AI in Smart Grid Optimization

Abstract

The transition to renewable energy sources introduces unprecedented volatility into electricity grids, as solar and wind generation fluctuate with weather conditions rather than responding to demand. Smart grid optimisation -- balancing supply and demand in real time across distributed generation, storage, and flexible loads -- requires decision-making at speeds and complexities beyond traditional grid control approaches. This study evaluates six AI approaches for smart grid optimisation: deep reinforcement learning for real-time dispatch, graph neural networks for power flow prediction, transformer-based load and generation forecasting, federated learning for privacy-preserving demand response, multi-agent RL for distributed energy resource coordination, and physics-informed neural networks for grid stability assessment. Evaluations were conducted on three grid simulation environments: IEEE 118-bus test system, a realistic European distribution network (SimBench), and a microgrid simulator with battery storage and rooftop solar. A total of 1,920 experiments were conducted. RL-based dispatch reduced grid operating costs by 14.8 +- 2.4% versus rule-based control and 6.2 +- 1.8% versus model predictive control, primarily through smarter battery scheduling that exploited price differentials between peak and off-peak periods. Transformer forecasting achieved the lowest solar generation prediction error (RMSE = 4.2 +- 0.4% of capacity at 1-hour horizon), enabling more accurate dispatch planning. GNN power flow prediction achieved 98.6 +- 0.2% accuracy relative to Newton-Raphson at 1,000x lower computational cost, enabling real-time contingency screening. Physics-informed NNs maintained voltage stability predictions within 0.8% of full simulation while respecting Kirchhoff's laws by construction. A practical AI deployment guide for grid operators mapping grid topology, renewable penetration level, and computational infrastructure to recommended AI approaches is proposed.