Application of Metaheuristic Algorithms for Generation System Adequacy Evaluation

• Main Authors: Amarasinghe, P. A. G. M., Abeygunawardane, S. K.
• Format: Conference Proceeding
• Language: English
• Subjects: capacity credit; Computational modeling; Estimation; evolutionary algorithms; generation system adequacy; metaheuristic algorithms; Metaheuristics; Monte Carlo methods; Power system planning; Renewable energy sources; swarm optimization; Wind power generation
• ISSN: 2691-364X
• DOI: 10.1109/MERCon60487.2023.10355460

The evaluation of generation system adequacy has become a complex procedure due to the variability of renewable power generation. Renewable power models associated with Monte Carlo Simulation (MCS) require a considerable amount of processing power, especially when periodical variations of renewables are modeled. This paper analyses the application of different metaheuristic algorithms for evaluating the adequacy of renewable-rich power generation systems. The IEEE reliability test system is modified and used for conducting several case studies. The utilized metaheuristic algorithms are validated using sequential simulations and it is found that problem-specific Evolutionary Swarm Algorithm (ESA) provides more accurate estimations for generation system adequacy indices. In this study, the improvement of generation system adequacy is analyzed when integrating different renewable power proportions into the system. The intra-day reliability variation of the system is analyzed for different solar and wind penetration levels. The reliability improvement provided by renewable generators to the generation system adequacy is quantified by estimating the respective Effective Load Carrying Capabilities (ELCCs) of solar and wind generation. The ELCCs of 100 MW solar and 100 MW wind generation are found to be 26 MW and 43 MW, respectively.