Deep reinforcement learning for real-time energy dispatch in smart grids with high renewable penetration
Litsenziya: cc-by
Annotatsiya
The increasing penetration of Renewable Energy (RE) in modern Smart Grids (SG) introduces substantial variability and uncertainty, posing critical challenges to real-time energy dispatch. Traditional optimization and rule-based methods, while effective under deterministic conditions, exhibit limited adaptability to stochastic RE generation and fluctuating demand. This study develops a Deep Reinforcement Learning (DRL) model for real-time dispatch in renewable-dominated SG, formulating the problem as a constrained Markov Decision Process (MDP). Actor-critic networks—Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—learn adaptive policies that jointly minimize operational costs, enhance renewable integration, and maintain grid reliability. A modified IEEE 33-bus distribution system with high RE diffusion is simulated using historical solar and wind profiles, storage dynamics, and realistic demand patterns. A comparative analysis of rule-based heuristics, deterministic Mixed-Integer Linear Programming (MILP), and two-stage stochastic optimization proves that DRL achieves superior performance across multiple dimensions. SAC delivers the best results, reducing operational costs by 20%, achieving 92.8% renewable application, and minimizing loss-of-load probability to 0.5%, while maintaining real-time computational feasibility (0.41 s per dispatch interval). Constraint satisfaction validation confirms 99.8% voltage compliance and 100% thermal limit adherence. Scalability analysis of the IEEE 123-bus network reveals sub-quadratic training-time scaling and effective model transferability under parameter variations. Sensitivity analyses confirm robustness under varying prediction errors, dispatch granularities, and storage configurations. These results establish DRL as a scalable, reliable, and cost-efficient model for next-generation SG dispatch under RE uncertainty.
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