A hybrid particle swarm optimization with small population size to solve the optimal short-term hydro-thermal unit commitment problem. (15th August 2016)

• Record Type: Journal Article
• Authors: Zhang, Jingrui; Tang, Qinghui; Chen, Yalin; Lin, Shuang
• Is Part Of: Energy. Volume 109(2016)
• Page Start: 765
• Page End: 780
• Publication Date: 2016-08-15
• Subjects: Hydro-thermal -- Unit commitment -- Particle swarm optimization -- Small population; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2016.05.057
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Hydro-thermal unit commitment (HTUC) is an extension of unit commitment (UC) problems. The hydro-thermal unit commitment problem considered in this study aims at minimizing the total fuel cost of thermal units while satisfying the constraints of spinning reserve, minimum online/offline, ramp rate, hydraulic networks, etc. A hybrid particle swarm optimization approach with small population size (HPSO-SP) is presented for solving the optimal short-term HTUC problem. In the proposed approach, three extra handling operations, i.e. mutation, DE-acceleration, and migration have been proposed for both binary and continuous variables to ensure the effects of small population. A repair strategy to the main equality and inequality constraints has also been employed to improve the searching efficiency of the algorithm. Several well-known UC test systems in literature are considered to test the proposed HPSO-SP approach first. After verification on UC problems, this approach is applied to solve several HTUC test systems and a practical hydro-thermal system in China. The final results show the feasibility and effectiveness of the HPSO-SP approach. Highlights: A hybrid PSO with small population size is proposed for the short-term HTUC problem. Three operations are modified to deal with the binary and continuous variables. A repair strategy to the main equality and inequality constraints is also employed. Performance of HPSO-SP is compared with other evolutionary algorithms in literature. A practical hydrothermal system is employed to verify the feasibility of HPSO-SP.