Abstract
In order to solve the problems of global warming and depletion of energy resource, renewable energy systems such as wind generation are getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to perfectly forecast wind power. This paper describes a method to use power forecast data of wind turbine generators considering wind power forecast error for optimal operation. The purpose in this paper is to smooth the output power fluctuation of a wind farm and to obtain more beneficial electrical power for selling.
References
1. New Energy and Industrial Technology Development Organization, “NEDO offshore wind energy progress,” 2013. Available at: http://www.nedo.go.jp/content/100515169.pdf?from=b.Search in Google Scholar
2. Tohoku Electric Power Co., Inc. “Annual Report,” 2012. Available at: http://www.tohoku-epco.co.jp/ir/report/pdf/ar2012.pdf.Search in Google Scholar
3. Le HT, Santoso S, Grady WM. Development and analysis of an ESS-based application for regulating wind farm power output variation. Proceedings of IEEE PES 2009 General Meeting, 8 pages (CD-ROM), 26–30 Jul 2009.10.1109/PES.2009.5275518Search in Google Scholar
4. Teleke S, Baran ME, Huang AQ, Bhattacharya S, Anderson L. Control strategies for battery energy storage for wind farm dispatching. IEEE Trans Energy Conver 2009;24:725–32.10.1109/TEC.2009.2016000Search in Google Scholar
5. Lee TY. Operating schedule of battery energy storage system in a time-of-use rate industrial user with wind turbine generators: a multipass iteration particle swarm optimization approach. IEEE Trans Energy Conver 2007;22:774–82.10.1109/TEC.2006.878239Search in Google Scholar
6. Castronuovo ED, Pecas Lopes JA. On the optimization of the daily operation of a wind-hydro power plant. IEEE Trans Power Syst 2004;19:1599–606.10.1109/TPWRS.2004.831707Search in Google Scholar
7. Lu MS, Chung-Liang C, Lee WJ, Wang L. Combining the wind power generation system with energy storage equipment. IEEE Trans Ind Appl 2009;45:2109–15.10.1109/TIA.2009.2031937Search in Google Scholar
8. Tanabe T, Tanikawa R, Aoki I, Funabashi T, Yokoyama R. Generation scheduling for wind power generation by storage battery system and meteorological forecast. Proceedings of IEEE PES 2008 General Meeting, 7 pages (CD-ROM), 20–24 Jul 2008.10.1109/PES.2008.4596116Search in Google Scholar
9. Methaprayoon K, Lee WJ, Yingvivatanapong C, Liao J. An integration of ANN wind power estimation into UC considering the forecasting uncertainty. Proceedings of IEEE Industrial & Commercial Power Systems Technical Conference (I&CPS), 2005:116–24.10.1109/ICPS.2005.1436364Search in Google Scholar
10. Senjyu T, Yona A, Urasaki N, Funabashi T. Application of recurrent neural network to long-term-ahead generating power forecasting for wind power generator, power system conference and exposition (PSCE), IEEE PES, 2006:1260–5.10.1109/PSCE.2006.296487Search in Google Scholar
11. Senjyu T, Yona A, Urasaki N, Funabashi T. Application of recurrent neural network to short-term-ahead generating power forecasting for wind power generator. Proceedings of International Conference on Electrical Engineering (ICEE), 6 pages (CD-ROM), 9–13 Jul 2006.10.1109/PSCE.2006.296487Search in Google Scholar
12. Uehara A, Senjyu T, Kikunaga Y, Yona A, Urasaki N, Funabashi T, et al. Study on optimum operation planning of wind farm/battery system using forecasted power data. Proceedings of International Conference on Power Electronics and Drive Systems (PEDS), 2–5 Nov 2009:907–12.10.1109/PEDS.2009.5385766Search in Google Scholar
13. Doherty R, O’Malley M. A new approach to quantify reserve demand in systems with significant installed wind capacity. IEEE Trans Power Syst 2005;20:587–95.10.1109/TPWRS.2005.846206Search in Google Scholar
14. Ummels BC, Gibescu M, Pelgrum E, Kling WL, Brand AJ. Impacts of wind power on thermal generation unit commitment and dispatch. IEEE Trans Energy Conver 2007;22:44–51.10.1109/TEC.2006.889616Search in Google Scholar
15. Tewari S, Geyer CJ, Mohan N. A statistical model for wind power forecast error and its application to the estimation of penalties in liberalized markets. IEEE Trans Power Syst 2011;26:2031–9.10.1109/TPWRS.2011.2141159Search in Google Scholar
© 2013 by Walter de Gruyter Berlin / Boston