Multi-objective Optimization of Accommodation Capacity for Distributed Generation Based on Mixed Strategy Nash Equilibrium, Considering Distribution Network Flexibility

Weisheng Liu,Fei Wang,Qiongdan Dai,Jian Wu,Yixin Huang,Li Yang

doi:10.3390/app9204395

Weisheng Liu, Fei Wang + Show 4 more

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https://doi.org/10.3390/app9204395

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Journal: Applied sciences	Publication Date: Oct 17, 2019
Citations: 2	License type: CC BY 4.0

Affiliation: Shandong University, Zhejiang University

Abstract

The increasing penetration of distributed generation (DG) brings about great fluctuation and uncertainty in distribution networks. In order to improve the ability of distribution networks to cope with disturbances caused by uncertainties and to evaluate the maximum accommodation capacity of DG, a multi-objective programming method for evaluation of the accommodation capacity of distribution networks for DG is proposed, considering the flexibility of distribution networks in this paper. Firstly, a multi-objective optimization model for determining the maximum accommodation of DG by considering the flexibility of distribution networks is constructed, aiming at maximizing the daily energy consumption, minimizing the voltage amplitude deviation, and maximizing the line capacity margin. Secondly, the comprehensive learning particle swarm optimization (CLPSO) algorithm is used to solve the multi-objective optimization model. Then, the mixed strategy Nash equilibrium is introduced to obtain the frontier solution with the optimal joint equilibrium value in the Pareto solution set. Finally, the effectiveness of the proposed method is demonstrated with an actual distribution network in China. The simulation results show that the proposed planning method can effectively find the Pareto optimal solution set by considering multiple objectives, and can obtain the optimal equilibrium solution for DG accommodation capacity and distribution network flexibility.

Highlights

Energy consumption has gradually changed from fossil fuels and other traditional energy sources to renewable energy sources, such as wind farms (WF), photovoltaic (PV) energy, and nuclear energy [1,2,3,4]
Maximum accommodation capacity of distributed generation (DG) are obtained solution tool which relies on external solvers for the low-level numerical solution of optimization by the proposed comprehensive learning particle swarm optimization (CLPSO) algorithm, and the objective function values of each solution are problem) toolbox on a PC with a Core i5 3570 CPU and 4GB of RAM
Considering flexibility of the distribution network is proposed in this paper, aiming at maximizing considering flexibility of the distribution network is proposed in this paper, aiming at maximizing the daily energy supply of DG, minimizing the voltage amplitude deviation, and maximizing the the daily energy supply of DG, minimizing the voltage amplitude deviation, and maximizing the line line capacity margin

Summary

Introduction

Energy consumption has gradually changed from fossil fuels and other traditional energy sources to renewable energy sources, such as wind farms (WF), photovoltaic (PV) energy, and nuclear energy [1,2,3,4]. With the reform of the power market in China, distributed WF and PV generation are developing rapidly, and the penetration of distributed generation (DG) in the distribution networks is increasing. When the capacity of DG in traditional medium- and low-voltage distribution networks approaches a high proportion (i.e., high penetration), there are difficulties in ensuring the power balance and the safe operation of distribution networks, and the reliability and power quality for customers [5,6]. The connection of DG into distribution networks helps to alleviate energy supply shortages, reduce network loss, and improve environmental benefits and the voltage quality of the distribution network.

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