Open Access

ARTICLE

Optimal Implementation of Photovoltaic and Battery Energy Storage in Distribution Networks

Hussein Abdel-Mawgoud¹, Salah Kamel¹, Hegazy Rezk^2,3, Tahir Khurshaid⁴, Sang-Bong Rhee^4,*

1 Department of Electrical Engineering, Faculty of Engineering, Aswan University, 81542 Aswan, Egypt
2 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, 11911 Al-Kharj, Saudi Arabia
3 Electrical Engineering Department, Faculty of Engineering, Minia University, 61517 Minia, Egypt
4 Department of Electrical Engineering, Yeungnam University, Gyeongsan, 38541, Korea

* Corresponding Author: Sang-Bong Rhee. Email:

Computers, Materials & Continua 2021, 69(2), 1463-1481. https://doi.org/10.32604/cmc.2021.017995

Received 20 February 2021; Accepted 04 March 2021; Issue published 21 July 2021

Abstract

Recently, implementation of Battery Energy Storage (BES) with photovoltaic (PV) array in distribution networks is becoming very popular in overall the world. Integrating PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural source. PV can be able to generate constant output power during 24-hours by installing BES with it. Therefore, this paper presents a new application of a recent metaheuristic algorithm, called Slime Mould Algorithm (SMA), to determine the best size, and location of photovoltaic alone or with battery energy storage in the radial distribution system (RDS). This algorithm is modeled from the behavior of SMA in nature. During the optimization process, the total active power loss during 24-hours is used as an objective function considering the equality and inequality constraints. In addition, the presented function is based on the probabilistic for PV output and different types of system load. The candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor (PLSF). IEEE 69-bus RDS with different types of loads is used as a test system. The effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms.

---

Keywords

---