Residential Sector Heating 1600 Demand Response 1400 District Heating Solar Thermal 1200Demand Served (PJ/year) Heat Pump 1000 Conservation 800 Wood Boiler Solid Fuel Boiler 600 Pellet Boiler 400 Oil Boiler 200 Gas Boiler 0 Coal Boiler 2000 2010 2020 2030 2040 2050 Direct Electric
CO2 Reduction Performance Which demand-side technology? How much CO2 will it save? Which “baseline” technology will it displace? Where there is an interaction with the electricity system, how much CO2 will be saved/produced for every kWh saved/used? What about interactions with other parts of the energy system – primary resource choice, sectoral focus of emissions reduction, etc?
The usual method Choose a baseline system E.g. For heating in the UK; the combustion of natural gas in a condensing boiler Figure out how much of each “energy carrier” the alternative system saves/produces (relative to the consumption/production of the baseline). E.g. A CHP system may consume an additional 3000kWh of gas/year, and produce an additional 2500kWh of electricity Multiply change in consumption for each energy carrier by the respective standard emissions rates; ~0.19kgCO2/kWh for gas, and 0.43kgCO2/kWh for electricity in the UK E.g. Change in CO2 = 0.19*3000 – 0.43*2500 = -1075kg CO2
An alternative method - marginal CO2 ratesThe CO2 actually saved due to a change in electricity demand isrelated to which power stations actually respond to that change.
The observed response of generators in GB ELEXON publishes pre-gate closure dispatch data for every “BM unit” in the GB system We know which generators these are, and their efficiency, so we can calculate the CO2 production rate change associated with a change in output We can do this for every generator, so we can find the aggregate change in CO2 produced in any ½ hour period, along with the change in aggregate system load We can create a scatter plot of these We can create a linear fit (through zero) The slope of the linear fit is an estimate of the marginal emissions rate for the system
GB Electricity Marginal Emissions 2002 to 2009 inclusive Change in System CO2 Rate (ktCO2/h) Linear Fit: y = 0.69 x Change in System Load (GWh/h)Source: Hawkes, A.D. (2010) Estimating Marginal Emissions Rates in National Electricity Systems. Energy Policy 38(10) 5977-5987.doi:10.1016/j.enpol.2010.05.053
Change in System CO2 Rate (ktCO2/h) y = 0.69 xChange in System Load (GWh/h) Marginal Emissions Factor (kgCO2/kWh) Stats of the MEFGB System Load (GW) Probability of System Load Number of ObservationsChange in System Load (GWh/h)
Change over time Decommissioning and commissioning of power stations. We know which “BM Units” will be decommissioned out to ~2020. National Grid also projects the types of new generators over the same period. We can replace the old with the new, and repeat the marginal emissions calculation. Resulting in... Time Period Marginal Emissions Rate (kgCO2/kWh) 2002-2009 0.69 kgCO2/kWh 2016 0.6 kgCO2/kWh 2020-2025 0.51 kgCO2/kWh
What does this mean? The actual marginal emissions rate from 2002-2009 was 60% higher than the figure typically used in policy analysis.
But... What about changes elsewhere in the energy system, and over a much longer timeframe? => Analysis using the UK MARKAL Model MARKAL (Market Allocation) chooses the least cost pathway for energy system change over a 50 year time horizon. It is an optimisation model, with objective function of discounted system cost, user-defined constraints, and thousands of decision variables.
MARKAL Analysis Method Constrain the introduction of micro-CHP and heat pumps into the energy system Zero to 10,000,000 installations, in 1,000,000 increments Run MARKAL, record change in total system CO2 emissions over the entire time horizon Calculate the abatement associated with the introduction of each system (i.e. CO2 reduction per system per year) Allow all other aspects of the energy system to respond dynamically to the “forced” introduction of the intervention
MARKAL Analysis Results
Conclusion From 2002-2009, the marginal CO2 intensity of grid electricity in Great Britain was 0.69 kgCO2/kWh. But the long term CO2 reduction brought about by a class of interventions is more reliant on long term system changes than short term....but MARKAL is a crude tool for such analyses, and more research would be required to make firm conclusions. A stronger link between demand-side modelling and system modelling is required to assess this situation more accurately.
Key Challenge: which margin?A hypothetical situation: 1. We adopt a new technology (e.g. an electric car) 2. This technology causes an increase in peak system load (i.e. it has negative capacity credit). 3. THEREFORE => the electric car is responsible for all the emissions increase/decrease associated with that power station. This is the BUILD MARGIN PERSPECTIVE. 4. BUT, when we actually charge the car, it is not the new power station that responds to this demand. 5. THEREFORE, the operational marginal emissions rate is appropriate. This is the OPERATING MARGIN PERSPECTIVE.