Optimal energy management framework of microgrid in Aljouf area considering demand response and renewable energy uncertainty

A novel optimal day-ahead scheduling framework that incorporates the recent starfish optimization algorithm (SFOA) is proposed in this research to manage the energy of a microgrid (MG) that includes electric vehicles (EVs) and renewable energy resources (RESs). The primary goal is to reduce MG's daily operational expenses. A time-of-use (TOU)-based demand response program (DRP) has been linked with the suggested schedule structure in order to lower demand consumption during expensive peak hours. The suggested method is distinguished by its rapid convergence rate, capability, and promotion of global convergence, in addition to its high search efficiency. The MG under consideration operates in actual weather circumstances of Sakaka, Aljouf area, Saudi Arabia which is situated at latitude 29° 58′ 15.13″N and longitude 40° 12′ 18.03″E. In addition to RESs like solar and wind turbines (WTs), the considered MG includes conventional resources of fuel cells (FCs), microturbines (MTs), storage batteries, and EVs. Furthermore, the uncertainty surrounding the production of RESs has been represented by the Beta and Weibull distributions. The analysis is performed with 24-h real data for bad day in January, average of entire year, good day in April, and forecasted at Aljouf location. Additionally, two EV charging modes are examined: smart and uncontrolled. Furthermore, contingency-based and uncertainty in DRP scenarios are analyzed. The suggested SFOA is verified through comparison with the published honey badger algorithm (HBA), particle swarm optimization (PSO), and weighted average algorithm (WAA). The ANOVA table, Friedman rank, Wilcoxon rank, and Kruskal Wallis statistical tests are used to statistically validate the suggested method. In comparison to the published HBA, the proposed method was successful in reducing the MG running cost while the EVs are unplugged by 11.71 %, 15.03 %, 17.21 %, and 17.28 % when it operates in bad, average, good, and predicted weather conditions, respectively. Also, when the EVs are plugged in uncontrolled charging mode SFOA mitigated the cost by 4.18 %, 2.84 %,17.23 %, and 4.76 % during the respective operating scenarios. Furthermore, by 2.56 %, 5.42 %, 41.99 %, and 7.32 % mitigation in MG cost the SFOA was the best during smart charging mode for EVs. The proposed approach can be endorsed as a successful MG energy management technique.