Hongsheng Su, Shilin Song*, Xingsheng Wang
Energy Engineering, Vol.121, No.11, pp. 3289-3303, 2024, DOI:10.32604/ee.2024.054643
- 21 October 2024
Abstract This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling More >
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