Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 37, Issue 2, Pages 1311-1325Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2021.3105418
Keywords
Uncertainty; Stochastic processes; Real-time systems; Wind power generation; Optimization; Robustness; Wind energy generation; Adaptive robust optimization; decision dependent uncertainty; endogenous uncertainty; virtual power plant
Categories
Funding
- National Natural Science Foundation of China (NSFC) [U1966601]
- State Grid Corporation of China [U1966601]
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This paper proposes a novel stochastic adaptive robust optimization model for determining the optimal self-scheduling plan for virtual power plants in the day-ahead energy-reserve market. By aggregating renewable generation units, conventional power plants, energy storages, and flexible demands, virtual power plants provide a flexible solution to distributed energy resources integration.
Virtual power plant (VPP) provides a flexible solution to distributed energy resources integration by aggregating renewable generation units, conventional power plants, energy storages, and flexible demands. This paper proposes a novel stochastic adaptive robust optimization (SARO) model for determining the optimal self-scheduling plan for VPP's participation in the day-ahead energy-reserve market. We consider exogenous uncertainties (or called decision-independent uncertainties, DIUs) associated with market clearing prices and available wind generation, as well as endogenous uncertainties (or called decision-dependent uncertainties, DDUs) pertaining to real-time reserve deployment requests. A tractable solution methodology based on modified Benders dual decomposition is developed to effectively solve the proposed SARO model with both DIUs and DDUs. Case studies are conducted to verify the efficiency and applicability of the proposed approach. Comparative results show that the proposed method can mitigate the conservatism of robust strategy by capturing a satisfactory trade-off between profitability and real-time operation feasibility.
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