Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

• DOI: 10.1016/j.aej.2024.02.069
• Publication Date: 2024-03-15T07:19:31Z
• Authors: Manoharan Premkumar; Tengku Juhana Ten...; Sowmya Ravichandran; Tan Ching Sin; Ramakrishnan Chan...; Anas R. Alsoud; Pradeep Jangir
• Keywords: Flow direction algorithm; Energy; Optimal power flow analysis; 0202 electrical engineering, electronic engineering, information engineering; ε-based constraint-handling; FACTS controllers; 02 engineering and technology; TA1-2040; Engineering (General). Civil engineering (General); Multiobjective optimization

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by integration unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind sources, along with a suite Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR phase shifters. primary objective is to solve OPF problem reducing generation costs while accommodating variable nature sources load demands. study prioritizes examination both constant fluctuating requirements. inherent variability PV energy, demand, captured through modelling probability density functions. approach enables more detailed optimization process, incorporating not just cost thermal but also scheduling associated penalty costs. Moreover, examines strategic placement sizing FACTS components, an aspect essential in minimizing overall production. Employing single- multi-objective algorithms, addresses modified IEEE-30 bus system various case studies. application recently developed flow direction algorithm, its variant ε-based constraint-handling mechanism contributions this work. results, benchmarked against several advanced metaheuristic reveal proposed algorithm's superior performance. comprehensive only underscores potential integrating grid highlights efficacy intelligent strategies managing complexities systems.