Deep reinforcement learning for joint dispatch of battery storage and gas turbines in renewable-powered microgrids

This study introduces a novel deep reinforcement learning (DRL) framework for the joint dispatch of Gas Turbines (GTs) and Battery Energy Storage Systems (BESs) in microgrids that face the variability of renewable energy sources and demands. BESs can store surplus renewable energy for nearly instantaneous use, while GTs offer sustained energy output over longer periods, offering complementary benefits. Previous studies often oversimplified GT operations, neglecting critical factors such as ramp-up times and increased degradation from frequent starts. This research addresses these gaps by proposing an advanced modeling framework that accurately captures the dynamic interaction between GTs and BESs, including GT ramp-up times and maintenance costs associated with operational cycles. Through extensive case studies involving diverse microgrid configurations, we demonstrate that DRL effectively learns dispatch policies directly from historical data, outperforming traditional optimization techniques. Deploying DRL to our framework yields more realistic dispatch policies, reducing GT maintenance costs by avoiding frequent starts. The proposed framework has significant potential to improve energy management strategies and to streamline the planning of hybrid energy systems. To encourage further research, we have released our codebase to the public, enabling the scientific community to build upon our findings. • Developed novel framework for joint GT-BES dispatch in renewable-powered microgrids. • Accurately modeled GT and BES operational dynamics and cost implications. • Compared DRL against traditional methods for GT and BES energy dispatch. • Publicly shared codebase and hyperparameters to promote reproducibility. • Demonstrated DRL’s potential in enhancing the planning of renewable power systems.

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