Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning.

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Power Systems

Background:

• Microgrids with distributed generation (DG) and energy storage (ES) are crucial for future power systems.
• Efficient distributed economic dispatch in microgrids is challenging due to the stochastic and nonlinear nature of DG units and loads.

Purpose of the Study:

• To propose a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids.
• To address the challenges of stochastic modeling and high computational complexity in microgrid management.

Main Methods:

• A cooperative reinforcement learning algorithm is developed, leveraging function approximation for large, continuous state spaces.
• A diffusion strategy is integrated to coordinate DG units and ES devices.
• The algorithm enables decentralized control, with nodes communicating only with local neighbors.

Main Results:

• The proposed algorithm effectively manages distributed economic dispatch without centralized controllers.
• Algorithm convergence is theoretically analyzed.
• Simulations using real-world data validate the algorithm's performance.

Conclusions:

• Cooperative reinforcement learning offers an efficient solution for distributed economic dispatch in microgrids.
• The decentralized approach simplifies microgrid operation and enhances scalability.
• The algorithm's ability to handle complex system dynamics is demonstrated through simulations.