Hosting Capacity Assessment in Electrical Power Distribution Systems Using Genetic Algorithm

• Published in: Electric power components and systems Vol. 51; no. 19; pp. 2354 - 2366
• Main Authors: Hanjalić, Merisa, Melić, Emina, Šarić, Mirza, Hivziefendić, Jasna
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 26.11.2023; Taylor & Francis Ltd
• Subjects: Alternative energy sources; Distributed generation; distribution network; Electric power systems; Electrical loads; Energy consumption; Energy resources; Genetic algorithms; hosting capacity; OpenDSS; Optimization; PV integration; Renewable energy sources; Smart grid
• ISSN: 1532-5008; 1532-5016
• DOI: 10.1080/15325008.2023.2227180

Environmental issues and the current global energy crisis serve as further motivators for the promotion of renewable energy sources. However, integrating these sources into existing power grids presents numerous challenges. As the connection capacity approaches its limits, it is imperative to employ innovative engineering methods to integrate distributed generation (DG) into resilient, self-healing smart grids of the future. One such tool is Hosting Capacity (HC) analysis, which is an emerging power system-planning tool used to position investments toward parts of the network that can absorb additional generation and promote efficient use of energy sources, avoiding overloading, inefficiencies, DG misallocations, and network failures. In this study, a technique for calculating the ideal HC in a power system is presented. The goal of this research is to develop a replicable optimization methodology for determining HC in smart distribution systems using a single objective constrained optimization problem solved through the use of genetic algorithm (GA). Detailed power system load and generation modeling and the use of advanced open-source research tool for load flow optimization improve the confidence in the proposed model. This research contributes to collective knowledge of the subject matter and establishes a reliable optimization methodology for determining HC in power systems.