A coordinated optimization framework for flexible operation of pumped storage hydropower system: Nonlinear modeling, strategy optimization and decision making

被引:91
作者
Zhao, Zhigao [1 ]
Yang, Jiandong [1 ]
Yang, Weijia [1 ]
Hu, Jinhong [1 ]
Chen, Man [2 ]
机构
[1] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430074, Peoples R China
[2] China Southern Power Grid Power Generat Co, Guangzhou 510630, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Pumped storage hydropower system; Multi-objective optimization; Reference vector guided evolutionary algorithm; Multiple constraints; Fuzzy analytic hierarchy process; GRAVITATIONAL SEARCH ALGORITHM; MULTIOBJECTIVE OPTIMIZATION; CONTROLLER; CHALLENGES; TRANSIENT; DESIGN; PLANTS; SOLAR; UNIT;
D O I
10.1016/j.enconman.2019.04.068
中图分类号
O414.1 [热力学];
学科分类号
摘要
Pumped storage hydropower system has now become a key support for integration of variable renewable energy. Along with the large-scale development of pumped storage plants all over the world, the importance of operation strategy is rapidly increasing with the fast-growing demand of flexibility. How to direct flexible operation and improve the stability of the system has become a key issue. This paper proposes a novel optimization framework to derive optimal operating policies for pumped storage hydropower system, which is divided into three coordinated stages: nonlinear modeling, strategy optimization and decision making. The real-time accurate equivalent circuit model is proposed in first stage, which can reconcile the conflict between simulation efficiency and accuracy, owing to the novel pump-turbine model and space-time discretization. The search corridor which consists of multiple constraints is defined to improve search efficiency. Reference vector guided evolutionary algorithm is performed to handle non-normalization objectives, multiple constraints and irregular Pareto fronts in second stage. Further, fuzzy analytic hierarchy process is innovatively introduced to select compatible solution under extreme conditions. The originality of the framework is embodied in multiple trade-offs, i.e. trade-off between accuracy and efficiency in the nonlinear model, trade-off between convergence and diversity in the strategy optimization, and trade-off in conflicting objectives of the decision making. Compared with the on-site operation, the maximum water pressure of volute and the vacuum at draft tube can be improved by 5.59% and 9.6%, the rotational speed oscillation also decreases with this framework in load rejection. The proposed framework can specify the optimal policy to enhance the system reliability, which can also serve as the basis for smart operation in various conditions.
引用
收藏
页码:75 / 93
页数:19
相关论文
共 53 条
[1]   An Enhanced Decomposition-Based Evolutionary Algorithm With Adaptive Reference Vectors [J].
Asafuddoula, Md ;
Singh, Hemant Kumar ;
Ray, Tapabrata .
IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (08) :2321-2334
[2]  
Chaudhry Hanif M., 2014, APPL HYDRAULIC TRANS
[3]   Economic viability of pumped-storage power plants participating in the secondary regulation service [J].
Chazarra, Manuel ;
Perez-Diaz, Juan I. ;
Garcia-Gonzalez, Javier ;
Praus, Roland .
APPLIED ENERGY, 2018, 216 :224-233
[4]   Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization [J].
Chen, Zhihuan ;
Yuan, Yanbin ;
Yuan, Xiaohui ;
Huang, Yuehua ;
Li, Xianshan ;
Li, Wenwu .
ISA TRANSACTIONS, 2015, 56 :173-187
[5]   Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II [J].
Chen, Zhihuan ;
Yuan, Xiaohui ;
Ji, Bin ;
Wang, Pengtao ;
Tian, Hao .
ENERGY CONVERSION AND MANAGEMENT, 2014, 84 :390-404
[6]   Improved gravitational search algorithm for parameter identification of water turbine regulation system [J].
Chen, Zhihuan ;
Yuan, Xiaohui ;
Tian, Hao ;
Ji, Bin .
ENERGY CONVERSION AND MANAGEMENT, 2014, 78 :306-315
[7]   China's small hydropower and its dispatching management [J].
Cheng, Chuntian ;
Liu, Benxi ;
Chau, Kwok-Wing ;
Li, Gang ;
Liao, Shengli .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 42 :43-55
[8]   Test Problems for Large-Scale Multiobjective and Many-Objective Optimization [J].
Cheng, Ran ;
Jin, Yaochu ;
Olhofer, Markus ;
Sendhoff, Bernhard .
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 47 (12) :4108-4121
[9]   A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization [J].
Cheng, Ran ;
Jin, Yaochu ;
Olhofer, Markus ;
Sendhoff, Bernhard .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2016, 20 (05) :773-791
[10]   Comparison of 100% renewable energy system scenarios with a focus on flexibility and cost [J].
Deason, Wesley .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 82 :3168-3178