A new spinning reserve requirement forecast method for deregulated electricity markets
Ancillary services are necessary for maintaining the security and reliability of power systems and constitute an important part of trade in competitive electricity markets. Spinning Reserve (SR) is one of the most important ancillary services for saving power system stability and integrity in response to contingencies and disturbances that continuously occur in the power systems. Hence, an accurate day-ahead forecast of SR requirement helps the Independent System Operator (ISO) to conduct a reliable and economic operation of the power system. However, SR signal has complex, non-stationary and volatile behavior along the time domain and depends greatly on system load. In this paper, a new hybrid forecast engine is proposed for SR requirement prediction. The proposed forecast engine has an iterative training mechanism composed of Levenberg-Marquadt (LM) learning algorithm and Real Coded Genetic Algorithm (RCGA), implemented on the Multi-Layer Perceptron (MLP) neural network. The proposed forecast methodology is examined by means of real data of Pennsylvania-New Jersey-Maryland (PJM) electricity market and the California ISO (CAISO) controlled grid. The obtained forecast results are presented and compared with those of the other SR forecast methods.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 87 (2010)
Issue (Month): 6 (June)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Abdou Kâ Diongue & Dominique Guegan & Bertrand Vignal, 2009.
"Forecasting electricity spot market prices with a k-factor GIGARCH process,"
Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers)
- Diongue, Abdou Kâ & Guégan, Dominique & Vignal, Bertrand, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Applied Energy, Elsevier, vol. 86(4), pages 505-510, April.
- Abdou Kâ Diongue & Dominique Guegan & Bertrand Vignal, 2007. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00188264, HAL.
- Abdou Kâ Diongue & Dominique Guégan & Bertrand Vignal, 2007. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Documents de travail du Centre d'Economie de la Sorbonne b07058, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Nov 2009.
- Pinson, P. & Nielsen, H.Aa. & Madsen, H. & Kariniotakis, G., 2009. "Skill forecasting from ensemble predictions of wind power," Applied Energy, Elsevier, vol. 86(7-8), pages 1326-1334, July.
- Delarue, Erik & D'haeseleer, William, 2008. "Adaptive mixed-integer programming unit commitment strategy for determining the value of forecasting," Applied Energy, Elsevier, vol. 85(4), pages 171-181, April.
- Georgopoulou, Chariklia A. & Giannakoglou, Kyriakos C., 2009. "Two-level, two-objective evolutionary algorithms for solving unit commitment problems," Applied Energy, Elsevier, vol. 86(7-8), pages 1229-1239, July.
- Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
- Aghaei, J. & Shayanfar, H.A. & Amjady, N., 2009. "Joint market clearing in a stochastic framework considering power system security," Applied Energy, Elsevier, vol. 86(9), pages 1675-1682, September.
- repec:hal:journl:halshs-00307606 is not listed on IDEAS
- Cai, Y.P. & Huang, G.H. & Yang, Z.F. & Tan, Q., 2009. "Identification of optimal strategies for energy management systems planning under multiple uncertainties," Applied Energy, Elsevier, vol. 86(4), pages 480-495, April.
When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:87:y:2010:i:6:p:1870-1879. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.