Planning and Optimization of Shared Energy Storage in Multi-Microgrids Based on LLM and Enhanced Deep Reinforcement Learning

Shared Energy Storage Centre (SESC) can enhance the flexibility of multi-microgrid (MMG) systems and improve renewable energy utilization. However, SESC planning remains constrained by long-term renewable and load uncertainty, stringent siting requirements, and the coupling between capacity allocation and profitability. This paper proposes an integrated framework combining large language model (LLM)-based multi-agent systems with exploration-enhanced deep reinforcement learning. The LLM agents address renewable, load forecasting and SESC siting, while the reinforcement learning module performs economic optimization. The upstream Multi-ChatGPT-Agent rolling forecast module enables multi-year joint forecasting of wind power, photovoltaic generation, and load. Comparative experiments show improved forecasting accuracy, and the prediction results are converted into representative datasets through clustering for downstream optimization. Guided by these forecasts, the ChatGPT-Agent infers SESC locations from microgrid coordinates and scale metrics while jointly considering wiring-distance minimisation, transmission-loss-cost minimisation, and practical siting constraints expressed in natural language. This allows language-based engineering requirements that are difficult to encode as mathematical or programmable constraints to be incorporated into the siting process. Subsequently, an Adaptive Count-Based Exploration Deep Q-Network (ACBE-DQN) is constructed to jointly learn capacity allocation and profitability strategies. By enhancing exploration through adaptive intrinsic reinforcement, ACBE-DQN mitigates sparse and non-stationary profit signals. Empirical studies show that, compared with standard DQN and Q-learning, the proposed model improves convergence stability and economic efficiency while maintaining feasible energy-state trajectories and operational constraints.