A New Approach to Short-term Price Forecast Strategy with an Artificial Neural Network Approach: Application to the Nord Pool

- Journal title : Journal of Electrical Engineering and Technology
- Volume 10, Issue 4, 2015, pp.1480-1491
- Publisher : The Korean Institute of Electrical Engineers
- DOI : 10.5370/JEET.2015.10.4.1480

Title & Authors

A New Approach to Short-term Price Forecast Strategy with an Artificial Neural Network Approach: Application to the Nord Pool

Kim, Mun-Kyeom;

Kim, Mun-Kyeom;

Abstract

In new deregulated electricity market, short-term price forecasting is key information for all market players. A better forecast of market-clearing price (MCP) helps market participants to strategically set up their bidding strategies for energy markets in the short-term. This paper presents a new prediction strategy to improve the need for more accurate short-term price forecasting tool at spot market using an artificial neural networks (ANNs). To build the forecasting ANN model, a three-layered feedforward neural network trained by the improved Levenberg-marquardt (LM) algorithm is used to forecast the locational marginal prices (LMPs). To accurately predict LMPs, actual power generation and load are considered as the input sets, and then the difference is used to predict price differences in the spot market. The proposed ANN model generalizes the relationship between the LMP in each area and the unconstrained MCP during the same period of time. The LMP calculation is iterated so that the capacity between the areas is maximized and the mechanism itself helps to relieve grid congestion. The addition of flow between the areas gives the LMPs a new equilibrium point, which is balanced when taking the transfer capacity into account, LMP forecasting is then possible. The proposed forecasting strategy is tested on the spot market of the Nord Pool. The validity, the efficiency, and effectiveness of the proposed approach are shown by comparing with time-series models

Keywords

Short-term forecasting;Locational marginal price;Artificial neural network;Levenberg-marquardt algorithm;Nord pool;

Language

English

References

1.

M. A. F. Ghazvini, B. Canizes, Z. Vale and H. Morais, “Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market,” Appl Energy, vol. 101, no. 1, pp. 667-677, Jan. 2013.

2.

M. Shahidehpour, H. Yamin and Z. Li, Market operations in electric power systems: John Wiley & Sons, Inc., Publication, 2002

3.

Gong Li and Jing Shi, “Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions,” Appl Energy, vol. 99, no. 1, pp. 13-22, Nov. 2012.

4.

D. W. Bunn, “Forecasting loads and prices in competitive power markets,” in Proceedings of IEEE, vol. 88, no. 2, pp. 163-69, Feb. 2000.

5.

J. Wang, S. Zhu, W. Zhang and H. Lu, “Combined modeling for electric load forecasting with adaptive particle swarm optimization,” Energy, vol. 35, no. 4, pp. 1671-1678, Apr. 2010.

6.

M.R. AlRashidi and K.M. EL-Naggar, “Long term electric load forecasting based on particle swarm optimization,” Appl Energy, vol. 87, no. 1, pp. 320-326, Jan. 2010.

7.

J. Wang, L. Li, D. Niu and Z. Tan, “An annual load forecasting model based on support vector regression with differential evolution algorithm,” Appl Energy, vol. 94, pp. 65-70, Jun. 2012.

8.

C. Wang, G. Grozev and S. Seo, “Decomposition and statistical analysis for regional electricity demand forecasting,” Energy, vol. 41, no. 1, pp. 313-325, May. 2012.

9.

R. Deb, R. Albert, L. L. Hsue and N. Brown, “How to incorporate volatility and risk in electricity price forecasting,” Electricity J, pp. 1-16, May. 2000.

10.

D. J. Pedregal and J. R. Trapero, “Mid-term hourly electricity forecasting based on a multi-rate approach,” Energy Convers Manage, vol. 51, no. 1, pp. 105-111, Jan. 2010.

11.

M. K. Kim, Y. W. Nam and J. K. Park, “Market-clearing for pricing system security based on voltage stability criteria,” Energy, vol. 36, no. 2, pp. 1255-1264, Feb. 2011.

12.

E. Ni and P. B. Luh, “Forecasting power market clearing price and its discrete PDF using a Bayesian-based classification method,” IEEE Power Eng. Soc. Winter Meeting 2001, Columbus, Jan. 2001.

13.

L. Zhang and P. B. Luh, “Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method,” IEEE Trans Power Syst, vol. 20, no. 1, pp. 59-66, Feb. 2005.

14.

M. K. Kim and D. Hur, “An optimal pricing scheme in electricity markets by parallelizing security constrained optimal power flow based market-clearing model,” Int J Electr Power Energy Syst, vol. 48, pp. 161-171, Jun. 2013.

15.

C. P. Rodriguez and G. J. Anders, “Energy price forecasting in the Ontario competitive power system market,” IEEE Trans Power Syst, vol. 19, pp. 366-374, Feb. 2004.

16.

F. J. Nogales, J. Contreras, A. J. Conejo and R. Espinola, “Forecasting next-day electricity prices by time series models,” IEEE Trans Power Syst, vol. 17, no. 2, pp. 342-348, May. 2002.

17.

J. Contreras, R. Espinola, F. J. Nogales and A. J. Conejo, “ARIMA models to predict next day electricity prices,” IEEE Trans Power Syst, vol. 18, no. 3, pp. 1014-1020, Aug. 2003.

18.

A. J. Conejo, M. A. Plazas, R. Espinola and A. B. Molina, “Day-ahead electricity price forecasting using the wavelet transform and ARIMA models,” IEEE Trans Power Syst, vol. 20, pp. 1035-1042, May. 2005.

19.

Z. Tan, J. Zhang, J. Wang and J. Xu, “Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models,” Appl Energy, vol. 87, no. 11, pp. 3606-3610, May. 2010.

20.

R. Garcia, J. Contreras, M. van Akkeren and J. Garcia , “A garch forecasting model to predict day-ahead electricity prices,” Power Systems, IEEE Trans Power Syst, vol. 20, no. 2, pp. 867-874, May. 2005.

21.

B. R. Szkuta, L. A. Sanabria and T. S. Dillon, “Electricity price short-term forecasting using artificial neural networks,” IEEE Trans Power Syst, vol. 14, no. 3, pp. 851-858, Aug. 1999.

22.

S. A. Kalogirou, “Applications of artificial neural-networks for energy systems,” Appl Energy, vol. 67, no. 1, pp. 17-35, Sept. 2000.

23.

L. Zhang, P. B. Luh and K. Kasivisvanathan, “Energy clearing price prediction and confidence interval estimation with cascaded neural networks,” IEEE Trans Power Syst, vol. 18, no. 1, pp. 99-105, Feb. 2003.

24.

H. Y. Yamin, S. M. Shahidehpour and Z. Li, “Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets,” Int J Electr Power Energy Syst, vol. 26, pp. 571-581, Oct. 2004.

25.

M. P. Garcia and D. S. Kirschen, “Forecasting system imbalance volumes in competitive electricity markets,” IEEE Trans Power Syst, vol. 21, pp. 240-248, Feb. 2006.

26.

S. K. Aggarwal, L. M. Saini and A. Kumar, “Electricity price forecasting in deregulated markets: a review and evaluation,” Int J Electr Power Energy Syst, vol. 31, no.1, pp. 13-22, Jan. 2009.

27.

R. Gareta, L. M. Romeo and A. Gil, “Forecasting of electricity prices with neural networks,” Energy Convers Manage, vol. 47, pp. 1770-1778, Aug. 2006.

28.

H. T. Nguyen and I. T. Nabney, “Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models,” Energy, vol. 35, no. 9, pp. 3674-3685, Jan. 2010.

29.

W. M. Lin, H. J. Gow and M. T. Tsai, “An enhanced radial basis function network for short-term electricity price forecasting,” Appl Energy, vol. 10, pp. 3226-3234, Oct, 2010.

30.

P. Mandal, T. Senjyu, N. Urasaki and T. Funabashi, “A neural network based several hour-ahead electric load forecasting using similar days approach,” Int J Electr Power Energy Syst, vol. 28, pp. 367-373, Jul. 2006.

31.

Y. Y. Hong and C. Y. Hsiao, “Locational marginal price forecasting in deregulated electricity markets using artificial intelligence” IEE Proc Generation Trans Distribution, vol. 149, no. 5, pp. 621-626, Sept. 2002.

32.

X. Lu, Z. Y. Dong and X. Li, “Electricity market price spike forecast with data mining techniques,” Electric Power Syst Res, vol. 73, pp. 19-29, Jan. 2005.

33.

J. H. Zhao, Z. Y. Dong, X. Li and K. P. Wong. “A general method for electricity market price spike analysis. In,” IEEE Power Eng. Soc. General Meeting, IEEE 2005, vol. 1, pp. 1286-1293, Jun. 2005.

34.

S. Haykin, Neural Networks: A Comprehensive Foundation: New York, Macmillan, 1994.

35.

N. Amjady and F. Keynia, “A new spinning reserve requirement forecast method for deregulated electricity markets,” Appl Energy, vol. 87, no. 6, pp. 1870-1879, Jun. 2010.

36.

M. H. Khosravi, S. Barghinia and P. Ansarimehr, “New momentum adjustment technique for Levenberg-Marquardt neural network training used in short term load forecasting”, Int Power Syst Conference, pp. 1782-1788, 2006.

37.

M. K. Kim and D. Hur, “Decomposition-coordination strategy to improve power transfer capability of interconnected systems,” Int J Electr Power Energy Syst, vol. 33, no. 10, pp. 1638-1647, 2011.

38.

Nord Pool spot market. [Online]. Available: <http://www.nordpoolspot.com/ >.

39.

The MathWorks, MATLAB. [Online]. Available: <http://www.mathworks.com >.