Zaijun Wu

Decentralized Multi-Agent System-Based Cooperative Frequency Control for Autonomous Microgrids With Communication Constraints

Based on power line carrier communication technology, a decentralized multi-agent system (DMAS)-based frequency control strategy is proposed and investigated in this study on an autonomous microgrid with communication constraints, where each agent can only communicate with its neighboring agents. Using the optimized average consensus algorithm, the global information (i.e., total active power deficiency of the microgrid) can be accurately shared in a decentralized way. Depending on the discovered global information, the cooperative frequency control strategy, which involves primary and secondary frequency control and multi-stage load shedding, is executed to achieve a cooperative frequency recovery. Simulation results indicate that the proposed frequency control approach can guarantee the consensus and coordination of the distributed agents and maintain the frequency stability of islanded microgrids even in emergency conditions.

Adaptive Decentralized Under-Frequency Load Shedding for Islanded Smart Distribution Networks

Frequency stability of islanded smart distribution networks with peer-to-peer (P2P) controlled distributed generators (DGs) has attracted special attention recently. Using power line communication (PLC) technology, a multi-agent system (MAS)-based, decentralized, under-frequency load shedding (UFLS) scheme is investigated in this study for smart distribution networks with the communication constraint that each agent can only communicate with its neighboring agents. The proposed scheme uses the two-layer nearest neighbor consensus algorithm (NNCA) to overcome the drawback of the leader-follower consensus algorithm and guarantee the implementation of a decentralized UFLS. Based on the global information (i.e., the magnitude of the total active power imbalance) discovered in the first layer of the NNCA, the multi-stage UFLS can be executed by the second layer of the NNCA. Simulation results demonstrate that the proposed scheme can effectively implement the decentralized UFLS while maintaining the frequency stability of an islanded smart distribution network.

Intelligent PHEV charging and discharging strategy in smart grid

The anticipation of a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) into the market brings up many new technical problems that need to be addressed. In the near future, a large number of PHEVs/PEVs connected to power grids will add a large-scale energy load, as well as add substantial energy resources that can be utilized. Vehicle-to-Grid (V2G) technology is a most promising opportunity in PHEV/PEV adoption. In this paper, the authors propose an intelligent method for optimally managing a large number of PHEVs/PEVs (e.g., 3,000) charging/discharging at a municipal parking deck. The authors used the Estimation of Distribution Algorithm (EDA) to determine the optimal charging/discharging times and patterns over a period of 24 hours. A mathematical framework for the objective function (i.e., maximizing the overall profit on a vehicle fleet base) is also given in detail. The authors characterized the performance of EDA-based energy management using a Matlab simulation, and compared it with other optimization techniques.