Novel Multiagent Based Load Restoration Algorithm for Microgrids

Once a fault in microgrids has been cleared, it is necessary to restore the unfaulted but out-of-service loads as much as possible in a timely manner. This paper proposes a novel fully distributed multiagent based load restoration algorithm. According to the algorithm, each agent makes synchronized load restoration decision according to discovered information. During the information discovery process, agents only communicate with their direct neighbors, and the global information is discovered based on the Average-Consensus Theorem. In this way, total net power, indexes and demands of loads that are ready for restoration can be obtained. Then the load restoration problem can be modeled and solved using existing algorithms for the 0-1 Knapsack problem. To achieve adaptivity and stability, a distributed algorithm for coefficient setting is proposed and compared against existing algorithms and a particle swarm optimization based algorithm. Theoretically, the proposed load restoration algorithm can be applied to systems of any size and structure. Simulation studies with power systems of different scale demonstrate the effectiveness of the proposed algorithm.

[1]  R. Olfati-Saber,et al.  Algebraic Connectivity Ratio of Ramanujan Graphs , 2007, 2007 American Control Conference.

[2]  N.N. Schulz,et al.  A Multi-Agent Solution to Distribution Systems Restoration , 2007, IEEE Transactions on Power Systems.

[3]  Palak Jain,et al.  Island identification in customer-driven micro-grids , 2010, IEEE PES T&D 2010.

[4]  Hiroshi Sasaki,et al.  A multiagent approach to distribution system restoration , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[5]  Stephen P. Boyd,et al.  Fast linear iterations for distributed averaging , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[6]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[7]  Dongbing Gu,et al.  Leader–Follower Flocking: Algorithms and Experiments , 2009, IEEE Transactions on Control Systems Technology.

[8]  Kai Huang,et al.  A Multiagent-Based Algorithm for Ring-Structured Shipboard Power System Reconfiguration , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  T. Nagata,et al.  An autonomous agent for power system restoration , 2004, IEEE Power Engineering Society General Meeting, 2004..

[10]  J.A. Momoh,et al.  Optimal Reconfiguration of the Navy Ship Power System using Agents , 2006, 2005/2006 IEEE/PES Transmission and Distribution Conference and Exhibition.

[11]  Mehran Mesbahi,et al.  Agreement over random networks , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[12]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[13]  Long Wang,et al.  Fast information sharing in networks of autonomous agents , 2008, 2008 American Control Conference.

[14]  A. Horn.  Doubly Stochastic Matrices and the Diagonal of a Rotation Matrix , 1954 .

[15]  Satish J. Ranade,et al.  Capacity Discovery in customer-driven micro-grids , 2009, 41st North American Power Symposium.

[16]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[17]  Stephen P. Boyd,et al.  Distributed average consensus with least-mean-square deviation , 2007, J. Parallel Distributed Comput..

[18]  G.T. Heydt,et al.  Optimal Restoration of Distribution Systems Using Dynamic Programming , 2008, IEEE Transactions on Power Delivery.

[19]  E. Lopez,et al.  Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operators: real application , 2006, IEEE Transactions on Power Systems.

[20]  Leonard L. Grigsby,et al.  Computational Methods for Electric Power Systems , 2012 .

[21]  Zhihong Man,et al.  Robust Finite-Time Consensus Tracking Algorithm for Multirobot Systems , 2009, IEEE/ASME Transactions on Mechatronics.

[22]  G. Lambert-Torres,et al.  Particle Swarm Optimization applied to system restoration , 2009, 2009 IEEE Bucharest PowerTech.

[23]  Jin-O Kim,et al.  Optimal service restoration and reconfiguration of network using Genetic-Tabu algorithm , 2004 .

[24]  A. G. Phadke,et al.  Artificial neural networks in power system restoration , 2002 .