Integration of Distributed Energy Resources into Electricity Systems and Markets

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Distributed energy resources (DER), including roof-top solar, small-scale wind turbines, home battery storage, electric vehicles (EV), and demand response, have become increasingly popular and essential components of power distribution systems. However, the growth of DER poses significant challenges to system operation, such as bidirectional power flow, intermittent power generation, and unexpected frequency/voltage fluctuation. DERs will also considerably impact electricity markets. For example, the Federal Energy Regulatory Commission (FERC) Order No. 2222, announced in 2020, requires grid operators in the US to establish DERs as market participants. Therefore, grid operators need to prepare for high-level DER penetration in the electricity system and market. Methods have been developed for DER integration, such as DER modeling, optimal power flow (OPF), and game theory. Among others, the applicant developed demand response, vehicle-to-grid, and battery models. Nonetheless, accurate and unified DER models are needed. Distributed, stochastic, and convexified OPF models should be developed for DER integration. Also, effective dynamic pricing to incentivize DER participants in the electricity market becomes increasingly crucial. The long-term goal of the proposed research program is to safely, reliably, and economically integrate DERs into electricity systems and markets. To reach this goal, the following learning and optimization models will be developed: 1) machine learning for modeling DERs; 2) OPF for economic dispatch, optimal sizing, and optimal placement of DERs; 3) distribution system reconfiguration methods to improve DER integration; 4) DERs management systems; and 5) game and mechanism design methods to promote DERs in markets. The short-term objectives will focus on developing accurate and unified DER, OPF, dynamic pricing, and game models. Novel learning and optimization methods will be developed for efficient and economic DER integration. The deep learning method and unified DER models can help grid operators to visualize DERs and simplify DER integration. The proposed OPF models will be quantitatively evaluated. The most suitable OPF model for high-level DER integration into large-scale power distribution systems will be identified. Grid operators can use this information to select the best model for their specific application. Using the proposed method, DERs can be controlled to increase power system stability, reduce generation cost, delay infrastructure investments, and contribute to Canada's commitment to greenhouse gas emission reduction. The proposed dynamic pricing can promote electricity market competition and increase DER owners' revenue. Also, five highly qualified personnel (HQP) will be trained with relevant knowledge, skills, and abilities, which are in high demand by governments, utilities, power industries, and academia. I will also strive to promote equity, diversity, and inclusion (EDI) in HQP training.