Monthly Hydropower Scheduling of Cascaded Reservoirs Using a Genetic Algorithm with a Simulation Procedure

Deji Baima, Guoyuan Qian, Jingzhen Luo, Pengcheng Wang, Hao Zheng () and Jinwen Wang
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Deji Baima: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
Guoyuan Qian: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
Jingzhen Luo: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
Pengcheng Wang: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China
Hao Zheng: Water Resources Department, Changjiang River Scientific Research Institute, 23 Huangpu Road, Wuhan 430010, China
Jinwen Wang: Institute of Water Resources and Hydropower, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China

Energies, 2024, vol. 17, issue 15, 1-17

Abstract: This study integrates genetic algorithms with simulation programs, applying the genetic algorithm’s (GA) fitness calculation within the simulation to reduce complexity and significantly improve the efficiency of the optimization process. Additionally, the simulation introduces the concept of “Field Leveling” (FL), utilizing a push–pull strategy to explore more space for absorbing and utilizing unnecessary spillage for energy generation, thereby maximizing electricity production and ensuring optimal reservoir scheduling. Two methods are provided, namely the field-leveling genetic algorithms GAFL1 and GAFL2. GAFL1 involves only pushing and does not include a push–pull process; thus, it cannot optimize spillage. On the other hand, GAFL2 implements a complete push–pull strategy, continuously exploring additional space to absorb and utilize unnecessary spillage. Both GAFL1 and GAFL2 achieved reasonable results; specifically, compared to SQP, GAFL1 improved firm yield by 8.3%, spillage increased by 2.2 times, and total energy decreased by 1.2%. GAFL2, building on the basis of GAFL1, effectively reduces spillage under all hydrological conditions without affecting the highest priority of stable output. However, the impact of reducing spillage on energy generation is not consistent; in wet and dry years, reducing spillage increases energy generation. However, in normal years, a reduction in spillage corresponds with decreased energy generation.

Keywords: monthly hydropower scheduling; cascaded reservoirs; genetic algorithm; simulation procedure; field-leveling procedure (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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