Why it’s Critical that PJM Load Forecasting Give Energy Efficiency Full Credit
Ken Kolkebeck and Austin Whitman of FirstFuel offer a way to ensure that PJM load forecasting takes energy efficiency fully into account.
With back-to-school season upon us, it seems fitting that the topic of “partial credit” should be getting some press. This is no A-minus-versus-A debate, however: in the mid-Atlantic states that make up the PJM Interconnection (PJM), partial credit could be costing electric customers millions of dollars.
The issue comes from an interesting report from the Brattle Group, recently commissioned by the Sustainable FERC Project, which argues that PJM’s load forecasting process only gives partial credit to energy efficiency – with significant impacts on customers. Brattle finds that PJM’s long-term load forecast leaves out key energy efficiency resources, and overestimates capacity resource needs. It’s important to get this forecast right: too high, and customers in PJM pay for too much capacity; too low, and there could be reliability issues. What’s more, long-term load forecasts often inform critical decisions by a host of stakeholders, market participants, and energy system planners.
Ken Kolkebeck, FirstFuel
The Brattle report says that PJM’s load forecast only includes historical demand-side energy efficiency resources and supply-side efficiency that clears the annual capacity auction. If energy efficiency isn’t bid in the auction, or doesn’t clear it, or happens in the future, it doesn’t show up in the forecast. While regional transmission organizations in New York (NYISO) and New England (ISO New England) have taken on this complicated issue through stakeholder working groups,
PJM has taken a different approach, with potentially adverse outcomes for customers.
We’re people who obsess about data and analytics, particularly when it comes to energy efficiency. So naturally, this issue gets us scratching our heads. Shouldn’t there be a way to create a credible energy efficiency forecast that doesn’t require drawn-out stakeholder compromises, hand-waving, or leaps of faith? An approach that’s scalable, replicable, and objective? One that system planners, such as PJM, can rely on when they’re modeling future capacity needs?
FirstFuel has figured out how to develop credible, weather- and occupancy-normalized forecasts of building energy consumption using deep insights from historical meter data. Utilities and their commercial customers are increasingly asking us to track energy savings at the whole building level, so that they can understand the impacts of energy efficiency investments, track portfolio results, and drive measure persistence. Although the intelligence we provide is system-, building-, and program-level, what’s new here is that it’s all based on meter data and extremely accurate. This means that for the first time, electricity meters can provide a “bottom-
up” view of the grid to inform resource planning processes, such as load forecasting.
Using data in this way, the next step might be to roll meter-level forecasts together into a “negative load” resource that shows up in load forecasts. It’s conceivable that PJM and other market operators could adopt a shared set of standards for using data analytics as a tool to inform energy efficiency forecasting.
Austin Whitman, FirstFuel
The importance of modeling energy efficiency savings isn’t going away, as states start to think about how best to use energy efficiency as the “fourth pillar” in their compliance strategies under EPA’s proposed Clean Power Plan. Many regions will likely turn to multi-state coalitions to minimize compliance costs. And then there’s the unfolding saga around FERC Order 745. The successful challenge to the Order has bolstered a broader industry effort to weaken energy efficiency policies and limit customer-side resource participation in wholesale markets. While Order 745 strictly dealt with demand response, aftershocks could hit the energy efficiency market.
It certainly wouldn’t resolve all of the market issues, but taking some of the uncertainty out of energy efficiency forecasting would strengthen the case for including it as a resource. What better way to demonstrate value and create accountability than to create robust, consistent, analytics-enabled efficiency forecasts across states and regions?
Energy efficiency and demand-side management already provide significant value to customers, in part by helping reduce energy and capacity market clearing prices. Let’s aspire to create markets that consistently award full credit to energy efficiency for these contributions to the electric grid.
Ken Kolkebeck is FirstFuel’s co-founder and SVP, products and buildings; Austin Whitman is director, regulatory affairs and development.
