Two-Stage Robust Optimization Considering the Uncertainty of Sources and Loads in Virtual Power Plants Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.1109/access.2025.3591459
· OA: W4412567147
Virtual power plants (VPPs) aggregate controllable distributed power sources, distributed new energy sources, distributed energy storage devices, loads, and other resources. As the proportion of new energy in VPPs continues to rise, the impacts of the uncertainty of internal new energy output and load have become increasingly significant, and the demand for flexible resources has also become more urgent. To address the challenges of system operation optimization and resource allocation caused by the integration of a large amount of renewable energy on the supply side of the energy system into VPPs and the flexible and changeable load on the demand side, this paper proposes a distributed robust optimization method that simultaneously considers the uncertainties of both the source and the load. First, regarding the uncertainties of wind power, photovoltaic output, and load, Monte Carlo scenario sampling and scenario reduction methods are employed to depict the typical scenarios of uncertainty, thereby determining the output interval ranges of wind power, photovoltaic, and load. On this basis, considering the uncertainties of renewable energy and load in multiple VPPs simultaneously, a distributed two-stage robust optimization model based on the min-max-min structure is established to obtain the optimal dispatching scheme for VPP operating costs under the most extreme scenarios. The model takes into account system power balance and output power constraints, demand response load constraints, and the interaction constraints of multiple VPPs. The uncertainty in VPPs is handled through an adjustable uncertainty control factor. Subsequently, the Benders decomposition algorithm is used to split the model into a master problem and a subproblem. Combining the duality theory, KKT conditions, and the Big-M method, the inner layer max-min optimization problem is transformed into a mixed integer linear programming problem. Finally, numerical simulations verify the correctness and effectiveness of the proposed model, as well as the positive role of this model in the economic and low-cost operation of VPPs when considering capacity configuration.
