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Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market

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  • Kun Li

    (Business School, Beijing Normal University, Beijing 100875, China)

  • Joseph D. Cursio

    (Stuart School of Business, Illinois Institute of Technology, Chicago, IL 60661, USA
    Goodwin School of Business, Benedictine University, Lisle, IL 60532, USA)

  • Yunchuan Sun

    (Business School, Beijing Normal University, Beijing 100875, China)

Abstract

Large price fluctuations have become a significant character and impede resource allocation in the electricity market. Negative prices and peak load spike prices coexist and represent over-supply and over-demand, respectively. It is important to interpret the impact of these extreme prices on sustainable power management from the perspective of economics. In this paper, we build a principal component analysis (PCA) to assess the impact of the two opposite phenomena on the smart grid electricity system. We perform a big-data study using intra-day data from the Pennsylvania, New Jersey, and Maryland (PJM) electricity system with over 11,000 transmission lines. As the contribution, this paper (1) measures the price fluctuations from the perspective of economics, (2) captures and observes the full-length behavior of negative and spike pricing in a modern smart grid system with multi-transmission lines and high-frequency price updates, and (3) employs methods with distinctive advantages to bring more in-depth findings to interpret the smart grid system. We find that spike prices hold the principal explanatory power for electricity market fluctuation in all the transmission lines. The results are consistent with previous studies about resolutions such as electrical energy storage, transmission capacity upgrade, and demand response.

Suggested Citation

  • Kun Li & Joseph D. Cursio & Yunchuan Sun, 2018. "Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4019-:d:180103
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    References listed on IDEAS

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    2. Khowaja, Kainat & Shcherbatyy, Mykhaylo & Härdle, Wolfgang Karl, 2021. "Surrogate Models for Optimization of Dynamical Systems," IRTG 1792 Discussion Papers 2021-001, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Li, Kun & Cursio, Joseph D. & Jiang, Mengfei & Liang, Xi, 2019. "The significance of calendar effects in the electricity market," Applied Energy, Elsevier, vol. 235(C), pages 487-494.
    4. Joseph Nyangon & Ruth Akintunde, 2024. "Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 13(1), January.
    5. Johannes Kaufmann & Philipp Artur Kienscherf & Wolfgang Ketter, 2020. "Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios," Energies, MDPI, vol. 13(14), pages 1-19, July.

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