Integrated Multiobjective Energy Management for a Smart Microgrid Incorporating Electric Vehicle Charging Stations and Demand Response Programs Under Uncertainty

Abstract

Listen

Translate article

This paper presents an innovative 24‐h scenario–based microgrid energy management system (MG‐EMS) designed to achieve cost reduction and emission reduction under conditions of uncertainty. Furthermore, a multiobjective hybrid heuristic algorithm, named hybrid multiobjective particle swarm optimization and lightning search algorithm (hMOPSO‐LSA), is introduced to tackle the MG‐EMS problem. This algorithm combines the LSA and the MOPSO algorithm. The MG under investigation comprises photovoltaic (PV) and wind turbine (WT) units, a combined heat and power (CHP) system, and employs multicarrier energy storage technology, specifically, power‐to‐gas (P2G) technology and an electric vehicle (EV) parking lot (PL). Flexible loads are incorporated into the MG to enhance cost and emission reduction through participation in the demand response program (DRP). The proposed MG‐EMS model utilizes probability density functions (PDFs) for modeling uncertainties and employs the Roulette wheel (RW) method for scenario selection. The simulations, carried out in MATLAB, encompass two different sections. In the first part, the accuracy and efficiency of the proposed algorithm were validated by solving the standard DTLZ benchmark functions and comparing the optimization results with those of several other optimization algorithms. In the second part, energy management in the MG was carried out using the proposed MG‐EMS model, solved by the hMOPSO‐LSA algorithm, both without flexible loads and with their inclusion. To provide a comprehensive evaluation, the problem was solved using the proposed hMOPSO‐LSA algorithm and compared against three benchmark algorithms: multiobjective flower pollination algorithm (MOFPA), MOPSO, and multiobjective dragonfly algorithm (MODA). The optimization results demonstrate that hMOPSO‐LSA achieves higher accuracy compared to other algorithms. Furthermore, the findings indicate that the participation of flexible loads in the DRP results in a 6.43% cost reduction and an 8.21% reduction in emissions. Additionally, P2G technology proves effective in cost and emission reduction, contributing 6.87% of the required gas supply within the MG.