Document Type
Article
Publication Date
11-3-2023
Department
Department of Electrical and Computer Engineering
Abstract
This paper proposes an algorithm for the optimal installation of inverter-based resources (IBR) composed of wind energy conversion systems, photovoltaic systems, and battery energy storage systems in distribution systems using genetic algorithm (GA) and the cuckoo search (CS) as optimization techniques. The OpenDSS software is used to calculate the power flow in the distribution system with different penetration levels of IBRs. It is used a standard load shape of the IEEE 123 bus system programmed in OpenDSS and irradiance, temperature, and wind speed curves from Brazil. The proposed algorithm, using a genetic algorithm and cuckoo search, was able to define the quantity and the location of hybrid renewable generation arrangements reducing electrical energy losses. Case studies were carried out for maximum penetration from 20% to 60%, totaling 5 cases, where each simulation was performed for a period of 24 hours. It is simple, fast and efficient, achieving satisfactory results and being able to be applied to larger systems. The proposed method stands out for the possibility of using IBR in conjunction with energy storage, in addition to having a customizable hybrid array and being able to carry out case studies with high penetration while optimally locating and sizing the hybrid array configured accordingly with the needs of each problem, reducing losses and maintaining the quality of the system's electrical voltage.
Publication Title
IEEE Access
Recommended Citation
Dantas, F.,
Fernandes, D.,
Neves, W.,
Branco, A.,
&
Costa, F.
(2023).
Optimal Inverter-Based Resource Installation to Minimize Technical Energy Losses in Distribution Systems.
IEEE Access,
11, 123961-123976.
http://doi.org/10.1109/ACCESS.2023.3330083
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/277
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2023 The Authors. Publisher’s version of record: https://doi.org/10.1109/ACCESS.2023.3330083