Optimum battery sizing, scheduling and demand management for microgrids using slime mould algorithm

Optimum battery energy storage systems (BESS) sizing, scheduling, and demand response management (DRM) play an important role in maintaining the cost-effective and reliable operation of microgrids (MG). This paper presents a slime mould algorithm (SMA)-based strategy to perform the optimum BESS sizing, scheduling, and DRM in the MG. The proposed strategy is composed of two layers i.e., the outer layer and the inner layer. The outer layer is responsible for performing the BESS sizing by utilizing the SMA. On the other hand, the inner layer is tasked with executing the optimum scheduling with and without DRM using SMA. The performance of the proposed strategy has been validated by using a grid-connected industrial MG for three different scenarios (scenario-I: median values of the selected year, scenario-II: a typical summer day, scenario-III: a typical winter day). The MG data including solar PV, hydropower, biogas, and load data are obtained from a Victorian water utility. The proposed SMA-based strategy has been compared with particle swarm optimization (PSO), grey wolf optimization (GWO), and Bat algorithm (BA) to highlight the superior performance of the proposed strategy. Numerical results demonstrate that the optimum BESS sizing and scheduling for scenarios I to III with DRM provides 6.38 %, 7.85 %, and 6.67 % lower total cost in a grid-connected industrial MG as compared to without DRM, respectively. Furthermore, the proposed SMA-based strategy reduces the operational cost of MG with DRM and BESS by 1 %, 4.17 %, and 14.05 % compared to PSO, GWO, and BA. The sensitivity analysis reveals that an increase in import price leads to a significant rise in the electricity bill across all scenarios. Conversely, the increments in solar PV, pumped hydropower generation, and biogas generation result in a substantial reduction in the electricity bill in all scenarios.