Optimization Model for the Short-Term Operation of Hydropower Plants Transmitting Power to Multiple Power Grids via HVDC Transmission Lines

Abstract

Long-distance and large-capacity hydropower transmission from large hydropower plants in southwestern China to load centers is an important and effective measure for the accommodation of large-scale hydropower in China. However, the load demands of the receiving-end power grids are not fully considered in the conventional power transmission mode, which has greatly affected their enthusiasm for the absorption of trans-regional hydro energy. In order to make full use of the peak shaving capability of the hydropower plants, this paper develops an optimization model for determining the hourly generation scheduling of the hydropower plants transmitting electric power to several power grids via high voltage direct current (HVDC) transmission lines. A large-capacity and highly complex multi-unit hydropower system, the Xiluodu plant in China, is taken as the case study. In the proposed model, minimizing the peak-valley differences of multiple receiving-end power grids is adopted as the objective to alleviate the peak shaving pressure of the receiving-end power grids. In addition to the traditional hydraulic constraints, the operation constraints of individual units and HVDC power transmission limits are well considered. The study focuses mainly on modeling the stair-like power transmission curve constraint which is discrete and nonlinear and has been rarely considered in previous studies. This constraint is then linearized through limiting the logical relations between multiple binary integer variables. Case studies demonstrate that the proposed model has high computational efficiency, and the peak-valley differences of the two receiving-end power grids, i.e., Zhejiang Power Grid and Guangdong Power Grid, are decreased by 15.2% and 7.33%, respectively. Moreover, frequent conversion of HVDC converter equipment can also be avoided, which makes the obtained generation schedule more executable.