Skip to main content

State-of-the-Art of Electricity Price Forecasting in a Grid Environment

  • Chapter
  • First Online:
Handbook of Power Systems II

Part of the book series: Energy Systems ((ENERGY))

Abstract

The purpose of electricity price forecasting is to estimate future electricity prices, particularly locational marginal prices (LMP), with consideration to both security and capacity constraints in a grid environment. Electricity price forecasting is vital to both market participants and market operators in wholesale electricity markets. Electricity price forecasts are used to assist the decision making of market participants on bidding submissions, asset allocations, bilateral trades, transmission and distribution planning, and generation construction locations. Electricity price forecasts are also used by market operators to uncover possible market power. The inaccuracy of electricity price forecasting is due to problems associated with volatility of prices, interpretability of explanatory variables, and underlying impacts of power grid security. This study classifies forecasting techniques common in the literature based on their objective, concept, time horizon, input–output specification, and level of accuracy. Thus the state-of-the-art of electricity price forecasting is described in this study. This survey facilitates the validation, comparison, and improvements of specific or combined methods of price forecasting in competitive electricity markets. Moreover, this study demonstrates a hybrid forecasting system, which combines fuzzy inference system and least-squares estimation. The proposed mechanism is applied to the day-ahead electricity price forecasting of an actual security-constrained, wholesale electricity market. This hybrid forecasting system provides both accuracy and transparency to electricity price forecasts. The forecasting information is also interpretable with respect to the fuzzy representations of selected inputs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Alvarado FL, Rajaraman R (2000) Understanding price volatility in electricity markets. In: Proceedings of the 33rd Hawaii International Conference on System Sciences

    Google Scholar 

  • Amjady N (2006) Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans Power Syst 21:887–896

    Article  Google Scholar 

  • Anderson CL, Davison M (2008) A hybrid system-econometric model for electricity spot prices: considering spike sensitivity to forced outage distributions. IEEE Trans Power Syst 23:927–937

    Article  Google Scholar 

  • Astrom KJ, Wittenmark B (1995) Adaptive control, 2nd edn. Addison-Wesley, Reading, MA

    Google Scholar 

  • Bastian J, Zhu J, Banunarayanan V, Mukerji R (1999) Forecasting energy prices in a competitive market. IEEE Comput Appl Power 12:40–45

    Article  Google Scholar 

  • Batlle C, Barqun J (2005) A strategic production costing model for electricity market price analysis. IEEE Trans Power Syst 20:67–74

    Article  Google Scholar 

  • Benini M, Marracci M, Pelacchi P, Venturini A (2002) Day-ahead market price volatility analysis in deregulated electricity markets. Proc IEEE Power Eng Soc Summer Meet 3:1354–1359

    Article  Google Scholar 

  • Botto C (1999) Price volatility-a natural consequence of electricity market deregulation and a trader’s delight. Proc IEEE Power Eng Soc Summer Meet 2:1272

    Google Scholar 

  • Bunn DW (2000) Forecasting loads and prices in competitive power markets. Proc IEEE 88: 163–169

    Article  Google Scholar 

  • Canazza V, Li G, Liu C-C, Lucarella D, Venturini A (2005) An intelligent system for price forecasting accuracy assessment. In: Proceedings of 13th International Conference on Intelligent Systems Application to Power Systems, Arlington, Virginia

    Google Scholar 

  • Chan KF, Gray P (2006) Using extreme value theory to measure value-at-risk for daily electricity spot prices. Int J Forecast 22:283–300

    Article  Google Scholar 

  • Chen J, Deng S, Huo X (2008) Electricity price curve modeling and forecasting by manifold learning. IEEE Trans Power Syst 23:877–888

    Article  Google Scholar 

  • Conejo AJ, Plazas MA, Espinola R, Molina AB (2005) Day-Ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans Power Syst 20:1035–1042

    Article  Google Scholar 

  • Contreras J, Espnola R, Nogales FJ, Conejo AJ (2003) ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst 18:1014–1020

    Article  Google Scholar 

  • Dahlgren R, Liu C-C, Lawarree J (2001) Volatility in the California power market: source, methodology and recommendations. IEE Proc Generation Transm Distrib 148:189–193

    Article  Google Scholar 

  • Deb R, Albert R, Hsue L-L, Brown N (2000) How to incorporate volatility and risk in electricity price forecasting. Electricity J 13(4):65–75

    Article  Google Scholar 

  • Deng S (2000) Pricing electricity derivatives under alternative stochastic spot price models. In: Proceedings of the 33rd Hawaii International Conference on System Sciences

    Google Scholar 

  • Deng S (1998) Stochastic models of energy commodity prices and their application: mean-reversion with jumps and spikes. PSERC report 98–28

    Google Scholar 

  • Ethier R, Mount T (1998) Estimating the volatility of spot prices in restructured electricity markets and the implications for option values. PSERC Working Paper

    Google Scholar 

  • Gao F, Guan X, Cao X, Papalexopoulos A (2000) Forecasting power market clearing price and quantity using a neural network method. In: Proceedings of the IEEE Power Engineering Society Summer Meeting, Seattle, WA, 2000, pp. 21832188

    Google Scholar 

  • Garcia RC, Contreras J, van Akkeren M, Garcia JBC (2005) A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 20:867–874

    Article  Google Scholar 

  • Gonzalez AM, Roque AMS, Garcia-Gonzalez J (2005) Modeling and forecasting electricity prices with input/output hidden markov models. IEEE Trans Power Syst 20:13–24

    Article  Google Scholar 

  • Guillaume S (2001) Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans Fuzzy Syst 9:426–443

    Article  Google Scholar 

  • Guillaume S, Charnomordic B (2004) Generating an interpretable family of fuzzy partitions from data. IEEE Trans Fuzzy Syst 12:324–335

    Article  Google Scholar 

  • Guo J-J, Luh PB (2004) Improving market clearing price prediction by using a committee machine of neural networks. IEEE Trans Power Syst 19:1867–1876

    Article  Google Scholar 

  • Hong Y-Y, Hsiao C-Y (2002) Locational marginal price forecasting in deregulated electric markets using artificial intelligence. IEE Proc Gener Thansm Distrib 149:621–626

    Article  Google Scholar 

  • Kian A, Keyhani A (2001) Stochastic price modeling of electricity in deregulated energy markets. In: Proceedings of the 34th Hawaii International Conference on System Sciences, vol. 2

    Google Scholar 

  • Kolos SP, Ronn EI (2008) Estimating the commodity market price of risk for energy prices. Energy Econ 30:621–641

    Article  Google Scholar 

  • Li G, Liu C-C, Mattson C, Lawarree J (2007) Day-ahead electricity price forecasting in a grid environment. IEEE Trans Power Syst 22:266–274

    Article  Google Scholar 

  • Li G, Liu C-C, Lawarree J, Gallanti M, Venturini A (2005) State-of-the-art of electricity price forecasting. In: Proceedings of the 2nd CIGRE/IEEE PES International Symposium 110–119

    Google Scholar 

  • Lucia JJ, Schwartz E (2002) Electricity prices and power derivatives: evidence from the nordic power exchange. Rev Derivatives Res 5:5–50

    Article  MATH  Google Scholar 

  • Nauck D (2000) Data analysis with neuro-fuzzy methods. International ISSEK Workshop on Data Fusion and Perception, Udine, Italy

    Google Scholar 

  • Ni E, Luh PB (2001) Forecasting Power market clearing price and its discrete PDF using a Bayesian-based classification method. IEEE Power Eng Soc Winter Meet 3:1518–1523

    Google Scholar 

  • Nicolaisen JD, Richter CW Jr., Shebl GB (2000) Price signal analysis for competitive electric generation companies. In: Proceedings of the Conference on Electric Utility Deregulation and Restructuring and Power Technologies 66–71

    Google Scholar 

  • Niimura T, Ko H-S, Ozawa K (2002) A day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market. Proc Int Joint Conf Neural Netw 2:1362–1366

    Google Scholar 

  • Nogales FJ, Contreras J, Conejo AJ, Espnola R (2002) Forecasting next-day electricity prices by time series models. IEEE Trans Power Syst 17:342–348

    Article  Google Scholar 

  • Olsson M, Soder L (2008) Modeling real-time balancing power market prices using combined SARIMA and Markov processes. IEEE Trans Power Syst 23:443–450

    Article  Google Scholar 

  • Pindoriya NM, Singh SN, Singh SK (2008) An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Trans Power Syst 23:1423–1432

    Article  Google Scholar 

  • Rodriguez CP, Anders GJ (2004) Energy price forecasting in the ontario competitive power system market. IEEE Trans Power Syst 19:366–374

    Article  Google Scholar 

  • Ruibal CM, Mazumdar M (2008) Forecasting the Mean and the Variance of electricity prices in deregulated markets. IEEE Trans Power Syst 23:25–32

    Article  Google Scholar 

  • Sansom DC, Downs T, Saha TK (2003) Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants. J Electr Electron Eng Aust 22(3):227–234

    Google Scholar 

  • Skantze P, Ilic M, Chapman J (2000) Stochastic modeling of electric power prices in a multi-market environment. In: Proceedings of Power Engineering Winter Meeting 1109–1114

    Google Scholar 

  • Stevenson M (2001) Filtering and forecasting spot electricity prices in the increasingly deregulated Australian electricity market. In: International Institute of Forecasters Conference, Atlanta, June 2001

    Google Scholar 

  • Szkuta BR, Sanavria LA, Dillon TS (1999) Electricity price short-term forecasting using artificial neural networks. IEEE Trans Power Syst 14:851–857

    Article  Google Scholar 

  • Valenzuela J, Mazumdar M (2001) On the computation of the probability distribution of the sport market price in a deregulated electricity market. In: Proceedings of the 21st Power Industry Computer Applications International Conference 268–271

    Google Scholar 

  • Wang AJ, Ramsay B (1998) A Neural network based estimator for electricity spot-pricing with particular reference to weekends and public holidays. Neurocomputing 23:47–57

    Article  Google Scholar 

  • Wang H, Kwong S, Jin Y, Wei W, Man K-F (2005) Agent-based evolutionary approach for interpretable rule-based knowledge extraction. IEEE Trans Syst Man Cybern C Appl Rev 35:143–155

    Article  Google Scholar 

  • Wang L-X, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22:1414–1427

    Article  MathSciNet  Google Scholar 

  • Yen J, Wang L, Gillespie, CW (1998) Improving the interpretability of TSK fuzzy models by combining global learning and local learning. IEEE Trans Fuzzy Syst 6:530–537

    Article  Google Scholar 

  • Zareipour H, Canizares CA, Bhattacharya K, Thomson J (2006) Application of public-domain market information to forecast ontario’s wholesale electricity prices. IEEE Trans Power Syst 21:1707–1717

    Article  Google Scholar 

  • Zhang L, Luh PB (2005) Neural Network-based market clearing price prediction and confidence interval estimation with an improved extended kalman filter method. IEEE Trans Power Syst 20:59–66

    Article  Google Scholar 

  • Zhang L, Luh PB, Kasiviswanathan K (2003) Energy clearing price prediction and confidence interval estimation with cascaded neural networks. IEEE Trans Power Syst 18:99–105

    Article  Google Scholar 

  • Zhao JH, Dong ZY, Li X, Wong KP (2007) A framework for electricity price spike analysis with advanced data mining methods. IEEE Trans Power Syst 22:376–385

    Article  Google Scholar 

  • Zhao JH, Dong ZY, Xu Z, Wong KP (2008) A statistical approach for interval forecasting of the electricity price. IEEE Trans Power Syst 23:267–276

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guang Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Li, G., Lawarree, J., Liu, CC. (2010). State-of-the-Art of Electricity Price Forecasting in a Grid Environment. In: Rebennack, S., Pardalos, P., Pereira, M., Iliadis, N. (eds) Handbook of Power Systems II. Energy Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12686-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12686-4_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12685-7

  • Online ISBN: 978-3-642-12686-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics