Joint Optimization of Wind Turbine Micrositing and Cabling in an Offshore Wind Farm

被引:45
作者
Tao S. [1 ]
Xu Q. [1 ]
Feijoo A. [2 ]
Zheng G. [2 ]
机构
[1] Department of Electrical Engineering, Southeast University, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Department of Enxeñería Eléctrica, Universidade de Vigo, Vigo
来源
Tao, Siyu (230188145@seu.edu.cn) | 1600年 / Institute of Electrical and Electronics Engineers Inc.卷 / 12期
基金
中国国家自然科学基金;
关键词
bi-level programming; Multi-objective optimization; optimal cabling; wind turbine micro-siting;
D O I
10.1109/TSG.2020.3022378
中图分类号
学科分类号
摘要
Wind farms (WFs) are important components of smart grid. The modeling and optimal planning of the WF is preliminary before its construction. In this article, a bi-level multi-objective optimization framework is presented, with the aim of simultaneously designing the configuration of wind turbines (WTs) as well as the topology of electrical collector system in an offshore WF. The installation capacity of the WF, the positioning of the WTs and the planning scheme of the electrical systemare balanced to achieve a better performance of the WF. In this proposal, there is an outer layer along with two inner layers. The objectives of the outer-layer model are the maximization of the WF's daily profit rate, the daily average capacity factor, and power quality. It is tackled by the Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The objectives of the two inner layer models are to determine the topology of the electrical system and the generation schedule of other generators, and are solved by means of the Binary Particle Swarm Optimization (BPSO) algorithm and the quadratic programming (QP) method respectively. The WF is assumed to be connected to the IEEE-24 bus test system. The simulation results validate the adaptability and effectiveness of the proposed approach with the main factors that affect the WF layout being analyzed. © 2020 IEEE.
引用
收藏
页码:834 / 844
页数:10
相关论文
共 42 条
[1]  
Kong F., Dong C., Liu X., Zeng H., Quantity versus quality: Optimal harvesting wind power for the smart grid, Proc. IEEE, 102, 11, pp. 1762-1776, (2014)
[2]  
Glinkowski M., Hou J., Rackliffe G., Advances in wind energy technologies in the context of smart grid, Proc. IEEE, 99, 6, pp. 1083-1097, (2011)
[3]  
Erlich I., Shewarega F., Feltes C., Koch F., Fortmann J., Offshore wind power generation technologies, Proc. IEEE, 101, 4, pp. 891-905, (2013)
[4]  
Record 6.1 GW of New Offshore Wind Capacity Installed Globally in 2019
[5]  
Gonzalez J., Payan M., Santos J., A new and efficient method for optimal design of large offshore wind power plants, IEEE Trans. Power Syst., 28, 3, pp. 3075-3084, (2013)
[6]  
Hou P., Zhu J., Ma K., Yang G., Hu W., Chen Z., A review of offshore wind farm layout optimization and electrical system design methods, J. Mod. Power Syst. Clean Energy, 7, 5, pp. 975-986, (2019)
[7]  
Mosetti G., Poloni C., Diviacco B., Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm, J. Wind Eng. Ind. Aerodyn., 51, 1, pp. 105-116, (1994)
[8]  
Grady S.A., Hussaini M.Y., Abdullah M.M., Placement of wind turbines using genetic algorithms, Renew. Energy, 30, 2, pp. 259-270, (2005)
[9]  
Emami A., Noghreh P., New approach on optimization in placement of wind turbines within wind farm by genetic algorithms, Renew. Energy, 35, 7, pp. 1559-1564, (2010)
[10]  
Pookpunt S., Ongsakul W., Optimal placement of wind turbines within wind farm using binary particle swarm optimization with timevarying acceleration coefficients, Renew. Energy, 55, pp. 266-276, (2013)