A cognitive control approach for microgrid performance optimization in unstable wireless communication

摘要

As a pivotal component of microgrid control and optimization, demand response management (DRM) is implemented in bidirectional communication between power suppliers and users to enhance the efficiency of the microgrid, and to balance supply and demand. Unstable communication, especially in wireless networks, will inevitably influence DRM performance. In this paper, a novel cognitive control approach is adopted to relieve the negative impacts on microgrid DRM performance. Unlike previous studies that have tried to resolve the impacts through communication methods, our machine learning method considers both communication quality and lost data estimates. The notion of information gap is introduced to represent information uncertainties caused by unstable communication. Moreover, cognitive control models are formulated in both the user agent and the microgrid control center agent, and a Q-learning algorithm is used as a cognitive learning method to generate different optimal policies for both types of agents based on attainable information. Our simulation results demonstrate the relationship between DRM performance and the unstable wireless environment. Moreover, the effectiveness of our proposed cognitive control approach is verified. (C) 2019 Elsevier B.V. All rights reserved.