Joint operation rules considering the uncertainty of energy available for multiple cascaded hydropower system

被引:1
作者
Wu, Xinyu [1 ]
Cheng, Ruixiang [1 ]
Cheng, Chuntian [1 ]
Ying, Qilin [2 ]
机构
[1] Dalian Univ Technol, Dept Inst Hydropower & Hydroinformat, Dalian, Peoples R China
[2] State Grid Corp China, Northeast Branch, LuYuan Hydropower Co, Shenyang, Peoples R China
来源
JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION | 2023年 / 59卷 / 01期
基金
中国国家自然科学基金;
关键词
multiple cascaded system; joint operation rules; hydropower; uncertainty of energy available; GENETIC ALGORITHM; RESERVOIR; MODEL; DERIVATION; CURVES; SIMULATION;
D O I
10.1111/1752-1688.13072
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Multi-reservoir operation rules have been widely used in practice operation. The operation rules are often derived from historical or simulated run-off information through implicit stochastic optimization or parameterization-simulation-optimization. The output decisions of operation rules are usually obtained without considering inflow forecasting or using perfect runoff forecast information, which is hardly implemented in practical applications. This paper proposes robust joint operation rules for multiple cascaded reservoirs considering the uncertainty of energy available. The rule parameters are optimized using multi-step genetic algorithm for minimum-power maximization. A case study for a three-cascaded-reservoirs system shows that, compared with deterministic joint operation rules, the accuracy of energy available estimation of joint operation rules is increased by 2.3%, considering inflow uncertainty. The simulated minimum-power decision of joint operation rules considering the uncertainty of energy available is 40.4% higher than that of determinate joint operation rules. Results indicate that there is a possibility of obtaining greater and more effective power decisions through the joint operation rules considering the uncertainty of available energy.
引用
收藏
页码:71 / 85
页数:15
相关论文
共 43 条
[1]   Optimizing operating rules for multi-reservoir hydropower generation systems: An adaptive hybrid differential evolution algorithm [J].
Ahmadianfar, Iman ;
Kheyrandish, Ali ;
Jamei, Mehdi ;
Gharabaghi, Bahram .
RENEWABLE ENERGY, 2021, 167 :774-790
[2]   Stochastic multiobjective reservoir operation under imprecise objectives: multicriteria decision-making approach [J].
Akbari, M. ;
Afshar, A. ;
Mousavi, S. Jamshid .
JOURNAL OF HYDROINFORMATICS, 2011, 13 (01) :110-120
[3]   Optimizing Watershed Management by Coordinated Operation of Storing Facilities [J].
Anghileri, Daniela ;
Castelletti, Andrea ;
Pianosi, Francesca ;
Soncini-Sessa, Rodolfo ;
Weber, Enrico .
JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 2013, 139 (05) :492-500
[4]   An aggregate stochastic dynamic programming model of multireservoir systems [J].
Archibald, TW ;
McKinnon, KIM ;
Thomas, LC .
WATER RESOURCES RESEARCH, 1997, 33 (02) :333-340
[5]   DERIVATION OF MONTHLY RESERVOIR RELEASE POLICIES [J].
BHASKAR, NR ;
WHITLATCH, EE .
WATER RESOURCES RESEARCH, 1980, 16 (06) :987-993
[6]   Input determination for neural network models in water resources applications. Part 1 - background and methodology [J].
Bowden, GJ ;
Dandy, GC ;
Maier, HR .
JOURNAL OF HYDROLOGY, 2005, 301 (1-4) :75-92
[7]   Tree-based reinforcement learning for optimal water reservoir operation [J].
Castelletti, A. ;
Galelli, S. ;
Restelli, M. ;
Soncini-Sessa, R. .
WATER RESOURCES RESEARCH, 2010, 46
[8]   Evaluation of stochastic reservoir operation optimization models [J].
Celeste, Alcigeimes B. ;
Billib, Max .
ADVANCES IN WATER RESOURCES, 2009, 32 (09) :1429-1443
[9]   Optimizing the reservoir operating rule curves by genetic algorithms [J].
Chang, FJ ;
Chen, L ;
Chang, LC .
HYDROLOGICAL PROCESSES, 2005, 19 (11) :2277-2289
[10]   A diversified multiobjective GA for optimizing reservoir rule curves [J].
Chen, Li ;
McPhee, James ;
Yeh, William W. -G. .
ADVANCES IN WATER RESOURCES, 2007, 30 (05) :1082-1093