Research on Distributed Optimization Scheduling and Its Boundaries in Virtual Power Plants

To improve the operational efficiency of the Virtual Power Plant (VPP) and the effectiveness and reliability of scheduling boundary characterization, this paper proposes a time-decoupled distributed optimization algorithm. First, based on the Lyapunov optimization theory, time decoupling is implemented within the VPP, transforming long-term optimization problems into single-period optimization problems, thereby reducing optimization complexity and improving operational efficiency. Second, the Alternating Direction Method of Multipliers (ADMM) framework is used to decompose the optimization problem into multiple subproblems, combined with a hybrid strategy to improve the particle swarm optimization algorithm for solving the problem, thus achieving distributed optimization for the VPP. Finally, to facilitate intra-day interaction between the VPP and the distribution network, the remaining controllable capacity of the VPP’s devices is used as the spinning reserve to address renewable energy fluctuations. A dynamic scheduling boundary model is constructed by introducing wind and solar fluctuation factors. Based on time decoupling and algorithm improvement, the scheduling boundaries are solved and updated on a rolling basis. Simulation results show that, firstly, the time decoupling strategy based on Lyapunov optimization has an error of less than 3%, and the solving time is reduced by 86.11% after decoupling, significantly improving solving efficiency and validating the feasibility and effectiveness of the time decoupling strategy. Secondly, the hybrid strategy-improved particle swarm optimization algorithm achieves improvements in convergence speed and accuracy compared to other algorithms. Finally, the VPP scheduling boundary and scheduling cost characterization times are 115 s and 6.7 s, respectively, effectively meeting the timeliness of VPP and distribution network interaction while ensuring the safety and reliability of the scheduling boundaries.