• Stackelberg Game-Based Optimal Solution Design • Hybrid Algorithm for Economic and Operational Optimization • Significant Performance Improvements in Configuration and Stability The optimal allocation of distributed energy storage systems (DESSs) is a high-dimensional, nonlinear, and complex optimization problem. Existing methods struggle to obtain high-quality solutions within a limited time, and most fail to fully consider the game behaviors among multiple stakeholders. Therefore, this paper proposes a Stackelberg-game-based optimal allocation method for DESSs to achieve optimal system economy and stability under multi-interest balance. By incorporating the loss cost of the distribution network, DESS investment cost, reactive power cost, price arbitrage revenue, and equipment utilization revenue, and by introducing environmental toxicity constraints and safety precautions, an economic optimization model is constructed that includes multi-dimensional constraints such as power balance, DESS capacity, node voltage, and state of charge. A multi-leader multi-follower Stackelberg game model is established, in which energy suppliers act as leaders in setting price strategies, while energy storage operators and users adjust their demand as followers, thereby realizing equilibrium analysis of multi-agent decision-making. The modified empirical mode decomposition (EMD) and cloud model-OWA operator are used to decompose fluctuation signals at multiple scales, and adjustment tasks are assigned to power-type and capacity-type energy storage devices. The Pareto file multi-objective particle swarm optimization algorithm is employed to solve the game equilibrium. Experimental results show that compared with existing methods, the proposed method reduces load volatility to 14.25%, which is 11.73 percentage points lower than the original volatility. The required DESS capacity is less than 500 kWh, reduced by approximately 85%. The average total cost is 1.8187 million yuan, with statistical significance (p < 0.01), indicating a marked improvement in economy. The significance of this research lies in integrating multi-stakeholder decision-making, fluctuation signal decomposition, and multi-objective optimization within a game-theoretic framework, providing theoretical foundations and engineering guidance for the life-cycle economy, environmental friendliness, and operational safety of DESSs.
Li et al. (Fri,) studied this question.