Optimization method for a hybrid microgrid energy management system with reserve margins

Abstract

The research study aims to provide an optimization technique for a hybrid microgrid energy management system with reserve margins. The load for the hybrid microgrid under consideration consists of grid-connected photovoltaic, wind, and battery energy storage devices and electric vehicles that may provide grid support. The recommended solution considers both an isolated mode of operation and a grid-connected operating situation. Isolated microgrids improve system resilience by distributing electricity to nearby loads from locally accessible resources. Furthermore, it is still challenging to govern, run, and protect these systems in grid-connected and islanded modes, cope with dispatch difficulties that decide the DRES's priority, and provide grid support, among other challenges. Furthermore, the BESS charging and discharging strategy should follow the Risk Mitigation Independent Power Producer Procurement Programme (RMIPPP) guidelines, charging predominantly from local renewable energy sources rather than the grid. This ensures that local South African legislation and requirements are observed throughout the investigation. The study focuses on optimizing and modeling a hybrid microgrid system incorporating different green energy sources, such as grid-tied solar photovoltaic, wind energy, and battery energy storage devices. The study uses sophisticated optimization techniques to increase the microgrid's efficiency and reliability. Specifically, particle swarm optimization and the genetic algorithm are used to solve the system model and address the difficulties of optimal energy generation, storage management, and hybrid integration. The findings illustrate the efficiency of these optimization approaches in improving overall performance, lowering costs, and assuring the microgrid's dispatch strategy under different operational situations.