Multiobjective Particle Swarm Optimization for Microgrids Pareto Optimization Dispatch

Qian Zhang,Weixiang Shen,Guoli Li,Jinhui Ma,Jinjin Ding

doi:10.1155/2020/5695917

Abstract

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.

Highlights

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
Based on the models and three objectives, the constrained MOO problem is formulated. en, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. e combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. e final trade-off solutions are decided based on the fuzzy set. e 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. e 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
The modelling of a PV-diesel-battery MG system is accomplished with the precise economic and environmental parameters. e multiobjective Pareto optimization dispatch is constructed as a MOO problem

Summary

Problem Formulation

3.1. e First Objective Function: Operation Cost. e optimization objective F1 contains energy consumption cost Cdg, the operation management cost Com, the depreciation cost of battery Cbd, and the energy interaction cost between the grid and MG Cgrid. Where NDG is a total number of DGs, Pi(t) is the energy output of the ith DG, and Pgrid(t) is the electricity exchange between the MG and power grid. Erefore, the environmental protective cost is considered in the dispatch model, which is defined as follows and in Table 3: 24 NP min F2 􏽘 􏽘 αi(t) + βi(t)􏼁Qi(t)􏼁. E constrained MOO problem deals with the dispatch of the DGs to satisfy the multiple objectives of the MG, limited by the constraints and operating limits. It can be formulated as min F(x) min F1(x), F2(x), F3(x)􏼁.

Pareto Particle Swarm Optimization Algorithm

40 Economic cost

Findings

Conclusion