Microgrid reinforcement learning program

Can deep reinforcement learning be used in microgrid control systems?

These real-world demonstrations further strengthen the foundation for DRL integration into microgrid control systems. In conclusion, the application of Deep Reinforcement Learning in microgrid control systems holds great promise for addressing key challenges in energy efficiency, renewable energy integration, and grid stability.

Can reinforcement learning improve microgrid management performance under stochastic wind speed conditions?

To evaluate the improvements of the performance indicators achieved by the application of the reinforcement learning for microgrid management under stochastic wind speed conditions, Ns = 50 independent simulation runs are executed. Each of these simulation runs generates a wind profile similar to the one depicted in Fig. 7.

What is a microgrid control system?

Typical hierarchical structure of microgrid control system. The control systems typically have to manage power source from the main grid and distributed energy resources (DER). Along with managing generation-load balance to ensure power quality and stability. 2.1. Linear control system approach

How to evaluate microgrid performance?

Evaluation of the microgrid performance The microgrid performance depends on the efficiency of the reinforcement learning algorithm. Usually the performance of the learning algorithm is analysed by representing the evolution of the Q -values of the actions , like in Section 4.3 ( Fig. 6 ), or the reward or penalty functions , .

Can RL control energy storage devices in microgrids?

Furthermore, recent findings in microgrid control systems have led to the use of RL application techniques to control energy storage devices in microgrids , . In some cases, microgrids may include different types of energy storage systems to better respond to changes in energy supply , , .

How does DRL integrate with a microgrid?

Despite the acknowledged complexities of DRL's learning, its integration provides a potent solution for real-time learning and adaptation in dynamic microgrid scenarios. The combination of perception and decision-making capabilities allows DRL to navigate uncertainties and optimize control strategies effectively over time.