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

Jun Jia, Guangming Zhang (), Xiaoxiong Zhou, Mingxiang Zhu, Zhihan Shi and Xiaodong Lv
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Jun Jia: College of Transportation Engineering, Nanjing Tech University, Nanjing 211899, China
Guangming Zhang: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China
Xiaoxiong Zhou: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China
Mingxiang Zhu: Taizhou College, Nanjing Normal University, Taizhou 225300, China
Zhihan Shi: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China
Xiaodong Lv: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China

Energies, 2024, vol. 17, issue 4, 1-18

Abstract: 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.

Keywords: cascade hydropower stations; inflow impact; multi-objective optimization; stochastic optimization; temporal correlation; mixed-integer programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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