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This initiative aims to enhance the efficiency of utility demand response (DR) operations by coordinating and integrating behind-the-meter (BTM) photovoltaic systems (PV) and energy storage (ES) using innovative machine learning software applications embedded in a distributed control architecture. The project is in the process of creating distributed energy resource (DER) learning agents and optimization engine within a hierarchical and layered distributed control architecture (DCA). These components work together to leverage aggregated DERs, providing more adaptable and swiftly responsive grid services tailored to a customized grid services set (GSS). They exchange information to facilitate the analysis, optimization, and dispatch of DERs for grid services. This paper outlines the DER Aggregation Model and the functional requirements of the DER Aggregation Engine, which delineates how participating DER assets will be grouped or aggregated for involvement in each GSS grid service. Based upon, we develop optimized command sets—establishing forecasted energy prices and substation level loads—utilizing DER excess capacity targeting five grid services: peak load management, energy arbitrage, frequency regulation, voltage support, and phase balance. In the end, sample customers’ bills with and without grid services will be compared for benchmarking associated tariffs.Keyword Aggregation model; distributed energy resources; grid services; machine learning; optimization
Rusakov et al. (Fri,) studied this question.
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