Optimising operation management for multi‐micro‐grids control

Nowadays, renewable energy sources in a micro‐grid (MG) system have increased challenges in terms of the irregularly and fluctuation of the photovoltaic and wind turbine units. It is necessary to develop battery energy storage. The MG central controller is helping to develop it in the MG system for...

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Vydané v:IET cyber-physical systems Ročník 3; číslo 1; s. 24 - 33
Hlavní autori: Kumrai, Teerawat, Ota, Kaoru, Dong, Mianxiong, Sato, Kazuhiko, Kishigami, Jay
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Southampton The Institution of Engineering and Technology 01.03.2018
John Wiley & Sons, Inc
Wiley
Predmet:
Alternative energy sources
battery energy storage
battery storage plants
Cloud computing
Computer simulation
Costs
covariance matrices
Covariance matrix
distributed power generation
Energy consumption
Energy costs
Energy efficiency
Energy storage
fitness-based modified game particle swarm optimisation algorithm
FMGPSO algorithm
Game theory
Genetic algorithms
Heuristic
Linear programming
Mathematical programming
MG central controller
multi microgrids control
multi mMG system
multiobjective covariance matrix adaptation evolution strategy
Neural networks
nondominated sorting genetic algorithm-III
operation management optimisation
particle swarm optimisation
Particle swarm optimization
photovoltaic power systems
photovoltaic units
pollutant emissions
Pollutants
power generation control
power system management
Renewable energy sources
Software
Sorting algorithms
speed-constrained multiobjective particle swarm optimisation
total energy expenses reduction
wind turbine units
Wind turbines
ISSN:2398-3396, 2398-3396
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Shrnutí:Nowadays, renewable energy sources in a micro‐grid (MG) system have increased challenges in terms of the irregularly and fluctuation of the photovoltaic and wind turbine units. It is necessary to develop battery energy storage. The MG central controller is helping to develop it in the MG system for improving the time of availability. Thus, reducing the total energy expenses of MG and improving the renewable energy sources (battery energy storage) are considered together with the operation management of the MG system. This study proposes fitness‐based modified game particle swarm optimisation (FMGPSO) algorithm to optimise the total costs of operation and pollutant emissions in the MG and multi‐MG system. The optimal size of battery energy storage is also considered. A non‐dominated sorting genetic algorithm‐III, a multi‐objective covariance matrix adaptation evolution strategy, and a speed‐constrained multi‐objective particle swarm optimisation are compared with the proposed FMGPSO to show the performance. The results of the simulation show that the FMGPSO outperforms both the comparison algorithms for the minimisation operation management problem of the MG and the multi‐MG system.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2398-3396
2398-3396
DOI:10.1049/iet-cps.2017.0079