Optimal Allocation of Energy Storage Systems Considering Demand Response: An Improved Ant Colony Optimization Approach

This study presents a novel approach for the optimal allocation of Energy Storage Systems (ESS) considering demand response scenarios, using an enhanced Ant Colony Optimization (ACO) algorithm. Recognizing the critical role of demand response and ESS in contemporary energy systems, we develop a cost-minimizing model integrating comprehensive constraints related to power balance, generation capacity, demand response, and energy storage operation. The proposed model is solved using an improved ACO algorithm, demonstrated to have superior performance over standard algorithms in test cases in terms of solution quality, computation time, and solution stability. However, its scalability in larger, more complex systems remains an area for future exploration. Despite this limitation, our research provides significant insights into optimal ESS allocation considering demand response, underscoring the potential of improved ACO as a promising tool for solving such complex optimization problems. Future work will focus on incorporating more realistic considerations into the model, testing the robustness of the improved ACO in larger-scale systems, and exploring the impacts of policy and market factors on optimal ESS allocation