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Multi-objective optimal scheduling of microgrid with electric vehicles

Citation

Mei, Y and Li, B and Wang, H and Wang, X and Negnevitsky, M, Multi-objective optimal scheduling of microgrid with electric vehicles, Energy Reports, 8 pp. 4512-4524. ISSN 2352-4847 (2022) [Refereed Article]


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DOI: doi:10.1016/j.egyr.2022.03.131

Abstract

With the increasing global attention to environmental protection, microgrids with efficient usage of renewable energy have been widely developed. Currently, the intermittent nature of renewable energy and the uncertainty of its demand affect the stable operation of a microgrid. Additionally, electric vehicles (EVs), as an impact load, could severely affect the safe dispatch of the microgrid. To solve these problems, a multi-objective optimization model was established based on the economy and the environmental protection of a microgrid including EVs. The linear weighting method based on two-person zero-sum game was used to coordinate the full consumption of renewable energy with the full bearing of load, and balance the two objectives better. Moreover, the adaptive simulated annealing particle swarm optimization algorithm (ASAPSO) was used to solve the multi-objective optimization model, and obtain the optimal solution in the unit. The simulation results showed that the multi-objective weight method could diminish the influence of uncertainty factors, promoting the full absorption of renewable energy and full load-bearing. Additionally, the orderly charging and discharging mode of EVs could reduce the operation cost and environmental protection cost of the microgrid. Therefore, the improved optimization algorithm was capable of improving the economy and environmental protection of the microgrid.

Item Details

Item Type:Refereed Article
Keywords:microgrid, electric vehicles, multi objective optimization, two-person zero-sum game, adaptive simulated annealing particles, swarm optimization algorithm
Research Division:Engineering
Research Group:Mechanical engineering
Research Field:Numerical modelling and mechanical characterisation
Objective Division:Energy
Objective Group:Energy storage, distribution and supply
Objective Field:Energy systems and analysis
UTAS Author:Wang, X (Professor Xiaolin Wang)
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:149452
Year Published:2022
Deposited By:Engineering
Deposited On:2022-03-31
Last Modified:2022-05-03
Downloads:0

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