Short-Term Hydropower Generation Scheduling Using an Improved Cloud Adaptive Quantum-Inspired Binary Social Spider Optimization Algorithm

Abstract

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.