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Model-Based Identification of Alternative Bidding Zones: Applications of Clustering Algorithms with Topology Constraints

Author

Listed:
  • Pietro Colella

    (ENSIEL-Politecnico di Torino Dipartimento Energia “Galileo Ferraris”, 10129 Torino, Italy)

  • Andrea Mazza

    (ENSIEL-Politecnico di Torino Dipartimento Energia “Galileo Ferraris”, 10129 Torino, Italy)

  • Ettore Bompard

    (ENSIEL-Politecnico di Torino Dipartimento Energia “Galileo Ferraris”, 10129 Torino, Italy)

  • Gianfranco Chicco

    (ENSIEL-Politecnico di Torino Dipartimento Energia “Galileo Ferraris”, 10129 Torino, Italy)

  • Angela Russo

    (ENSIEL-Politecnico di Torino Dipartimento Energia “Galileo Ferraris”, 10129 Torino, Italy)

  • Enrico Maria Carlini

    (Terna Rete Italia SpA Dispacciamento e Conduzione, 00144 Roma, Italy)

  • Mauro Caprabianca

    (Terna Rete Italia SpA Dispacciamento e Conduzione, 00144 Roma, Italy)

  • Federico Quaglia

    (Terna Rete Italia SpA Dispacciamento e Conduzione, 00144 Roma, Italy)

  • Luca Luzi

    (Terna Rete Italia SpA Dispacciamento e Conduzione, 00144 Roma, Italy)

  • Giuseppina Nuzzo

    (Terna Rete Italia SpA Dispacciamento e Conduzione, 00144 Roma, Italy)

Abstract

The definition of bidding zones is a relevant question for electricity markets. The bidding zones can be identified starting from information on the nodal prices and network topology, considering the operational conditions that may lead to congestion of the transmission lines. A well-designed bidding zone configuration is a key milestone for an efficient market design and a secure power system operation, being the basis for capacity allocation and congestion management processes, as acknowledged in the relevant European regulation. Alternative bidding zone configurations can be identified in a process assisted by the application of clustering methods, which use a predefined set of features, objectives and constraints to determine the partitioning of the network nodes into groups. These groups are then analysed and validated to become candidate bidding zones. The content of the manuscript can be summarized as follows: (1) A novel probabilistic multi-scenario methodology was adopted. The approach needs the analysis of features that are computed considering a set of scenarios defined from solutions in normal operation and in planned maintenance cases. The weights of the scenarios are indicated by TSOs on the basis of the expected frequency of occurrence; (2) The relevant features considered are the Locational Marginal Prices ( LMP s) and the Power Transfer Distribution Factors ( PTDF s); (3) An innovative computation procedure based on clustering algorithms was developed to group nodes of the transmission electrical network into bidding zones considering topological constraints. Several settings and clustering algorithms were tested in order to evaluate the robustness of the identified solutions.

Suggested Citation

  • Pietro Colella & Andrea Mazza & Ettore Bompard & Gianfranco Chicco & Angela Russo & Enrico Maria Carlini & Mauro Caprabianca & Federico Quaglia & Luca Luzi & Giuseppina Nuzzo, 2021. "Model-Based Identification of Alternative Bidding Zones: Applications of Clustering Algorithms with Topology Constraints," Energies, MDPI, vol. 14(10), pages 1-17, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2763-:d:552731
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    References listed on IDEAS

    as
    1. Paul L Joskow, 2019. "Challenges for wholesale electricity markets with intermittent renewable generation at scale: the US experience," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 35(2), pages 291-331.
    2. Frank A. Wolak, 2011. "Measuring the Benefits of Greater Spatial Granularity in Short-Term Pricing in Wholesale Electricity Markets," American Economic Review, American Economic Association, vol. 101(3), pages 247-252, May.
    3. Karl-Kiên Cao & Johannes Metzdorf & Sinan Birbalta, 2018. "Incorporating Power Transmission Bottlenecks into Aggregated Energy System Models," Sustainability, MDPI, vol. 10(6), pages 1-32, June.
    4. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
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    Cited by:

    1. Gianfranco Chicco & Andrea Mazza & Salvatore Musumeci & Enrico Pons & Angela Russo, 2022. "Editorial for the Special Issue “Verifying the Targets—Selected Papers from the 55th International Universities Power Engineering Conference (UPEC 2020)”," Energies, MDPI, vol. 15(15), pages 1-8, August.
    2. La Guardia, Marcello & D'Ippolito, Filippo & Cellura, Maurizio, 2022. "A GIS-based optimization model finalized to the localization of new power-to-gas plants: The case study of Sicily (Italy)," Renewable Energy, Elsevier, vol. 197(C), pages 828-835.
    3. Cristian Bovo & Valentin Ilea & Enrico Maria Carlini & Mauro Caprabianca & Federico Quaglia & Luca Luzi & Giuseppina Nuzzo, 2021. "Optimal Computation of Network Indicators for Electricity Market Bidding Zones Configuration Considering Explicit N-1 Security Constraints," Energies, MDPI, vol. 14(14), pages 1-31, July.

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