Assessing the Effect of Wind Farm Layout on Energy Storage Requirement for Power Fluctuation Mitigation

• Published in: IEEE transactions on sustainable energy Vol. 10; no. 2; pp. 558 - 568
• Main Authors: Kazari, Hessam, Oraee, Hashem, Pal, Bikash C.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-04-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; battery energy storage; Energy; Energy levels; Energy storage; Fluctuations; genetic algorithm (GA); Genetic algorithms; Layout; Layouts; Manganese; Mitigation; Objective function; optimal layout; Optimization; Power fluctuation; Wind effects; wind farm; Wind farms; Wind forecasting; Wind power; Wind power generation; Wind speed; Wind turbines
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2018.2837060

Optimization of wind farm (WF) layout has been studied in the literature with the objective of maximizing the wind energy capture. Based on the power spectrum density theorem, this paper shows that the WF layout affects not only the total harvested energy but also the level of power fluctuation, which, in turn, influences required capacity of battery energy storage system (BESS) needed to mitigate the inherent power fluctuation of the WFs. Since, both harvested energy level and BESS capacity directly influence the profit of WF owner, the effect of WF layout on these quantities is taken into account simultaneously, and WF layout optimization problem is redefined. Genetic algorithm is then employed in order to optimize the resulting objective function. The proposed method and optimization process are performed on the layout of an actual offshore WF using real wind data. Anew index is introduced to quantify the power fluctuations, and energy curtailment is assessed. The comparative analysis between the actual layout performance and the optimal layout in different scenarios is conducted, showing the reduction of power fluctuations and improvement of energy curtailment. In addition, different BESS technologies have been analyzed to study the impact of their parameters on the optimization results.