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Deploying EE to Lower C02 Emissions and Comply with the CPP
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Strategies

This article is republished from the May 2016 issue of Strategies, AESP’s exclusive magazine for members. To receive Strategies, please consider joining AESP.
 
Deploying Energy Efficiency to Lower C02 Emissions and Comply with the Clean Power Plan: Two Case Studies
By Frank Stern, Rob Neumann, Amanvir Chahal and David Purcell

Stern(1).jpgNeumann(1).jpgIntroduction
There has been a great deal of discussion on compliance with the Clean Power Plan (CPP). Surprisingly, there is little discussion of specific costs and benefits in leveraging energy efficiency (EE) to reduce CO2 and move toward complying with the CPP. Navigant investigated the effects of deploying additional EE resources to decrease CO2 emissions in two regions – California and PJM.

PJM coordinates movement of electricity through all or parts of Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia and the District of Columbia. 

 

Chahal.jpgPurcell(1).jpgOur analysis shows that deploying additional EE for CPP compliance results in reduced CO2, as would be expected, but it also reduces costs and system congestion. Additional EE can reduce cost to serve load by 3 percent to 5 percent in California and PJM, which reduces costs annually up to $825 million in California and $1.5 billion in PJM. Another benefit of deployed EE is system congestion relief which reduces the cost to serve load – this is important since large, urban utilities are focused on reducing congestion points and EE can be used as a solution.

 

CPP and CO2 Reduction Timeline
The CPP has been “stayed” by the U.S. Supreme Court until final resolution of the case through the federal courts. The U.S. Supreme Court may not have final resolution of the case until 2018, although it could be sooner. Regardless, many states and regions continue to move toward the CPP goals to reduce carbon emissions, plan for an advanced energy economy, and meet cleaner generation goals. It is not known at this time if the deadlines in the CPP will be modified.
 
Modeling EE for CO2 Reduction
Navigant has been modeling supply resources for many years and has been including EE as a modeled resource. For this analysis, we focused on modeling PJM Transmission Interconnection and the state of California. To establish our EE “base case” across California and PJM, we included levels of EE modeled in each of Navigant’s most recent PROMOD and POM1 transmission model runs. The data and assumptions in these runs are updated and verified with industry experts each quarter. Variables in the model include (i) rate of EE adoption over time, (ii) amount of EE compared to new generation, and (iii) varying amounts of EE deployed. EE was modeled across California and PJM for the three cases (high/medium/low) - each case was run for 2025 and 2030. These years are important since 2025 is the middle of the CPP implementation period and 2030 is the first year of full compliance with the rule (final goal). The low case included a 50 percent reduction in EE from the base case, while the high case included a 50 percent increase in EE from the base case - the base case in 2030 is 33 million MWh for PJM and 24 million MWh for California.
 
Modeled Results
Deployed EE can provide up to 8.8 percent of California’s and 3.6 percent of PJM’s overall CPP Compliance goal in 2030. There is also a reduction in the cost to serve generation load based upon deployed EE. In PJM, the cost savings from the low EE case to the high EE case results in over $1.5 billion in savings annually in 2030, 3.6 percent of total cost to serve load, while in California, the same metric results in up to $825 million in savings annually in 2030, 4.7 percent of the cost to serve load. To state it in different terms, the cost to increase EE in 2030 to assist meeting CPP requirements is approximately $900 million in PJM and $550 million in California which results in an EE return on investment of $600 million in PJM and $300 million in California. This lowers 2030 system capacity requirements by 5.6 percent in PJM and 10.7 percent in California. The lower savings and returns in California are due to aggressive renewable and EE policies already underway today in advance of CPP compliance. 
 
Another benefit of deployed EE is reduced system congestion which reduces the cost to serve load. EE will lower the need for new thermal generation on the system and put downward pressure on capacity and resource prices. Our model shows that system congestion is reduced by approximately 1.5 percent and is seen system-wide. This amounts to cost reductions of more than $765 million a year in PJM and $270 million a year in California. This system congestion finding is important since there are various efforts underway across the nation to improve congestion (e.g., Con Edison Brooklyn/Queens Demand Management Initiative). 
 
Conclusion
CPP initiatives would be greatly benefited by incorporating additional EE into the planning process. EE reduces emissions and systems costs and pushes out the need for large, costly new generation projects. Specifically, we showed that CO2  emissions would be significantly lowered in PJM and California in both 2025 and 2030, while system costs are lowered in PJM and California by at least 3 percent and 5 percent, respectively. This all adds up to longer glide paths for meeting regulatory requirements or when state goals have to be implemented. By including EE as a resource into the resource mix, system planners and environmental offices gain significant benefits in the form of decreasing costs, flattening demand and a zero-emitting resource.
 
Frank Stern is Managing Director; Rob Neumann is Associate Director; Amanvir Chahal is Associate Director; and David Purcell is Senior Consultant at Navigant. This article was contributed by the AESP Business Issues and Models Topic Committee.
 
[1] PROMOD IV is a detailed hourly chronological market model that simulates the dispatch and operation of the wholesale electricity market. It replicates the least cost optimization decision criteria used by system operators and utilities in the market while observing generating operational limitations and transmission constraints. The Proprietary Portfolio Optimization Model (POM) is leveraged for regional analysis of regulatory impacts.

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