Optimal Placement and Sizing of Distributed Generators Based on Multiobjective Particle Swarm Optimization

Deyu Yang,Wenchao Cai,Ke Luo,Bo Yang,Dong An,Junqing Jia,Wenli Wu

doi:10.3389/fenrg.2021.770342

Abstract

To solve the problems of environmental pollution and energy consumption, the development of renewable energy sources becomes the top priority of current energy transformation. Therefore, distributed power generation has received extensive attention from engineers and researchers. However, the output of distributed generation (DG) is generally random and intermittent, which will cause various degrees of impact on the safe and stable operation of power system when connected to different locations, different capacities, and different types of power grids. Thus, the impact of sizing, type, and location needs to be carefully considered when choosing the optimal DG connection scheme to ensure the overall operation safety, stability, reliability, and efficiency of power grid. This work proposes a distinctive objective function that comprehensively considers power loss, voltage profile, pollution emissions, and DG costs, which is then solved by the multiobjective particle swarm optimization (MOPSO). Finally, the effectiveness and feasibility of the proposed algorithm are verified based on the IEEE 33-bus and 69-bus distribution network.

Highlights

With the rapid development of the world’s electric power industry, the total amount of social electricity consumption has risen sharply over the last decade (Yang et al, 2016; Yang et al, 2017; Zhang et al, 2021)
The simulation results obtained by multiobjective particle swarm optimization (MOPSO) and the voltage distribution of the optimized IEEE 33-bus distribution network are shown in Table 1 and Figure 7, respectively
The power losses and voltage profile of the distribution network are significantly improved after different types of distributed generation (DG) are configured because DG is always installed near the load

Summary

INTRODUCTION

With the rapid development of the world’s electric power industry, the total amount of social electricity consumption has risen sharply over the last decade (Yang et al, 2016; Yang et al, 2017; Zhang et al, 2021). With the increasing requirements for power system reliable operation, the problem of DG location and constant sizing has developed from a single-objective problem that only considers the minimum network loss to a multiobjective optimization problem that comprehensively considers voltage quality, current quality, and environmental factors. Genetic algorithm, and other methods have been applied to solve such multiobjective location and constant volume problem These methods all need to set weights to transform the multiobjective problem into a single-objective problem for proper solutions (Murty and Kumar, 2015); these weights are often difficult to determine in actual operation. The remaining of this paper is organized as follows: Mathematical Optimization Model of DG Planning develops the objective function.

Objective

CONCLUSION

DATA AVAILABILITY STATEMENT