Day-Ahead Optimal Scheduling for a Full-Scale PV–Energy Storage Microgrid: From Simulation to Experimental Validation

Abstract

Microgrids facilitate the complementary and collaborative operation of various distributed energy resources. Implementing effective day-ahead scheduling strategies can significantly enhance the economic efficiency and operational stability of microgrid systems. In this study, the long short-term memory (LSTM) neural network is first employed to forecast photovoltaic (PV) power generation and load demand, using operational data from a full-scale microgrid system. Subsequently, an optimization model for a full-scale PV–energy storage microgrid is developed, integrating a PV power generation system, a battery energy storage system, and a specific industrial load. The model aims to minimize the total daily operating cost of the system while satisfying a set of system operational constraints, with particular emphasis on the safety requirements for grid exchange power. The formulated optimization problem is then transformed into a mixed-integer linear programming (MILP) model, which is solved using a computational solver to derive the day-ahead economic scheduling scheme. Finally, the proposed scheduling scheme is validated through field experiments conducted on the full-scale PV–energy storage microgrid system across various operational scenarios. By comparing the simulation results with the experimental outcomes, the effectiveness and practicality of the proposed day-ahead economic scheduling scheme for the microgrid are demonstrated.