Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-13
Main Authors: Ma, Jinhui, Shen, Weixiang, Ding, Jinjin, Zhang, Qian, Li, Guoli
Format: Article
Language:English
Subjects:
Classification
Computer simulation
Consumption
Distributed generation
Economic analysis
Economic models
Electric vehicles
Electricity
Electricity distribution
Emissions control
Energy
Engineering
Fuzzy sets
Genetic algorithms
Greenhouse effect
Greenhouse gases
Linear programming
Multiple objective analysis
Objectives
Optimization algorithms
Pareto optimization
Particle swarm optimization
Power supply
Purchasing
Reliability
Search algorithms
Sorting algorithms
Tradeoffs
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Summary:Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/5695917