Multi-Agent Deep Reinforcement Learning Framework for Adaptive Energy Management in Smart Grids

Abstract

The shift of contemporary smart grids toward decentralized and data-based structures brings new challenges related to stability, adaptability, and balance of energy. We present a multi-agent deep reinforcement learning framework for adaptive energy management based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The presented model allows distributed energy agents—representing generation, consumption, and storage units—to jointly control the power flow in real time, in dynamic and unsteady conditions. The algorithm robustness and generalization were evaluated in four operational scenarios: static, seasonal, strongly variable and storm spikes. Presented results indicate that the MADDPG controller achieves better performance in comparison with previously used rule-based or random baselines, effectively reducing average value of the energy imbalance and increasing stability of the system for all environments. The received policies are characterised by a great convergence and adaptability to nonlinear fluctuations, allowing for coordinated decision making in between distributed agents. It is shown that MADDPG provides a scalable and resilient base for real-time control in smart grids. This research contributes to the development of multi-agent methodology of reinforcement learning concerning energy systems of the next generation, in those autonomous cooperation and adaptive control become crucial for their universally sustainable and efficient functioning.