The Research of Microgrid Optimized Dispatching Method Based on MEA

Integration of large scale renewable energy sources and electric cars into power grid will bring new opportunities and challenges to the operation, control of the power grid and the power market .The microgrid , which integration the distributed generation systems, energy storage element and loads ,...

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Vydáno v:Applied Mechanics and Materials Ročník 494-495; číslo Current Development of Mechanical Engineering and Energy; s. 1841 - 1844
Hlavní autoři: Zhang, Shao Min, Wang, Bao Yi, Wei, Chen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Zurich Trans Tech Publications Ltd 01.01.2014
Témata:
Algorithms
Computer simulation
Distributed generation
Electric cars
Electric power generation
Electric power grids
Markets
Nonlinear programming
Microgrids (MGs)
V2G
Mind Evolutionary Algorithm (MEA)
Optimized Dispatching
ISBN:3038350036, 9783038350033
ISSN:1660-9336, 1662-7482, 1662-7482
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Shrnutí:Integration of large scale renewable energy sources and electric cars into power grid will bring new opportunities and challenges to the operation, control of the power grid and the power market .The microgrid , which integration the distributed generation systems, energy storage element and loads ,has been seen as one effective way to solve the problems. This paper proposed a MEA-based scheduling algorithm, and studied a microgrid that contains 32 nodes and 50 electric cars. Both the MEA algorithm and mixed integer nonlinear programming algorithm (MINLP) can solve the energy resource scheduling problem,but the results of our simulation shows that the total cost of MEA is higher than that of the MINLP,but there is a significant improvement in the efficiency of execution.The results shows that we can use MEA to achieve optimal scheduling,it also verify the effectiveness of the algorithm.
Bibliografie:Selected, peer reviewed papers from the 2013 International Symposium on Vehicle, Mechanical, and Electrical Engineering (ISVMEE 2013), December 21-22, 2013, Taiwan, China
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ISBN:3038350036
9783038350033
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.494-495.1841