Long-Term Generation Scheduling of Hydropower System Using Multi-Core Parallelization of Particle Swarm Optimization

Sheng-li Liao, Ben-xi Liu (), Chun-tian Cheng, Zhi-fu Li and Xin-yu Wu
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Sheng-li Liao: Dalian University of Technology
Ben-xi Liu: Dalian University of Technology
Chun-tian Cheng: Dalian University of Technology
Zhi-fu Li: Dalian University of Technology
Xin-yu Wu: Dalian University of Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 9, No 16, 2807 pages

Abstract: Abstract A multi-core parallel Particle Swarm Optimization (MPPSO) algorithm is developed to improve computational efficiency for long-term optimal hydropower system operation, in response to rapidly increasing size and complexity of hydropower systems, especially in China. The MPPSO can be implemented in three steps with easily accessible multi-core hardware platforms. First, a multi-group parallel computing strategy is introduced to maintain the diversity of population for finding the global optima. Second, the fork/join framework based on divide-and-conquer strategy is adopted to distribute multiple populations to different CPU cores for parallel calculations to take full advantage of CPU performance. Third, the results generated in different CPUs are merged to achieve an improved acceleration effect on computational time cost and more accurate optimal scheduling solution. Results for a system of twelve hydropower stations in the Guizhou Power Grid in China demonstrate that the proposed algorithm makes full use of multi-core resources, and significantly improves the computational efficiency and accuracy of the optimal solution, in addition to its low parallelization cost and low implementation cost. These suggest that the proposed algorithm has great potential for future optimal operation of hydropower systems.

Keywords: Hydropower; Long-term optimal operation; Multi-core parallel; Fork/join; Particle swarm optimization (PSO) (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s11269-017-1662-1

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