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Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition

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  • Elmore, Clay T.
  • Dowling, Alexander W.

Abstract

Energy markets balance electricity generation (supply) and demand while ensuring non-discriminatory access. Understanding energy market dynamics is essential to improving grid efficiency and optimizing the development of new energy conversion and storage technologies. Accurate energy price forecasts are important for many energy storage technologies to be profitable from price arbitrage. In this paper, we apply Dynamic Mode Decomposition (DMD), a popular spatial-temporal reduced-form modeling technique, to forecast 6587 locational marginal prices in the California Day-Ahead Market (DAM). DMD is a promising equation-free modeling technique in systems with inherent periodic tendencies in time such as financial markets and fluid dynamics. Yet we show, for the first time, that DMD cannot reliably forecast day-ahead energy prices due to the periodicity of the data. Instead, we show Augmented DMD (ADMD) overcomes these limitations and is a fast and accurate price forecaster. We benchmark DMD, ADMD, and backcasting forecasting methods for optimal price arbitrage with an energy storage system. Using ADMD, a market-connected energy storage system can capture up to 92% of allowable revenues in rolling horizon simulations. Moreover, ADMD is up to 1000-times faster than time-series forecasting methods and requires orders of magnitude less data than deep/machine learning techniques.

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  • Elmore, Clay T. & Dowling, Alexander W., 2021. "Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition," Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:energy:v:232:y:2021:i:c:s0360544221012615
    DOI: 10.1016/j.energy.2021.121013
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    1. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    2. Zhang, Jinliang & Tan, Zhongfu & Wei, Yiming, 2020. "An adaptive hybrid model for short term electricity price forecasting," Applied Energy, Elsevier, vol. 258(C).
    3. Bhar, Ramaprasad & Colwell, David B. & Xiao, Yuewen, 2013. "A jump diffusion model for spot electricity prices and market price of risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3213-3222.
    4. Dowling, Alexander W. & Kumar, Ranjeet & Zavala, Victor M., 2017. "A multi-scale optimization framework for electricity market participation," Applied Energy, Elsevier, vol. 190(C), pages 147-164.
    5. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    6. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    7. 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.
    8. 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.
    9. 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.
    10. Otashu, Joannah I. & Baldea, Michael, 2020. "Scheduling chemical processes for frequency regulation," Applied Energy, Elsevier, vol. 260(C).
    11. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    12. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    13. Ziel, Florian & Steinert, Rick, 2018. "Probabilistic mid- and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 251-266.
    14. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    15. Rafal Weron, 2014. "A review of electricity price forecasting: The past, the present and the future," HSC Research Reports HSC/14/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    16. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    17. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    18. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    19. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    20. Jordan Mann & J. Nathan Kutz, 2016. "Dynamic mode decomposition for financial trading strategies," Quantitative Finance, Taylor & Francis Journals, vol. 16(11), pages 1643-1655, November.
    21. Hua, Jia-Chen & Roy, Sukesh & McCauley, Joseph L. & Gunaratne, Gemunu H., 2016. "Using dynamic mode decomposition to extract cyclic behavior in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 172-180.
    22. Lizarraga-Garcia, Enrique & Ghobeity, Amin & Totten, Mark & Mitsos, Alexander, 2013. "Optimal operation of a solar-thermal power plant with energy storage and electricity buy-back from grid," Energy, Elsevier, vol. 51(C), pages 61-70.
    23. Cui, Ling-xiao & Long, Wen, 2016. "Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 498-508.
    24. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    25. Kost, Christoph & Flath, Christoph M. & Möst, Dominik, 2013. "Concentrating solar power plant investment and operation decisions under different price and support mechanisms," Energy Policy, Elsevier, vol. 61(C), pages 238-248.
    26. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
    27. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    28. Mitra, Sumit & Sun, Lige & Grossmann, Ignacio E., 2013. "Optimal scheduling of industrial combined heat and power plants under time-sensitive electricity prices," Energy, Elsevier, vol. 54(C), pages 194-211.
    29. Florian Ziel & Rick Steinert, 2017. "Probabilistic Mid- and Long-Term Electricity Price Forecasting," Papers 1703.10806, arXiv.org, revised May 2018.
    30. Zhang, Jinliang & Wei, Yiming & Tan, Zhongfu, 2020. "An adaptive hybrid model for short term wind speed forecasting," Energy, Elsevier, vol. 190(C).
    31. Walawalkar, Rahul & Apt, Jay & Mancini, Rick, 2007. "Economics of electric energy storage for energy arbitrage and regulation in New York," Energy Policy, Elsevier, vol. 35(4), pages 2558-2568, April.
    32. Fares, Robert L. & Meyers, Jeremy P. & Webber, Michael E., 2014. "A dynamic model-based estimate of the value of a vanadium redox flow battery for frequency regulation in Texas," Applied Energy, Elsevier, vol. 113(C), pages 189-198.
    33. Sioshansi, Ramteen & Denholm, Paul & Jenkin, Thomas & Weiss, Jurgen, 2009. "Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects," Energy Economics, Elsevier, vol. 31(2), pages 269-277, March.
    34. Luo, Shuman & Weng, Yang, 2019. "A two-stage supervised learning approach for electricity price forecasting by leveraging different data sources," Applied Energy, Elsevier, vol. 242(C), pages 1497-1512.
    35. Dowling, Alexander W. & Zheng, Tian & Zavala, Victor M., 2017. "Economic assessment of concentrated solar power technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1019-1032.
    36. Agrawal, Rahul Kumar & Muchahary, Frankle & Tripathi, Madan Mohan, 2019. "Ensemble of relevance vector machines and boosted trees for electricity price forecasting," Applied Energy, Elsevier, vol. 250(C), pages 540-548.
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