Advanced optimization approaches for demand side management in renewable energy generation system

Demand Side Optimization (DSO) is a critical approach to enhancing energy efficiency and sustainability by actively managing and reducing energy consumption at the consumer level. This study investigates the performance of three prominent optimization algorithms the Backtracking Search Algorithm (BSA), the Artificial Bee Colony Algorithm (ABC), and Binary Particle Swarm Optimization (BPSO) for demand-side energy management applications. Experimental evaluations were conducted for sample sizes of 100 and 600 consumers. For a sample size of 100 iterations, BPSO exhibited superior performance with a minimum cost of $0.031, an execution time of 0.49145 seconds, and a mean deviation (MD) of 25%. The ABC algorithm achieved a best cost of $0.32 in 1.89324 seconds (MD = 25%), while BSA reached a best cost of $ 0.022 in 0.96125 seconds with the same MD. For a larger sample size of 600 iterations, both BPSO and BSA attained a best cost of $405, whereas ABC recorded $433. The comparative results highlight the distinct strengths of each algorithm: BSA’s efficient search space exploration, ABC’s balance between exploration and exploitation, and BPSO’s effective handling of binary decision variables. The integration of these optimization algorithms with real-time analytics, smart metering, and intelligent energy management systems can significantly improve the reliability, economic efficiency, and environmental sustainability of demand-side energy systems. • Comparative study of BSA, ABC, and BPSO algorithms for effective Demand Side Optimization in energy management. • Evaluation based on execution time, best cost, and mean deviation across different sample sizes (100 and 600). • BPSO demonstrated the fastest execution time and efficiency in handling binary decision variables. • BSA showed superior exploration capabilities, while ABC balanced exploration and exploitation effectively. • Integration potential of these algorithms with real-time analytics and smart metering for sustainable and cost-effective energy systems.