Electric companies, electricity industry stakeholders, and regulators are placing an increasing emphasis on accurately representing distributed energy resources in electric company resource planning efforts. While there is substantial literature on the qualitative advantages and disadvantages of distributed energy resource modeling methods, there has been little research that quantitatively assesses implications across methods. This study quantitatively demonstrates the impact of a variety of approaches to representing one distributed energy resource—behind-the-meter energy efficiency (EE)—in electric company resource planning models on planning results.
Two established approaches to modeling EE are compared and contrasted: (1) a static (i.e., demand-side) approach that screens each EE measure for cost-effectiveness outside the resource planning model, subtracts the cost-effective EE impacts from the model’s input load forecast, and then models generation resource decisions subject to this net load forecast; and (2) a dynamic (i.e., supply-side) approach that represents each EE measure as part of an EE supply curve, which directly competes with generation resources in the model’s capacity planning decision algorithm. Within these two methods, alternative formulations and sensitivities such as the impact of bundling and uncertain EE adoption are also tested and discussed.
A comparison of key planning results such as EE investment costs, total system costs, and the marginal value of EE for each modeling method has yielded several valuable insights for resource planners. Overall, findings show that there are conditions under which the dynamic approach and conditions under which the static approach may be more appropriate than the other for resource planning efforts.