Abstract:

With the growth of global energy demand and the advancement of sustainable development goals, energy management of microgrids, as an important means to address energy supply, improve energy efficiency and promote green energy utilization, is faced with high dimensionality, complexity and dynamic challenges. In this paper, we propose a distributed reinforcement learning-driven energy optimization and management strategy for microgrid clusters, aiming to enhance the efficiency and sustainability of microgrid clusters in energy scheduling and management through intelligent means. Aiming at the challenges of the microgrid cluster, such as large dynamic changes in load and complex topology, adaptive decision-making and collaborative optimization of the microgrid cluster in a distributed environment is achieved by constructing an objective optimization function and introducing a distributed reinforcement learning algorithm; and power generation of the microgrid cluster is achieved by treating each power point in the microgrid as an agent and utilizing information sharing to achieve the maximization of the global benefit and minimization of the power generation cost, storage and load demand management; finally, the results of the real case show that the proposed strategy is able to maintain the dynamic balance between power supply and demand, resulting in a saving of about 18% of the total power generation cost compared to the traditional methodology techniques.

Key words: distributed reinforcement learning, dynamic energy management, microgrid clusters, dynamic balancing, agent