Advanced control and energy management algorithm for a multi-source microgrid incorporating renewable energy and electric vehicle integration

Abstract

This paper presents a comprehensive study on advanced control and energy management in a multi-source/multi-load microgrid. The microgrid integrates solar panels, a wind turbine system, a Li-ion battery-based storage system, an electric vehicle (EV), and a DC load, all inter-connected through power electronic converters to a DC bus and linked to the AC grid via a DC/AC inverter. The study outlines the primary control objectives: DC bus voltage regulation, optimization of photovoltaic and wind energy conversion, and maintaining high-quality energy injection into the grid. Given the intermittent nature of solar and wind energy and the varying energy demands that affect battery life and performance, a novel energy management algorithm is introduced. This algorithm addresses different operating modes, such as Constant Current (CC) and Constant Voltage (CV), to enhance network stability, energy quality, and optimize energy distribution between the various sources and the EV. The proposed fuzzy logic controllers, known for their robustness to uncertainties, flexibility, and ease of implementation, are employed to achieve these objectives. The Particle Swarm Optimization (PSO) is used to fine-tune the fuzzy logic control parameters, ensuring optimal performance. This optimization process included minimizing Integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Square Error (ITSE) as objective functions. Extensive simulations under various operating scenarios demonstrated significant improvements in control accuracy, stability, and system performance.