Title :
Study on optimum operation planning of wind farm/battery system using forecasted power data
Author :
Uehara, Akie ; Senjyu, Tomonobu ; Kikunaga, Yasuaki ; Yona, Atsushi ; Urasaki, Naomitsu ; Funabashi, Toshihisa ; Kim, Chul-Hwan
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of the Ryukyus, Nishihara, Japan
Abstract :
To reduce the impact of large output power fluctuations of a wind turbine generator (WTG) integrated in a power system, a battery energy storage system (BESS) is installed with the WTG. However, if a large battery is installed, the capital cost of the wind turbine system will increase. Hence, the smaller size of battery is preferable to reduce the capital cost and the battery should be utilized effectively. Therefore, optimum operational planning is required. In this context, this paper presents an optimization approach to determine operational planning of a wind farm (WF) generated power coupled with a BESS. In this optimization method, it is assumed that a forecasted generated power data is available 3 hours ahead, which is utilized in the operational planning of the WF and the BESS. The optimization program searches for the smoothing command of combined output power and BESS charge/discharge operational scheme 3 hours ahead in order to obtain more benefit of electrical power selling and the smoothing of the output power fluctuations. The optimization method uses genetic algorithm (GA). The effectiveness of the proposed optimization method is verified by simulation results.
Keywords :
energy storage; genetic algorithms; load forecasting; power system planning; secondary cells; wind power plants; wind turbines; battery energy storage system; electrical power selling; forecasted power data; genetic algorithm; optimization; optimum operation planning; power fluctuations; wind farm/battery system; wind turbine generator; Batteries; Costs; Fluctuations; Optimization methods; Power generation; Power system planning; Wind energy generation; Wind farms; Wind forecasting; Wind turbines; Wind farm; battery energy storage system; forecasted generated power; genetic algorithm;
Conference_Titel :
Power Electronics and Drive Systems, 2009. PEDS 2009. International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-4166-2
Electronic_ISBN :
978-1-4244-4167-9
DOI :
10.1109/PEDS.2009.5385766