Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm
- 11 Downloads
Short-term hydropower generation scheduling (STHGS), a highly complicated nonlinear optimization problem with various equality and inequality constraints, plays an important role in the utilization of hydropower and water resources. To overcome the complexity and nonlinearity of STHGS problem effectively, an improved cloud adaptive quantum-inspired binary social spider optimization (ICAQBSSO) algorithm is proposed in this paper. Quantum bit (q-bit) and quantum rotation gate are used to improve its code mode and search mode and enable it to optimize discrete problems. The improved cooperative operators of ICAQBSSO overcome the problem of unreasonable parameters and elements in its original cooperative operators. With the heuristic strategies for repairing minimum uptime/downtime constraint and spinning reserve capacity constraint, the ICAQBSSO algorithm is coupled with an optimal stable load distribution table (OSLDT) to optimize the sub-problems of STHGS, unit commitment (UC) and economic load dispatch (ELD). In the case study of the STHGSs for Three Gorges hydropower station, corresponding to 75 m, 88 m and 107 m water heads, the results of the proposed algorithm and other intelligent algorithms show the feasibility and effectiveness of the proposed algorithm for obtaining near-optimal solutions in less time.
KeywordsShort-term hydropower generation scheduling Social spider optimization Quantum computing Cloud model Optimal stable load distribution table
The achievements are funded by the National Science Support Plan Project of China (2009BAC56B03) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Compliance with Ethical Standards
Conflict of Interest
- Allawi MF, Jaafar O, Ehteram M, Hamzah FM, El-Shafie A (2018) Synchronizing artificial intelligence models for operating the dam and reservoir system. Water Resour Manag:1–17Google Scholar
- Ohishi, T., Santos, E., Arce, A., Kadowaki, M., Cicogna, M., & Soares, S. (2005). Comparison of two heuristic approaches to hydro unit commitment. Power Tech, 2005 IEEE Russia (pp.1–7). IEEEGoogle Scholar
- Peng L, Zhou J, Wang C, Qiao Q, Li M (2015) Short-term hydro generation scheduling of xiluodu and xiangjiaba cascade hydropower stations using improved binary-real coded bee colony optimization algorithm. Energy Conv Manag 91(91):19–31Google Scholar
- Shi, L., Hao, J., Zhou, J., & Xu, G. (2004). Short-term generation scheduling with reliability constraint using ant colony optimization algorithm. Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on (Vol.6, pp.5102–5106 Vol.6). IEEEGoogle Scholar
- Tawhid MA, Ali AF (2016) A hybrid social spider optimization and genetic algorithm for minimizing molecular potential energy function. Soft Comput 21(21):1–16Google Scholar
- Wood AJ, Wollenberg B (1996) Power generation operation and control - 2nd edition. IEEE Power Energy Mag 12(4):90–93Google Scholar