An Introduction to MultiAgent Systems.

In this paper, future distribution network operation is discussed under assumption of multimicrogrids (MMGs) concept. The economic operation of MMGs is formulated as an optimization problem. A stochastically and probabilistic modeling of both small-scale energy resources (SSERs) and load demand at each microgrids (MGs) is done to determine the optimal economic operation of each MGs with minimum cost based on the power transaction between the MGs and main grids. The balance between the total power generation in each MGs and the load demand is determined regarding the sold or purchase power either by MG or by main grid. Based on the results, the mean, standard deviation, and probability density function of each generated power with SSERs is determined considering optimization constraints. A statistical analysis for generated power and costs is given. The power interchange between MGs is considered. The particle swarm optimization is applied to minimize the cost function as an optimization algorithm. Results show that it is possible to regulate the power demand and power transaction between each MGs and the main grid. Moreover, it is indicated that the power sharing between MGs with main grid can reduce the total operation cost of the future distribution network.

Optimal Power Dispatch of Multi-Microgrids at Future Smart Distribution Grids

In this paper, we introduce the incremental welfare consensus algorithm for solving the energy management problem in a smart grid environment populated with distributed generators and responsive demands. The proposed algorithm is distributed and cooperative such that it eliminates the need for a central energy-management unit, central price coordinator, or leader. The optimum energy solution is found through local peer-to-peer communications among smart devices. Each distributed generation unit is connected to a local price regulator, as is each consumer unit. In response to the price of energy proposed by the local price regulators, the power regulator on each generation/consumer unit determines the level of generation/consumption power needed to optimize the benefit of the device. The consensus-based coordination among price regulators drives the behavior of the overall system toward the global optimum, despite the greedy behavior of each unit. The primary advantages of the proposed approach are: 1) convergence to the global optimum without requiring a central controller/coordinator or leader, despite the greedy behavior at the individual level and limited communications; and 2) scalability in terms of per-node computation and communications burden.

Incremental Welfare Consensus Algorithm for Cooperative Distributed Generation/Demand Response in Smart Grid

To achieve accurate reactive power sharing regardless of the effects of mismatched line impedance, this paper proposes a reactive power sharing method that employs both consensus control and adaptive virtual impedance control for islanded microgrids. The consensus control is used to find the reactive power mismatch among distributed generation (DG) units. The reactive power mismatch term is fed to a proportional integral controller to generate the virtual impedance correction. With fully distributed regulation of the DG virtual impedance, the load reactive power will be shared accurately among DGs and the circulating line currents among DGs are effectively suppressed. The control strategy does not require the knowledge of the line impedances. Also, the average voltage restoration based on a dynamic consensus control is proposed to recover the decreased output voltage of each DG due to the droop action and the added virtual impedance. Simulation results show the effectiveness of the proposed method in achieving load reactive power sharing and the voltage restoration.

Distributed Adaptive Virtual Impedance Control for Accurate Reactive Power Sharing Based on Consensus Control in Microgrids

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.

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

If generation in a power system is insufficient to power all loads, efficient load shedding operations may need to be deployed to maintain the supply-demand balance. This paper proposes a distributed multi-agent-based load shedding algorithm, which can make efficient load shedding decision based on discovered global information. During the information discovery process, only communications between immediate neighboring agents are used. The information discovery algorithm is represented as a discrete time linear system and the stability of which is analyzed according to average-consensus theorem. According to rigorous stability analysis, convergence of the designed algorithm can be guaranteed. To improve the speed of the algorithm, particle swarm optimization (PSO) is used to optimize the coefficients for information exchange so that the second largest eigenvalue of the iteration matrix is minimized. According to the designed algorithm, total net active power and operating status of loads can be discovered accurately even with faults. Based on the discovered information, coordinated load shedding decision can be made.

Jun Gong

Papers

Stable Multi-Agent-Based Load Shedding Algorithm for Power Systems