Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid

This article proposes an approach to optimize energy management (EM) in a microgrid (MG) with a battery storage system (BSS) and distributed generation. The primary objective is to address the challenge of costeffective and environmental-friendly operation of the microgrid, with a specific focus on minimizing operational cost and emission through optimal BSS utilization in the daily schedule. Balancing economic and environmental factors through optimization poses a complex problem, which is addressed here by applying the slime mould algorithm (SMA) along with the weighted sum and fuzzy decision-making method. Three scenarios are considered to demonstrate the MG's EM. In Scenario A, SMA outperforms differential evolution (DE), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA) by reducing operational cost by 17.59 %, 8.19 %, and 9.84 %, respectively, and achieves better emission reduction with reductions of 4.93 %, 15.74 %, and 9.91 % compared to DE, SCA, and GOA, respectively. SMA also dominates DE, SCA, and GOA in other scenarios, producing superior Pareto-optimal solutions. Additionally, comparing the Pareto-optimal results achieved with SMA, Scenario C outperforms Scenarios A and B by achieving reductions of 25.47 % and 35.20 % in operational cost, respectively, along with a corresponding 6.4 % and 36.26 % decrease in emission, highlighting the superior performance of Scenario C.