Abstract
The short-term hydropower generation scheduling (STHGS) is a complicated problem in the utilization of hydropower and water resources. An improved binary-coded whale optimization algorithm (IBWOA) is proposed in this paper to solve the complex nonlinear problem. The STHGS problem is divided into unit combination (UC) subproblem and economic load distribution (ELD) subproblem. For the UC subproblem, we use the sigmoid function (SF) to generate a binary array representing the start/stop state of the unit. 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. Each generation solution is optimized by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint. For ELD subproblem, the optimal stable load distribution table (OSLDT) is used to distribute the load quickly. 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. Finally, the proposal is applied to solve the STHGS of the Three Gorges Hydropower Station. When the water head is 75 m,88 m, and 107 m, the minimum water consumption calculated by the IBWOA algorithm is 1,058,323,464 m3, 892,524,696 m3, and 745,272,216 m3, respectively. Compared with the traditional whale optimization algorithm, the water consumption of the IBOWA algorithm corresponding to 75 m, 88 m, and 107 m water heads is reduced by 0.76%, 0.26%, and 0.05%, respectively. The comparison between the IBWOA algorithm and other heuristic algorithms shows that the IBWOA has good feasibility and high optimization accuracy.
Highlights
Hydropower is an ideal renewable and clean resource with many advantages, such as pollution-free, low operating cost, and strong peak-regulating capacity
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
The unit commitment (UC) subproblem was solved by two repair strategies, and the economic load scheduling (ELD) subproblem was solved by an optimal stable load distribution table (OSLDT)
Summary
Hydropower is an ideal renewable and clean resource with many advantages, such as pollution-free, low operating cost, and strong peak-regulating capacity. A variety of heuristic optimization algorithms have been proposed for the STHGS problem in order to deal with the complex problem of multi-constraint nonlinearity These are genetic algorithm (GA) (Senthil and Mohan 2010; Zheng et al 2013), particle swarm optimization (PSO)(Kumar et al 2011; Fakhar et al 2015), chaotic optimization algorithm (COA) (Cai et al 2007), ant colony optimization (ACO) (Shi et al 2004), simulated annealing (SA)(Dudek et al 2010; Saraiva et al 2011), bee colony optimization (BCO) (Peng et al 2015), and bat algorithm (BA) (Su et al 2019). A whale algorithm with low parameter requirement and good optimization effect is proposed to solve this problem. The results show that the algorithm has high effectiveness and feasibility
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