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Electricity Restructuringand Competition.docx

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This document discusses electricity restructuring and competition, including: - Traditionally, electricity provision was regulated vertically integrated utilities. In the last 25 years, restructuring introduced competition through wholesale electricity markets. - Factors leading to restructuring included deregulation of other industries and high electricity prices. Wholesale electricity trading illustrated the feasibility of electricity markets. - Key aspects of restructuring included unbundling generation from transmission/distribution, establishing organized wholesale markets, and setting up independent system operators to manage grid operations and reliability.

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Electricity Restructuring and Competition Electricity MarketsTraditional Organization of Electricity Provision in U.S.Govt regulated, vertically integrated public utilitiesLast 25 YearsWave of restructuring and introduction of electricity trading and competition across U.S. and other nationsNow large-scale wholesale electricity markets operate across many parts of U.S. Factors leading to restructuring & deregulation Prior track record of deregulating energy markets and other industries once considered natural monopolies telecomm, airlines, railroads, High electricity prices in spite of regulation In U.S. states such as NY, California, New England
In other countries, such as U.K. As the U.S. electricity transmission grid was built out in the 2nd half of 20th century, electric utilities started buying and selling energy from each other This illustrated feasibility of trading energy across the grid in markets Political Push 2nd Bush President Electricity Restructuring – A How-To GuideUnbundle generation from transmission and distributionIt’s important to allow multiple business firms to own and operate generation plants (independent power producers (IPPs) or merchant generators)This may require forcing some vertically integrated IOUs to divest some of their generation assets Electricity Restructuring – A How-To GuideEstablish an organized market for wholesale electricity tradingSellers are firms like merchant generators that inject energy into the gridBuyers are retail distributors and industrial users that withdraw energy from the gridMarket rules might include an advance-trading (e.g., day-ahead) market as well as a real-time balancing market Electricity Restructuring – A How-To GuideSet up an organization to manage and coordinate energy flows over the
gridThis could be an independent system operator (ISO) or a regional transmission organization (RTO)Key responsibilitiesBalance energy supply and demand in real timeMaintain system reliabilityCoordinate new transmission investments Wholesale Market Competition Electricity Restructuring – A How-To GuideRetail distributionOwnership and operation of local distribution networks would typically continue as regulated public utilities (natural monopoly rationale)But it is feasible to introduce retail competition via brokers and energy re-sellers who purchase wholesale energy, re-sell it to retail customers, and pay fees to local distribution companies for use of the local network. Wholesale Electricity MarketsLet’s examine a perfect competition model of a short-run (hourly) wholesale electricity marketDemand SideDemand varies hour by hour, as weather conditions and desired electricity usage changeVery price inelastic – most retail customers pay fixed retail pricesSupply SideMost supply comes from fossil fuel generatorsFF supply is driven by cost of generation and capacities of generation units.Generation from renewables is intermittent Fossil Fuel Generation and SupplySupply curve from FF generation is comprised of a series of steps –Width of step is generation capacity of a unitHeight of step is marginal cost
(MC) of the unitFF MC of generationMC = heat rate x fuel cost + emissions rate x emissions tax rate Generation Example TypeMarginal Operating CostCapacity Nuclear$12/MWh1000 MW Coal$25/MWh2500 MW Gas$55/MWh1500 MW Turbine (peaker)$90/MWh 500 MW Example with Supply and DemandDraw the competitive supply curve for the production of electric energy on this system Assume that demand is 3000 MW and is completely price inelastic in the very short run. What would be the spot price in a perfectly competitive wholesale electricity market? Assume that demand is 4500 MW and is completely price inelastic in the very short run. What would be the competitive spot price be? What if there is a downward sloping demand function:D(P) = 5050 – 10PWhat is the spot price in a perfectly competitive market?Assume that demand is 6000 MW for prices up to $110/MWh, but that 600 MW of this demand would be willing to be curtailed for prices above $110/MWh or more. What is the perfectly competitive market price in this case? Competitive Supply Curve
Example - continuedLet’s use the example to tease out wholesale purchase costs for buyers (distributors).Assume that demand is 3000 MW for 8 hours/dayAssume that demand is 4500 MW for 12 hours/dayAssume that demand is 6000 MW for prices up to $110/MWh, but that 600 MW of this demand would be willing to be curtailed for prices above $110/MWh or more for 4 hours/day Example - continuedLet’s use the example to tease out wholesale purchase costs for buyers (distributors).Assume that demand is 3000 MW for 8 hours/dayAssume that demand is 4500 MW for 12 hours/dayAssume that demand is 6000 MW for prices up to $110/MWh, but that 600 MW of this demand would be willing to be curtailed for prices above $110/MWh or more for 4 hours/dayThen prices are:P = $25 for 8 hours/dayP = $55 for 12 hours/dayP = $110 for 4 hours dayAverage purchase cost for buyers = (8*$25+12*$55+4*$110)/24 = $50.4/MWh Renewable Energy in Wholesale MarketsWhat happens when renewable energy (wind turbines, solar panels) generation capacity is added?Let’s look at how the example changes when 1000 MW of solar photovoltaic generation capacity is added to the grid.Impact on wholesale prices and price volatility?
Competitive Supply Curve with 1000 MW renewable capacity added Electricity Markets over the GridWholesale Electricity CompetitionThe scope and size of the market can be expanded by using the transmission network to connect buyers and sellers. A bigger market can enable more suppliers to compete for sales.If there are no transmission bottlenecks, then a grid- connected area can operate as a single market with a single price.But sometimes transmission links will be capacity- constrained, and prices in different parts of the network will differ.Locational Marginal PricesCompetitive prices at each node of the network that take into account transmission constraints.See next 2 slides for LMP contour mapsGoogle MISO-PJM LMP contour map to see current map MISO-PJM pricing contour map – evening March 11, 2019 MISO-PJM pricing contour map – afternoon March 12, 2019 Trouble w/ Electricity Markets?California restructured its electricity industry and opened wholesale market to competition in 1998California Energy Crisis of 2000Why?High summer demandLarge drop in hydro-electricity importsSpike in natural
gas pricesMarket power of IPPs – especially at peak demand timesImpactBIG increase in wholesale prices (~400- 500%)Distco’s squeezedRolling blackouts End of California CrisisDistribution utilities nearly bankrupt by early 2001California ended its restructuring experiment in 2001State govt negotiates new supply contracts for utilities (very costly contracts!)Arnold gets elected governorRestructuring grinds to a halt in most of Western U.S. Why did restructuring flop in CA?Problems with CA restructuring planLimits on long-term forward contracts between generation suppliers and distribution utilitiesLack of retail buyer price response (no real time pricing)NIMBY obstacles to generation investments (not enough slack in system) 18 THE STANDARD PRESCRIPTION FIGURE 2.1 Physical functions of electricity. FINAL CUSTOMERS ( RETAIL SALES ) OFFICE HOUSEFACTORY LOCAL DISTRIBUTION
SYSTEM GENERATION ( POWER PLANTS ) TRANSMISSION NETWORKS ( GRID ) FLOW OF POWERMETER SYSTEM OPERATIONS Reforming the Industr y 45 This is all we will say about Model 2 structural issues. Models 3 and 4 are where the action is. It is, however, worth remembering that the new trading arrangements can be developed and put into operation before Mod- els 3 and 4 are actually introduced. Transmission and system operations can be separated from generation and new trading arrangements instituted be- fore any deregulation takes place or any competitors enter the market. In the United States, this would be somewhat similar to the operation of the old tight pools, where (for many years, and well before the introduction of competition) the final price to customers was regulated but the dispatch and transmission were coordinated over a wide area, and the pricing
rules for wholesale sales between companies were approved by FERC. This should not be taken as a proposal that the rules of the old tight pools should be adopted in the United States. However, the rules we do propose for trading arrangements later in the text could be adopted in modified form even be- fore wholesale competition is introduced. M o d e l 3 : W h o l e s a l e C o m p e t i t i o n Model 3 as we define it here has a fully competitive generating sector. There is no cost-of-service regulated generation. Distribution companies (now FIGURE 3.3 Model 3—wholesale competition. DISTCO CUSTOMER DISTCOLARGECUSTOMER LARGE CUSTOMER CUSTOMER ENERGY SALES IPPIPP IPP IPP IPP TRANSMISSION WIRES
WHOLESALE MARKETPLACE Renewable Energy Main Types of Renewable EnergyDispatchable RenewablesHydroelectric PowerGeothermalVariable Energy ResourcesWind TurbinesOn-shoreOff-shoreSolarHot water heatersConcentrating solar power (also called, solar thermal)Solar Photovoltaic (PV) Concentrating Solar Power Solana station 280 MW parabolic trough solar plant, 70 miles SW of Phoenix
Ivanpah, CA Solar Tower Plant (377MW, $2.2 billion) Solar PVA PV cell consists of two or more thin layers of semi- conducting material, most commonly silicon. When the silicon is exposed to light, electrical charges are generated and this can be conducted away by metal contacts as direct current (DC). The electrical output from a single cell is small, so multiple cells are connected together and encapsulated (usually behind glass) to form a module (sometimes referred to as a "panel"). The PV module is the principle building block of a PV system and any number of modules can be connected together to give the desired electrical output.Modules are connected to inverters, which convert DC power into AC power. Crystalline silicon PV panels Solar PVTypesCrystalline siliconThin filmConcentrating PVSet UpFixed tilt (land vs. rooftop); facing directionSingle axis trackingDouble axis trackingScaleUtility scale > 1 MW Distributed generation (rooftops, building sites) 1 kW – 1 MW
Room for solar?Consider the following factsUS electricity consumption = 4 billion MWh/yrSolar PV capacity factor (AZ) = 20 %Solar PV space requirement = 3 acres/MW640 acres land per square mileHow much land would be required to supply all US electricity consumption from solar PV? Growth in RenewablesI’ll focus on wind and solar – Fastest growing renewables in electricityWind turbine capacity growing ~ 20%/yr in USSolar PV capacity growing ~ 30%/yr in USOther renewablesHydro, geo-thermal, bio-fuelsWhy so much growth in wind & solar?Falling prices Favorable policies – subsidies, tax credits, RPS, …Concerns about environmental impacts of fossil fuel use Solar Generation as % of Total Generation, 2018 Experience Curve (Learning by Doing)
Economic comparisons of electricity generation technologiesCommon metric in energy industry is the levelized cost of energy (LCOE)LCOE is equal to the constant price per unit energy that would equate NPV of revenues to NPV of costs; alternatively; a measure of the real average total cost of generation over the lifetime of a generation plant EIA estimates of LCOE* * Advanced coal has total system LCOE = 139 Classifying generation technologiesDispatchable generatorsCoal, gas combined-cycle, nuclear,…Can be controlled by a system operator; can be turned on and off based on economic conditionsCan provide electricity generation as well as reliability services – such as spinning reserves and frequency regulationIntermittent generatorsWind, solar PV, solar thermalProduction depends on weather conditionsCan’t be controlled by system operator (unless coupled with energy storage) Public (or Social) vs Private Economics of Renewable EnergyPublicFocus on overall economic costs and benefits, taking into account things like environmental benefits, time- varying benefits and costs, electricity system reliability, …
Public (or Social) vs Private Economics of Renewable EnergyPublicFocus on overall economic costs and benefits, taking into account things like environmental benefits, time- varying benefits and costs, electricity system reliability, …PrivateLook at costs and benefits from viewpoint of an individual decision-maker:household considering installing solar panels (EXCEL)Electric utility considering investing in wind turbinesThese costs and benefits would be evaluated after the effects of subsidies and/or tax breaks Economic value of intermittent renewable generationTiming of generationDoes generation occur when electricity is valuable?Timing of (on-shore) wind, vs solar PVSolar PV vs. solar thermalEnvironmental benefitsWhat fossil fuel generators are displaced? How much of each type of emission is reduced; how do you value emissions reductions?Grid integration costsAt higher penetration of renewables, intermittency leads to more supply variability and could reduce system reliability.Does this require more spinning reserves, more backup generation capacity? Short run value of intermittent renewable generation*Notationy = electricity load (quantity demanded) per period (random)C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)EM(x) = emissions associated with producing qty x; ϕ =EM’(x)τ = $ damages per ton of emissionsK = renewable generation capacitys = renewable generation output per unit of capacity (random)
* See Baker, et al, “Economics of Solar Electricity” Annual Review of Resource Economics, 2013. Short run value of intermittent renewable generationNotationy = electricity load (quantity demanded) per period (random)C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)EM(x) = emissions associated with producing qty x; ϕ =EM’(x)τ = $ damages per ton of emissionsK = renewable generation capacitys = renewable generation output per unit of capacity (random)Value of KV(K)=E[[C(y) – C(y-sK) +τ EM(y) – τ EM(y-sK)] Short run value of intermittent renewable generationNotationy = electricity load (quantity demanded) per period (random)C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)EM(x) = emissions associated with producing qty x; ϕ =EM’(x)τ = $ damages per ton of emissionsK = renewable generation capacitys = renewable generation output per unit of capacity (random)Value of KV(K)=E[C(y) – C(y-sK) +τ EM(y) – τ EM(y-sK)]Marginal value of KV’(K)=E[λ]E[s] + Cov[λ,s] + τ E[ϕ]E[s] + τ Cov[ϕ,s] Marginal Value of Renewable CapacityMarginal value depends on:E[s] = Capacity factor of renewable E[λ] = Average of marginal cost (MC) of displaced fossil fuel gen Cov[λ,s] = Covariance of renewable generation and MCτ = $ damages per ton of emissionsE[ϕ] = Average of marginal emission ratesCov[ϕ,s] = Covariance of renewable generation and ϕVariation in marginal value:Different types of renewable
energy – e.g., wind vs. solar – will differ across many of these variables, and so their marginal value may be quite differentEven for a given type – e.g., solar – marginal value will vary based on local weather conditions and grid conditions Net Metering for Solar PVA typical residential PV array will sometimes yield more energy than the household is using. Net Metering is a policy in which the distributor (eg, utility) buys back or credits the household for energy it puts into the grid.2005 federal law - all utilities are required to offer net metering to their customers.ACC recently approved change from 1-for-1 net metering credits, to credits for excess generation based on (lower) avg wholesale elec rate Grid Parity for renewables?Grid parity for a renewable technology is sometimes described as the point at which its LCOE matches that of fossil fuel generation.Basic idea is that once grid parity is achieved, that renewable technology wouldn’t need subsidies or special incentives to compete with fossil fuel technologies.OK, but here are a few other considerationsIs it dispatchable; can it be used for reliability svcs?Timing of generation and value?Magnitude of environmental benefits?Are there major grid integration costs? 100% Renewables Goalhttps://www.forbes.com/sites/trevornace/2017/08/01/califor nia-goes-all-in-100-percent-renewable-energy-by- 2045/#3409408f570fhttp://www.pnas.org/content/pnas/112/49/1 5060.full.pdfhttp://www.pnas.org/content/pnas/early/2017/06/16 /1610381114.full.pdf
Jacobson, et al Proceedings of National Academy of Science 2015 The large-scale conversion to 100% wind, water, and solar (WWS) power for all purposes (electricity, transportation, heating/cooling, and industry) is currently inhibited by a fear of grid instability and high cost due to the variability and uncertainty of wind and solar. This paper couples numerical simulation of time- and space-dependent weather with simulation of time-dependent power demand, storage, and demand response to provide low-cost solutions to the grid reliability problem with 100% penetration of WWS across all energy sectors in the continental United States between 2050 and 2055. Solution s are obtained without higher-cost stationary battery storage by prioritizing storage of heat in soil and water; cold in water and ice; and electricity in phase-change materials, pumped hydro, hydropower, and hydrogen. PNAS 2017 – An assessment of 100% renewables claim Previous analyses have found that the most feasible route to a
low-carbon energy future is one that adopts a diverse portfolio of technologies. In contrast, Jacobson et al. (2015) consider whether the future primary energy sources for the United States could be narrowed to almost exclusively wind, solar, and hydroelectric power and suggest that this can be done at “low- cost” in a way that supplies all power with a probability of loss of load “that exceeds electric-utility industry standards for reliability”. We find that their analysis involves errors, inappropriate methods, and implausible assumptions. Their study does not provide credible evidence for rejecting the conclusions of previous analyses that point to the benefits of considering a broad portfolio of energy system options. A policy prescription that overpromises on the benefits of relying on a narrower portfolio of technologies options could be counterproductive, seriously impeding the move to a cost effective decarbonized energy system. Renewable SubsidiesThere is a vast array of federal, state, local and utility subsidy programs for renewables – dsireusa.org is a good info sourceMajor federal subsidies30% investment tax creditElectricity production tax creditState subsidy
examplesRenewable Portfolio Standard (RPS) – eg California, ArizonaSales tax exemptions - AZInterest rate subsidies for loans - TexasCity solar PV subsidies - Texs Grid integration and reliability challenges for renewablesIntermittencyTiming and correlation with electricity demandRenewable cost and capacity factorDispatchable vs non- dispatchable generatorsSee Paul Joskow, American Econ Rev (2012) Load and solar output at 4 sites; 3 days in August 2011 California Duck Curve
The duck sinks – negative mid-day wholesale prices in CAISO Why were generators willing to sell at negative prices??The production tax credit. Some renewables owners (mainly wind) are eligible for a production tax credit, which essentially pays them for every MWh they produce. So, not producing means foregoing this credit. In theory, producers will pay to sell into the wholesale market as long as they’re paying less than the tax credit. Why were generators willing to sell at negative prices??The Renewable Portfolio Standard. Under California’s Renewable Portfolio Standard (RPS), utilities are on the hook to provide 60% of their electricity from renewable sources by 2030 and 100% by 2045. The utilities sign contacts with renewable providers in order to try to meet their RPS targets; utilities pay a penalty if not met. So utilities want to encourage the renewable providers to produce. For example, under a very simple power purchase agreement, the utility would pay the renewable provider a pre-specified price per MWh irrespective
of the wholesale market price, leaving the renewable provider no incentive to shut down when prices are negative. Why were generators willing to sell at negative prices??Operating constraints. For some power plants, varying the output level entails high costs, particularly starting and stopping the plant. I think of those as analogous to the extra fuel, plus wear and tear, planes expend taking off. So, if it costs a lot to restart a nuclear plant or a coal plant, for example, you’re willing to pay not to have to turn it off to avoid incurring those costs. Renewable Intermittency & Grid ReliabilityElectricity system operators need to balance demand and supply of electricity in real time to maintain reliabilityManaged via operating reserves and back-up generatorsIntermittency of renewables poses risks to reliability at high penetrationRole for large-scale energy storageAbsent more storage, system operators must to carry more operating reserves and back-up generatorsRole for improved use of demand-response
Research ApproachesStructural model #1G. Gowrisankaran, S. Reynolds, M. Samano “Intermittency and the value of renewable energy” Jour Political Economy (2016)“This paper develops a method to quantify the social costs and reductions in carbon emissions from large-scale renewable energy generation. We estimate social costs by solving for the decisions that maximize total surplus under different levels of renewable energy capacity. Social costs depend crucially on (1) the variability of the source including the extent to which the variability correlates with demand; (2) the extent to which output from the source is forecastable; and (3) the costs of building backup generation required to maintain system reliability.” “Intermittency and the value of renewable energy” Data from TEP, Tucson solar sites, EPA, NOAA, EIAModel of optimal elec utility operations – includes generator dispatch, operating reserves, investment, demand response, forecastingModel parameters either estimated by GRS or drawn from estimates in other studies.Results for 10 – 20% solar generation mandatesIntermittency adds 2 – 3.5 ¢/kWh to solar PV costsSolar mandates estimated to be very costly at time of
study, based on solar PV cost of $4.40/W; current solar PV costs much lower20% solar mandate is ‘welfare-neutral’ at PV cost of $1.50/W if SCC = $40/ton CO2 * Today in Energy March 6, 2017 U.S. wind generating capacity surpasses hydro capacity at the end of 2016 Source: U.S. Energy Information Administration, Preliminary Monthly Electric Generator Inventory Note: Data include facilities with a nameplate capacity of one megawatt and above. Installed wind electric generating capacity in the United States surpassed conventional hydroelectric generating capacity, long the nation’s largest source of renewable electricity, after 8,727 megawatts (MW) of new wind capacity came online in 2016.
However, given the hydro fleet’s higher average capacity factors and the above- normal precipitation on the West Coast so far this year, hydro generation will likely once again exceed wind generation in 2017. Source: U.S. Energy Information Administration, Electricity Data Browser Note: Data include facilities with a nameplate capacity of one megawatt and above. Wind and hydro generation both follow strong seasonal patterns. Hydro generation typically reaches its seasonal peak in the spring and early summer, especially in the Pacific Northwest and California where about half of U.S. hydropower is produced. Across most of the https://www.eia.gov/todayinenergy/detail.php?id=30212# http://www.eia.gov/electricity/data/eia860m/ http://www.eia.gov/electricity/monthly/epm_table_grapher.cfm? t=epmt_6_02_b https://www.eia.gov/todayinenergy/detail.php?id=14611 http://www.eia.gov/electricity/data/browser/ https://www.eia.gov/todayinenergy/detail.php?id=16891 http://www.eia.gov/todayinenergy/detail.php?id=20112
Today in Energy March 6, 2017 U.S. wind generating capacity surpasses hydro capacity at the end of 2016 Source: U.S. Energy Information Administration, Preliminary Monthly Electric Generator Inventory Note: Data include facilities with a nameplate capacity of one megawatt and above. Installed wind electric generating capacity in the United States surpassed conventional hydroelectric generating capacity, long the nation’s largest source of renewable electricity, after 8,727 megawatts (MW) of new wind capacity came online in 2016. However, given the hydro fleet’s higher average capacity factors and the above- normal precipitation on the West Coast so far this year, hydro generation will likely once again exceed wind generation in 2017. Source: U.S. Energy Information Administration, Electricity Data Browser Note: Data include facilities with a nameplate capacity of one megawatt and above. Wind and hydro generation both follow strong seasonal patterns. Hydro generation typically reaches its seasonal peak in the spring and
early summer, especially in the Pacific Northwest and California where about half of U.S. hydropower is produced. Across most of the NREL | 26NREL | 26 19.0% 12.7% 11.2% 11.0% 10.7% 6.5% 6.4% 5.4% 4.7% 4.2% 2.3% 0% 2%
4% 6% 8% 10% 12% 14% 16% 18% 20% CA NV HI VT MA AZ UT NC NM NJ U.S. So la r G
en er at io n as a P er ce nt ag e of T ot
al N et G en er at io n CSP DPV UPV Solar Generation as a Percentage of Total Generation, 2018 • The role of utility versus distributed solar varies by state, with northeastern states and Hawaii relying more on DPV. Note: EIA monthly data for 2018 are not final. Additionally,
smaller utilities report information to EIA on a yearly basis, and therefore, a certain amount of solar data has not yet been reported. “Net Generation” includes DPV generation. Net generation does not take into account imports and exports to and from each state and therefore the percentage of solar consumed in each state may vary from its percentage of net generation. Source: U.S. Energy Information Administration, “Electricity Data Browser.” Accessed April 3, 2019. NREL | 62NREL | 62 PV Experience Curve • This experience curve displays the relationship, in logarithmic form, between the average selling price of a PV module and the cumulative global shipments of PV modules. As shown, for every doubling of cumulative PV shipments, there is on average a corresponding ~22% reduction in PV module price. – In 2010, the experience rate was 20%
• Since 2012, module ASP has been below the historical experience curve. • Analysts project that by 2022 ASP will be approximately $0.2/W and globally we will have shipped a terawatt. Source: 1976-2018: Paula Mints. "Photovoltaic Manufacturer Capacity, Shipments, Price & Revenues 2018/2019." SPV Market Research. Report SPV-Supply6. April 2019. 0.1 1 10 100 1,000 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 M
od ul e A SP (2 01 8 $/ W ) Cumulative Global Shipments (MW) Historic ASP U.S. Energy Information Administration | AEO2016 Levelized Costs 6
Table 1a. Estimated LCOE (weighted average of regional values based on projected capacity additions) for new generation resources, plants entering service in 2022 Plant Type Capacity Factor (%) U.S. Capacity-Weighted1 Average LCOE (2015 $/MWh) for Plants Entering Service in 2022 Levelized Capital Cost Fixed
O&M Variable O&M (including fuel) Transmission Investment Total System LCOE Levelized Tax Credit Total LCOE
including Tax Credit2 Dispatchable Technologies Advanced Coal with CCS3 N/B Natural Gas-fired Conventional Combined Cycle 87 12.8 1.4 41.2 1.0 56.4 N/A 56.4 Advanced Combined Cycle 87 15.4 1.3 38.1 1.1 55.8 N/A 55.8 Advanced CC with CCS N/B Conventional Combustion Turbine 30 37.1 6.5 58.9 2.9 105.4 N/A 105.4 Advanced Combustion Turbine 30 25.9 2.5 61.9 3.3 93.6 N/A 93.6 Advanced Nuclear 90 75.0 12.4 11.3 1.0 99.7 N/A 99.7 Geothermal 91 27.8 13.1 0.0 1.4 42.3 -2.8 39.5
Biomass N/B Non-Dispatchable Technologies Wind 42 43.3 12.5 0.0 2.7 58.5 -7.6 50.9 Wind – Offshore N/B Solar PV4 26 61.2 9.5 0.0 3.5 74.2 -15.9 58.2 Solar Thermal N/B Hydroelectric5 60 54.1 3.1 5.0 1.5 63.7 N/A 63.7 1The capacity-weighted average is the average levelized cost per technology, weighted by the new capacity coming online in each region. The capacity additions for each region were based on additions in 2018 -2022. Technologies for which capacity additions are not expected do not have a capacity-weighted average, and are marked as “N/B.” 2The tax credit component is based on targeted federal tax credits such as the production or investment tax credit available for some technologies. It only reflects tax credits available for plants entering service in 2022. EIA models renewable tax credits as follows: new solar thermal and PV
plants are eligible to receive a 30% investment tax credit on capital expenditures if under construction before the end of 2019, and then tax credits taper off to 26% in 2020, 22% in 2021, and 10% thereafter. New wind, geothermal, and biomass plants receive a $23.0/MWh ($12.0/MWh for technologies other than wind, geothermal and closed-loop biomass) inflation-adjusted production tax credit over the plant’s first ten years of service if they are under construction before the end of 2016, with the tax credit for wind declining by 20% in 2017, 40% in 2018, 60% in 2019, and expiring completely in 2020. Up to 6 GW of new nuclear plants are eligible to receive an $18/MWh production tax credit if in service by 2020. Not all technologies have tax credits, and are indicated as “N/A.” The results are based on a regional model and state or local incentives are not included in LCOE calculations. 3Due to new regulations (CAA 111b), conventional coal plants cannot be built without CCS because they are required to meet specific CO2 emission standards. The coal with CCS technology modeled is assumed to remove 30% of the plant’s CO2 emissions. Coal plants have a 3 percentage-point
adder to their cost-of-capital. 4Costs are expressed in terms of net AC power available to the grid for the installed capacity. 5As modeled, hydroelectric is assumed to have seasonal storage so that it can be dispatched within a season, but overall operation is limited by resources available by site and season. Source: U.S. Energy Information Administration, Annual Energy Outlook 2016, April 2016, DOE/EIA-0383(2016). U.S. Energy Information Administration | AEO2016 Levelized Costs 6 Table 1a. Estimated LCOE (weighted average of regional values based on projected capacity additions) for new generation resources, plants entering service in 2022 Plant Type Capacity Factor (%) U.S. Capacity-Weighted 1 Average LCOE (2015 $/MWh) for Plants Entering Service in
2022 Levelized Capital Cost Fixed O&M Variable O&M (including fuel) Transmission Investment Total System LCOE Levelized Tax Credit Total LCOE including Tax Credit 2 Dispatchable Technologies Advanced Coal with CCS 3
N/B Natural Gas-fired Conventional Combined Cycle 87 12.8 1.4 41.2 1.0 56.4 N/A 56.4 Advanced Combined Cycle 87 15.4 1.3 38.1 1.1 55.8 N/A 55.8 Advanced CC with CCS N/B Conventional Combustion Turbine 30 37.1 6.5 58.9 2.9 105.4 N/A 105.4 Advanced Combustion Turbine 30 25.9 2.5 61.9 3.3 93.6 N/A 93.6 Advanced Nuclear 90 75.0 12.4 11.3 1.0 99.7 N/A 99.7 Geothermal 91 27.8 13.1 0.0 1.4 42.3 -2.8 39.5 Biomass N/B Non-Dispatchable Technologies Wind 42 43.3 12.5 0.0 2.7 58.5 -7.6 50.9 Wind – Offshore N/B Solar PV 4
26 61.2 9.5 0.0 3.5 74.2 -15.9 58.2 Solar Thermal N/B Hydroelectric 5 60 54.1 3.1 5.0 1.5 63.7 N/A 63.7 1 The capacity-weighted average is the average levelized cost per technology, weighted by the new capacity coming online in each region. The capacity additions for each region were based on additions in 2018 -2022. Technologies for which capacity additions are not expected do not have a capacity-weighted average, and are marked as “N/B.” 2 The tax credit component is based on targeted federal tax credits such as the production or investment tax credit available for some technologies. It only reflects tax credits available for plants entering service in 2022. EIA models renewable tax credits as follows: new solar thermal and PV plants are eligible to receive a 30% investment tax credit on capital expenditures if under construction before the end of 2019, and then tax credits
taper off to 26% in 2020, 22% in 2021, and 10% thereafter. New wind, geothermal, and biomass plants receive a $23.0/MWh ($12.0/MWh for technologies other than wind, geothermal and closed-loop biomass) inflation-adjusted production tax credit over the plant’s first ten years of service if they are under construction before the end of 2016, with the tax credit for wind declining by 20% in 2017, 40% in 2018, 60% in 2019, and expiring completely in 2020. Up to 6 GW of new nuclear plants are eligible to receive an $18/MWh production tax credit if in service by 2020. Not all technologies have tax credits, and are indicated as “N/A.” The results are based on a regional model and state or local incentives are not included in LCOE calculations. 3 Due to new regulations (CAA 111b), conventional coal plants cannot be built without CCS because they are required to meet specific CO 2 emission standards. The coal with CCS technology modeled is assumed to remove 30% of the plant’s CO2 emissions. Coal plants have a 3 percentage-point
adder to their cost-of-capital. 4 Costs are expressed in terms of net AC power available to the grid for the installed capacity. 5 As modeled, hydroelectric is assumed to have seasonal storage so that it can be dispatched within a season, but overall operation is limited by resources available by site and season. Source: U.S. Energy Information Administration, Annual Energy Outlook 2016, April 2016, DOE/EIA-0383(2016). In addition, Arizona’s Renewable Portfolio Standard mandates that 30% of total renew- able energy consist of distributed generation, e.g. solar PV on customers’ rooftops. Our model allows distributed solar to have di↵ erent capital costs from non-distributed solar and to reduce electricity transmission costs.
Figure 2: Load and solar output for di↵ erent U.S. sites, Aug. 14-16, 2011 0.00 0.25 0.50 0.75 1.00 Aug 14 00:00 Aug 14 12:00 Aug 15 00:00 Aug 15 12:00 Aug 16 00:00 Aug 16 12:00 Aug 17 00:00 Local Time N or m al iz
ed S ol ar O ut pu t ( O ut pu t/C ap ac ity )
Legend Berthoud, CO Rated 9.88 kW New York, NY Rated 5.7 kW San Diego, CA Rated 5.7 kW Tucson, AZ Rated 9.2 kW (One Site Used in Analysis) 0.00 0.25 0.50 0.75 1.00
Aug 14 00:00 Aug 14 12:00 Aug 15 00:00 Aug 15 12:00 Aug 16 00:00 Aug 16 12:00 Aug 17 00:00 Local Time N or m al iz ed L oa d (L oa d/ M ax
im um L oa d) Legend Xcel Energy Inc. (Berthoud, CO) New York ISO (New York, NY) San Diego Gas & Electric (San Diego, CA) Tucson Electric Power (Tucson, AZ) Note: The Berthoud, New York, and San Diego solar generation data are from SMA Solar Technology AG’s Sunny Portal (https://www.sunnyportal.com/). The Tucson data
displayed here are from one of 58 sites used in our main analysis and are from the University of Arizona Photovoltaics Lab. The site shown here was chosen because it is the near the center of the city. The load data are from Federal Energy Regulation Commission Form 714. Load is measured hourly while solar output is measured at the 15-minute level. To illustrate the issues of intermittency, Figure 2 shows load and solar PV output for four sites across the U.S., for three summer days during our sample period, Aug. 14-16, 2011. We chose three sites from the Western U.S. with high solar potential (Figure 1), as well as New York. During these three days, the solar installation in New York produces far below its rated capacity at all times. The other installations all reach a peak output of 75% of 9
The first ramp of 8,000 MW in the upward direction (duck’s tail) occurs in the morning starting around 4:00 a.m. as people get up and go about their daily routine. The second, in the downward direction, occurs after the sun comes up around 7:00 a.m. when on-line conventional generation is replaced by supply from solar generation resources (producing the belly of the duck). As the sun sets starting around 4:00 p.m., and solar generation ends, the ISO must dispatch resources that can meet the third and most significant daily ramp (the arch of the duck’s neck). Immediately following this steep 11,000 MW ramp up, as demand on the system deceases into the evening hours, the ISO must reduce or shut down that generation to meet the final downward ramp. Flexible resources needed To ensure reliability under changing grid conditions, the ISO needs resources with ramping flexibility and the ability to start and stop multiple times per day. To ensure supply and demand match at all times, controllable resources will need the flexibility to change output levels and start and stop as dictated by
real-time grid conditions. Grid ramping conditions will vary through the year. The net load curve or duck chart in Figure 2 illustrates the steepening ramps expected during the spring. The duck chart shows the system requirement to supply an additional 13,000 MW, all within approximately three hours, to replace the electricity lost by solar power as the sun sets. Oversupply mitigation Oversupply is when all anticipated generation, including renewables, exceeds the real-time demand. The potential for this increases as more renewable energy is added to the grid but demand for electricity does not increase. This is a concern because if the market cannot automatically manage oversupply it can lead to overgeneration, which requires manual intervention of the market to maintain reliability. During oversupply times, wholesale prices can be very low and even go negative in which generators have
to pay utilities to take the energy. But the market often remedies the oversupply situation and automatically works to restore the balance between supply and demand. In almost all cases, oversupply is a manageable condition but it is not a sustainable condition over time — and this drives the need for proactive policies and actions to avoid the situation. The duck curve in Figure 2 shows that oversupply is expected to occur during the middle of the day as well. Because the ISO must continuously balance supply and demand, steps must be taken to mitigate Figure 2: The duck curve shows steep ramping needs and overgeneration risk www.caiso.com | 250 Outcropping Way, Folsom, CA 95630 | 916.351.4400 CommPR/2016 © 2016 California ISO California Independent System Operator 3
Energy, Externalities & Climate Emissions & ExternalitiesMuch of energy produced and used in U.S. and around the world is from fossil fuelsBurning fossil fuels yield air-borne emissionsSO2NOxCH4VOCPM2.5Mercury (from coal)CO2 Emissions & ExternalitiesMuch of energy produced and used in U.S. and around the world is from fossil fuelsBurning fossil fuels yield air-borne emissionsSO2NOxCH4VOCPM2.5Mercury (from coal)CO2So??Damage to natural environment, cropsDamage to buildings, infrastructureMost important of all – ill health and even deathCO2 – greenhouse gas that can affect
climate EmissionsOK, sure there is some bad stuff coming from fossil fuels, but they yield a tremendous amount of valuable, low-cost energyThe economic problem is negative externalities. If the people who produce and/or consume fossil fuels actually (somehow) bear the costs and adverse consequences of emissions, then you could say the benefits of this energy outweigh the costs.But if NOT then this production/consumption imposes negative externalities on others. As a result, too many fossil fuels are produced and consumed. Energy and the EnvironmentBefore Climate Change (BCC) EraMajor environmental challengesAcid rain – SO2 and NOx emissions from burning coalUrban smog and air pollution – mainly from NOx , PM, and VOC from cars/trucksPolicies in BCC EraClean Air Act Amendments of 1990Established EPA Acid Rain Program – Innovative Cap & Trade ProgramRestricted auto emissions via technology standards
(e.g., catalytic converters), tighter CAFÉ standards, and blended fuel requirements Energy and Climate Change Energy and Climate ChangeProduction and consumption of fossil fuels results in greenhouse gas (GhG) emissions – mainly CO2 and CH4 (methane)The greenhouse effect from GhG emissions results in climate change – a multi-faceted impactHow has CO2 in atmosphere been changing?
Energy and Climate ChangeProduction and consumption of fossil fuels results in greenhouse gas (GhG) emissions – mainly CO2 and CH4 (methane)The greenhouse effect from GhG emissions results in climate change – a multi-faceted impactHow has CO2 in atmosphere been changing?And what are the impacts of this? Economic Analysis of Environmental ImpactsBefore we go too far into climate issues, let’s look at how we can analyze environmental policy related to energy issues.And look at lessons from prior environmental policy efforts
Model of emissions regulationTwo sides of emissionsDamages – Changes in emissions result in marginal cost of emissionsFor CO2, we refer to MC of emissions as Social Cost of Carbon (SCC)“Benefits” – Emissions are a side-effect of productive economic activity (like burning natural gas to generate electricity)Marginal benefit (MB) of emissions is extra benefit as emissions increase.Mirror image of MB is marginal abatement cost of reducing emissions. Managing Emissions Externalities $ E MB MC Managing Emissions Externalities
$ E MB MC abatement marginal abatement cost Social Optimum $ E MB MC abatement marginal abatement cost social optimum emissions
Policy OptionsDo NothingSo called business as usualEmissions TaxWhere do tax revenues go?Emissions CapTechnology Standard(s)Cap and Trade ProgramIssue emissions permits equal to capped emissions levelAuction off permits or give away (grandfather) Managing Emissions Externalities $ E MB MC abatement marginal abatement cost Managing Emissions Externalities $ E MB MC
abatement marginal abatement cost Managing Emissions Externalities $ E MB abatement marginal abatement cost CAP Cap & Trade $ E MB abatement marginal abatement cost CAP
Measuring Economic Damages from EmissionsA wide variety of damagesContaminated air and water, damage to crops, illness/deathTwo main approaches for measuring damagesContingent ValuationAsk people about WTP for better environmental quality, reduced health/mortality risksBased on survey or questionnaire responsesMarket-based ValuationIndirect method to reveal WTP for enviro quality, reduced health/mortality risksBased on market price or wage changes as enviro quality, health/mortality risks vary Illustration – Mortality RiskSuppose you knew for sure that reducing air emissions by some amount would save one life; you don’t know which person’s life, just that one life is saved. How much is this worth in dollars? How much should society and policy-makers value this life? Illustration – Mortality RiskSuppose you knew for sure that reducing air emissions by some amount would save one life; you
don’t know which person’s life, just that one life is saved. How much is this worth in dollars? How much should society and policy-makers value this life?Gov. Andrew Cuomo: “My mother’s not expendable. You cannot put a value on human life. You do the right thing. That’s what Pop taught us.”But let’s suppose you needed to come up with a $ value for saving a life – how would you do it? What would you come up with? Illustration – Mortality RiskSuppose you knew for sure that reducing air emissions by some amount would save one life; you don’t know which person’s life, just that one life is saved. How much is this worth in dollars? How much should society and policy-makers value this life?Gov. Andrew Cuomo: “My mother’s not expendable. You cannot put a value on human life. You do the right thing. That’s what Pop taught us.”But let’s suppose you needed to come up with a $ value for saving a life – how would you do it? What would you come up with? Putting an economic value on lifeContingent Valuation ApproachGather survey responses about (hypothetical) WTP to avoid various mortality risks [reduced auto crash risk,
workplace death risk, etc.] From EPA website … In the scientific literature, these estimates of willingness to pay for small reductions in mortality risks are often referred to as the "value of a statistical life.” This is because these values are typically reported in units that match the aggregate dollar amount that a large group of people would be willing to pay for a reduction in their individual risks of dying in a year, such that we would expect one fewer death among the group during that year on average. From EPA website … This is best explained by way of an example. Suppose each person in a sample of 100,000 people were asked how much he or she would be willing to pay for a reduction in their individual risk of dying of 1 in 100,000, or 0.001%, over the next year. Since this reduction in risk would mean that we would expect one fewer death among the sample of 100,000 people over the next year on average, this is sometimes described as "one statistical life saved.”
Now suppose that the average response to this hypothetical question was $100. Then the total dollar amount that the group would be willing to pay to save one statistical life in a year would be $100 per person × 100,000 people, or $10 million. This is what is meant by the "value of a statistical life.” Importantly, this is not an estimate of how much money any single individual or group would be willing to pay to prevent the certain death of any particular person. Putting an economic value on lifeMarket-based Valuation ApproachCompensating wage differences – how do wages differ across occupations as mortality risk varies?You can find this via regression analysis of market wage dataCan derive VSL – value of a statistical life – based on the mortality risk coefficient in a wage regression. Earnings Regression Approach Run a regression of annual income (I) on explanatory variables including occupation (OCCP) and mortality rate for jobs in occupation (MORT), where MORT indicates number of deaths
per 100,000 workers per year. I = a + b*EDUC + c*OCCP + d*MORT + … + error How do you interpret d coefficient? Earnings Regression Approach Run a regression of annual income (I) on explanatory variables including occupation (OCCP) and mortality rate for jobs in occupation (MORT), where MORT indicates number of deaths per 100,000 workers per year. I = a + b*EDUC + c*OCCP + d*MORT + … + error d = △I/ △MORT = Change in annual income for having one more death/100,000 per year. VSL = d*100,000 Unique Environmental Challenges Posed by Climate ChangeHealthy climate is global public goodMultiple large
uncertaintiesInequality and welfare analysisLong-term persistent impactsRole of discount rate for NPV analysis Climate, the Economy, and Climate PolicyMany areas of the natural and social sciences involve complex systems that link multiple areas and disciplines. This is particularly true for the science, economics, and policy of climate change, which involve a wide variety of fields from atmospheric chemistry to game theory.Integrated assessment analyses and models play a key role in putting the pieces together. Integrated assessment models (IAMs) integrate knowledge from two or more domains into a single framework. These are sometimes theoretical but are increasingly computerized, empirical, dynamic, non-linear models of varying levels of complexity.
Climate, the Economy, and Climate PolicyMany areas of the natural and social sciences involve complex systems that link multiple areas and disciplines. This is particularly true for the science, economics, and policy of climate change, which involve a wide variety of fields from atmospheric chemistry to game theory.Integrated assessment analyses and models play a key role in putting the pieces together. Integrated assessment models (IAMs) integrate knowledge from two or more domains into a single framework. These are sometimes theoretical but are increasingly computerized, empirical, dynamic, non-linear models of varying levels of complexity. STERN REVIEW: The Economics of Climate Change iv Figure 1 Greenhouse-gas emissions in 2000, by source Power (24%)
Transport (14%) Buildings (8%) Industry (14%) Other energy related (5%) Waste (3%) Agriculture (14%) Land use (18%) NON-ENERGY EMISSIONS ENERGY EMISSIONS
Energy emissions are mostly CO2 (some non-CO2 in industry and other energy related). Non-energy emissions are CO2 (land use) and non-CO2 (agriculture and waste). Total emissions in 2000: 42 GtCO2e. Source: Prepared by Stern Review, from data drawn from World Resources Institute Climate Analysis Indicators Tool (CAIT) on-line database version 3.0. Under a BAU scenario, the stock of greenhouse gases could more than treble by the end of the century, giving at least a 50% risk of exceeding 5°C global average temperature change during the following decades. This would take humans into unknown territory. An illustration of the scale of such an increase is that we are now only around 5°C warmer than in the last ice age.
Such changes would transform the physical geography of the world. A radical change in the physical geography of the world must have powerful implications for the human geography - where people live, and how they live their lives. Figure 2 summarises the scientific evidence of the links between concentrations of greenhouse gases in the atmosphere, the probability of different levels of global average temperature change, and the physical impacts expected for each level. The risks of serious, irreversible impacts of climate change increase strongly as concentrations of greenhouse gases in the atmosphere rise. STERN REVIEW: The Economics of Climate Change iv Figure 1 Greenhouse-gas emissions in 2000, by source Power (24%) Transport (14%)
Buildings (8%) Industry (14%) Other energy related (5%) Waste (3%) Agriculture (14%) Land use (18%) NON-ENERGY EMISSIONS ENERGY EMISSIONS Energy emissions are mostly CO 2 (some non-CO 2 in industry and other energy related). Non-energy emissions are CO 2 (land use) and non-CO 2 (agriculture and waste). Total emissions in 2000: 42 GtCO2e.
Source: Prepared by Stern Review, from data drawn from World Resources Institute Climate Analysis Indicators Tool (CAIT) on-line database version 3.0. Under a BAU scenario, the stock of greenhouse gases could more than treble by the end of the century, giving at least a 50% risk of exceeding 5°C global average temperature change during the following decades. This would take humans into unknown territory. An illustration of the scale of such an increase is that we are now only around 5°C warmer than in the last ice age. Such changes would transform the physical geography of the world. A radical change in the physical geography of the world must have powerful implications for the human geography - where people live, and how they live their lives. Figure 2 summarises the scientific evidence of the links
between concentrations of greenhouse gases in the atmosphere, the probability of different levels of global average temperature change, and the physical impacts expected for each level. The risks of serious, irreversible impacts of climate change increase strongly as concentrations of greenhouse gases in the atmosphere rise. STERN REVIEW: The Economics of Climate Change v Figure 2 Stabilisation levels and probability ranges for temperature increases The figure below illustrates the types of impacts that could be experienced as the world comes into equilibrium with more greenhouse gases. The top panel shows the range of temperatures projected at stabilisation levels between 400ppm and 750ppm CO2e at
equilibrium. The solid horizontal lines indicate the 5 - 95% range based on climate sensitivity estimates from the IPCC 20012 and a recent Hadley Centre ensemble study3. The vertical line indicates the mean of the 50th percentile point. The dashed lines show the 5 - 95% range based on eleven recent studies4. The bottom panel illustrates the range of impacts expected at different levels of warming. The relationship between global average temperature changes and regional climate changes is very uncertain, especially with regard to changes in precipitation (see Box 4.2). This figure shows potential changes based on current scientific literature. 1°C 2°C 5°C4°C3°C Risk of weakening of natural carbon absorption and possible increasing natural methane releases and weakening of the Atlantic THC 400 ppm CO2e 450 ppm CO2e
550 ppm CO2e 650ppm CO2e 750ppm CO2e 5% 95% Sea level rise threatens major world cities, including London, Shanghai, New York, Tokyo and Hong Kong Falling crop yields in many developing regions FoodFood WaterWater EcosystemsEcosystems Risk of rapid Risk of rapid climate climate change and change and major major irreversible irreversible impactsimpacts
Eventual Temperature change (relative to pre-industrial) 0°C Rising crop yields in high-latitude developed countries if strong carbon fertilisation Yields in many developed regions decline even if strong carbon fertilisation Large fraction of ecosystems unable to maintain current form Increasing risk of abrupt, large-scale shifts in the climate system (e.g. collapse of the Atlantic THC and the West Antarctic Ice Sheet) Significant changes in water availability (one study projects more than a billion people suffer water shortages in the 2080s, many in Africa, while a similar number gain waterSmall mountain glaciers disappear worldwide – potential threat to water supplies in several areas Greater than 30% decrease
in runoff in Mediterranean and Southern Africa Coral reef ecosystems extensively and eventually irreversibly damaged Possible onset of collapse of part or all of Amazonian rainforest Onset of irreversible melting of the Greenland ice sheet Extreme Extreme Weather Weather EventsEvents Rising intensity of storms, forest fires, droughts, flooding and heat waves Small increases in hurricane intensity lead to a doubling of
damage costs in the US Many species face extinction (20 – 50% in one study) Severe impacts in marginal Sahel region Rising number of people at risk from hunger (25 – 60% increase in the 2080s in one study with weak carbon fertilisation), with half of the increase in Africa and West Asia. Entire regions experience major declines in crop yields (e.g. up to one third in Africa) 2 Wigley, T.M.L. and S.C.B. Raper (2001): 'Interpretation of high projections for global-mean warming', Science 293: 451-454 based on Intergovernmental Panel on Climate Change (2001): 'Climate change 2001: the scientific basis.
Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change' [Houghton JT, Ding Y, Griggs DJ, et al. (eds.)], Cambridge: Cambridge University Press. 3 Murphy, J.M., D.M.H. Sexton D.N. Barnett et al. (2004): 'Quantification of modelling uncertainties in a large ensemble of climate change simulations', Nature 430: 768 - 772 4 Meinshausen, M. (2006): 'What does a 2°C target mean for greenhouse gas concentrations? A brief analysis based on multi-gas emission pathways and several climate sensitivity uncertainty estimates', Avoiding dangerous climate change, in H.J. Schellnhuber et al. (eds.), Cambridge: Cambridge University Press, pp.265 - 280. STERN REVIEW: The Economics of Climate Change v Figure 2 Stabilisation levels and probability ranges for temperature increases The figure below illustrates the types of impacts that could be experienced as the world comes into equilibrium with more greenhouse gases. The top panel shows the range of temperatures projected at stabilisation levels between 400ppm and 750ppm CO2e at equilibrium. The solid horizontal lines indicate the 5 - 95% range based on climate sensitivity estimates from the IPCC 20012 and a recent Hadley Centre ensemble study3. The vertical line indicates the mean of the 50th percentile point. The dashed lines show the 5 - 95%
range based on eleven recent studies4. The bottom panel illustrates the range of impacts expected at different levels of warming. The relationship between global average temperature changes and regional climate changes is very uncertain, especially with regard to changes in precipitation (see Box 4.2). This figure shows potential changes based on current scientific literature. 1°C2°C 5°C4°C3°C Risk of weakening of natural carbon absorption and possible increasing natural methane releases and weakening of the Atlantic THC 400 ppm CO 2 e 450 ppm CO 2 e 550 ppm CO 2 e 650ppm CO 2 e 750ppm CO 2 e
5% 95% Sea level rise threatens major world cities, including London, Shanghai, New York, Tokyo and Hong Kong Falling crop yields in many developing regions Food Food Water Water Ecosystems Ecosystems Risk of rapid Risk of rapid climate climate change and change and major major irreversible irreversible impacts impacts Eventual Temperature change (relative to pre-industrial)
0°C Rising crop yields in high-latitude developed countries if strong carbon fertilisation Yields in many developed regions decline even if strong carbon fertilisation Large fraction of ecosystems unable to maintain current form Increasing risk of abrupt, large-scale shifts in the climate system (e.g. collapse of the Atlantic THC and the West Antarctic Ice Sheet) Significant changes in water availability (one study projects more than a billion people suffer water shortages in the 2080s, many in Africa, while a similar number gain water Small mountain glaciers disappear worldwide – potential threat to water supplies in several areas Greater than 30% decrease in runoff in Mediterranean and Southern Africa Coral reef ecosystems extensively and eventually irreversibly damaged Possible onset of collapse
of part or all of Amazonian rainforest Onset of irreversible melting of the Greenland ice sheet Extreme Extreme Weather Weather Events Events Rising intensity of storms, forest fires, droughts, flooding andheat waves Small increases in hurricane intensity lead to a doubling of damage costs in the US Many species face extinction (20 –50% in one study) Severe impacts in marginal Sahel region Rising number of people at risk from hunger (25 –60% increase in the 2080s in one study with weak carbon fertilisation), with half of the increase in Africa and West Asia. Entire regions experience
major declines in crop yields (e.g. up to one third in Africa) 2 Wigley, T.M.L. and S.C.B. Raper (2001): 'Interpretation of high projections for global-mean warming', Science 293: 451-454 based on Intergovernmental Panel on Climate Change (2001): 'Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change' [Houghton JT, Ding Y, Griggs DJ, et al. (eds.)], Cambridge: Cambridge University Press. 3 Murphy, J.M., D.M.H. Sexton D.N. Barnett et al. (2004): 'Quantification of modelling uncertainties in a large ensemble of climate change simulations', Nature 430: 768 - 772 4 Meinshausen, M. (2006): 'What does a 2°C target mean for greenhouse gas concentrations? A brief analysis based on multi-gas emission pathways and several climate sensitivity uncertainty estimates', Avoiding dangerous climate change, in H.J. Schellnhuber et al. (eds.), Cambridge:
Cambridge University Press, pp.265 - 280. 13 new investments made, new infrastructure put in place, and changes occur in the decisions, practices, and behaviors of millions of business managers, workers, and consumers. Exhibit 4 A “CARBON REVOLUTION” NEEDS TO BE THREE TIMES FASTER THAN THE INDUSTRIAL REVOLUTION RISE IN LABOR PRODUCTIVITY Source: Contours of the World Economy 1 – 2030 A.D., Maddison, 2007; McKinsey analysis 0
2 4 6 8 10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Index Year 0 = 1 Years Carbon productivity growth required 2008–50 US labor productivity growth 1830–1955
Technological innovation often plays a critical role in productivity growth, but just as important are changes in the wider political, institutional, and cultural environment that enable technologies to be exploited and provide incentives for their deployment. For example, the productivity increases of the Industrial Revolution were partly the result of technological innovations such as the spinning jenny and the steam engine. But just as important were innovations in the way people organized and managed their businesses, such as Richard Arkwright’s creation of the first large-scale factories, Henry Ford’s invention of the production line, or Alfred Sloan’s development of the divisionalized
corporation. These technological and organizational innovations were in turn encouraged and enabled by a series of changes in government policy, institutional structures, and the regulatory environment. For example, governments created a legal framework for public companies, enabling large amounts of capital to be pooled for the first time. They also strengthened property rights, enabling businesses to make long-term investments. And they passed consumer protection laws, enabling customers to trust the products and services they were buying, thus spurring demand.
15 Exhibit 5 The first conclusion that stands out is that a significant portion of the abatement potential, approximately 7 gigatons of annual emissions on the left side of the curve, would be at a negative cost to society. In other words, these actions would earn a positive economic return derived largely from savings in energy costs through, for example, more energy-efficient lighting or more fuel-efficient vehicles. The second key point is that under the cost curve’s assumptions, the world can achieve the 27 gigatons per year of abatement required in 2030
to stay below 500 ppmv for a marginal cost of under €40 per ton. Finally, the cost curve counters a number of myths about carbon abatement—for example, that there are only limited low-cost abatement opportunities in the developed world or that we can only achieve abatement with new technologies (Exhibit 6). If the world were to take these abatement actions, the annual total cost to society would be €500 billion–€1,100 billion in 2030 or 0.6–1.4 percent of that year’s projected global GDP, assuming growth continues on its long-term trend. This cost estimate is roughly in the middle of the range of 0.2– 3.0 percent of
Stanley Reynolds Energy Efficiency Energy EfficiencyImprovements in energy efficiency are a major part of all proposals for significant reductions in global GhG emissions. Higher energy efficiency can also yield other air quality improvements as energy use fallslower emissions from autos & electricity generatorsMoreover, energy efficiency gains are often described as being among the very least expensive ways to reduce emissions. Indeed, it is often argued that many energy efficiency gains can be achieved with direct
economic benefits that exceed the costs. As environmental economist Robert Stavins puts it: EE gains are not just a free lunch, they will pay you to have lunch.For example, see the McKinsey global GhG emissions abatement curve Energy EfficiencyImprovements in energy efficiency are a major part of all proposals for significant reductions in global GhG emissions. Higher energy efficiency can also yield other air quality improvements as energy use fallslower emissions from autos & electricity generatorsMoreover, energy efficiency gains are often described as being among the very least expensive ways to reduce emissions. Indeed, it is often argued that many energy efficiency gains can be achieved with direct economic benefits that exceed the costs. As environmental economist Robert Stavins puts it: EE gains are not just a free lunch, they will pay you to have lunch.For example, see the McKinsey global GhG emissions abatement curve Global CO2 Abatement Cost
An Energy Efficiency (EE) GapMcKinsey MAC graph highlights EE opportunitiesIt includes many EE improvements with positive NPV (negative abatement cost)But there is also lots of evidence that firms and households are slow to make EE investments on autos, appliances, HVAC, housing, etc.Big question – WHY aren’t more of these investments being made?? An Energy Efficiency Gap Economic studies from 1980s document very high implicit discount rates implied by consumer choices over appliances with different costs and energy efficiencies. Analysts using a variety of methodologies found implicit discount rates ranging from 25 % to over 100 %.incentives (e.g., landlord vs tenant) An Energy Efficiency Gap Economic studies from 1980s document very high implicit discount rates implied by consumer
choices over appliances with different costs and energy efficiencies. Analysts using a variety of methodologies found implicit discount rates ranging from 25 % to over 100 %.Possible reasons for an EE gap? Lack of information about EE opportunities, or about size of savingsLack of access to financing for householdsMyopia or bounded-rationality of decision-makersSplit incentives (e.g., landlord vs tenant) Federal EE Programs National Appliance Energy Conservation Act (1987) Mandatory standards for energy efficiency of household appliances. To ensure that manufacturers are building products that are at the maximum energy efficiency levels that are technically feasible and economically justified. Energy Star program - https://www.energystar.gov/ Corporate Average Fuel Economy (CAFÉ) Regulations first enacted by Congress in 1975, after the 1973– 74 Arab Oil Embargo, to improve the average fuel economy of cars and light trucks produced for sale in the US
State/Local EE ProgramsState ProgramsThere are many, many state-level EE programs – see the Database for State Incentives for Renewables and Energy Efficiency (DSIRE)ArizonaProperty tax exemption for certain energy-efficient technologies or improvementsAZ Corporation Commission Energy Efficiency mandate for utilities Utility Programshttps://www.tep.com/efficiency/ Potential Market FailuresPotential Policy Options Energy market failures Environmental externalities Emissions pricing (tax, cap-and- trade) Average-cost electricity pricing Real-time pricing; market pricing Energy security Energy taxation; strategic reserves Capital market failures Liquidity constraints Financing/loan programs Innovation market failures
R&D spillovers R&D tax credits; public funding Learning-by-doing spillovers Incentives for early market adoption Information problems Lack of information; asymmetric info Information programs Principal–agent problems Information programs Learning-by-using Information programs Potential Behavioral Failures Bounded rationality Education; information; product standards Heuristic decision-making Education; information; product standards Buildings and EEWe know that there are some market failures regarding energy use for buildingsWhat does EE look like in the building/construction sector? U.S. Energy Consumption by Sector*
* DOE Buildings and Energy Data Book (2010): http://buildingsdatabook.eere.energy.gov/ Residential BuildingUses more energy than commercial buildingsMajor industry in Arizona (historically)Pepper Viner HomesWhat are main household energy uses? See next slide * DOE Buildings and Energy Data Book (2010): http://buildingsdatabook.eere.energy.gov/ How do I know if my house or building is green?The world of environmental certification for homes and buildingsUS Green Building Council introduced its Leadership in Energy and Environmental Design (LEED) program in 2000LEED Video ClipOther private programsE.g. – NAHBGreen – National Green Building ProgramFederal programsEnergy Star
Evidence on value of green certification for commercial buildings? Split incentive problem can cause market failure – Do we need government intervention to solve this?A couple of recent studies provide evidence that green certification is a signal translates into extra market value – the studies and results are described in: http://insight.gbig.org/leed-value-of-a-market- signal/ Installed costs lagged wholesale PV module price (2007-2009) decline $0.2/W compare with $1.3/W * Economic Impact of EEEE programs lead to appliances – refrigerators, AC units, TV’s, … – that are less costly to use, since they use less energy.Does this cause behavior to change?? Do EE standards deliver?Note that more energy efficiency means that the effective price-per-unit of energy services fallsGreater EE implies LOWER price per unit of energy
servicesWhat happens when you lower the price of something??For EE, this change in behavior is called the rebound effect.An extreme version is, backfire effect. Energy Efficiency in TransportationThe centerpiece of US energy efficiency policy for transportation is the Corporate Average Fuel Economy (CAFÉ) policyCAFÉ established in 1975Mandated an 18 MPG fleet average for model year 1978CAFÉ progressively raised since 1978 for cars and light trucks CAFÉ over time Year CarsLight Trucks 1980 2015 1990 27.520 2000 27.520.7 2010 27.523.5 2015 35.028.2 2025* (goals) 54 46
CAFÉ ComputationsEach automaker is required to meet the CAFÉ MPG standard for the average MPG of their fleet of new model autos (and light trucks).An automaker that sells cars in U.S. must pay penalties if its fleet doesn’t meet CAFÉ standardCurrently $55 per MPG above standard, per vehicle sold (had been scheduled to rise to $140) Do EE standards deliver? Reformed CAFÉ*In 2008, under the Energy Independence and Security Act (EISA), NHTSA’s authority to set the CAFE standards was altered. First, EISA mandated attribute-based standards for cars, meaning that each vehicle would be subject to its own standard based on its attributes (in this case vehicle size), rather than using one standard for all cars or for all trucks. In addition, under the new rules, NHTSA was required to set standards for vehicle fuel efficiency each year (whereas
before, the agency was allowed to do so) and was required to set them at the “maximum feasible” levels through 2030. The other major change was that EPA was given authority to regulate GHG emissions and would now do so under the CAFE standards. The so- called “reformed standards” were established jointly by NHTSA and EPA, with the first phase for model year (MY) 2012–2016 vehicles finalized in 2011, and the second phase for MY 2017– 2025 vehicles finalized in August of 2012. * See Virginia McConnell, RFF 2013 Main Economic Effects of CAFÉ StandardBenefits of reducing U.S. demand for oilSmaller macroeconomic effects of oil price shocksCounter OPEC market power (reduce oil price mark-ups and final oil prices)Reduced environmental impactsLower CO2 emissionsCan we quantify these benefits?Improvements in local air quality? Main Economic Effects of
CAFÉ StandardChanges in incentives for automakersPricing and production of auto and truck models – go through analysis in classIncentives for design features and for R&D and innovationAre there market failures here?Auto designAuto R&D and innovation Other Economic Effects of CAFÉ StandardRole of prior fuel taxesRebound EffectCAFÉ standard reduces gallons/mile; may increase # miles travelled (by lowering relative cost of driving)Rebound effect likely offsets 10 – 20% of overall fuel reductionMore miles travelled Greater congestion costsMore accidents/deathsFleet mix and safety 15 Exhibit 5 The first conclusion that stands out is that a significant portion of the abatement
potential, approximately 7 gigatons of annual emissions on the left side of the curve, would be at a negative cost to society. In other words, these actions would earn a positive economic return derived largely from savings in energy costs through, for example, more energy-efficient lighting or more fuel-efficient vehicles. The second key point is that under the cost curve’s assumptions, the world can achieve the 27 gigatons per year of abatement required in 2030 to stay below 500 ppmv for a marginal cost of under €40 per ton. Finally, the cost curve counters a number of myths about carbon abatement—for example, that there
are only limited low-cost abatement opportunities in the developed world or that we can only achieve abatement with new technologies (Exhibit 6). If the world were to take these abatement actions, the annual total cost to society would be €500 billion–€1,100 billion in 2030 or 0.6–1.4 percent of that year’s projected global GDP, assuming growth continues on its long-term trend. This cost estimate is roughly in the middle of the range of 0.2– 3.0 percent of 1
Economics 473 Spring 2020 Problem Set Four – Answer Notes 1. (10 points) The graph below depicts the marginal benefit (MB) of carbon emissions and the social cost of carbon (SCC) curve. Use this graph to answer the questions below. a) Explain in words why the socially optimal level of Carbon emissions is not zero. In the graph above, at a level of zero emissions, the marginal benefit (MB) from emissions is much higher than the marginal cost (or, social cost of carbon, SCC) of emissions. From an economic point of view, some modest positive level of emissions has economic benefits that dramatically exceed the economic damages associated with the emissions. My own view is that the real-world empirical situation regarding zero emissions is similar to what is depicted
in the graph above. b) Suppose that the government sets a tax on Carbon emissions (e.g., a carbon tax). Show the optimal level for the tax on emissions on the graph. Explain in words how the tax is determined. Whatever the per unit tax on carbon emissions is, polluters would (collectively) choose their amount of emissions at the quantity where the tax rate intersects the MB curve. So, if you want to achieve the optimal emissions level E* (and optimal emissions reductions), set the tax rate at the dollar amount equal to the dollar amount where MB and SCC intersect. In order to implement this in practice, you would have to know – or have estimates of – the MB and SCC curves. c) Suppose instead that the government establishes a cap and trade program for Carbon emissions. Show the optimal level for the emissions cap on the graph. Compare the outcome this cap and trade program with the outcome for the emissions tax from part (b). The cap should be set equal to E*. The outcome in terms of
emissions and emissions abatement would be the same as with the tax in part (b). The cost of abatement would also be the same. The cost of acquiring permits in the cap and trade program may differ from the tax payments under emissions taxation, depending on how permits are issued under cap and trade. $ Emissions SCC MB E* E’ p’ 2
d) Now suppose that the government sets a cap that is too lenient; that is, the cap allows more emissions than the optimal cap you identified in part (c). Show the equilibrium trading price for emissions permits with this ‘too lenient’ cap, and compare this equilibrium trading price with the optimal emissions tax from part (b). Let’s say the cap is set at E’ > E*. The equilibrium trading price is determined by the MB at the capped emissions quantity of E’. This is illustrated by price p’ on the graph. e) The social cost of carbon is based on the net present value of current and future damages from an additional ton of carbon emissions now. Suppose that a higher social discount rate is used to calculate these damages. Explain how this change in the social discount rate would affect the position of the SCC curve, and the optimal current level of emissions and emissions abatement. Since much of the damage from CO2 emissions are in the
future, a higher social discount rate would lower the NPV of damages from current CO2 emissions and reduce (shift down) the SCC curve. This shift would increase optimal emissions and reduce optimal emissions abatement. 2. (5 points) Explain in words the meaning of the term, ‘levelized cost of energy’. LCOE is often used to compare the costs of different technologies for generating electricity. Explain pros and cons of using LCOE for such comparisons. LCOE is equal to the constant price per unit energy that would equate NPV of revenues to NPV of costs. Alternatively, a measure of the real average total cost of generation over the lifetime of a generation plant. PRO – LCOE provides a way to compare average costs of different methods for generating electricity. CON – LCOE is focused on cost rather than benefits or value. Value of different technologies may differ due to: (1) timing of generation – e.g., wind turbines tend to generate electricity at night when
electricity is not valuable, (2) dispatchable vs. non-dispatchable generators. 3. (10 points) A variety of government policies promote greater energy efficiency. Examples include programs for more energy efficient lighting and the federal CAFÉ program for autos and light trucks. Consider the following claim – “Government policies promoting energy efficiency are bound to fail because of the rebound effect. These policies have the unintended consequence of lowering the effective price of energy services and thereby encouraging greater energy use.” Discuss this claim and explain whether or not you agree with it. It’s true that EE policies have this unintended consequence. You can make an argument on either side of this. The argument turns on how large the rebound effect is. If this effect is large for a particular EE program, then the claim is basically correct – an example would be the Mexico Cash for Coolers program for air conditioners. If the effect is small – which is likely true for most programs, then the claim is wrong.
Final Take-Home Exam Economics 473 - Spring 2020 NAME________________________ There are 6 questions worth a total of 150 points. Make sure to answer all parts of all questions fully on the exam. Write your answers in sentences. Make sure to explain where any numerical results that you derive come from. A final answer without a clear explanation of how you obtained it is a failing answer. In cases where your final answer may be incorrect, many of the logical steps you followed might well be correct. We will not be able to award you partial credit if you do not show the process through which you obtained your answer. You may use the lecture notes, the videos, the slides, D2L course material, your assignments, and the readings when answering the questions. You may not use any other material. Good luck and Perform well. 1. (20 points) Renewable energy technologies such as wind turbines and solar photovoltaic panels are characterized by variable and intermittent power generation. a. Why does renewable intermittency present problems for the
electric power grid? b. What can electricity utilities do to address problems posed by intermittency? 2. (36 points) Consider a dispatchable electricity generation technology and an intermittent renewable generation technology. The 2 technologies have the following characteristics: Dispatchable Technology Renewable Technology Annual Construction cost ($/MW/yr) $200,000 $100,000 O&M fixed cost ($/MW/yr) $36,520
$22,640 Operating cost ($/MWh) $30 $0 Capacity factor 90 % 20 % Note that construction and O&M fixed costs have been listed on an annual basis per MW of capacity. a) Find the levelized cost of energy (LCOE) in $/MWh for each technology. b) Would a subsidy for the renewable technology be required in order for it to achieve the same LCOE as the dispatchable technology? c) Electricity generated from these technologies can be sold into the wholesale market. Suppose the wholesale price of electricity is $100/MWh during peak hours (which occur one-half of the time) and $30/MWh during off-peak hours (the other half of the time). Assume that one-half of generation occurs during peak
hours and the other half occurs during off-peak hours for each technology. Find the wholesale market profits per MW per year for each technology. Does the technology with the lower LCOE yield the most profit per MW per year? Explain why or why not. d) Suppose as in part c that electricity generated from these technologies can be sold into the wholesale market. Assume that the wholesale price of electricity is $100/MWh during peak hours (which occur one-half of the time) and $30/MWh during off-peak hours (the other half of the time). Now assume that one-half of dispatchable generation occurs during peak hours and the other half occurs during off-peak hours and assume that all of the renewable generation occurs during peak hours. Find the wholesale market profits per MW per year for each technology. Does the technology with the lower LCOE yield the most profit per MW per year? Explain why or why not. 3. (24 points) The graph below depicts the marginal benefit (MB) of carbon emissions and the social cost of carbon (SCC) curve. The MB curve is linear, starting at $100/ton at zero emissions and falling to $0/ton at emissions equal to 1,000 tons/day. SCC is constant at $60/ton. Use this graph to answer the questions below.
$100 SCC $60 MB CO2 Emissions 1,000 tons/day
a) Suppose that the government sets a tax on Carbon emissions (e.g., a carbon tax). What is the optimal tax on CO2 emissions for the situation depicted in the graph? How much emissions abatement would occur? How much tax revenue would this tax raise? b) Suppose instead that the government establishes a cap and trade program for CO2 emissions, and sets the cap for CO2 emissions at 800 tons/day. How much emissions abatement is required to meet this cap? What is the total cost of emissions abatement? c) The social cost of carbon is based on the net present value of current and future damages from an additional ton of carbon emissions now. Suppose that a higher social discount rate is used to calculate these damages. Explain how this change in the social discount rate would affect the position of the SCC curve, and the optimal current level of emissions and emissions abatement. 4. (30 points) The government provides subsidies for consumers who purchase energy efficient appliances and automobiles. a) What is the argument for providing such subsidies? What
market failure(s) are these subsidies aimed at correcting? One type of unintended consequence of this kind of subsidy policy is the rebound effect. b) Explain what the rebound effect is; use one or more examples in your explanation. Explain why it is important to consider the rebound effect when estimating the impact of the subsidy policy on energy use. c) How would the size of a rebound effect depend on the price elasticity of household demand for energy (for example, the demand for electricity for appliances or the demand for gasoline for autos)? Explain. 5. (24 points) Environmental externalities can lead to illness and death for people. a. Explain the concept of 'value of statistical life' (VSL). b. Explain how VSL factors into the economic benefits of reducing greenhouse gas emissions. c. Suppose that new evidence emerges to show that VSL is lower than previously believed. How would this new evidence affect your recommendations about what to do about greenhouse gas emissions and global warming.
6. (16 points) Electricity restructuring led to deregulated wholesale electricity markets in many parts of the U.S. Merchant generation suppliers compete with one another to supply electricity in these markets. Market power (or, monopoly power) is a potential concern when the number of suppliers in a market is fairly low. Why might supplier market power be a serious concern in wholesale electricity markets? Explain your answer. 1

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Electricity Restructuringand Competition.docx

  • 1. Electricity Restructuring and Competition Electricity MarketsTraditional Organization of Electricity Provision in U.S.Govt regulated, vertically integrated public utilitiesLast 25 YearsWave of restructuring and introduction of electricity trading and competition across U.S. and other nationsNow large-scale wholesale electricity markets operate across many parts of U.S. Factors leading to restructuring & deregulation Prior track record of deregulating energy markets and other industries once considered natural monopolies telecomm, airlines, railroads, High electricity prices in spite of regulation In U.S. states such as NY, California, New England
  • 2. In other countries, such as U.K. As the U.S. electricity transmission grid was built out in the 2nd half of 20th century, electric utilities started buying and selling energy from each other This illustrated feasibility of trading energy across the grid in markets Political Push 2nd Bush President Electricity Restructuring – A How-To GuideUnbundle generation from transmission and distributionIt’s important to allow multiple business firms to own and operate generation plants (independent power producers (IPPs) or merchant generators)This may require forcing some vertically integrated IOUs to divest some of their generation assets Electricity Restructuring – A How-To GuideEstablish an organized market for wholesale electricity tradingSellers are firms like merchant generators that inject energy into the gridBuyers are retail distributors and industrial users that withdraw energy from the gridMarket rules might include an advance-trading (e.g., day-ahead) market as well as a real-time balancing market Electricity Restructuring – A How-To GuideSet up an organization to manage and coordinate energy flows over the
  • 3. gridThis could be an independent system operator (ISO) or a regional transmission organization (RTO)Key responsibilitiesBalance energy supply and demand in real timeMaintain system reliabilityCoordinate new transmission investments Wholesale Market Competition Electricity Restructuring – A How-To GuideRetail distributionOwnership and operation of local distribution networks would typically continue as regulated public utilities (natural monopoly rationale)But it is feasible to introduce retail competition via brokers and energy re-sellers who purchase wholesale energy, re-sell it to retail customers, and pay fees to local distribution companies for use of the local network. Wholesale Electricity MarketsLet’s examine a perfect competition model of a short-run (hourly) wholesale electricity marketDemand SideDemand varies hour by hour, as weather conditions and desired electricity usage changeVery price inelastic – most retail customers pay fixed retail pricesSupply SideMost supply comes from fossil fuel generatorsFF supply is driven by cost of generation and capacities of generation units.Generation from renewables is intermittent Fossil Fuel Generation and SupplySupply curve from FF generation is comprised of a series of steps –Width of step is generation capacity of a unitHeight of step is marginal cost
  • 4. (MC) of the unitFF MC of generationMC = heat rate x fuel cost + emissions rate x emissions tax rate Generation Example TypeMarginal Operating CostCapacity Nuclear$12/MWh1000 MW Coal$25/MWh2500 MW Gas$55/MWh1500 MW Turbine (peaker)$90/MWh 500 MW Example with Supply and DemandDraw the competitive supply curve for the production of electric energy on this system Assume that demand is 3000 MW and is completely price inelastic in the very short run. What would be the spot price in a perfectly competitive wholesale electricity market? Assume that demand is 4500 MW and is completely price inelastic in the very short run. What would be the competitive spot price be? What if there is a downward sloping demand function:D(P) = 5050 – 10PWhat is the spot price in a perfectly competitive market?Assume that demand is 6000 MW for prices up to $110/MWh, but that 600 MW of this demand would be willing to be curtailed for prices above $110/MWh or more. What is the perfectly competitive market price in this case? Competitive Supply Curve
  • 5. Example - continuedLet’s use the example to tease out wholesale purchase costs for buyers (distributors).Assume that demand is 3000 MW for 8 hours/dayAssume that demand is 4500 MW for 12 hours/dayAssume that demand is 6000 MW for prices up to $110/MWh, but that 600 MW of this demand would be willing to be curtailed for prices above $110/MWh or more for 4 hours/day Example - continuedLet’s use the example to tease out wholesale purchase costs for buyers (distributors).Assume that demand is 3000 MW for 8 hours/dayAssume that demand is 4500 MW for 12 hours/dayAssume that demand is 6000 MW for prices up to $110/MWh, but that 600 MW of this demand would be willing to be curtailed for prices above $110/MWh or more for 4 hours/dayThen prices are:P = $25 for 8 hours/dayP = $55 for 12 hours/dayP = $110 for 4 hours dayAverage purchase cost for buyers = (8*$25+12*$55+4*$110)/24 = $50.4/MWh Renewable Energy in Wholesale MarketsWhat happens when renewable energy (wind turbines, solar panels) generation capacity is added?Let’s look at how the example changes when 1000 MW of solar photovoltaic generation capacity is added to the grid.Impact on wholesale prices and price volatility?
  • 6. Competitive Supply Curve with 1000 MW renewable capacity added Electricity Markets over the GridWholesale Electricity CompetitionThe scope and size of the market can be expanded by using the transmission network to connect buyers and sellers. A bigger market can enable more suppliers to compete for sales.If there are no transmission bottlenecks, then a grid- connected area can operate as a single market with a single price.But sometimes transmission links will be capacity- constrained, and prices in different parts of the network will differ.Locational Marginal PricesCompetitive prices at each node of the network that take into account transmission constraints.See next 2 slides for LMP contour mapsGoogle MISO-PJM LMP contour map to see current map MISO-PJM pricing contour map – evening March 11, 2019 MISO-PJM pricing contour map – afternoon March 12, 2019 Trouble w/ Electricity Markets?California restructured its electricity industry and opened wholesale market to competition in 1998California Energy Crisis of 2000Why?High summer demandLarge drop in hydro-electricity importsSpike in natural
  • 7. gas pricesMarket power of IPPs – especially at peak demand timesImpactBIG increase in wholesale prices (~400- 500%)Distco’s squeezedRolling blackouts End of California CrisisDistribution utilities nearly bankrupt by early 2001California ended its restructuring experiment in 2001State govt negotiates new supply contracts for utilities (very costly contracts!)Arnold gets elected governorRestructuring grinds to a halt in most of Western U.S. Why did restructuring flop in CA?Problems with CA restructuring planLimits on long-term forward contracts between generation suppliers and distribution utilitiesLack of retail buyer price response (no real time pricing)NIMBY obstacles to generation investments (not enough slack in system) 18 THE STANDARD PRESCRIPTION FIGURE 2.1 Physical functions of electricity. FINAL CUSTOMERS ( RETAIL SALES ) OFFICE HOUSEFACTORY LOCAL DISTRIBUTION
  • 8. SYSTEM GENERATION ( POWER PLANTS ) TRANSMISSION NETWORKS ( GRID ) FLOW OF POWERMETER SYSTEM OPERATIONS Reforming the Industr y 45 This is all we will say about Model 2 structural issues. Models 3 and 4 are where the action is. It is, however, worth remembering that the new trading arrangements can be developed and put into operation before Mod- els 3 and 4 are actually introduced. Transmission and system operations can be separated from generation and new trading arrangements instituted be- fore any deregulation takes place or any competitors enter the market. In the United States, this would be somewhat similar to the operation of the old tight pools, where (for many years, and well before the introduction of competition) the final price to customers was regulated but the dispatch and transmission were coordinated over a wide area, and the pricing
  • 9. rules for wholesale sales between companies were approved by FERC. This should not be taken as a proposal that the rules of the old tight pools should be adopted in the United States. However, the rules we do propose for trading arrangements later in the text could be adopted in modified form even be- fore wholesale competition is introduced. M o d e l 3 : W h o l e s a l e C o m p e t i t i o n Model 3 as we define it here has a fully competitive generating sector. There is no cost-of-service regulated generation. Distribution companies (now FIGURE 3.3 Model 3—wholesale competition. DISTCO CUSTOMER DISTCOLARGECUSTOMER LARGE CUSTOMER CUSTOMER ENERGY SALES IPPIPP IPP IPP IPP TRANSMISSION WIRES
  • 10. WHOLESALE MARKETPLACE Renewable Energy Main Types of Renewable EnergyDispatchable RenewablesHydroelectric PowerGeothermalVariable Energy ResourcesWind TurbinesOn-shoreOff-shoreSolarHot water heatersConcentrating solar power (also called, solar thermal)Solar Photovoltaic (PV) Concentrating Solar Power Solana station 280 MW parabolic trough solar plant, 70 miles SW of Phoenix
  • 11. Ivanpah, CA Solar Tower Plant (377MW, $2.2 billion) Solar PVA PV cell consists of two or more thin layers of semi- conducting material, most commonly silicon. When the silicon is exposed to light, electrical charges are generated and this can be conducted away by metal contacts as direct current (DC). The electrical output from a single cell is small, so multiple cells are connected together and encapsulated (usually behind glass) to form a module (sometimes referred to as a "panel"). The PV module is the principle building block of a PV system and any number of modules can be connected together to give the desired electrical output.Modules are connected to inverters, which convert DC power into AC power. Crystalline silicon PV panels Solar PVTypesCrystalline siliconThin filmConcentrating PVSet UpFixed tilt (land vs. rooftop); facing directionSingle axis trackingDouble axis trackingScaleUtility scale > 1 MW Distributed generation (rooftops, building sites) 1 kW – 1 MW
  • 12. Room for solar?Consider the following factsUS electricity consumption = 4 billion MWh/yrSolar PV capacity factor (AZ) = 20 %Solar PV space requirement = 3 acres/MW640 acres land per square mileHow much land would be required to supply all US electricity consumption from solar PV? Growth in RenewablesI’ll focus on wind and solar – Fastest growing renewables in electricityWind turbine capacity growing ~ 20%/yr in USSolar PV capacity growing ~ 30%/yr in USOther renewablesHydro, geo-thermal, bio-fuelsWhy so much growth in wind & solar?Falling prices Favorable policies – subsidies, tax credits, RPS, …Concerns about environmental impacts of fossil fuel use Solar Generation as % of Total Generation, 2018 Experience Curve (Learning by Doing)
  • 13. Economic comparisons of electricity generation technologiesCommon metric in energy industry is the levelized cost of energy (LCOE)LCOE is equal to the constant price per unit energy that would equate NPV of revenues to NPV of costs; alternatively; a measure of the real average total cost of generation over the lifetime of a generation plant EIA estimates of LCOE* * Advanced coal has total system LCOE = 139 Classifying generation technologiesDispatchable generatorsCoal, gas combined-cycle, nuclear,…Can be controlled by a system operator; can be turned on and off based on economic conditionsCan provide electricity generation as well as reliability services – such as spinning reserves and frequency regulationIntermittent generatorsWind, solar PV, solar thermalProduction depends on weather conditionsCan’t be controlled by system operator (unless coupled with energy storage) Public (or Social) vs Private Economics of Renewable EnergyPublicFocus on overall economic costs and benefits, taking into account things like environmental benefits, time- varying benefits and costs, electricity system reliability, …
  • 14. Public (or Social) vs Private Economics of Renewable EnergyPublicFocus on overall economic costs and benefits, taking into account things like environmental benefits, time- varying benefits and costs, electricity system reliability, …PrivateLook at costs and benefits from viewpoint of an individual decision-maker:household considering installing solar panels (EXCEL)Electric utility considering investing in wind turbinesThese costs and benefits would be evaluated after the effects of subsidies and/or tax breaks Economic value of intermittent renewable generationTiming of generationDoes generation occur when electricity is valuable?Timing of (on-shore) wind, vs solar PVSolar PV vs. solar thermalEnvironmental benefitsWhat fossil fuel generators are displaced? How much of each type of emission is reduced; how do you value emissions reductions?Grid integration costsAt higher penetration of renewables, intermittency leads to more supply variability and could reduce system reliability.Does this require more spinning reserves, more backup generation capacity? Short run value of intermittent renewable generation*Notationy = electricity load (quantity demanded) per period (random)C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)EM(x) = emissions associated with producing qty x; ϕ =EM’(x)τ = $ damages per ton of emissionsK = renewable generation capacitys = renewable generation output per unit of capacity (random)
  • 15. * See Baker, et al, “Economics of Solar Electricity” Annual Review of Resource Economics, 2013. Short run value of intermittent renewable generationNotationy = electricity load (quantity demanded) per period (random)C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)EM(x) = emissions associated with producing qty x; ϕ =EM’(x)τ = $ damages per ton of emissionsK = renewable generation capacitys = renewable generation output per unit of capacity (random)Value of KV(K)=E[[C(y) – C(y-sK) +τ EM(y) – τ EM(y-sK)] Short run value of intermittent renewable generationNotationy = electricity load (quantity demanded) per period (random)C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)EM(x) = emissions associated with producing qty x; ϕ =EM’(x)τ = $ damages per ton of emissionsK = renewable generation capacitys = renewable generation output per unit of capacity (random)Value of KV(K)=E[C(y) – C(y-sK) +τ EM(y) – τ EM(y-sK)]Marginal value of KV’(K)=E[λ]E[s] + Cov[λ,s] + τ E[ϕ]E[s] + τ Cov[ϕ,s] Marginal Value of Renewable CapacityMarginal value depends on:E[s] = Capacity factor of renewable E[λ] = Average of marginal cost (MC) of displaced fossil fuel gen Cov[λ,s] = Covariance of renewable generation and MCτ = $ damages per ton of emissionsE[ϕ] = Average of marginal emission ratesCov[ϕ,s] = Covariance of renewable generation and ϕVariation in marginal value:Different types of renewable
  • 16. energy – e.g., wind vs. solar – will differ across many of these variables, and so their marginal value may be quite differentEven for a given type – e.g., solar – marginal value will vary based on local weather conditions and grid conditions Net Metering for Solar PVA typical residential PV array will sometimes yield more energy than the household is using. Net Metering is a policy in which the distributor (eg, utility) buys back or credits the household for energy it puts into the grid.2005 federal law - all utilities are required to offer net metering to their customers.ACC recently approved change from 1-for-1 net metering credits, to credits for excess generation based on (lower) avg wholesale elec rate Grid Parity for renewables?Grid parity for a renewable technology is sometimes described as the point at which its LCOE matches that of fossil fuel generation.Basic idea is that once grid parity is achieved, that renewable technology wouldn’t need subsidies or special incentives to compete with fossil fuel technologies.OK, but here are a few other considerationsIs it dispatchable; can it be used for reliability svcs?Timing of generation and value?Magnitude of environmental benefits?Are there major grid integration costs? 100% Renewables Goalhttps://www.forbes.com/sites/trevornace/2017/08/01/califor nia-goes-all-in-100-percent-renewable-energy-by- 2045/#3409408f570fhttp://www.pnas.org/content/pnas/112/49/1 5060.full.pdfhttp://www.pnas.org/content/pnas/early/2017/06/16 /1610381114.full.pdf
  • 17. Jacobson, et al Proceedings of National Academy of Science 2015 The large-scale conversion to 100% wind, water, and solar (WWS) power for all purposes (electricity, transportation, heating/cooling, and industry) is currently inhibited by a fear of grid instability and high cost due to the variability and uncertainty of wind and solar. This paper couples numerical simulation of time- and space-dependent weather with simulation of time-dependent power demand, storage, and demand response to provide low-cost solutions to the grid reliability problem with 100% penetration of WWS across all energy sectors in the continental United States between 2050 and 2055. Solution s are obtained without higher-cost stationary battery storage by prioritizing storage of heat in soil and water; cold in water and ice; and electricity in phase-change materials, pumped hydro, hydropower, and hydrogen. PNAS 2017 – An assessment of 100% renewables claim Previous analyses have found that the most feasible route to a
  • 18. low-carbon energy future is one that adopts a diverse portfolio of technologies. In contrast, Jacobson et al. (2015) consider whether the future primary energy sources for the United States could be narrowed to almost exclusively wind, solar, and hydroelectric power and suggest that this can be done at “low- cost” in a way that supplies all power with a probability of loss of load “that exceeds electric-utility industry standards for reliability”. We find that their analysis involves errors, inappropriate methods, and implausible assumptions. Their study does not provide credible evidence for rejecting the conclusions of previous analyses that point to the benefits of considering a broad portfolio of energy system options. A policy prescription that overpromises on the benefits of relying on a narrower portfolio of technologies options could be counterproductive, seriously impeding the move to a cost effective decarbonized energy system. Renewable SubsidiesThere is a vast array of federal, state, local and utility subsidy programs for renewables – dsireusa.org is a good info sourceMajor federal subsidies30% investment tax creditElectricity production tax creditState subsidy
  • 19. examplesRenewable Portfolio Standard (RPS) – eg California, ArizonaSales tax exemptions - AZInterest rate subsidies for loans - TexasCity solar PV subsidies - Texs Grid integration and reliability challenges for renewablesIntermittencyTiming and correlation with electricity demandRenewable cost and capacity factorDispatchable vs non- dispatchable generatorsSee Paul Joskow, American Econ Rev (2012) Load and solar output at 4 sites; 3 days in August 2011 California Duck Curve
  • 20. The duck sinks – negative mid-day wholesale prices in CAISO Why were generators willing to sell at negative prices??The production tax credit. Some renewables owners (mainly wind) are eligible for a production tax credit, which essentially pays them for every MWh they produce. So, not producing means foregoing this credit. In theory, producers will pay to sell into the wholesale market as long as they’re paying less than the tax credit. Why were generators willing to sell at negative prices??The Renewable Portfolio Standard. Under California’s Renewable Portfolio Standard (RPS), utilities are on the hook to provide 60% of their electricity from renewable sources by 2030 and 100% by 2045. The utilities sign contacts with renewable providers in order to try to meet their RPS targets; utilities pay a penalty if not met. So utilities want to encourage the renewable providers to produce. For example, under a very simple power purchase agreement, the utility would pay the renewable provider a pre-specified price per MWh irrespective
  • 21. of the wholesale market price, leaving the renewable provider no incentive to shut down when prices are negative. Why were generators willing to sell at negative prices??Operating constraints. For some power plants, varying the output level entails high costs, particularly starting and stopping the plant. I think of those as analogous to the extra fuel, plus wear and tear, planes expend taking off. So, if it costs a lot to restart a nuclear plant or a coal plant, for example, you’re willing to pay not to have to turn it off to avoid incurring those costs. Renewable Intermittency & Grid ReliabilityElectricity system operators need to balance demand and supply of electricity in real time to maintain reliabilityManaged via operating reserves and back-up generatorsIntermittency of renewables poses risks to reliability at high penetrationRole for large-scale energy storageAbsent more storage, system operators must to carry more operating reserves and back-up generatorsRole for improved use of demand-response
  • 22. Research ApproachesStructural model #1G. Gowrisankaran, S. Reynolds, M. Samano “Intermittency and the value of renewable energy” Jour Political Economy (2016)“This paper develops a method to quantify the social costs and reductions in carbon emissions from large-scale renewable energy generation. We estimate social costs by solving for the decisions that maximize total surplus under different levels of renewable energy capacity. Social costs depend crucially on (1) the variability of the source including the extent to which the variability correlates with demand; (2) the extent to which output from the source is forecastable; and (3) the costs of building backup generation required to maintain system reliability.” “Intermittency and the value of renewable energy” Data from TEP, Tucson solar sites, EPA, NOAA, EIAModel of optimal elec utility operations – includes generator dispatch, operating reserves, investment, demand response, forecastingModel parameters either estimated by GRS or drawn from estimates in other studies.Results for 10 – 20% solar generation mandatesIntermittency adds 2 – 3.5 ¢/kWh to solar PV costsSolar mandates estimated to be very costly at time of
  • 23. study, based on solar PV cost of $4.40/W; current solar PV costs much lower20% solar mandate is ‘welfare-neutral’ at PV cost of $1.50/W if SCC = $40/ton CO2 * Today in Energy March 6, 2017 U.S. wind generating capacity surpasses hydro capacity at the end of 2016 Source: U.S. Energy Information Administration, Preliminary Monthly Electric Generator Inventory Note: Data include facilities with a nameplate capacity of one megawatt and above. Installed wind electric generating capacity in the United States surpassed conventional hydroelectric generating capacity, long the nation’s largest source of renewable electricity, after 8,727 megawatts (MW) of new wind capacity came online in 2016.
  • 24. However, given the hydro fleet’s higher average capacity factors and the above- normal precipitation on the West Coast so far this year, hydro generation will likely once again exceed wind generation in 2017. Source: U.S. Energy Information Administration, Electricity Data Browser Note: Data include facilities with a nameplate capacity of one megawatt and above. Wind and hydro generation both follow strong seasonal patterns. Hydro generation typically reaches its seasonal peak in the spring and early summer, especially in the Pacific Northwest and California where about half of U.S. hydropower is produced. Across most of the https://www.eia.gov/todayinenergy/detail.php?id=30212# http://www.eia.gov/electricity/data/eia860m/ http://www.eia.gov/electricity/monthly/epm_table_grapher.cfm? t=epmt_6_02_b https://www.eia.gov/todayinenergy/detail.php?id=14611 http://www.eia.gov/electricity/data/browser/ https://www.eia.gov/todayinenergy/detail.php?id=16891 http://www.eia.gov/todayinenergy/detail.php?id=20112
  • 25. Today in Energy March 6, 2017 U.S. wind generating capacity surpasses hydro capacity at the end of 2016 Source: U.S. Energy Information Administration, Preliminary Monthly Electric Generator Inventory Note: Data include facilities with a nameplate capacity of one megawatt and above. Installed wind electric generating capacity in the United States surpassed conventional hydroelectric generating capacity, long the nation’s largest source of renewable electricity, after 8,727 megawatts (MW) of new wind capacity came online in 2016. However, given the hydro fleet’s higher average capacity factors and the above- normal precipitation on the West Coast so far this year, hydro generation will likely once again exceed wind generation in 2017. Source: U.S. Energy Information Administration, Electricity Data Browser Note: Data include facilities with a nameplate capacity of one megawatt and above. Wind and hydro generation both follow strong seasonal patterns. Hydro generation typically reaches its seasonal peak in the spring and
  • 26. early summer, especially in the Pacific Northwest and California where about half of U.S. hydropower is produced. Across most of the NREL | 26NREL | 26 19.0% 12.7% 11.2% 11.0% 10.7% 6.5% 6.4% 5.4% 4.7% 4.2% 2.3% 0% 2%
  • 27. 4% 6% 8% 10% 12% 14% 16% 18% 20% CA NV HI VT MA AZ UT NC NM NJ U.S. So la r G
  • 29. al N et G en er at io n CSP DPV UPV Solar Generation as a Percentage of Total Generation, 2018 • The role of utility versus distributed solar varies by state, with northeastern states and Hawaii relying more on DPV. Note: EIA monthly data for 2018 are not final. Additionally,
  • 30. smaller utilities report information to EIA on a yearly basis, and therefore, a certain amount of solar data has not yet been reported. “Net Generation” includes DPV generation. Net generation does not take into account imports and exports to and from each state and therefore the percentage of solar consumed in each state may vary from its percentage of net generation. Source: U.S. Energy Information Administration, “Electricity Data Browser.” Accessed April 3, 2019. NREL | 62NREL | 62 PV Experience Curve • This experience curve displays the relationship, in logarithmic form, between the average selling price of a PV module and the cumulative global shipments of PV modules. As shown, for every doubling of cumulative PV shipments, there is on average a corresponding ~22% reduction in PV module price. – In 2010, the experience rate was 20%
  • 31. • Since 2012, module ASP has been below the historical experience curve. • Analysts project that by 2022 ASP will be approximately $0.2/W and globally we will have shipped a terawatt. Source: 1976-2018: Paula Mints. "Photovoltaic Manufacturer Capacity, Shipments, Price & Revenues 2018/2019." SPV Market Research. Report SPV-Supply6. April 2019. 0.1 1 10 100 1,000 0 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 M
  • 32. od ul e A SP (2 01 8 $/ W ) Cumulative Global Shipments (MW) Historic ASP U.S. Energy Information Administration | AEO2016 Levelized Costs 6
  • 33. Table 1a. Estimated LCOE (weighted average of regional values based on projected capacity additions) for new generation resources, plants entering service in 2022 Plant Type Capacity Factor (%) U.S. Capacity-Weighted1 Average LCOE (2015 $/MWh) for Plants Entering Service in 2022 Levelized Capital Cost Fixed
  • 35. including Tax Credit2 Dispatchable Technologies Advanced Coal with CCS3 N/B Natural Gas-fired Conventional Combined Cycle 87 12.8 1.4 41.2 1.0 56.4 N/A 56.4 Advanced Combined Cycle 87 15.4 1.3 38.1 1.1 55.8 N/A 55.8 Advanced CC with CCS N/B Conventional Combustion Turbine 30 37.1 6.5 58.9 2.9 105.4 N/A 105.4 Advanced Combustion Turbine 30 25.9 2.5 61.9 3.3 93.6 N/A 93.6 Advanced Nuclear 90 75.0 12.4 11.3 1.0 99.7 N/A 99.7 Geothermal 91 27.8 13.1 0.0 1.4 42.3 -2.8 39.5
  • 36. Biomass N/B Non-Dispatchable Technologies Wind 42 43.3 12.5 0.0 2.7 58.5 -7.6 50.9 Wind – Offshore N/B Solar PV4 26 61.2 9.5 0.0 3.5 74.2 -15.9 58.2 Solar Thermal N/B Hydroelectric5 60 54.1 3.1 5.0 1.5 63.7 N/A 63.7 1The capacity-weighted average is the average levelized cost per technology, weighted by the new capacity coming online in each region. The capacity additions for each region were based on additions in 2018 -2022. Technologies for which capacity additions are not expected do not have a capacity-weighted average, and are marked as “N/B.” 2The tax credit component is based on targeted federal tax credits such as the production or investment tax credit available for some technologies. It only reflects tax credits available for plants entering service in 2022. EIA models renewable tax credits as follows: new solar thermal and PV
  • 37. plants are eligible to receive a 30% investment tax credit on capital expenditures if under construction before the end of 2019, and then tax credits taper off to 26% in 2020, 22% in 2021, and 10% thereafter. New wind, geothermal, and biomass plants receive a $23.0/MWh ($12.0/MWh for technologies other than wind, geothermal and closed-loop biomass) inflation-adjusted production tax credit over the plant’s first ten years of service if they are under construction before the end of 2016, with the tax credit for wind declining by 20% in 2017, 40% in 2018, 60% in 2019, and expiring completely in 2020. Up to 6 GW of new nuclear plants are eligible to receive an $18/MWh production tax credit if in service by 2020. Not all technologies have tax credits, and are indicated as “N/A.” The results are based on a regional model and state or local incentives are not included in LCOE calculations. 3Due to new regulations (CAA 111b), conventional coal plants cannot be built without CCS because they are required to meet specific CO2 emission standards. The coal with CCS technology modeled is assumed to remove 30% of the plant’s CO2 emissions. Coal plants have a 3 percentage-point
  • 38. adder to their cost-of-capital. 4Costs are expressed in terms of net AC power available to the grid for the installed capacity. 5As modeled, hydroelectric is assumed to have seasonal storage so that it can be dispatched within a season, but overall operation is limited by resources available by site and season. Source: U.S. Energy Information Administration, Annual Energy Outlook 2016, April 2016, DOE/EIA-0383(2016). U.S. Energy Information Administration | AEO2016 Levelized Costs 6 Table 1a. Estimated LCOE (weighted average of regional values based on projected capacity additions) for new generation resources, plants entering service in 2022 Plant Type Capacity Factor (%) U.S. Capacity-Weighted 1 Average LCOE (2015 $/MWh) for Plants Entering Service in
  • 40. N/B Natural Gas-fired Conventional Combined Cycle 87 12.8 1.4 41.2 1.0 56.4 N/A 56.4 Advanced Combined Cycle 87 15.4 1.3 38.1 1.1 55.8 N/A 55.8 Advanced CC with CCS N/B Conventional Combustion Turbine 30 37.1 6.5 58.9 2.9 105.4 N/A 105.4 Advanced Combustion Turbine 30 25.9 2.5 61.9 3.3 93.6 N/A 93.6 Advanced Nuclear 90 75.0 12.4 11.3 1.0 99.7 N/A 99.7 Geothermal 91 27.8 13.1 0.0 1.4 42.3 -2.8 39.5 Biomass N/B Non-Dispatchable Technologies Wind 42 43.3 12.5 0.0 2.7 58.5 -7.6 50.9 Wind – Offshore N/B Solar PV 4
  • 41. 26 61.2 9.5 0.0 3.5 74.2 -15.9 58.2 Solar Thermal N/B Hydroelectric 5 60 54.1 3.1 5.0 1.5 63.7 N/A 63.7 1 The capacity-weighted average is the average levelized cost per technology, weighted by the new capacity coming online in each region. The capacity additions for each region were based on additions in 2018 -2022. Technologies for which capacity additions are not expected do not have a capacity-weighted average, and are marked as “N/B.” 2 The tax credit component is based on targeted federal tax credits such as the production or investment tax credit available for some technologies. It only reflects tax credits available for plants entering service in 2022. EIA models renewable tax credits as follows: new solar thermal and PV plants are eligible to receive a 30% investment tax credit on capital expenditures if under construction before the end of 2019, and then tax credits
  • 42. taper off to 26% in 2020, 22% in 2021, and 10% thereafter. New wind, geothermal, and biomass plants receive a $23.0/MWh ($12.0/MWh for technologies other than wind, geothermal and closed-loop biomass) inflation-adjusted production tax credit over the plant’s first ten years of service if they are under construction before the end of 2016, with the tax credit for wind declining by 20% in 2017, 40% in 2018, 60% in 2019, and expiring completely in 2020. Up to 6 GW of new nuclear plants are eligible to receive an $18/MWh production tax credit if in service by 2020. Not all technologies have tax credits, and are indicated as “N/A.” The results are based on a regional model and state or local incentives are not included in LCOE calculations. 3 Due to new regulations (CAA 111b), conventional coal plants cannot be built without CCS because they are required to meet specific CO 2 emission standards. The coal with CCS technology modeled is assumed to remove 30% of the plant’s CO2 emissions. Coal plants have a 3 percentage-point
  • 43. adder to their cost-of-capital. 4 Costs are expressed in terms of net AC power available to the grid for the installed capacity. 5 As modeled, hydroelectric is assumed to have seasonal storage so that it can be dispatched within a season, but overall operation is limited by resources available by site and season. Source: U.S. Energy Information Administration, Annual Energy Outlook 2016, April 2016, DOE/EIA-0383(2016). In addition, Arizona’s Renewable Portfolio Standard mandates that 30% of total renew- able energy consist of distributed generation, e.g. solar PV on customers’ rooftops. Our model allows distributed solar to have di↵ erent capital costs from non-distributed solar and to reduce electricity transmission costs.
  • 44. Figure 2: Load and solar output for di↵ erent U.S. sites, Aug. 14-16, 2011 0.00 0.25 0.50 0.75 1.00 Aug 14 00:00 Aug 14 12:00 Aug 15 00:00 Aug 15 12:00 Aug 16 00:00 Aug 16 12:00 Aug 17 00:00 Local Time N or m al iz
  • 46. Legend Berthoud, CO Rated 9.88 kW New York, NY Rated 5.7 kW San Diego, CA Rated 5.7 kW Tucson, AZ Rated 9.2 kW (One Site Used in Analysis) 0.00 0.25 0.50 0.75 1.00
  • 47. Aug 14 00:00 Aug 14 12:00 Aug 15 00:00 Aug 15 12:00 Aug 16 00:00 Aug 16 12:00 Aug 17 00:00 Local Time N or m al iz ed L oa d (L oa d/ M ax
  • 48. im um L oa d) Legend Xcel Energy Inc. (Berthoud, CO) New York ISO (New York, NY) San Diego Gas & Electric (San Diego, CA) Tucson Electric Power (Tucson, AZ) Note: The Berthoud, New York, and San Diego solar generation data are from SMA Solar Technology AG’s Sunny Portal (https://www.sunnyportal.com/). The Tucson data
  • 49. displayed here are from one of 58 sites used in our main analysis and are from the University of Arizona Photovoltaics Lab. The site shown here was chosen because it is the near the center of the city. The load data are from Federal Energy Regulation Commission Form 714. Load is measured hourly while solar output is measured at the 15-minute level. To illustrate the issues of intermittency, Figure 2 shows load and solar PV output for four sites across the U.S., for three summer days during our sample period, Aug. 14-16, 2011. We chose three sites from the Western U.S. with high solar potential (Figure 1), as well as New York. During these three days, the solar installation in New York produces far below its rated capacity at all times. The other installations all reach a peak output of 75% of 9
  • 50. The first ramp of 8,000 MW in the upward direction (duck’s tail) occurs in the morning starting around 4:00 a.m. as people get up and go about their daily routine. The second, in the downward direction, occurs after the sun comes up around 7:00 a.m. when on-line conventional generation is replaced by supply from solar generation resources (producing the belly of the duck). As the sun sets starting around 4:00 p.m., and solar generation ends, the ISO must dispatch resources that can meet the third and most significant daily ramp (the arch of the duck’s neck). Immediately following this steep 11,000 MW ramp up, as demand on the system deceases into the evening hours, the ISO must reduce or shut down that generation to meet the final downward ramp. Flexible resources needed To ensure reliability under changing grid conditions, the ISO needs resources with ramping flexibility and the ability to start and stop multiple times per day. To ensure supply and demand match at all times, controllable resources will need the flexibility to change output levels and start and stop as dictated by
  • 51. real-time grid conditions. Grid ramping conditions will vary through the year. The net load curve or duck chart in Figure 2 illustrates the steepening ramps expected during the spring. The duck chart shows the system requirement to supply an additional 13,000 MW, all within approximately three hours, to replace the electricity lost by solar power as the sun sets. Oversupply mitigation Oversupply is when all anticipated generation, including renewables, exceeds the real-time demand. The potential for this increases as more renewable energy is added to the grid but demand for electricity does not increase. This is a concern because if the market cannot automatically manage oversupply it can lead to overgeneration, which requires manual intervention of the market to maintain reliability. During oversupply times, wholesale prices can be very low and even go negative in which generators have
  • 52. to pay utilities to take the energy. But the market often remedies the oversupply situation and automatically works to restore the balance between supply and demand. In almost all cases, oversupply is a manageable condition but it is not a sustainable condition over time — and this drives the need for proactive policies and actions to avoid the situation. The duck curve in Figure 2 shows that oversupply is expected to occur during the middle of the day as well. Because the ISO must continuously balance supply and demand, steps must be taken to mitigate Figure 2: The duck curve shows steep ramping needs and overgeneration risk www.caiso.com | 250 Outcropping Way, Folsom, CA 95630 | 916.351.4400 CommPR/2016 © 2016 California ISO California Independent System Operator 3
  • 53. Energy, Externalities & Climate Emissions & ExternalitiesMuch of energy produced and used in U.S. and around the world is from fossil fuelsBurning fossil fuels yield air-borne emissionsSO2NOxCH4VOCPM2.5Mercury (from coal)CO2 Emissions & ExternalitiesMuch of energy produced and used in U.S. and around the world is from fossil fuelsBurning fossil fuels yield air-borne emissionsSO2NOxCH4VOCPM2.5Mercury (from coal)CO2So??Damage to natural environment, cropsDamage to buildings, infrastructureMost important of all – ill health and even deathCO2 – greenhouse gas that can affect
  • 54. climate EmissionsOK, sure there is some bad stuff coming from fossil fuels, but they yield a tremendous amount of valuable, low-cost energyThe economic problem is negative externalities. If the people who produce and/or consume fossil fuels actually (somehow) bear the costs and adverse consequences of emissions, then you could say the benefits of this energy outweigh the costs.But if NOT then this production/consumption imposes negative externalities on others. As a result, too many fossil fuels are produced and consumed. Energy and the EnvironmentBefore Climate Change (BCC) EraMajor environmental challengesAcid rain – SO2 and NOx emissions from burning coalUrban smog and air pollution – mainly from NOx , PM, and VOC from cars/trucksPolicies in BCC EraClean Air Act Amendments of 1990Established EPA Acid Rain Program – Innovative Cap & Trade ProgramRestricted auto emissions via technology standards
  • 55. (e.g., catalytic converters), tighter CAFÉ standards, and blended fuel requirements Energy and Climate Change Energy and Climate ChangeProduction and consumption of fossil fuels results in greenhouse gas (GhG) emissions – mainly CO2 and CH4 (methane)The greenhouse effect from GhG emissions results in climate change – a multi-faceted impactHow has CO2 in atmosphere been changing?
  • 56. Energy and Climate ChangeProduction and consumption of fossil fuels results in greenhouse gas (GhG) emissions – mainly CO2 and CH4 (methane)The greenhouse effect from GhG emissions results in climate change – a multi-faceted impactHow has CO2 in atmosphere been changing?And what are the impacts of this? Economic Analysis of Environmental ImpactsBefore we go too far into climate issues, let’s look at how we can analyze environmental policy related to energy issues.And look at lessons from prior environmental policy efforts
  • 57. Model of emissions regulationTwo sides of emissionsDamages – Changes in emissions result in marginal cost of emissionsFor CO2, we refer to MC of emissions as Social Cost of Carbon (SCC)“Benefits” – Emissions are a side-effect of productive economic activity (like burning natural gas to generate electricity)Marginal benefit (MB) of emissions is extra benefit as emissions increase.Mirror image of MB is marginal abatement cost of reducing emissions. Managing Emissions Externalities $ E MB MC Managing Emissions Externalities
  • 58. $ E MB MC abatement marginal abatement cost Social Optimum $ E MB MC abatement marginal abatement cost social optimum emissions
  • 59. Policy OptionsDo NothingSo called business as usualEmissions TaxWhere do tax revenues go?Emissions CapTechnology Standard(s)Cap and Trade ProgramIssue emissions permits equal to capped emissions levelAuction off permits or give away (grandfather) Managing Emissions Externalities $ E MB MC abatement marginal abatement cost Managing Emissions Externalities $ E MB MC
  • 60. abatement marginal abatement cost Managing Emissions Externalities $ E MB abatement marginal abatement cost CAP Cap & Trade $ E MB abatement marginal abatement cost CAP
  • 61. Measuring Economic Damages from EmissionsA wide variety of damagesContaminated air and water, damage to crops, illness/deathTwo main approaches for measuring damagesContingent ValuationAsk people about WTP for better environmental quality, reduced health/mortality risksBased on survey or questionnaire responsesMarket-based ValuationIndirect method to reveal WTP for enviro quality, reduced health/mortality risksBased on market price or wage changes as enviro quality, health/mortality risks vary Illustration – Mortality RiskSuppose you knew for sure that reducing air emissions by some amount would save one life; you don’t know which person’s life, just that one life is saved. How much is this worth in dollars? How much should society and policy-makers value this life? Illustration – Mortality RiskSuppose you knew for sure that reducing air emissions by some amount would save one life; you
  • 62. don’t know which person’s life, just that one life is saved. How much is this worth in dollars? How much should society and policy-makers value this life?Gov. Andrew Cuomo: “My mother’s not expendable. You cannot put a value on human life. You do the right thing. That’s what Pop taught us.”But let’s suppose you needed to come up with a $ value for saving a life – how would you do it? What would you come up with? Illustration – Mortality RiskSuppose you knew for sure that reducing air emissions by some amount would save one life; you don’t know which person’s life, just that one life is saved. How much is this worth in dollars? How much should society and policy-makers value this life?Gov. Andrew Cuomo: “My mother’s not expendable. You cannot put a value on human life. You do the right thing. That’s what Pop taught us.”But let’s suppose you needed to come up with a $ value for saving a life – how would you do it? What would you come up with? Putting an economic value on lifeContingent Valuation ApproachGather survey responses about (hypothetical) WTP to avoid various mortality risks [reduced auto crash risk,
  • 63. workplace death risk, etc.] From EPA website … In the scientific literature, these estimates of willingness to pay for small reductions in mortality risks are often referred to as the "value of a statistical life.” This is because these values are typically reported in units that match the aggregate dollar amount that a large group of people would be willing to pay for a reduction in their individual risks of dying in a year, such that we would expect one fewer death among the group during that year on average. From EPA website … This is best explained by way of an example. Suppose each person in a sample of 100,000 people were asked how much he or she would be willing to pay for a reduction in their individual risk of dying of 1 in 100,000, or 0.001%, over the next year. Since this reduction in risk would mean that we would expect one fewer death among the sample of 100,000 people over the next year on average, this is sometimes described as "one statistical life saved.”
  • 64. Now suppose that the average response to this hypothetical question was $100. Then the total dollar amount that the group would be willing to pay to save one statistical life in a year would be $100 per person × 100,000 people, or $10 million. This is what is meant by the "value of a statistical life.” Importantly, this is not an estimate of how much money any single individual or group would be willing to pay to prevent the certain death of any particular person. Putting an economic value on lifeMarket-based Valuation ApproachCompensating wage differences – how do wages differ across occupations as mortality risk varies?You can find this via regression analysis of market wage dataCan derive VSL – value of a statistical life – based on the mortality risk coefficient in a wage regression. Earnings Regression Approach Run a regression of annual income (I) on explanatory variables including occupation (OCCP) and mortality rate for jobs in occupation (MORT), where MORT indicates number of deaths
  • 65. per 100,000 workers per year. I = a + b*EDUC + c*OCCP + d*MORT + … + error How do you interpret d coefficient? Earnings Regression Approach Run a regression of annual income (I) on explanatory variables including occupation (OCCP) and mortality rate for jobs in occupation (MORT), where MORT indicates number of deaths per 100,000 workers per year. I = a + b*EDUC + c*OCCP + d*MORT + … + error d = △I/ △MORT = Change in annual income for having one more death/100,000 per year. VSL = d*100,000 Unique Environmental Challenges Posed by Climate ChangeHealthy climate is global public goodMultiple large
  • 66. uncertaintiesInequality and welfare analysisLong-term persistent impactsRole of discount rate for NPV analysis Climate, the Economy, and Climate PolicyMany areas of the natural and social sciences involve complex systems that link multiple areas and disciplines. This is particularly true for the science, economics, and policy of climate change, which involve a wide variety of fields from atmospheric chemistry to game theory.Integrated assessment analyses and models play a key role in putting the pieces together. Integrated assessment models (IAMs) integrate knowledge from two or more domains into a single framework. These are sometimes theoretical but are increasingly computerized, empirical, dynamic, non-linear models of varying levels of complexity.
  • 67. Climate, the Economy, and Climate PolicyMany areas of the natural and social sciences involve complex systems that link multiple areas and disciplines. This is particularly true for the science, economics, and policy of climate change, which involve a wide variety of fields from atmospheric chemistry to game theory.Integrated assessment analyses and models play a key role in putting the pieces together. Integrated assessment models (IAMs) integrate knowledge from two or more domains into a single framework. These are sometimes theoretical but are increasingly computerized, empirical, dynamic, non-linear models of varying levels of complexity. STERN REVIEW: The Economics of Climate Change iv Figure 1 Greenhouse-gas emissions in 2000, by source Power (24%)
  • 68. Transport (14%) Buildings (8%) Industry (14%) Other energy related (5%) Waste (3%) Agriculture (14%) Land use (18%) NON-ENERGY EMISSIONS ENERGY EMISSIONS
  • 69. Energy emissions are mostly CO2 (some non-CO2 in industry and other energy related). Non-energy emissions are CO2 (land use) and non-CO2 (agriculture and waste). Total emissions in 2000: 42 GtCO2e. Source: Prepared by Stern Review, from data drawn from World Resources Institute Climate Analysis Indicators Tool (CAIT) on-line database version 3.0. Under a BAU scenario, the stock of greenhouse gases could more than treble by the end of the century, giving at least a 50% risk of exceeding 5°C global average temperature change during the following decades. This would take humans into unknown territory. An illustration of the scale of such an increase is that we are now only around 5°C warmer than in the last ice age.
  • 70. Such changes would transform the physical geography of the world. A radical change in the physical geography of the world must have powerful implications for the human geography - where people live, and how they live their lives. Figure 2 summarises the scientific evidence of the links between concentrations of greenhouse gases in the atmosphere, the probability of different levels of global average temperature change, and the physical impacts expected for each level. The risks of serious, irreversible impacts of climate change increase strongly as concentrations of greenhouse gases in the atmosphere rise. STERN REVIEW: The Economics of Climate Change iv Figure 1 Greenhouse-gas emissions in 2000, by source Power (24%) Transport (14%)
  • 71. Buildings (8%) Industry (14%) Other energy related (5%) Waste (3%) Agriculture (14%) Land use (18%) NON-ENERGY EMISSIONS ENERGY EMISSIONS Energy emissions are mostly CO 2 (some non-CO 2 in industry and other energy related). Non-energy emissions are CO 2 (land use) and non-CO 2 (agriculture and waste). Total emissions in 2000: 42 GtCO2e.
  • 72. Source: Prepared by Stern Review, from data drawn from World Resources Institute Climate Analysis Indicators Tool (CAIT) on-line database version 3.0. Under a BAU scenario, the stock of greenhouse gases could more than treble by the end of the century, giving at least a 50% risk of exceeding 5°C global average temperature change during the following decades. This would take humans into unknown territory. An illustration of the scale of such an increase is that we are now only around 5°C warmer than in the last ice age. Such changes would transform the physical geography of the world. A radical change in the physical geography of the world must have powerful implications for the human geography - where people live, and how they live their lives. Figure 2 summarises the scientific evidence of the links
  • 73. between concentrations of greenhouse gases in the atmosphere, the probability of different levels of global average temperature change, and the physical impacts expected for each level. The risks of serious, irreversible impacts of climate change increase strongly as concentrations of greenhouse gases in the atmosphere rise. STERN REVIEW: The Economics of Climate Change v Figure 2 Stabilisation levels and probability ranges for temperature increases The figure below illustrates the types of impacts that could be experienced as the world comes into equilibrium with more greenhouse gases. The top panel shows the range of temperatures projected at stabilisation levels between 400ppm and 750ppm CO2e at
  • 74. equilibrium. The solid horizontal lines indicate the 5 - 95% range based on climate sensitivity estimates from the IPCC 20012 and a recent Hadley Centre ensemble study3. The vertical line indicates the mean of the 50th percentile point. The dashed lines show the 5 - 95% range based on eleven recent studies4. The bottom panel illustrates the range of impacts expected at different levels of warming. The relationship between global average temperature changes and regional climate changes is very uncertain, especially with regard to changes in precipitation (see Box 4.2). This figure shows potential changes based on current scientific literature. 1°C 2°C 5°C4°C3°C Risk of weakening of natural carbon absorption and possible increasing natural methane releases and weakening of the Atlantic THC 400 ppm CO2e 450 ppm CO2e
  • 75. 550 ppm CO2e 650ppm CO2e 750ppm CO2e 5% 95% Sea level rise threatens major world cities, including London, Shanghai, New York, Tokyo and Hong Kong Falling crop yields in many developing regions FoodFood WaterWater EcosystemsEcosystems Risk of rapid Risk of rapid climate climate change and change and major major irreversible irreversible impactsimpacts
  • 76. Eventual Temperature change (relative to pre-industrial) 0°C Rising crop yields in high-latitude developed countries if strong carbon fertilisation Yields in many developed regions decline even if strong carbon fertilisation Large fraction of ecosystems unable to maintain current form Increasing risk of abrupt, large-scale shifts in the climate system (e.g. collapse of the Atlantic THC and the West Antarctic Ice Sheet) Significant changes in water availability (one study projects more than a billion people suffer water shortages in the 2080s, many in Africa, while a similar number gain waterSmall mountain glaciers disappear worldwide – potential threat to water supplies in several areas Greater than 30% decrease
  • 77. in runoff in Mediterranean and Southern Africa Coral reef ecosystems extensively and eventually irreversibly damaged Possible onset of collapse of part or all of Amazonian rainforest Onset of irreversible melting of the Greenland ice sheet Extreme Extreme Weather Weather EventsEvents Rising intensity of storms, forest fires, droughts, flooding and heat waves Small increases in hurricane intensity lead to a doubling of
  • 78. damage costs in the US Many species face extinction (20 – 50% in one study) Severe impacts in marginal Sahel region Rising number of people at risk from hunger (25 – 60% increase in the 2080s in one study with weak carbon fertilisation), with half of the increase in Africa and West Asia. Entire regions experience major declines in crop yields (e.g. up to one third in Africa) 2 Wigley, T.M.L. and S.C.B. Raper (2001): 'Interpretation of high projections for global-mean warming', Science 293: 451-454 based on Intergovernmental Panel on Climate Change (2001): 'Climate change 2001: the scientific basis.
  • 79. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change' [Houghton JT, Ding Y, Griggs DJ, et al. (eds.)], Cambridge: Cambridge University Press. 3 Murphy, J.M., D.M.H. Sexton D.N. Barnett et al. (2004): 'Quantification of modelling uncertainties in a large ensemble of climate change simulations', Nature 430: 768 - 772 4 Meinshausen, M. (2006): 'What does a 2°C target mean for greenhouse gas concentrations? A brief analysis based on multi-gas emission pathways and several climate sensitivity uncertainty estimates', Avoiding dangerous climate change, in H.J. Schellnhuber et al. (eds.), Cambridge: Cambridge University Press, pp.265 - 280. STERN REVIEW: The Economics of Climate Change v Figure 2 Stabilisation levels and probability ranges for temperature increases The figure below illustrates the types of impacts that could be experienced as the world comes into equilibrium with more greenhouse gases. The top panel shows the range of temperatures projected at stabilisation levels between 400ppm and 750ppm CO2e at equilibrium. The solid horizontal lines indicate the 5 - 95% range based on climate sensitivity estimates from the IPCC 20012 and a recent Hadley Centre ensemble study3. The vertical line indicates the mean of the 50th percentile point. The dashed lines show the 5 - 95%
  • 80. range based on eleven recent studies4. The bottom panel illustrates the range of impacts expected at different levels of warming. The relationship between global average temperature changes and regional climate changes is very uncertain, especially with regard to changes in precipitation (see Box 4.2). This figure shows potential changes based on current scientific literature. 1°C2°C 5°C4°C3°C Risk of weakening of natural carbon absorption and possible increasing natural methane releases and weakening of the Atlantic THC 400 ppm CO 2 e 450 ppm CO 2 e 550 ppm CO 2 e 650ppm CO 2 e 750ppm CO 2 e
  • 81. 5% 95% Sea level rise threatens major world cities, including London, Shanghai, New York, Tokyo and Hong Kong Falling crop yields in many developing regions Food Food Water Water Ecosystems Ecosystems Risk of rapid Risk of rapid climate climate change and change and major major irreversible irreversible impacts impacts Eventual Temperature change (relative to pre-industrial)
  • 82. 0°C Rising crop yields in high-latitude developed countries if strong carbon fertilisation Yields in many developed regions decline even if strong carbon fertilisation Large fraction of ecosystems unable to maintain current form Increasing risk of abrupt, large-scale shifts in the climate system (e.g. collapse of the Atlantic THC and the West Antarctic Ice Sheet) Significant changes in water availability (one study projects more than a billion people suffer water shortages in the 2080s, many in Africa, while a similar number gain water Small mountain glaciers disappear worldwide – potential threat to water supplies in several areas Greater than 30% decrease in runoff in Mediterranean and Southern Africa Coral reef ecosystems extensively and eventually irreversibly damaged Possible onset of collapse
  • 83. of part or all of Amazonian rainforest Onset of irreversible melting of the Greenland ice sheet Extreme Extreme Weather Weather Events Events Rising intensity of storms, forest fires, droughts, flooding andheat waves Small increases in hurricane intensity lead to a doubling of damage costs in the US Many species face extinction (20 –50% in one study) Severe impacts in marginal Sahel region Rising number of people at risk from hunger (25 –60% increase in the 2080s in one study with weak carbon fertilisation), with half of the increase in Africa and West Asia. Entire regions experience
  • 84. major declines in crop yields (e.g. up to one third in Africa) 2 Wigley, T.M.L. and S.C.B. Raper (2001): 'Interpretation of high projections for global-mean warming', Science 293: 451-454 based on Intergovernmental Panel on Climate Change (2001): 'Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change' [Houghton JT, Ding Y, Griggs DJ, et al. (eds.)], Cambridge: Cambridge University Press. 3 Murphy, J.M., D.M.H. Sexton D.N. Barnett et al. (2004): 'Quantification of modelling uncertainties in a large ensemble of climate change simulations', Nature 430: 768 - 772 4 Meinshausen, M. (2006): 'What does a 2°C target mean for greenhouse gas concentrations? A brief analysis based on multi-gas emission pathways and several climate sensitivity uncertainty estimates', Avoiding dangerous climate change, in H.J. Schellnhuber et al. (eds.), Cambridge:
  • 85. Cambridge University Press, pp.265 - 280. 13 new investments made, new infrastructure put in place, and changes occur in the decisions, practices, and behaviors of millions of business managers, workers, and consumers. Exhibit 4 A “CARBON REVOLUTION” NEEDS TO BE THREE TIMES FASTER THAN THE INDUSTRIAL REVOLUTION RISE IN LABOR PRODUCTIVITY Source: Contours of the World Economy 1 – 2030 A.D., Maddison, 2007; McKinsey analysis 0
  • 86. 2 4 6 8 10 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Index Year 0 = 1 Years Carbon productivity growth required 2008–50 US labor productivity growth 1830–1955
  • 87. Technological innovation often plays a critical role in productivity growth, but just as important are changes in the wider political, institutional, and cultural environment that enable technologies to be exploited and provide incentives for their deployment. For example, the productivity increases of the Industrial Revolution were partly the result of technological innovations such as the spinning jenny and the steam engine. But just as important were innovations in the way people organized and managed their businesses, such as Richard Arkwright’s creation of the first large-scale factories, Henry Ford’s invention of the production line, or Alfred Sloan’s development of the divisionalized
  • 88. corporation. These technological and organizational innovations were in turn encouraged and enabled by a series of changes in government policy, institutional structures, and the regulatory environment. For example, governments created a legal framework for public companies, enabling large amounts of capital to be pooled for the first time. They also strengthened property rights, enabling businesses to make long-term investments. And they passed consumer protection laws, enabling customers to trust the products and services they were buying, thus spurring demand.
  • 89. 15 Exhibit 5 The first conclusion that stands out is that a significant portion of the abatement potential, approximately 7 gigatons of annual emissions on the left side of the curve, would be at a negative cost to society. In other words, these actions would earn a positive economic return derived largely from savings in energy costs through, for example, more energy-efficient lighting or more fuel-efficient vehicles. The second key point is that under the cost curve’s assumptions, the world can achieve the 27 gigatons per year of abatement required in 2030
  • 90. to stay below 500 ppmv for a marginal cost of under €40 per ton. Finally, the cost curve counters a number of myths about carbon abatement—for example, that there are only limited low-cost abatement opportunities in the developed world or that we can only achieve abatement with new technologies (Exhibit 6). If the world were to take these abatement actions, the annual total cost to society would be €500 billion–€1,100 billion in 2030 or 0.6–1.4 percent of that year’s projected global GDP, assuming growth continues on its long-term trend. This cost estimate is roughly in the middle of the range of 0.2– 3.0 percent of
  • 91. Stanley Reynolds Energy Efficiency Energy EfficiencyImprovements in energy efficiency are a major part of all proposals for significant reductions in global GhG emissions. Higher energy efficiency can also yield other air quality improvements as energy use fallslower emissions from autos & electricity generatorsMoreover, energy efficiency gains are often described as being among the very least expensive ways to reduce emissions. Indeed, it is often argued that many energy efficiency gains can be achieved with direct
  • 92. economic benefits that exceed the costs. As environmental economist Robert Stavins puts it: EE gains are not just a free lunch, they will pay you to have lunch.For example, see the McKinsey global GhG emissions abatement curve Energy EfficiencyImprovements in energy efficiency are a major part of all proposals for significant reductions in global GhG emissions. Higher energy efficiency can also yield other air quality improvements as energy use fallslower emissions from autos & electricity generatorsMoreover, energy efficiency gains are often described as being among the very least expensive ways to reduce emissions. Indeed, it is often argued that many energy efficiency gains can be achieved with direct economic benefits that exceed the costs. As environmental economist Robert Stavins puts it: EE gains are not just a free lunch, they will pay you to have lunch.For example, see the McKinsey global GhG emissions abatement curve Global CO2 Abatement Cost
  • 93. An Energy Efficiency (EE) GapMcKinsey MAC graph highlights EE opportunitiesIt includes many EE improvements with positive NPV (negative abatement cost)But there is also lots of evidence that firms and households are slow to make EE investments on autos, appliances, HVAC, housing, etc.Big question – WHY aren’t more of these investments being made?? An Energy Efficiency Gap Economic studies from 1980s document very high implicit discount rates implied by consumer choices over appliances with different costs and energy efficiencies. Analysts using a variety of methodologies found implicit discount rates ranging from 25 % to over 100 %.incentives (e.g., landlord vs tenant) An Energy Efficiency Gap Economic studies from 1980s document very high implicit discount rates implied by consumer
  • 94. choices over appliances with different costs and energy efficiencies. Analysts using a variety of methodologies found implicit discount rates ranging from 25 % to over 100 %.Possible reasons for an EE gap? Lack of information about EE opportunities, or about size of savingsLack of access to financing for householdsMyopia or bounded-rationality of decision-makersSplit incentives (e.g., landlord vs tenant) Federal EE Programs National Appliance Energy Conservation Act (1987) Mandatory standards for energy efficiency of household appliances. To ensure that manufacturers are building products that are at the maximum energy efficiency levels that are technically feasible and economically justified. Energy Star program - https://www.energystar.gov/ Corporate Average Fuel Economy (CAFÉ) Regulations first enacted by Congress in 1975, after the 1973– 74 Arab Oil Embargo, to improve the average fuel economy of cars and light trucks produced for sale in the US
  • 95. State/Local EE ProgramsState ProgramsThere are many, many state-level EE programs – see the Database for State Incentives for Renewables and Energy Efficiency (DSIRE)ArizonaProperty tax exemption for certain energy-efficient technologies or improvementsAZ Corporation Commission Energy Efficiency mandate for utilities Utility Programshttps://www.tep.com/efficiency/ Potential Market FailuresPotential Policy Options Energy market failures Environmental externalities Emissions pricing (tax, cap-and- trade) Average-cost electricity pricing Real-time pricing; market pricing Energy security Energy taxation; strategic reserves Capital market failures Liquidity constraints Financing/loan programs Innovation market failures
  • 96. R&D spillovers R&D tax credits; public funding Learning-by-doing spillovers Incentives for early market adoption Information problems Lack of information; asymmetric info Information programs Principal–agent problems Information programs Learning-by-using Information programs Potential Behavioral Failures Bounded rationality Education; information; product standards Heuristic decision-making Education; information; product standards Buildings and EEWe know that there are some market failures regarding energy use for buildingsWhat does EE look like in the building/construction sector? U.S. Energy Consumption by Sector*
  • 97. * DOE Buildings and Energy Data Book (2010): http://buildingsdatabook.eere.energy.gov/ Residential BuildingUses more energy than commercial buildingsMajor industry in Arizona (historically)Pepper Viner HomesWhat are main household energy uses? See next slide * DOE Buildings and Energy Data Book (2010): http://buildingsdatabook.eere.energy.gov/ How do I know if my house or building is green?The world of environmental certification for homes and buildingsUS Green Building Council introduced its Leadership in Energy and Environmental Design (LEED) program in 2000LEED Video ClipOther private programsE.g. – NAHBGreen – National Green Building ProgramFederal programsEnergy Star
  • 98. Evidence on value of green certification for commercial buildings? Split incentive problem can cause market failure – Do we need government intervention to solve this?A couple of recent studies provide evidence that green certification is a signal translates into extra market value – the studies and results are described in: http://insight.gbig.org/leed-value-of-a-market- signal/ Installed costs lagged wholesale PV module price (2007-2009) decline $0.2/W compare with $1.3/W * Economic Impact of EEEE programs lead to appliances – refrigerators, AC units, TV’s, … – that are less costly to use, since they use less energy.Does this cause behavior to change?? Do EE standards deliver?Note that more energy efficiency means that the effective price-per-unit of energy services fallsGreater EE implies LOWER price per unit of energy
  • 99. servicesWhat happens when you lower the price of something??For EE, this change in behavior is called the rebound effect.An extreme version is, backfire effect. Energy Efficiency in TransportationThe centerpiece of US energy efficiency policy for transportation is the Corporate Average Fuel Economy (CAFÉ) policyCAFÉ established in 1975Mandated an 18 MPG fleet average for model year 1978CAFÉ progressively raised since 1978 for cars and light trucks CAFÉ over time Year CarsLight Trucks 1980 2015 1990 27.520 2000 27.520.7 2010 27.523.5 2015 35.028.2 2025* (goals) 54 46
  • 100. CAFÉ ComputationsEach automaker is required to meet the CAFÉ MPG standard for the average MPG of their fleet of new model autos (and light trucks).An automaker that sells cars in U.S. must pay penalties if its fleet doesn’t meet CAFÉ standardCurrently $55 per MPG above standard, per vehicle sold (had been scheduled to rise to $140) Do EE standards deliver? Reformed CAFÉ*In 2008, under the Energy Independence and Security Act (EISA), NHTSA’s authority to set the CAFE standards was altered. First, EISA mandated attribute-based standards for cars, meaning that each vehicle would be subject to its own standard based on its attributes (in this case vehicle size), rather than using one standard for all cars or for all trucks. In addition, under the new rules, NHTSA was required to set standards for vehicle fuel efficiency each year (whereas
  • 101. before, the agency was allowed to do so) and was required to set them at the “maximum feasible” levels through 2030. The other major change was that EPA was given authority to regulate GHG emissions and would now do so under the CAFE standards. The so- called “reformed standards” were established jointly by NHTSA and EPA, with the first phase for model year (MY) 2012–2016 vehicles finalized in 2011, and the second phase for MY 2017– 2025 vehicles finalized in August of 2012. * See Virginia McConnell, RFF 2013 Main Economic Effects of CAFÉ StandardBenefits of reducing U.S. demand for oilSmaller macroeconomic effects of oil price shocksCounter OPEC market power (reduce oil price mark-ups and final oil prices)Reduced environmental impactsLower CO2 emissionsCan we quantify these benefits?Improvements in local air quality? Main Economic Effects of
  • 102. CAFÉ StandardChanges in incentives for automakersPricing and production of auto and truck models – go through analysis in classIncentives for design features and for R&D and innovationAre there market failures here?Auto designAuto R&D and innovation Other Economic Effects of CAFÉ StandardRole of prior fuel taxesRebound EffectCAFÉ standard reduces gallons/mile; may increase # miles travelled (by lowering relative cost of driving)Rebound effect likely offsets 10 – 20% of overall fuel reductionMore miles travelled Greater congestion costsMore accidents/deathsFleet mix and safety 15 Exhibit 5 The first conclusion that stands out is that a significant portion of the abatement
  • 103. potential, approximately 7 gigatons of annual emissions on the left side of the curve, would be at a negative cost to society. In other words, these actions would earn a positive economic return derived largely from savings in energy costs through, for example, more energy-efficient lighting or more fuel-efficient vehicles. The second key point is that under the cost curve’s assumptions, the world can achieve the 27 gigatons per year of abatement required in 2030 to stay below 500 ppmv for a marginal cost of under €40 per ton. Finally, the cost curve counters a number of myths about carbon abatement—for example, that there
  • 104. are only limited low-cost abatement opportunities in the developed world or that we can only achieve abatement with new technologies (Exhibit 6). If the world were to take these abatement actions, the annual total cost to society would be €500 billion–€1,100 billion in 2030 or 0.6–1.4 percent of that year’s projected global GDP, assuming growth continues on its long-term trend. This cost estimate is roughly in the middle of the range of 0.2– 3.0 percent of 1
  • 105. Economics 473 Spring 2020 Problem Set Four – Answer Notes 1. (10 points) The graph below depicts the marginal benefit (MB) of carbon emissions and the social cost of carbon (SCC) curve. Use this graph to answer the questions below. a) Explain in words why the socially optimal level of Carbon emissions is not zero. In the graph above, at a level of zero emissions, the marginal benefit (MB) from emissions is much higher than the marginal cost (or, social cost of carbon, SCC) of emissions. From an economic point of view, some modest positive level of emissions has economic benefits that dramatically exceed the economic damages associated with the emissions. My own view is that the real-world empirical situation regarding zero emissions is similar to what is depicted
  • 106. in the graph above. b) Suppose that the government sets a tax on Carbon emissions (e.g., a carbon tax). Show the optimal level for the tax on emissions on the graph. Explain in words how the tax is determined. Whatever the per unit tax on carbon emissions is, polluters would (collectively) choose their amount of emissions at the quantity where the tax rate intersects the MB curve. So, if you want to achieve the optimal emissions level E* (and optimal emissions reductions), set the tax rate at the dollar amount equal to the dollar amount where MB and SCC intersect. In order to implement this in practice, you would have to know – or have estimates of – the MB and SCC curves. c) Suppose instead that the government establishes a cap and trade program for Carbon emissions. Show the optimal level for the emissions cap on the graph. Compare the outcome this cap and trade program with the outcome for the emissions tax from part (b). The cap should be set equal to E*. The outcome in terms of
  • 107. emissions and emissions abatement would be the same as with the tax in part (b). The cost of abatement would also be the same. The cost of acquiring permits in the cap and trade program may differ from the tax payments under emissions taxation, depending on how permits are issued under cap and trade. $ Emissions SCC MB E* E’ p’ 2
  • 108. d) Now suppose that the government sets a cap that is too lenient; that is, the cap allows more emissions than the optimal cap you identified in part (c). Show the equilibrium trading price for emissions permits with this ‘too lenient’ cap, and compare this equilibrium trading price with the optimal emissions tax from part (b). Let’s say the cap is set at E’ > E*. The equilibrium trading price is determined by the MB at the capped emissions quantity of E’. This is illustrated by price p’ on the graph. e) The social cost of carbon is based on the net present value of current and future damages from an additional ton of carbon emissions now. Suppose that a higher social discount rate is used to calculate these damages. Explain how this change in the social discount rate would affect the position of the SCC curve, and the optimal current level of emissions and emissions abatement. Since much of the damage from CO2 emissions are in the
  • 109. future, a higher social discount rate would lower the NPV of damages from current CO2 emissions and reduce (shift down) the SCC curve. This shift would increase optimal emissions and reduce optimal emissions abatement. 2. (5 points) Explain in words the meaning of the term, ‘levelized cost of energy’. LCOE is often used to compare the costs of different technologies for generating electricity. Explain pros and cons of using LCOE for such comparisons. LCOE is equal to the constant price per unit energy that would equate NPV of revenues to NPV of costs. Alternatively, a measure of the real average total cost of generation over the lifetime of a generation plant. PRO – LCOE provides a way to compare average costs of different methods for generating electricity. CON – LCOE is focused on cost rather than benefits or value. Value of different technologies may differ due to: (1) timing of generation – e.g., wind turbines tend to generate electricity at night when
  • 110. electricity is not valuable, (2) dispatchable vs. non-dispatchable generators. 3. (10 points) A variety of government policies promote greater energy efficiency. Examples include programs for more energy efficient lighting and the federal CAFÉ program for autos and light trucks. Consider the following claim – “Government policies promoting energy efficiency are bound to fail because of the rebound effect. These policies have the unintended consequence of lowering the effective price of energy services and thereby encouraging greater energy use.” Discuss this claim and explain whether or not you agree with it. It’s true that EE policies have this unintended consequence. You can make an argument on either side of this. The argument turns on how large the rebound effect is. If this effect is large for a particular EE program, then the claim is basically correct – an example would be the Mexico Cash for Coolers program for air conditioners. If the effect is small – which is likely true for most programs, then the claim is wrong.
  • 111. Final Take-Home Exam Economics 473 - Spring 2020 NAME________________________ There are 6 questions worth a total of 150 points. Make sure to answer all parts of all questions fully on the exam. Write your answers in sentences. Make sure to explain where any numerical results that you derive come from. A final answer without a clear explanation of how you obtained it is a failing answer. In cases where your final answer may be incorrect, many of the logical steps you followed might well be correct. We will not be able to award you partial credit if you do not show the process through which you obtained your answer. You may use the lecture notes, the videos, the slides, D2L course material, your assignments, and the readings when answering the questions. You may not use any other material. Good luck and Perform well. 1. (20 points) Renewable energy technologies such as wind turbines and solar photovoltaic panels are characterized by variable and intermittent power generation. a. Why does renewable intermittency present problems for the
  • 112. electric power grid? b. What can electricity utilities do to address problems posed by intermittency? 2. (36 points) Consider a dispatchable electricity generation technology and an intermittent renewable generation technology. The 2 technologies have the following characteristics: Dispatchable Technology Renewable Technology Annual Construction cost ($/MW/yr) $200,000 $100,000 O&M fixed cost ($/MW/yr) $36,520
  • 113. $22,640 Operating cost ($/MWh) $30 $0 Capacity factor 90 % 20 % Note that construction and O&M fixed costs have been listed on an annual basis per MW of capacity. a) Find the levelized cost of energy (LCOE) in $/MWh for each technology. b) Would a subsidy for the renewable technology be required in order for it to achieve the same LCOE as the dispatchable technology? c) Electricity generated from these technologies can be sold into the wholesale market. Suppose the wholesale price of electricity is $100/MWh during peak hours (which occur one-half of the time) and $30/MWh during off-peak hours (the other half of the time). Assume that one-half of generation occurs during peak
  • 114. hours and the other half occurs during off-peak hours for each technology. Find the wholesale market profits per MW per year for each technology. Does the technology with the lower LCOE yield the most profit per MW per year? Explain why or why not. d) Suppose as in part c that electricity generated from these technologies can be sold into the wholesale market. Assume that the wholesale price of electricity is $100/MWh during peak hours (which occur one-half of the time) and $30/MWh during off-peak hours (the other half of the time). Now assume that one-half of dispatchable generation occurs during peak hours and the other half occurs during off-peak hours and assume that all of the renewable generation occurs during peak hours. Find the wholesale market profits per MW per year for each technology. Does the technology with the lower LCOE yield the most profit per MW per year? Explain why or why not. 3. (24 points) The graph below depicts the marginal benefit (MB) of carbon emissions and the social cost of carbon (SCC) curve. The MB curve is linear, starting at $100/ton at zero emissions and falling to $0/ton at emissions equal to 1,000 tons/day. SCC is constant at $60/ton. Use this graph to answer the questions below.
  • 116. a) Suppose that the government sets a tax on Carbon emissions (e.g., a carbon tax). What is the optimal tax on CO2 emissions for the situation depicted in the graph? How much emissions abatement would occur? How much tax revenue would this tax raise? b) Suppose instead that the government establishes a cap and trade program for CO2 emissions, and sets the cap for CO2 emissions at 800 tons/day. How much emissions abatement is required to meet this cap? What is the total cost of emissions abatement? c) The social cost of carbon is based on the net present value of current and future damages from an additional ton of carbon emissions now. Suppose that a higher social discount rate is used to calculate these damages. Explain how this change in the social discount rate would affect the position of the SCC curve, and the optimal current level of emissions and emissions abatement. 4. (30 points) The government provides subsidies for consumers who purchase energy efficient appliances and automobiles. a) What is the argument for providing such subsidies? What
  • 117. market failure(s) are these subsidies aimed at correcting? One type of unintended consequence of this kind of subsidy policy is the rebound effect. b) Explain what the rebound effect is; use one or more examples in your explanation. Explain why it is important to consider the rebound effect when estimating the impact of the subsidy policy on energy use. c) How would the size of a rebound effect depend on the price elasticity of household demand for energy (for example, the demand for electricity for appliances or the demand for gasoline for autos)? Explain. 5. (24 points) Environmental externalities can lead to illness and death for people. a. Explain the concept of 'value of statistical life' (VSL). b. Explain how VSL factors into the economic benefits of reducing greenhouse gas emissions. c. Suppose that new evidence emerges to show that VSL is lower than previously believed. How would this new evidence affect your recommendations about what to do about greenhouse gas emissions and global warming.
  • 118. 6. (16 points) Electricity restructuring led to deregulated wholesale electricity markets in many parts of the U.S. Merchant generation suppliers compete with one another to supply electricity in these markets. Market power (or, monopoly power) is a potential concern when the number of suppliers in a market is fairly low. Why might supplier market power be a serious concern in wholesale electricity markets? Explain your answer. 1