Multi-Time Scale Optimal Dispatch of Distribution Network with Pumped Storage Station Based on Model Predictive Control

As the penetration of renewable energy increases, the distribution grid faces great challenges in integrating large amounts of distributed energy sources and dealing with their output uncertainty. To address this, a multi-time scale optimal dispatch method based on model predictive control is proposed, including a day-ahead stage and an intra-day rolling stage. In the day-ahead stage, to fully utilize the flexibility of variable speed pumped storage hydropower, the generating/pumping phase modulation condition is considered, not just generating or pumping. Day-ahead optimal dispatch is established with the objective of minimizing the operation economy and node voltage deviation of the distribution network. In the intra-day rolling stage, model predictive control with finite time domain rolling optimal dispatch is used to replace the traditional single-time section optimal dispatch, considering the forecast data of wind, photovoltaic (PV), and load within the finite time domain, so that can respond in advance to smooth the generator output. At the same time, the uncertainty problem of the distribution network is solved effectively by rolling optimization and feedback correction of model predictive control. In order to consider the daily operating capacity balance of energy storage in the intra-day stage, the capacity imbalance penalty is added to the intra-day rolling optimization objective function, so that the energy storage capacity tries to track the results of the day-ahead optimization, achieving the long-term development of energy storage. Simulation analysis proves the feasibility and effectiveness of the proposed method. The proposed method enhances the generation–load–storage coordinated dispatching ability, effectively improving the distribution network’s capability to respond to fluctuations of renewable energy.