Coordinated Optimization of Active Distribution Network and Multi-Microgrids Considering Voltage Robustness and Economic Efficiency: A Distributed Model Predictive Control Method

Abstract

With the high penetration of renewable energy, the active distribution network (ADN) and multi-microgrids (MMGs), as emerging multi-layered energy management systems, face challenges such as voltage violations and conflicts of interest among multiple agents. To address these distributed coordinating optimization issues in the ADN-MMGs system, this paper proposes a coordinating optimization method for ADN-MMGs systems that balances voltage robustness and system economics. In the day-ahead stage, the method considers MMGs' participation in the voltage regulation ancillary service market and decentralized peer-to-peer (P2P) trading market, and baseline plans are developed for voltage stability and economic operation. In the intra-day stage, ADN and MMGs are treated as subsystems. Using voltage sensitivity matrices and consensus algorithms, a state-space-based distributed model predictive control (Di-MPC) coordination model is constructed, effectively mitigating short-term power fluctuations from renewable energy and load variability. An analysis on a modified IEEE 33-bus system demonstrates that the proposed method enhances economic efficiency and voltage robustness by coordinating subsystem energy flows. Compared to the distributed economic model predictive control (MPC) scheme, the proposed method reduces the state of charge (SOC) tracking error of MMGs by 51.21% and ensures that the deviation in planned daily operating cost for the ADN-MMGs system does not exceed 1%.