
Energies, Journal Year: 2024, Volume and Issue: 17(24), P. 6371 - 6371
Published: Dec. 18, 2024
Fast charge/discharge scheduling of battery storage systems is essential in microgrids to effectively balance variable renewable energy sources, meet fluctuating demand, and maintain grid stability. To achieve this, parallel processing employed, allowing batteries respond instantly dynamic conditions. By managing the complexity, high data volume, rapid decision-making requirements real time, ensures that microgrid operates with stability, efficiency, safety. With application deep reinforcement learning (DRL) algorithm design, demand for computational power has further increased significantly. address this challenge, we propose a Ray-based framework accelerate development fast microgrids. We demonstrate how implement real-world problem framework. focused on minimizing losses reducing ramping rate net loads by leveraging Asynchronous Advantage Actor Critic (A3C) algorithms features Ray cluster real-time decision making. Multiple instances OpenDSS were executed concurrently, each instance simulating distinct environment efficiently input data. Additionally, Numba CUDA was utilized facilitate GPU acceleration shared memory, significantly enhancing performance computationally intensive reward function A3C. The proposed enhanced performance, enabling efficient management complex, environments.
Language: Английский