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Browsing Journals and Magazines by Author "Abeygunawardane, SK"

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    Adequacy evaluation of composite power systems using an evolutionary swarm algorithm
    (IEEE, 2022) Amarasinghe, PAGM; Abeygunawardane, SK; Singh, C
    The generation and transmission capacities of many power systems in the world are significantly increasing due to the escalating global electricity demand. Therefore, the adequacy evaluation of power systems has become a computationally challenging and time-consuming task. Recently, population-based intelligent search methods such as Genetic Algorithms (GAs) and Binary Particle Swarm Optimization (BPSO) have been successfully employed for evaluating the adequacy of power generation systems. In this work, the authors propose a novel Evolutionary Swarm Algorithm (ESA) for the adequacy evaluation of composite generation and transmission systems. The random search guiding mechanism of the ESA is based on the underlying philosophies of GAs and BPSO. The main objective of the ESA is to find out the most probable system failure states that significantly affect the adequacy of composite systems. The identified system failure states can be directly used to estimate the system adequacy indices. The proposed ESA-based framework is used to evaluate the adequacy of the IEEE Reliability Test System (RTS). The estimated annualized and annual adequacy indices such as Probability of Load Curtailments (PLC), Expected Duration of Load Curtailments (EDLC), Expected Energy Not Supplied (EENS) and Expected Frequency of Load Curtailments (EFLC) are compared with those obtained using Sequential Monte Carlo Simulation (SMCS), GA and BPSO. The results show that the accuracy, computational efficiency, convergence characteristics, and precision of the ESA outperform those of GA and BPSO. Moreover, compared to SMCS, the ESA can estimate the adequacy indices in a more time-efficient manner.
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    Kernel density estimation based time-dependent approach for analyzing the impact of increasing renewable on generation system adequacy
    (Elsevier, 2020) Abeygunawardane, SK; Singh, C; Amarasinghe, PAGM
    Integration of non-conventional renewables such as wind and solar to the power system may affect the system reliability, especially when the proportion of renewable power in the system is large. Therefore, with a significant level of renewable penetration, the intermittency and both diurnal and seasonal variations of renewable power generation should be deliberately modeled in order to accurately quantify the power system reliability. This paper presents a novel method based on Kernel Density Estimation (KDE) for modeling intermittency and both diurnal and seasonal variations of wind and solar power generation using historical renewable power generation data. The proposed KDE based renewable power models are used with non-sequential Monte Carlo simulation to evaluate the generation system adequacy. Several case studies are conducted on IEEE reliability test system to analyze the impact of increasing renewables on the generation system adequacy. The results show that the generation system adequacy tends to decay exponentially when the renewable integration is increased. It is shown that the reliability values obtained using the proposed approach are very close to those provided by the time-consuming sequential simulations. Importance of modeling seasonal variations of wind and solar is also investigated.