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

Pietro Colella, Andrea Mazza, Ettore Bompard, Gianfranco Chicco, Angela Russo, Enrico Maria Carlini, Mauro Caprabianca, Federico Quaglia, Luca Luzi and Giuseppina Nuzzo
Additional contact information
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

Energies, 2021, vol. 14, issue 10, 1-17

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.

Keywords: bidding zones; clustering; locational marginal prices; power transfer distribution factors; weighted scenarios (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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