Enhanced optimization of photovoltaic units EVCSs and BESS integration in radial distribution networks using a hybrid sine-cosine gorilla search algorithm.
Source: PubMed, NCBI / U.S. National Library of Medicine
The implementation of multiple distributed energy resources (DER) into the radial distribution networks which includes Photovoltaic (PV) systems, Electric Vehicle Charging Stations (EVCSs), and Battery Energy Storage Systems (BESS) presents a number of challenges that include voltage regulation and power loss as well as the challenges in managing load demand. In this paper, the author presents a superior optimization model that employs a new Hybrid Sine Cosine Gorilla Search Algorithm (HSCGSA) to effectively coordinate the addition of PV units, EVCS charging loads and BESS in the radial distribution systems. The offered approach will seek to maximize the position and size of DERS to reduce active and reactive power losses and ensure the stability of the voltage throughout the network. The HSCGSA is a hybrid approach to enhancement of the exploratory ability of the Sine Cosine Algorithm (SCA) and the exploitation power of the Gorilla Troops Optimizer (GTO), which provides a balanced solution to the intricate and complex optimization problems involving multiple planning criteria. The performance of the proposed algorithm is validated through simulations on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The results demonstrate that the proposed HSCGSA significantly improves system performance compared with the conventional Genetic Algorithm (GA). For the IEEE 33-bus system, the active power loss is reduced from 3477.166 kW to 1577.06 kW, representing a r
Abstract
The implementation of multiple distributed energy resources (DER) into the radial distribution networks which includes Photovoltaic (PV) systems, Electric Vehicle Charging Stations (EVCSs), and Battery Energy Storage Systems (BESS) presents a number of challenges that include voltage regulation and power loss as well as the challenges in managing load demand. In this paper, the author presents a superior optimization model that employs a new Hybrid Sine Cosine Gorilla Search Algorithm (HSCGSA) to effectively coordinate the addition of PV units, EVCS charging loads and BESS in the radial distribution systems. The offered approach will seek to maximize the position and size of DERS to reduce active and reactive power losses and ensure the stability of the voltage throughout the network. The HSCGSA is a hybrid approach to enhancement of the exploratory ability of the Sine Cosine Algorithm (SCA) and the exploitation power of the Gorilla Troops Optimizer (GTO), which provides a balanced solution to the intricate and complex optimization problems involving multiple planning criteria. The performance of the proposed algorithm is validated through simulations on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The results demonstrate that the proposed HSCGSA significantly improves system performance compared with the conventional Genetic Algorithm (GA). For the IEEE 33-bus system, the active power loss is reduced from 3477.166 kW to 1577.06 kW, representing a reduction of approximately 54.6%, while the minimum bus voltage improves from 0.9224 pu to 0.9706 pu. In the IEEE 69-bus system, the active power loss decreases from 3859.99 kW to 1381.28 kW, corresponding to a reduction of about 64.2%, along with a substantial enhancement in the voltage stability index. Similarly, in the IEEE 118-bus system, the active power loss is reduced from 2279.42 kW to 608.55 kW, achieving nearly 73% reduction when multiple DER units are optimally allocated. These results confirm that the proposed HSCGSA provides superior capability for optimal placement and sizing of PV units, EV charging stations, and battery energy storage systems in radial distribution networks.