Stochastic Optimization Framework for Realizing Combined Value Streams From Customer-Side Resources–Self Service and Distribution System Operations

Customer-side resources are becoming increasingly crucial in grid modernization and transitioning to a sustainable energy future. Although most load-serving entities (LSEs) typically consider customer-side resources for single applications, these resources can be used for multiple applications simultaneously. This paper proposes a stochastic optimization framework to help LSEs capture multiple value streams from customer-side resources within their network. Specifically, we consider self-service applications - peak shaving, energy arbitrage, ramp rate reduction - and distribution system operational applications - loss reduction and voltage management. The framework is also adapted to handle the impacts of the activities of third-party aggregators on the LSE’s network. We also evaluate the performance of two algorithms - decision rule-based and optimal real-time dispatch - for dispatching the customer-side resources in the face of different sources and levels of uncertainty. Simulations were run using modified IEEE test systems within the OpenDSS simulation tool. The results show the value of customer-side resources can be maximized when multiple applications are simultaneously considered, and that value increases with increasing levels of forecast uncertainties.