A Study of Reinforcement Learning Framework for Energy Management in Smart Grids: Integrating Market Trading, Load Forecasting, and Vertical Agents

This paper proposes an integrated reinforcement learning (RL) framework for optimizing energy management in smart and micro grids that addresses both real-time operations and day-ahead market trading. By designing reward functions that incorporate real-time market prices, grid demand, peak penalties, and forecasted load values, the framework directs optimal charging, discharging, or holding actions of a Battery Energy Storage System (BESS). A comprehensive battery model captures state-of-charge (SoC) dynamics with round-trip efficiency losses, cycle-based degradation using rainflow counting algorithms, and operational constraints including ramp rate limits. This physics-based degradation modeling, which accounts for nonlinear depth-of-discharge effects and electrochemical aging mechanisms (SEI growth, lithium plating, electrode stress), enables the RL agent to balance immediate energy arbitrage profits against long-term asset preservation through optimized shallow cycling strategies. The framework employs Proximal Policy Optimization (PPO) for stable multi-objective policy learning and integrates day-ahead load forecasting using Transformer models. A novel contribution is the application of vertical agents powered by small-scale large language models (sLLM) to translate RL decisions into executable schedules through an intuitive human-machine interface, bridging the gap between optimal policies and practical implementation.