Title :
Novel Multiagent Based Load Restoration Algorithm for Microgrids
Author :
Xu, Yinliang ; Liu, Wenxin
Author_Institution :
Klipsch Sch. of Electr. & Comput. En gineering, New Mexico State Univ., Las Cruces, NM, USA
fDate :
3/1/2011 12:00:00 AM
Abstract :
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.
Keywords :
distributed algorithms; knapsack problems; load management; multi-agent systems; particle swarm optimisation; power grids; 0-1 knapsack problem; average-consensus theorem; distributed algorithm; distributed multiagent based load restoration; global information; information discovery; load restoration problem; microgrids; out-of-service loads; particle swarm optimization; stability; synchronized load restoration decision; total net power; Algorithm design and analysis; Eigenvalues and eigenfunctions; Generators; Indexes; Power system stability; Stability analysis; Average-consensus theorem; load restoration; microgrid; multiagent system; particle swarm optimization;
Journal_Title :
Smart Grid, IEEE Transactions on
DOI :
10.1109/TSG.2010.2099675