Real‑Time Dynamic Dispatch Strategy for High‑Penetration Wind‑Solar Power Systems Based on Deep Reinforcement Learning

Abstract

High-penetration wind and photovoltaic integration is driving smart grids from conventional deterministic dispatch toward real-time dynamic dispatch under random fluctuation and fast response requirements. Wind and solar outputs are strongly affected by weather conditions, and forecast errors may lead to rapid net-load ramping, insufficient reserve capacity, branch overload, and limited renewable accommodation. To address the operational requirements of high-penetration wind-solar power systems, a real-time dynamic dispatch strategy based on deep reinforcement learning is constructed. Nodal load, wind-solar forecast errors, conventional generator output, battery state of charge, branch loading ratio, and real-time electricity price are incorporated into the state space, while the SAC algorithm outputs continuous dispatch actions for coordinated thermal generation adjustment, battery charging and discharging, and interruptible load response. Experiments are conducted on a modified IEEE 118-bus system, where MATPOWER is used for AC power-flow verification and Python is used to build the reinforcement learning environment. PPO, DDPG, and rolling economic dispatch are selected as comparative methods. The results show that SAC reduces the daily operation cost to 45,820 USD, decreases the renewable curtailment rate to 4.82%, and lowers load shedding to 0.71 MWh under 70% wind-solar penetration. It also effectively controls branch-overload events in most medium forecast-error scenarios. These results indicate that deep reinforcement learning can form a stable dispatch balance among economic efficiency, renewable accommodation, and security constraints, providing a feasible computational solution for real-time control in high-renewable smart grids.