Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Thang Trung Nguyen,Le Van Dai,Le Chi Kien,Thai Dinh Pham

doi:10.1155/2020/1603802

Abstract

This paper proposes an improved coyote optimization algorithm (ICOA) for optimizing the location and sizing of solar photovoltaic distribution generation units (PVDGUs) in radial distribution systems. In the considered problem, four single objectives consisting of total power losses, capacity of all PVDGUs, voltage profile index, and harmonic distortions are minimized independently while satisfying branch current limits, voltage limits, and harmonic distortion limits exactly and simultaneously. The performance of the proposed ICOA method has been improved significantly since two improvements were carried out on the two new solution generations of the conventional coyote optimization algorithm (COA). By finding four single objectives from two IEEE distribution power systems with 33 buses and 69 buses, the impact of each proposed improvement and two proposed improvements on the real performance of ICOA has been investigated. ICOA was superior to COA in terms of capability of finding higher quality solutions, more stable search ability, and faster convergence speed. Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. The result comparisons have also indicated the outstanding performance of ICOA because it could find much better results than these methods, especially SFO, SSA, and GA. Consequently, the proposed ICOA is a very effective method for finding the optimal location and capacity of PVDGUs in radial distribution power systems.

Highlights

In order to investigate the real performance of the proposed improved coyote optimization algorithm (ICOA) method in finding the optimal location and sizing of photovoltaic distribution generation units (PVDGUs), IEEE 33-bus distribution power network and IEEE 69-bus distribution power network [26, 49] are used as two main studied tests. e IEEE 33-bus distribution power network shown in Figure 2 has a total load demand of 3715 kW and 2300 kVAR and operates at a nominal voltage of 12.66 kV. e IEEE 69-bus distribution power network shown in Figure 3 operates at a nominal voltage of 12.66 kV and supplies power to total load demand of 3802 kW and 2694 kVAR. e two systems are considering linear loads
Due to the total load demand as well as the system dimension, three PVDGUs are installed in the IEEE 33-bus distribution network whereas four PVDGUs are installed in the IEEE 69bus distribution network. e optimal location and optimal capacity of PVDGUs in the two systems aim at reaching four single-objective functions considered in the four following cases
Results for comparison are reported in Table 8. e best fitness function of 0.0937 can point out that the proposed ICOA is the best method among compared methods in finding the optimal solution. e lowest average fitness function and the lowest worst fitness function confirm that the proposed method is the most stable tool with the lowest fluctuations. e improvement level of the best fitness is from 0.531% to 17.590%. e improvement levels of the average fitness and the worst fitness are high, up to 49.739% and 53.211%, respectively

Summary

Introduction

Importance of Solar Photovoltaic for Distribution Power Networks. The production of electricity from fossil fuels has been causing many negative impacts such as air pollution, water pollution, and increasing temperature. In many countries around the world, solar photovoltaic distribution generation units (PVDGUs) are being popularly used as a main renewable energy source due to its economic, technical, and environmental benefits [1]. During the last decades, the annual growth rate of distributed generation based on solar photovoltaic increased higher than 40% [2]. Many studies have shown that the received benefits will depend on the site and rated power of distributed generation units (DGUs) in general and PVDGUs in particular [2,3,4,5,6].

Methods

Results

Conclusion