Haofan Yu

To address the grid volatility issues caused by the integration of electric vehicles and distributed energy storage, a multi-timescale real-time rolling optimization scheduling strategy for virtual power plants based on model predictive control (MPC) is proposed. Considering the particularity of the electric vehicle units aggregated in the virtual power plant, an analysis of electric vehicle travel behavior is conducted to enable effective participation of electric vehicles in the charging and discharging scheduling plan of the virtual power plant. A multi-timescale day-ahead and intra-day operational framework for the virtual power plant is constructed, considering the constraints involved in system operation. An objective function incorporating intra-day balancing market and incentive-based demand response price incentives is introduced, and a multi-objective, multi-timescale optimization scheduling model based on the MPC rolling optimization algorithm is established for day-ahead and intra-day scheduling. By employing the MPC algorithm for real-time rolling optimization, the optimal day-ahead and intra-day scheduling plan for the virtual power plant is obtained. Simulation results indicate that the proposed strategy improved economic performance by 6.7%, increased carbon-credit revenues by 43.6%, reduced photovoltaic curtailment by 8291 kW, and decreased the output variability of distributed energy resources by 32%. The multi-timescale rolling optimization scheduling strategy presented in this paper addresses the volatility challenges of integrating distributed energy into the power grid. It offers theoretical value and serves as a practical reference for studying the large-scale integration of distributed energy resources on the user side.

To address the source‐load mismatch and photovoltaic (PV) curtailment caused by the large‐scale integration of distributed PV into distribution networks, this paper proposes an optimal control strategy for PV accommodation considering the coordination of a centralized energy storage system (CESS) and demand response. To achieve coordinated regulation on both the supply and demand sides, a multiobjective optimization model is established. It minimizes the comprehensive operating cost, including electricity purchasing, PV curtailment, CESS operation and maintenance, and the peak‐to‐valley load difference. On this basis, a time‐of‐use pricing mechanism is introduced to establish a piecewise linear load transfer rate model, which quantifies users’ dynamic electricity consumption behavior and optimizes the allocation of peak and valley loads. The proposed model is solved using particle swarm optimization (PSO). Case study results show that, compared with the scenario considering only CESS, the coordinated strategy reduces PV curtailment by 12.34%, electricity purchasing by 10.0%, and the total operating cost by 8.37%, demonstrating good performance in improving PV accommodation and the economic efficiency of the distribution network.

Large‐scale deployment of distributed photovoltaic (PV) systems brings challenges such as voltage violation and increased active power loss. Due to its high efficiency and low cost, the centralized energy storage system (CESS) has become a key technology to optimize distribution network operation. This paper proposes a multiobjective optimization model for CESS configuration in typical scenarios of Chinese rural residential users and enterprises, which considers voltage deviation, active power loss, and comprehensive costs. The analytic hierarchy process (AHP) is used to assign differentiated weights to different scenarios, transforming the multiobjective problem into a single‐objective problem. The particle swarm optimization (PSO) algorithm is then used to solve the problem, finally achieving collaborative optimization of the access location and capacity configuration of CESS. The validity of the proposed optimization model is confirmed by case studies in typical scenarios. In the rural residential scenario, CESS controls the voltage deviation within ±5%, with costs reduced by more than 50% compared to distributed energy storage systems (DESSs). In the enterprise scenario, CESS eliminates the voltage overlimit and reduces the cost by 53.95%. The proposed optimal configuration method for CESS effectively improves the voltage stability and economic performance, providing a low‐cost energy storage solution for the large‐scale application of distributed PV.