MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Abstract

Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

Distributed Peer-to-Peer Electricity Trading Considering Network Loss in a Distribution System

“…Generally, TOU tariffs have the constraint s t ≤ b t [10]. When a DNO purchases/sells the power P t from/to the MGO n/LA c at the time slot t, the revenue/cost of the MGO n/LA c is…”

“…Recently, enormous interest has grown in exploring different strategies to improve the benefits of participants in the future electricity market [6][7][8][9][10][11][12][13]. For an LA of residential consumers, it can reduce electricity cost by shifting the load from peak hours to off-peak hours or purchasing from other electricity suppliers when the time-of-use (TOU) tariffs of the corresponding DNO are at the peak [6][7][8].…”

“…Furthermore, the LA can also benefit from providing ancillary services to power systems such as frequency regulation service [9]. For an MGO, the basic strategy is to schedule the controllable units within a microgrid, such as formulating charging and discharging strategies for energy storage, and demand response mechanisms for internal loads [10]. Moreover, the self-interested MGOs can benefit from directly exchanging electricity among microgrid clusters or with consumers under signed agreements [11][12][13].…”

Energy Market Management for Distribution Network with a Multi-Microgrid System: A Dynamic Game Approach

“…Due to its environmental benefits and sustainability, it has received much attention from the power industry in recent years [1][2][3][4]. When multiple MGs are connected to a local distribution network (DN) of a power grid, they can form a multi-microgrid (MMG) [5][6][7]. The coordination and control of these multiple MGs and the DN are required to improve the economic benefits of MGs and the reliability and stability of power grids, which brings us many challenges.…”

Consensus-based Hierachical Demand Side Management in Microgrid

“…Thus, a hierarchical control strategy is presented to consider the economic dispatch problem, in order to improve efficiency, reliability and controllability. The primary and advanced level of the droop control can be implemented in the lower level of hierarchical control architecture, whereas tertiary control can be achieved in the top level [5], [6]. Tertiary control aims at optimizing the operation status of the microgrid based on economic dispatching law, market signal and forecasting results.…”