Distributed optimal energy scheduling for grid connected multi-microgrids with architecturized load characteristics

This paper proposes load characteristics-based multiple microgrids (Ch-MMGs) architecture and utilizes distributed model predictive control (DMPC) based energy management for its day-ahead scheduling in grid-connected mode. Different from existing work, which consider MMG with composite load architecture, the MMG architecture is re-organized by categorizing consumer's load into different groups according to their operational characteristics. By doing so, each characteristics microgrid (Ch-MG) would have specific load pattern, which can significantly improve power utilization more economically and optimally. Demand Response Program (DRP) is incorporated for reducing the peak load demand of categorized groups in accordance to Time-of-Use (ToU) prices. In order to meet the supply and demand of each Ch-MG more economically, a DMPC based two level optimal energy scheduling scheme has been developed, where distributed network operator (DNO) acts as coordinating entity at upper level. It facilitates economic energy exchange among Ch-MMGs through providing optimal reference signal to Ch-MG at lower level. The Ch-MG performs local optimization in according to reference signal from upper level DNO. The uncertainty in renewable power generation are handled with probabilistic scenarios using Monte-Carlo simulation. Comparative case studies are conducted on conventional and proposed MMG architecture. The results indicate that the operation of individual MGs with proposed scheme can effectively reduce system operation cost and achieve system-wide and local supply-demand balance more economically. (C) 2022 The Author(s). Published by Elsevier Ltd.