Adapting design and dispatch optimization models to better inform energy planning decisions in the deployment of distributed renewable energy technologies

Abstract

Energy models are widely used to evaluate the technical and economic feasibility of energy efficiency, renewable energy, and sustainable transportation, and to guide the economic deployment of clean energy technologies. However, a gap exists between theoretical recommendations, and what individuals, businesses, or utilities choose to deploy. This research explores why these gaps exist, and how changes to technical modeling capabilities and the communication of corresponding results can increase clean energy deployment. We first develop and document an energy decision model, Renewable Energy Optimization and Integration (REopt) that optimizes technology size and dispatch strategy with the objective of minimizing lifecycle cost of energy for a building or campus. We then conduct a study of prior REopt users to understand how the model was employed to inform energy decisions. From this, we develop recommendations for modifications to tool capabilities that may increase successful deployment of clean energy. We then implement suggested capabilities, including co-optimizing among multiple goals such as cost and resilience, and integrating more qualitative values that influence decisions, but are not typically included in models. Through these tactics, we aim to narrow the gap between modeling and deployment by enabling building owners and campus energy managers to make more effective and efficient decisions that will, in turn, increase the speed and scale of clean energy implementation.