Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations

• Published in: Energies (Basel) Vol. 17; no. 4; p. 772
• Main Authors: Jia, Jun, Zhang, Guangming, Zhou, Xiaoxiong, Zhu, Mingxiang, Shi, Zhihan, Lv, Xiaodong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2024
• Subjects: Analysis; cascade hydropower stations; Case studies; Decision making; Efficiency; Electricity distribution; Electricity generation; Energy resources; Energy storage; Flexibility; Hydroelectric power; inflow impact; mixed-integer programming; multi-objective optimization; Optimization; Power; Probability distribution; Scheduling; stochastic optimization; temporal correlation; Water-power; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17040772

There exists a temporal and spatial coupling effect among the hydropower units in cascade hydropower stations which constitutes a complex planning problem. Researching the multi-objective optimization scheduling of cascade hydropower stations under various spatiotemporal inflow impacts is of significant importance. Previous studies have typically only focused on the economic dispatch issues of cascade hydropower stations, with little attention given to their coupling mechanism models and the uncertainty impacts of inflows. Firstly, this paper establishes a coupled optimization scheduling model for cascade hydropower stations and elaborates on the operational mechanism of cascade hydropower stations. Secondly, according to the needs of actual scenarios, two types of optimization objectives are set, considering both the supply adequacy and peak-shaving capacity as indicators, with the total residual load and the peak-valley difference of the residual load as comprehensive optimization objectives. Subsequently, considering the uncertainty impact of the inflow side, a stochastic optimization model for inflow is established based on a normal distribution probability. Finally, case study analyses demonstrate that the proposed model not only effectively achieves supply stability but also reduces the peak-valley difference in load, and can achieve optimized scheduling under the uncertain environment of inflow.