Multi-objective energy management of multiple microgrids under random electric vehicle charging

In view of the increasing development of decentralized power systems and electric vehicles, this paper seeks to improve the energy management performance of multiple microgrid systems under the uncertainty associated with electric vehicle charging. A multi-objective optimization model is established...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 208; p. 118360
Main Authors: Tan, Bifei, Chen, Haoyong
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.10.2020
Elsevier BV
Subjects:
Adaptive algorithms
Algorithms
Back propagation networks
carbon
case studies
Computer simulation
Consistency algorithm
Decomposition
Deep learning
Distributed generation
Economic models
Electric power systems
Electric vehicle charging
Electric vehicles
Electrical loads
energy
Energy management
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Long short-term memory
Machine learning
Mathematical models
Multi-microgrids
Multiple objective analysis
Neural networks
Operating costs
Optimization
Pareto optimality
Shannon-Wiener index
Transmission loss
uncertainty
Long short-term memory
Pareto optimality
Electric vehicles
Shannon-Wiener index
Multi-microgrids
Consistency algorithm
ISSN:0360-5442, 1873-6785
Online Access:Get full text
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Summary:In view of the increasing development of decentralized power systems and electric vehicles, this paper seeks to improve the energy management performance of multiple microgrid systems under the uncertainty associated with electric vehicle charging. A multi-objective optimization model is established for minimizing the transmission losses, operating costs, and carbon emissions of multiple microgrid systems. Firstly, a novel method is proposed for forecasting electric vehicle charging loads based on a back propagation neural network improved by long short-term memory deep learning. Based on the forecast data, a double layer solution algorithm is proposed, which consists of an adaptive multi-objective evolutionary algorithm based on decomposition and differential evolution at the multiple microgrids layer and a modified consistency algorithm for fast economic scheduling at the single microgrid layer. Finally, a model system composed of four interconnected IEEE microgrids is simulated as a case study, and the performance of the proposed algorithm is compared with that of conventional multi-objective evolutionary algorithms based on decomposition. The simulation results demonstrate the superiority of the global search performance and the rapid convergence performance of the proposed improved algorithm. •Forecasting is applied to the charging loads of electric vehicles.•A neural network improved by deep learning reduces the forecasting error by 36.86%.•A multi-objective optimization model is proposed for multiple microgrids.•The optimization algorithm is improved using consistency and differential theory.•The improved algorithm provides better Pareto solutions and convergence.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2020.118360