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Extreme prices in electricity balancing markets from an approach of statistical physics

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  • Mureddu, Mario
  • Meyer-Ortmanns, Hildegard

Abstract

An increase in energy production from renewable energy sources is viewed as a crucial achievement in most industrialized countries. The higher variability of power production via renewables leads to a rise in ancillary service costs over the power system, in particular costs within the electricity balancing markets, mainly due to an increased number of extreme price spikes. This study analyzes the impact of an increased share of renewable energy sources on the behavior of price and volumes of the Italian balancing market. Starting from configurations of load and power production, which guarantee a stable performance, we implement fluctuations in the load and in renewables; in particular we artificially increase the contribution of renewables as compared to conventional power sources to cover the total load. We then determine the amount of requested energy in the balancing market and its fluctuations, which are induced by production and consumption. Within an approach of agent-based modeling we estimate the resulting energy prices and costs. While their average values turn out to be only slightly affected by an increased contribution from renewables, the probability for extreme price events is shown to increase along with undesired peaks in the costs. Our methodology provides a tool for estimating outliers in prices obtained in the energy balancing market, once data of consumption, production and their typical fluctuations are provided.

Suggested Citation

  • Mureddu, Mario & Meyer-Ortmanns, Hildegard, 2018. "Extreme prices in electricity balancing markets from an approach of statistical physics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1324-1334.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1324-1334
    DOI: 10.1016/j.physa.2017.09.001
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    References listed on IDEAS

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    1. Mario Mureddu & Guido Caldarelli & Alessandro Chessa & Antonio Scala & Alfonso Damiano, 2015. "Green Power Grids: How Energy from Renewable Sources Affects Networks and Markets," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-15, September.
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    7. Lun, Isaac Y.F & Lam, Joseph C, 2000. "A study of Weibull parameters using long-term wind observations," Renewable Energy, Elsevier, vol. 20(2), pages 145-153.
    8. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
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    Cited by:

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    2. Emilio Ghiani & Marco Galici & Mario Mureddu & Fabrizio Pilo, 2020. "Impact on Electricity Consumption and Market Pricing of Energy and Ancillary Services during Pandemic of COVID-19 in Italy," Energies, MDPI, vol. 13(13), pages 1-19, July.
    3. Marco Galici & Mario Mureddu & Emilio Ghiani & Fabrizio Pilo, 2022. "Blockchain-Based Hardware-in-the-Loop Simulation of a Decentralized Controller for Local Energy Communities," Energies, MDPI, vol. 15(20), pages 1-25, October.
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    5. Su, Yufei & Kern, Jordan D. & Reed, Patrick M. & Characklis, Gregory W., 2020. "Compound hydrometeorological extremes across multiple timescales drive volatility in California electricity market prices and emissions," Applied Energy, Elsevier, vol. 276(C).

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