IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v32y2010i5p1025-1033.html
   My bibliography  Save this article

An agent-based approach with collaboration among agents: Estimation of wholesale electricity price on PJM and artificial data generated by a mean reverting model

Author

Listed:
  • Sueyoshi, Toshiyuki

Abstract

This study examines the performance of MAIS (Multi-Agent Intelligent Simulator) equipped with various learning capabilities. In addition to the learning capabilities, the proposed MAIS incorporates collaboration among agents. The proposed MAIS is applied to estimate a dynamic change of wholesale electricity price in PJM (Pennsylvania-New Jersey-Mainland) and an artificial data set generated by a mean reverting model. Using such different types of data sets, the methodological validity of MAIS is confirmed by comparing it with other well-known alternatives in computer science. This study finds that the MAIS needs to incorporate both the mean reverting model and the collaboration behavior among agents in order to enhance its estimation capability. The MAIS discussed in this study will provide research on energy economics with a new numerical capability that can investigate a dynamic change of not only wholesale electricity price but also speculation and learning process of traders.

Suggested Citation

  • Sueyoshi, Toshiyuki, 2010. "An agent-based approach with collaboration among agents: Estimation of wholesale electricity price on PJM and artificial data generated by a mean reverting model," Energy Economics, Elsevier, vol. 32(5), pages 1025-1033, September.
    • Handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1025-1033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140-9883(10)00053-8
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    2. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    3. Alvaro Cartea & Marcelo Figueroa, 2005. "Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(4), pages 313-335.
    4. Derek W. Bunn and Fernando Oliveira, 2001. "An Application of Agent-based Simulation to the New Electricity Trading Arrangements of England and Wales," Computing in Economics and Finance 2001 93, Society for Computational Economics.
    5. Bask, Mikael & Widerberg, Anna, 2009. "Market structure and the stability and volatility of electricity prices," Energy Economics, Elsevier, vol. 31(2), pages 278-288, March.
    6. Demers, Jean-Guy, 2009. "Multiple zone power forwards: A value at risk framework," Energy Economics, Elsevier, vol. 31(5), pages 714-726, September.
    7. Goto, Mika & Tsutsui, Miki, 2008. "Technical efficiency and impacts of deregulation: An analysis of three functions in U.S. electric power utilities during the period from 1992 through 2000," Energy Economics, Elsevier, vol. 30(1), pages 15-38, January.
    8. Sueyoshi, Toshiyuki, 2010. "An agent-based approach equipped with game theory: Strategic collaboration among learning agents during a dynamic market change in the California electricity crisis," Energy Economics, Elsevier, vol. 32(5), pages 1009-1024, September.
    9. Benth, Fred Espen & Koekebakker, Steen, 2008. "Stochastic modeling of financial electricity contracts," Energy Economics, Elsevier, vol. 30(3), pages 1116-1157, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sueyoshi, Toshiyuki & Goto, Mika, 2015. "Environmental assessment on coal-fired power plants in U.S. north-east region by DEA non-radial measurement," Energy Economics, Elsevier, vol. 50(C), pages 125-139.
    2. Young, David & Poletti, Stephen & Browne, Oliver, 2014. "Can agent-based models forecast spot prices in electricity markets? Evidence from the New Zealand electricity market," Energy Economics, Elsevier, vol. 45(C), pages 419-434.
    3. Goto, Hisanori & Goto, Mika & Sueyoshi, Toshiyuki, 2011. "Consumer choice on ecologically efficient water heaters: Marketing strategy and policy implications in Japan," Energy Economics, Elsevier, vol. 33(2), pages 195-208, March.
    4. Sueyoshi, Toshiyuki & Goto, Mika & Sugiyama, Manabu, 2013. "DEA window analysis for environmental assessment in a dynamic time shift: Performance assessment of U.S. coal-fired power plants," Energy Economics, Elsevier, vol. 40(C), pages 845-857.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2017. "Measurement of returns to scale on large photovoltaic power stations in the United States and Germany," Energy Economics, Elsevier, vol. 64(C), pages 306-320.
    6. Lin, Boqiang & Wang, Yao, 2020. "Analyzing the elasticity and subsidy to reform the residential electricity tariffs in China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 189-206.
    7. Sueyoshi, Toshiyuki & Wang, Derek, 2017. "Measuring scale efficiency and returns to scale on large commercial rooftop photovoltaic systems in California," Energy Economics, Elsevier, vol. 65(C), pages 389-398.
    8. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis," Energy Economics, Elsevier, vol. 42(C), pages 271-288.
    9. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Environmental assessment by DEA radial measurement: U.S. coal-fired power plants in ISO (Independent System Operator) and RTO (Regional Transmission Organization)," Energy Economics, Elsevier, vol. 34(3), pages 663-676.
    10. Sun, Chuanwang & Lin, Boqiang, 2013. "Reforming residential electricity tariff in China: Block tariffs pricing approach," Energy Policy, Elsevier, vol. 60(C), pages 741-752.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    2. Timothy Christensen & Stan Hurn & Kenneth Lindsay, 2009. "It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-48.
    3. Joanna Janczura, 2012. "Pricing electricity derivatives within a Markov regime-switching model," Papers 1203.5442, arXiv.org.
    4. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    5. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    6. Antonio Bello & Javier Reneses & Antonio Muñoz, 2016. "Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case," Energies, MDPI, vol. 9(3), pages 1-27, March.
    7. Adam Clements & Joanne Fuller & Stan Hurn, 2013. "Semi-parametric Forecasting of Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 89(287), pages 508-521, December.
    8. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    9. Bannör, Karl & Kiesel, Rüdiger & Nazarova, Anna & Scherer, Matthias, 2016. "Parametric model risk and power plant valuation," Energy Economics, Elsevier, vol. 59(C), pages 423-434.
    10. Mayer, Klaus & Trück, Stefan, 2018. "Electricity markets around the world," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 77-100.
    11. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    12. Clements, A.E. & Herrera, R. & Hurn, A.S., 2015. "Modelling interregional links in electricity price spikes," Energy Economics, Elsevier, vol. 51(C), pages 383-393.
    13. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2012. "Forecasting spikes in electricity prices," International Journal of Forecasting, Elsevier, vol. 28(2), pages 400-411.
    14. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    15. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    16. Jun Maekawa & Koji Shimada, 2019. "A Speculative Trading Model for the Electricity Market: Based on Japan Electric Power Exchange," Energies, MDPI, vol. 12(15), pages 1-15, July.
    17. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    18. Marcelo G. Figueroa, 2006. "Pricing Multiple Interruptible-Swing Contracts," Birkbeck Working Papers in Economics and Finance 0606, Birkbeck, Department of Economics, Mathematics & Statistics.
    19. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    20. Michel Culot & Valérie Goffin & Steve Lawford & Sébastien de Meten & Yves Smeers, 2013. "Practical stochastic modelling of electricity prices," Post-Print hal-01021603, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1025-1033. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.