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Identifying high value chp candidates
- 1. Main Headquarters: 120 Water Street, Suite 350, North Andover, MA 01845 With offices in: NY, ME, TX, CA, OR www.ers-inc.com IDENTIFYING HIGH VALUE CHP USING A LOW-COST METHODOLOGY By: Susan Haselhorst
- 2. BACKGROUND Legislation in Massachusetts in 2008 Almost tripled energy efficiency goals for the mandated electric company run energy efficiency programs CHP envisioned providing 20% of the C&I electric portfolio saving • New measure for the program administrators
- 3. HOW TO PREVENT THIS? 100% 80% 60% 40% 20% 0% 20% 40% 60% 80% 100% Age0 Age1 Age2 Age3 Age6 Age0 Age1 Age2 Age3 Age0 Age1 Age2 Age3 Age4 Age5 Age6 Age7 Age0 Age1 Age2 Age3 Age4 Age5 Age6 Age0 Age1 Age2 Age3 Age4 Age5 Age6 Age7 FC GT ICE MT ALL ←OffOn→ Capacityfactor Unused capacitywhile on Off > 3 day duration Off 1 to 3 day duration Off <1 dayduration
- 4. BETTER CHP PROGRAM DESIGN Thermal following Driven by energy efficiency, not grid benefits Combined annual efficiency >60% Cost-benefit screen requires 5000 FLH+ Educated consumer Rigorous technical assistance Discourages exporting Long term O&M contracts
- 5. WHO ARE THE GOOD CUSTOMERS? CHP Market Characterization KEMA evaluation team Itron conducted qualitative assessment ERS conducted the quantitative assessment NGRID study manager
- 6. Inputs C&I Monthly gas bills 115,000 accounts Service town Some business type Hourly weather data Gas & electric commodity cost Electric distribution rates Average CHP performance and cost, by size range Existing distributed generation lists Base load shape library Outputs For each gas account Optimum thermal following CHP size Annual kWh generated Annual fuel fired Annual useful heat System cost O&M cost Payback Aggregated accounts By Program Administrator By customer type By CHP size range Lead lists of specific customers CUSTOMER CHP POTENTIAL MODEL 6/9/2014 6
- 7. UNDER THE HOOD 1. Use bills to quantify the base and weather sensitive load 2. 8760 hourly analysis using weather and library of base load profiles 3. Selecting an optimally sized system
- 8. FROM GAS BILLS Highly certain of base and weather sensitive load magnitudes
- 9. WS LOAD PROFILE Distribute the annual load through the heating season using hourly weather data
- 10. BASE LOAD SERVED BY HOT WATER Building type inferred from SIC or name of the account Discount a portion of base usage Assign an hourly profile
- 11. OPTIMIZED CHP SIZE – DOWNSIZE
- 12. BUILDING LOCATION Use location Identify program administrator Located in municipal service territory Exclude sites where interconnect is difficult
- 13. MODEL WEAKNESSES Overstate potential Site already generates electricity Facility CHP potential is limited by electrical usage Average pricing doesn’t screen expensive sites About 25% of heating systems not conducive Understate potential Emerging technologies aren’t captured Additional potential with absorption cooling Doesn’t capture oil-fired potential Right perspective for a lead list
- 14. A FEW GOOD UNITS ….
- 15. BY SYSTEM SIZE
- 16. BY BUILDING TYPE
- 17. Examine impact of assumptions WHAT IF … 17 Payback - Years Num Accts Pct Change from Baseline Capacity (kW) Pct Change from Baseline Average System Size kW Payback <3 199 -86% 157,644 -67% 791 Payback <4 1,153 -21% 378,265 -20% 328 Baseline for payback <5 years 1,464 0% 475,167 0% 325 Payback <6 1,882 29% 556,145 17% 295 Payback <8 2,515 72% 660,168 39% 263
- 18. INTERESTING FINDINGS Car washes perceived as good CHP candidates Car washes, however, do not use much gas Wash water is not heated Mismatch between electric and thermal loads Manual check of 127accounts Insufficient electrical use at half of the viable residential sites
- 19. Rich and immediately useable results Lead lists Market focus Technique could be adapted Improved estimates of economic potential Could test other screening models Assess other gas measures CONCLUSIONS 19
Editor's Notes
- Begin the story where many EE stories begin – the state legislature. In 2008 MA legistilature passed the Green Communities Act which almost tripled program goals. The good news: CHP was now an eligible measure, where it had been excluded before. PAs were pleased to have another means of meeting those tough goals. The bad news: well, Eric showed you the bad news.
- How could the PAs avoid this?
- Lessons learned from NY and CA experiences. Key lesson learned is to drive systems to a thermal following design. CA programs, as noted by Eric were grid congestion driven. Another element of the design is the PAs are working hard to educate their customers. Rigorous technical assistance which verifies thermal load profiles and models of hourly performance Make clear the consequences of exporting Require long therm This creates a strong economic basis for the projects so they can withstand the vicissitudes of spark spread, burn-in periods, O&M and other issues impacting this complex measures
- PA wished to jump start the CHP sales Commissioned by the program administrators as a task of the KEMA evaluation team Itron conducted qualitative assessment ERS conducted the quantitative assessment – which I am speaking about today NGRID study manager
- We built the model. I’d like to take you under the hood - to look at a few of the important model concepts. Complicated flow diagram in the paper where you can see more details
- Use the monthly bills to separate out the base use from the weather sensitive usage. Base usage is the average summer load, extrapolated across the year and the weather sensitive load is the remainder. Simple BUT – it exactly characterizes the customers thermal load for that time period.
- Typical commercial building has a large weather sensitive heating component. We do an hourly model using hourly weather data. In this illustration – three days are concatenated Jan, Mar, Apr. At the building balance point, the load is zero. As the OA temperature fluctuates, so does the thermal load. Above the balance point, there is no loading. ERS has completed a number of gas program evaluations which show that this is a pretty good assumption.
- The base load – although often smaller in magnitude than the WS load is important to economic CHP. First – we discounted a portion of the base load determined from the bill to account for load that cannot be served by CHP. For example, in a restaurant, CHP will not displace the gas grill. Need to distribute the base load determined across the year by hour. We do that using a load profile based on the building type. The load profiles were derived from the CEUS data – CA End Use Building type is derived from the SIC or customer name. More uncertainty in this analysis – however, again, must reconcile with the bills.
- Rarely, however, is it cost-effective for a CHP system to meet all the thermal requirements of a site. This is a bit counter-intuitive, so bear with me. The graph displays the hourly thermal loading of a facility in January. At this point, the unit is operating at 100% - it should be at the design temperature. But if the unit is sized to meet this load – it operates at part load through the rest of the month. The full load hours for the month is the sum of all the individual hour part loads. If the unit is sized to meet the entire load, it only runs 444 full load hours – which is a 60% load factor. However, if you downsize the unit – to say – 45% of the maximum load, it runs at 100% capacity for most of the month. Only here and here – 725 hours 97% capacity factor Sized at 15% - the capacity factor is 100% it runs full out for 744 hours. More hours of out put per installed kW. Smaller unit is more cost-effective. The model tests increments of the initial maximum size to find the largest unit that has < 5 year payback.
- Last detail about the model – and that isn’t one of the four concept points, but is kind of interesting is how we used location. We had the location of each account – which meant we knew Identify program administrator Located in municipal service territory Exclude sites where interconnect is difficult
- Before I review some of the results – I’d like to touch on some of the model weaknesses and mitigiation. If sites already generate – its likely its with gas and that gas load is not a thermal load that can be displaced by CHP. Most sites are thermal limited – that is they can use all the electricity generated. In some cases, the site is electrically limited and there isn’t enough electric usage to displace. Next item is interesting – average pricing is not a bellshape curve. At an average price of $2500 kW – the price can’t be lower than $0/kW. However the upper price is unlimited. CHP will not be viable at sites where direct fired heating is used – But because we are generating lead lists – we didn’t want any false negatives. Sites can be quickly screened for most of these factors as well.
- Let’s look at the results. First – after processing the individual gas accounts what did we end up with? We screened out most of the accounts because the resulting CHP systems were too small. 60 kW was assumed to be the threshold for widely available CHP systems. The next step screened out accounts with large enough CHP, but failed to pass the 5 year payback test. We screened out sites located in networks where interconnect was difficult. Screening out existing CHP pulled out a big chunk of the potential leaving a list of 1464 viable accounts.
- It’s a simple matter to aggregate the individual units into different categories. I have two examples of the aggregations. Here we compiled units by the estimated CHP size. This is useful because CHP performance, vendor community, target market, and costs vary by the size of the CHP. You will see MT and ICE dominating the smaller systems with a vendor community. Gas turbines will dominate the very large systems.
- Note the unknown – one PA chose not to provide either customer name or SIC codes. Could deduce the very largest customers. Note – Office – surprising. May represent larger base loads due to reheat. Industrial – not many sites, but relatively large systems. Compared to residential many fairly small systems. Residential includes: condos, public housing, apartment buildings Other: By PA, by gas sales quartile.
- Examined impact of incentives, standby rates.
- Car washes were identified in at least two studies as good CHP candidates. Electric use by blower motors and thermal load heating wash water. But a review of dozens of car wash accounts using customer name and SIC showed very little usage. No systematic way to match an electric account to a gas account for a site. We were able to test whether a particular sector was vulnerable to exporting: health clubs, schools, residential.