Application of novel evolutionary algorithms for analyzing the impact of integrating renewables on the adequacy of composite power systems

Abstract

The adequacy evaluation of modern renewable-rich power systems tends to be a computationally challenging task due to variations of renewable power generation. Recently, more computationally efficient evolutionary algorithms and swarm intelligence-based methods are utilized for evaluating the adequacy of power systems. In this paper, the authors have proposed a wind and solar integrated composite system adequacy evaluation framework using an Evolutionary Swarm Algorithm (ESA). The system failure states which have a higher probability of occurrence are explored using the ESA to estimate the adequacy indices of the system. The wind and solar power generation are modeled using a clustering-based method considering their annual effective power output. Moreover, the correlation between the system load and renewable power generation is modeled in the adequacy evaluation framework. Using the proposed framework, several case studies are conducted on the IEEE Reliability Test System to analyze the impact of integrating renewables on the adequacy of composite systems. The results show that wind generation tends to improve system reliability than solar due to its higher availability. In addition, the equivalent capacities of wind and solar generators are found to be 125MW and 215MW against a 50MW hydro generator.