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Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey

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  • Gokturk Poyrazoglu

    (Electrical & Electronics Engineering, Ozyegin University, Istanbul 34794, Turkey)

Abstract

In the electricity market, different pricing models can be applied to increase market competitiveness. Different electricity systems use different market structures. Uniform marginal pricing, zonal marginal pricing, and nodal marginal pricing methods are commonly used market structures. For markets wishing to move from a uniform pricing structure to a more competitive zonal pricing structure, the determination of price zones is critical for achieving a competitive market that generates accurate price signals. Three different pricing zone detection algorithms are analyzed in this paper including the k -means clustering and queen/rook spatially constraint clustering. Finally, the results of a case study for the Turkish electricity system are shared to compare each method.

Suggested Citation

  • Gokturk Poyrazoglu, 2021. "Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey," Energies, MDPI, vol. 14(4), pages 1-13, February.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1014-:d:499791
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    References listed on IDEAS

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    1. Shengkun Xie, 2019. "Defining Geographical Rating Territories in Auto Insurance Regulation by Spatially Constrained Clustering," Risks, MDPI, vol. 7(2), pages 1-20, April.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Zhengwei Qu & Hongwen Li & Yunjing Wang & Jiaxi Zhang & Ahmed Abu-Siada & Yunxiao Yao, 2020. "Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier," Energies, MDPI, vol. 13(8), pages 1-20, April.
    4. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
    5. Bae-Geun Lee & Joonwoo Lee & Soobae Kim, 2020. "Development of a Static Equivalent Model for Korean Power Systems Using Power Transfer Distribution Factor-Based k -Means++ Algorithm," Energies, MDPI, vol. 13(24), pages 1-12, December.
    6. Piotr F. Borowski, 2020. "Zonal and Nodal Models of Energy Market in European Union," Energies, MDPI, vol. 13(16), pages 1-21, August.
    7. Bublitz, Andreas & Keles, Dogan & Zimmermann, Florian & Fraunholz, Christoph & Fichtner, Wolf, 2019. "A survey on electricity market design: Insights from theory and real-world implementations of capacity remuneration mechanisms," Energy Economics, Elsevier, vol. 80(C), pages 1059-1078.
    8. Maher AbuBaker, 2019. "Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine," Energies, MDPI, vol. 12(22), pages 1-29, November.
    9. Robert Thorndike, 1953. "Who belongs in the family?," Psychometrika, Springer;The Psychometric Society, vol. 18(4), pages 267-276, December.
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    Cited by:

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    2. Hansol Shin & Tae Hyun Kim & Kyuhyeong Kwag & Wook Kim, 2021. "A Comparative Study of Pricing Mechanisms to Reduce Side-Payments in the Electricity Market: A Case Study for South Korea," Energies, MDPI, vol. 14(12), pages 1-19, June.
    3. Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.
    4. Štefan Bojnec, 2023. "Electricity Markets, Electricity Prices and Green Energy Transition," Energies, MDPI, vol. 16(2), pages 1-4, January.
    5. Samar Fatima & Verner Püvi & Ammar Arshad & Mahdi Pourakbari-Kasmaei & Matti Lehtonen, 2021. "Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks," Energies, MDPI, vol. 14(9), pages 1-23, April.

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