Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm

Abstract

An improved binary-coded whale optimization algorithm (IBWOA) is proposed to solve the complex nonlinear problem of short-term hydropower generation scheduling (STHGS). The spatial optimal load distribution is combined with the temporal unit commitment combination model, and the binary array is used to represent the start/stop state of the unit. Sigmoid Function (SF) is used to solve the correspondence between binary array and real number. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. The unit commitment (UC) subproblem was solved by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint, and the economic load scheduling (ELD) subproblem was solved by an optimal stable load distribution table (OSLDT). The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. The proposal is applied to solve the STHGS of the Three Gorges hydropower station. The results show that the method has good convergence, stability, fast calculation speed, and high optimization accuracy.

A New Approach for Seepage Parameters Inversion Analysis Using Improved Whale Optimization Algorithm and Support Vector Regression

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Variational Bayes to Accelerate the Lagrange Multipliers towards the Dual Optimization of Reliability and Cost in Renewable Energy Systems

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