Leveraging modified golden jackal optimization for enhanced demand-side management in microgrids with different tariffs

This paper presents a novel Demand Side Management (DSM) system for microgrids, designed to minimize electricity costs and optimize social welfare for consumers. The proposed system leverages the Golden Jackal Optimization (GJO) algorithm to determine the optimal solution, considering multiple objective functions and system constraints. Additionally, a Modified Golden Jackal Optimization (MGJO) algorithm is introduced to further refine the optimization results. The primary objectives of this system are to reduce electricity bills, peak-to-average ratio (PAR), carbon dioxide (CO 2 ) emissions, and user waiting times. To achieve these goals, the system utilizes three dynamic pricing schemes: Time-of-Use (ToU), Critical Peak Pricing (CPP), and Real-Time Pricing (RTP). The system categorizes loads into fixed, shiftable, and non-shiftable types, balancing user comfort with energy efficiency by considering the trade-off between shiftable load start times and expected appliance operation times. The efficacy of the proposed system is demonstrated through various case studies conducted on a hypothetical smart building. The proposed algorithm, MGJO, achieved significant improvements, reducing total electricity costs by 53.86 % under ToU, 60.25 % under RTP, and 55.89 % under CPP, while also lowering the PAR by 37.11 % and improving user waiting time by 39.26 %, 48.89 %, and 28.53 % for the respective pricing schemes. The results consistently highlight the effectiveness of the MGJO algorithm in minimizing electricity costs while maintaining user comfort.