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Electricity Market Structure and Pricing Analyses

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Optimization Methods and Applications

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 130))

Abstract

In this chapter, we provide an overview of the electricity market structure and discuss its characteristics. We also survey the regulation policies on electricity prices and the existing price forecasting techniques in a market-driven electricity industry. The complex nature of electricity markets makes it difficult to design optimal policies for the policy makers. It also makes it challenging for market participants to conduct price forecasting. Additionally, the dynamic nature of the electricity markets creates strong demand for researchers to come up with a more accurate prediction.

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Notes

  1. 1.

    Electricity market restructuring started in Australia, Chile, and the U.K. in the 1980s, followed by a larger-scale liberalization in the European Union and the U.S. in the 1990s.

  2. 2.

    Additional costs may arise if transmission congestion occurs. In such cases, the locational marginal price (LMP) or the zonal market clearing price (ZMCP) mechanisms may be implemented. LMP is defined as specific “network buses” while ZMCP is defined by different regions (zones) which can include many “network buses.” Both the LMP and the ZMCP incorporate the additional transmission congestion costs to determine prices.

  3. 3.

    Despite concerns over the growing retail tariff deficit due to suboptimal rates, the Spanish government imposed price controls that restricted the annual rate increases to be below 1.4%. Since 2000, the retail tariff revenues have been too small to recover the costs of power network operation creating large government deficits which are estimated to be 26.9 Billion euros [16].

  4. 4.

    Several states in the U.S. implemented retail rate controls to reduce electricity prices by 3–20% followed by a rate freeze during the electricity market restructuring. These price controls were motivated by concerns of the market power in the wholesale and retail sectors during the transition period [28]. In the electricity crisis in California, for example, an unexpected rise in natural gas prices caused the equilibrium electricity wholesale prices to exceed the mandated retail prices by up to 500% [12]. Retail price controls, therefore, are cited as one of the factors that led to the 2000–2001 California electricity crisis [15].

  5. 5.

    A covariance-stationary process is referred to as a weakly stationary process. A strong stationary process can have a finite mean and/or variance.

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Acknowledgements

Work of P.M. Pardalos is partially supported by the Paul and Heidi Brown Preeminent Professorship at ISE, University of of Florida.

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Authors and Affiliations

  1. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611, USA

    Panos M. Pardalos & Anil Singh

  2. Sport Management, School of Kinesiology, University of Michigan, Ann Arbor, MI, 48109, USA

    Wenche Wang

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  1. Panos M. Pardalos
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  2. Anil Singh
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  3. Wenche Wang
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Correspondence to Wenche Wang .

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Editors and Affiliations

  1. Industrial & Systems Engineering, Texas A&M University, College Station, Texas, USA

    Sergiy Butenko

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  3. Methods of Discrete Optimization, V.M. Glushkov Institute of Cybernetics, Kyiv, Ukraine

    Volodymyr Shylo

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Pardalos, P.M., Singh, A., Wang, W. (2017). Electricity Market Structure and Pricing Analyses. In: Butenko, S., Pardalos, P., Shylo, V. (eds) Optimization Methods and Applications . Springer Optimization and Its Applications, vol 130. Springer, Cham. https://doi.org/10.1007/978-3-319-68640-0_18

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