Risk assessment of electric power generation systems using modified jellyfish search algorithm

Abstract

An electric utility's main goal is to fulfil the requirements and expectations of its customers by providing power. When there are uncertainties, like equipment failures, system reliability evaluation offers a framework to guarantee that the system will still function properly. A modified Jellyfish Search Algorithm (JFSA) has been proposed for estimation of Electric power generation system reliability indices. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and other modified versions of algorithms have been used in algorithms that use optimization methods for the assessment of reliability indices. Jelly Fish Search Algorithm has been used in power systems to find the economic load dispatch of generating units, for integration of Distributed Generation (DG) units, Maximum Power tracking of PV system and Optimal Power Flow solutions etc. However, JFSA has not been implemented for the evaluation of reliability indices for electric power generation system. In this context a modified JFSA algorithm is developed for evaluation of certain reliability indices such as Loss of Load Expectation (LOLE), and Expected Demand Not Supplied (EDNS), Loss of Load Probability (LOLP). The algorithm presented is implemented on two test system which are RBTS 6 bus system and IEEE 24 bus Reliability Test System. The Results obtained are compared for different models of Generation and Load and are analysed.