An Artificial Rabbits’ Optimization to Allocate PVSTATCOM for Ancillary Service Provision in Distribution Systems

• Published in: Mathematics (Basel) Vol. 11; no. 2; p. 339
• Main Authors: Elshahed, Mostafa, Tolba, Mohamed A., El-Rifaie, Ali M., Ginidi, Ahmed, Shaheen, Abdullah, Mohamed, Shazly A.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023
• Subjects: Allocations; Alternative energy sources; ancillary service provision; Ancillary services; artificial rabbits’ optimization; Codes; distribution systems; Electric potential; Electricity; Electricity distribution; Evolutionary algorithms; Evolutionary computation; Food science; Mathematical models; Monte Carlo simulation; Optimization; Optimization algorithms; Parameter identification; PVSTATCOM allocation; Rabbits; Renewable resources; Solar power generation; Static synchronous compensators; Voltage
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math11020339

Attaining highly secure and safe operation of the grid with acceptable voltage levels has become a difficult issue for electricity companies that must adopt remedial actions. The usage of a PV solar farm inverter as a static synchronous compensator (or PVSTATCOM device) throughout the night has recently been proposed as a way to enhance the system performance. In this article, the novel artificial rabbits’ optimization algorithm (AROA) is developed for minimizing both the daily energy losses and the daily voltage profile considering different 24 h loadings. The novel AROA is inspired from the natural surviving strategies of rabbits. The novel AROA is tested on a typical IEEE 33-node distribution network including three scenarios. Different scenarios are implemented considering PV/STATCOM allocations throughout the day. The effectiveness of the proposed AROA is demonstrated in comparison to differential evolution (DE) algorithm and golden search optimization (GSO). The PVSTATCOM is adequately allocated based on the proposed AROA, where the energy losses are greatly reduced with 54.36% and the voltage deviations are greatly improved with 43.29%. Moreover, the proposed AROA provides no violations in all constraints while DE fails to achieve these limits. Therefore, the proposed AROA shows greater dependability than DE and GSO. Moreover, the voltage profiles at all distribution nodes all over the daytime hours are more than the minimum limit of 95%.