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Federico Quaglia
Terna Rete Italia SpA Dispacciamento e Conduzione, 00144 Roma, Italy

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Journal article
Published: 14 July 2021 in Energies
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In this paper an optimization problem designed to calculate electric grid specific indicators to be used within model-based methodologies for the definition of alternative electricity market bidding zone configurations is designed. The approach integrates within the framework of a bidding zone review process aligned to the specifications of the Commission Regulation (EU) 2015/1222 (CACM) and Regulation (EU) 2019/943 of the European Parliament and of the Council (CEP). The calculated solution of the optimization provides locational marginal prices and allows to determine, outside the optimization problem, the power transfer distribution factors for critical elements. Both indicators can be used as inputs by specially designed clustering algorithms to identify model-based electricity market bidding zone configurations, as alternative to the current experience-based configurations. The novelty of the optimization problem studied in this paper consists in integrating the N-1 security criteria for transmission network operation in an explicit manner, rather than in a simplified and inaccurate manner, as encountered in the literature. The optimization problem is evaluated on a set of historical and significant operating scenarios of the Italian transmission network, carefully selected by the Italian transmission system operator. The results show the optimization problem capability to produce insightful results for supporting a bidding zone review process and its advantages with respect to simplified methodologies encountered in the literature.

ACS Style

Cristian Bovo; Valentin Ilea; Enrico Carlini; Mauro Caprabianca; Federico Quaglia; Luca Luzi; Giuseppina Nuzzo. Optimal Computation of Network Indicators for Electricity Market Bidding Zones Configuration Considering Explicit N-1 Security Constraints. Energies 2021, 14, 4267 .

AMA Style

Cristian Bovo, Valentin Ilea, Enrico Carlini, Mauro Caprabianca, Federico Quaglia, Luca Luzi, Giuseppina Nuzzo. Optimal Computation of Network Indicators for Electricity Market Bidding Zones Configuration Considering Explicit N-1 Security Constraints. Energies. 2021; 14 (14):4267.

Chicago/Turabian Style

Cristian Bovo; Valentin Ilea; Enrico 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 14, no. 14: 4267.

Journal article
Published: 12 May 2021 in Energies
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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 (LMPs) and the Power Transfer Distribution Factors (PTDFs); (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.

ACS Style

Pietro Colella; Andrea Mazza; Ettore Bompard; Gianfranco Chicco; Angela Russo; Enrico Carlini; Mauro Caprabianca; Federico Quaglia; Luca Luzi; Giuseppina Nuzzo. Model-Based Identification of Alternative Bidding Zones: Applications of Clustering Algorithms with Topology Constraints. Energies 2021, 14, 2763 .

AMA Style

Pietro Colella, Andrea Mazza, Ettore Bompard, Gianfranco Chicco, Angela Russo, Enrico Carlini, Mauro Caprabianca, Federico Quaglia, Luca Luzi, Giuseppina Nuzzo. Model-Based Identification of Alternative Bidding Zones: Applications of Clustering Algorithms with Topology Constraints. Energies. 2021; 14 (10):2763.

Chicago/Turabian Style

Pietro Colella; Andrea Mazza; Ettore Bompard; Gianfranco Chicco; Angela Russo; Enrico 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 14, no. 10: 2763.

Review article
Published: 19 November 2020 in Journal of Environmental Economics and Management
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The negative demand shock due to the COVID-19 lockdown has reduced net demand for electricity—system demand less amount of energy produced by intermittent renewables, hydroelectric units, and net imports—that must be served by controllable generation units. Under normal demand conditions, introducing additional renewable generation capacity reduces net demand. Consequently, the lockdown can provide insights about electricity market performance with a large share of renewables. We find that although the lockdown reduced average day-ahead prices in Italy by 45%, re-dispatch costs increased by 73%, both relative to the average of the same magnitude for the same period in previous years. We estimate a deep-learning model using data from 2017 to 2019 and find that predicted re-dispatch costs during the lockdown period are only 26% higher than the same period in previous years. We argue that the difference between actual and predicted lockdown period re-dispatch costs is the result of increased opportunities for suppliers with controllable units to exercise market power in the re-dispatch market in these persistently low net demand conditions. Our results imply that without grid investments and other technologies to manage low net demand conditions, an increased share of intermittent renewables is likely to increase costs of maintaining a reliable grid.

ACS Style

Christoph Graf; Federico Quaglia; Frank A. Wolak. (Machine) learning from the COVID-19 lockdown about electricity market performance with a large share of renewables. Journal of Environmental Economics and Management 2020, 105, 102398 .

AMA Style

Christoph Graf, Federico Quaglia, Frank A. Wolak. (Machine) learning from the COVID-19 lockdown about electricity market performance with a large share of renewables. Journal of Environmental Economics and Management. 2020; 105 ():102398.

Chicago/Turabian Style

Christoph Graf; Federico Quaglia; Frank A. Wolak. 2020. "(Machine) learning from the COVID-19 lockdown about electricity market performance with a large share of renewables." Journal of Environmental Economics and Management 105, no. : 102398.

Journal article
Published: 06 June 2020 in Energies
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Over the last years, power systems around the globe experienced deep changes in their operation, mainly induced by the widespread of Intermittent Renewable Energy Sources (IRES). These changes involved a review of market and operational rules, in the direction of a stronger integration. At European level, this integration is in progress, driven by the new European guidelines and network codes, which deal with multiple issues, from market design to operational security. In this framework, the project TERRE (Trans European Replacement Reserve Exchange) is aimed at the realization of a European central platform, called LIBRA, for the exchange of balancing resources and, in particular, for the activation of the procured Replacement Reserve (RR) resources. The Italian Transmission System Operator (TSO), TERNA, is a participant of the project and it is testing new methodologies for the sizing of RR and its required activation throughout the TERRE process. The aim of the new methodologies is to find areas of potential improvement in the sizing of RR requirements and activation, which open up the possibility for a reduction of the procurement cost, without endangering the security of the power system. This paper describes a new RR sizing methodology, proposed by TERNA, which is based on a persistence method, showing its results on real data and highlighting key advantages and potential limitations of this approach. In order to overcome these limitations, a literature review on alternative approaches has been carried out, identifying nowcasting techniques as a relevant alternative for the very short term forecast horizon. These one could be further investigated and tested in the future, using the proposed persistence method as a benchmark.

ACS Style

Mauro Caprabianca; Maria Carmen Falvo; Lorenzo Papi; Lucrezia Promutico; Viviana Rossetti; Federico Quaglia. Replacement Reserve for the Italian Power System and Electricity Market. Energies 2020, 13, 2916 .

AMA Style

Mauro Caprabianca, Maria Carmen Falvo, Lorenzo Papi, Lucrezia Promutico, Viviana Rossetti, Federico Quaglia. Replacement Reserve for the Italian Power System and Electricity Market. Energies. 2020; 13 (11):2916.

Chicago/Turabian Style

Mauro Caprabianca; Maria Carmen Falvo; Lorenzo Papi; Lucrezia Promutico; Viviana Rossetti; Federico Quaglia. 2020. "Replacement Reserve for the Italian Power System and Electricity Market." Energies 13, no. 11: 2916.