IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2763-d552731.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2763/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/10/2763/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Keppler, Jan Horst & Quemin, Simon & Saguan, Marcelo, 2022. "Why the sustainable provision of low-carbon electricity needs hybrid markets," Energy Policy, Elsevier, vol. 171(C).
    2. Gils, Hans Christian & Gardian, Hedda & Kittel, Martin & Schill, Wolf-Peter & Zerrahn, Alexander & Murmann, Alexander & Launer, Jann & Fehler, Alexander & Gaumnitz, Felix & van Ouwerkerk, Jonas & Bußa, 2022. "Modeling flexibility in energy systems — comparison of power sector models based on simplified test cases," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    3. Simshauser, Paul, 2024. "On static vs. dynamic line ratings in renewable energy zones," Energy Economics, Elsevier, vol. 129(C).
    4. Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
    5. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
    6. Karsten Neuhoff & Jörn C. Richstein & Mats Kröger, 2023. "Reacting to Changing Paradigms: How and Why to Reform Electricity Markets," DIW Berlin: Politikberatung kompakt, DIW Berlin, German Institute for Economic Research, volume 127, number pbk189, January.
    7. Mier, Mathias, 2021. "Efficient pricing of electricity revisited," Energy Economics, Elsevier, vol. 104(C).
    8. Lundin, Erik, 2022. "Geographic price granularity and investments in wind power: Evidence from a Swedish electricity market splitting reform," Energy Economics, Elsevier, vol. 113(C).
    9. Kwonsik Song & Kyle Anderson & SangHyun Lee & Kaitlin T. Raimi & P. Sol Hart, 2020. "Non-Invasive Behavioral Reference Group Categorization Considering Temporal Granularity and Aggregation Level of Energy Use Data," Energies, MDPI, vol. 13(14), pages 1-21, July.
    10. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
    11. Pollitt, M. G., 2023. "Locational Marginal Prices (LMPs) for Electricity in Europe? The Untold Story," Cambridge Working Papers in Economics 2352, Faculty of Economics, University of Cambridge.
    12. Villalobos, Cristian & Negrete-Pincetic, Matías & Figueroa, Nicolás & Lorca, Álvaro & Olivares, Daniel, 2021. "The impact of short-term pricing on flexible generation investments in electricity markets," Energy Economics, Elsevier, vol. 98(C).
    13. Brown, David P. & Sappington, David E. M., 2022. "The Impact of Wholesale Price Caps on Forward Contracting," Working Papers 2022-12, University of Alberta, Department of Economics.
    14. Hossain, Eklas & Roy, Shidhartho & Mohammad, Naeem & Nawar, Nafiu & Dipta, Debopriya Roy, 2021. "Metrics and enhancement strategies for grid resilience and reliability during natural disasters," Applied Energy, Elsevier, vol. 290(C).
    15. Martin Kueppers & Christian Perau & Marco Franken & Hans Joerg Heger & Matthias Huber & Michael Metzger & Stefan Niessen, 2020. "Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization," Energies, MDPI, vol. 13(16), pages 1-15, August.
    16. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    17. Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2020. "Electricity market integration, decarbonisation and security of supply: Dynamic volatility connectedness in the Irish and Great Britain markets," Energy Economics, Elsevier, vol. 92(C).
    18. Radulescu, Doina & Sulger, Philippe, 2022. "Interdependencies between countries in the provision of energy," Energy Economics, Elsevier, vol. 107(C).
    19. André Quites Ordovás Santos & Adriel Rodrigues da Silva & Jorge Javier Gimenez Ledesma & Adriano Batista de Almeida & Marco Roberto Cavallari & Oswaldo Hideo Ando Junior, 2021. "Electricity Market in Brazil: A Critical Review on the Ongoing Reform," Energies, MDPI, vol. 14(10), pages 1-23, May.
    20. Eicke, Anselm & Schittekatte, Tim, 2022. "Fighting the wrong battle? A critical assessment of arguments against nodal electricity prices in the European debate," Energy Policy, Elsevier, vol. 170(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2763-:d:552731. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.