Distributed Model-Predictive Real-Time Optimal Operation of a Network of Smart Microgrids

In this paper, we study multiple interconnected smart microgrids, and develop an efficient strategy for their internal device scheduling and energy trading. This paper achieves the following objectives. First, each microgrid holistically manages its internal devices, such as local storage systems and reactive power compensation levels, and external active/reactive energy trading (with the external grid and other microgrids) simultaneously for real-time optimization. Scenarios of social cooperation and game in interconnected microgrids are formulated as distinct objectives of the microgrids, which aim to optimize their own performance and gain benefits through energy trading. Second, microgrids of different network topologies (radial and meshed) and nominal voltages are fully incorporated. Third, a fully distributed model-predictive and computational-intelligence-based algorithm is proposed, so that microgrids' devices operate autonomously with minimum communication exchange, obviating the need for a central controller. Convergence properties of the proposed distributed algorithm are analyzed and benchmarked with a state-of-the-art distributed algorithm, and numerical simulations for different scenarios are performed demonstrating that the proposed distributed strategy can be feasibly applied to real-world microgrids.