Abstract
With structural changes in power consumption, peak load demands and peak-valley differences are growing, which motivates peak shaving operations of hydropower systems. A multiobjective mixed-integer nonlinear programming model is presented for short-term hydropower generation scheduling that aims to regulate peak loads and enhance power efficiency. The combined feasible operation zones and the aggregate generation function of each plant are embedded in this model to deal with the operation constraints of units. A set of Pareto solutions of this model is generated by an improved normalized constraint method that narrows the optimization range to ensure the practicality and compact distribution of solutions, each of which represents an aggregated scheduling of cascaded hydropower plants. Then, a mixed integer nonlinear programming formulation is developed to solve the unit commitment problem for each solution, and a fuzzy-based membership value assignment method is employed to extract a best compromise solution for final implementation. A case study of the hydropower plants of the Beipanjiang cascade in China is used to test the proposed approach, verifying its success in generating a practical operating schedule while providing abundant information to decision makers for decision support.
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