Optimal Computation of Network Indicators for Electricity Market Bidding Zones Configuration

The paper presents an optimization model to compute network indicators for a model-based approach to define alternative bidding zone configurations in the framework of a Bidding Zone Review process compliant with the Commission Regulation (EU) 2015/1222 (CACM) and Regulation (EU) 2019/943 of the European Parliament and of the Council (CEP). The model is used to compute Locational Marginal Prices and, in the optimal point, Power Transfer Distribution Factors for critical elements. These indicators can be processed by clustering algorithms to identify alternative bidding zone configurations. The proposed model considers explicitly the N-1 security criteria (largely neglected or very simplified in literature) for transmission system operation. The algorithm is tested on relevant historical operating scenarios of the Italian transmission network, showing the model’s ability to both provide results supporting the existing bidding zone configuration, as well as suggesting interesting alternatives.

[1]  Valentin Ilea,et al.  Impact of the price coupling of regions project on the day-ahead electricity market in Italy , 2017, 2017 IEEE Manchester PowerTech.

[2]  Andrea Mazza,et al.  Applications of Clustering Techniques to the Definition of the Bidding Zones , 2019, 2019 54th International Universities Power Engineering Conference (UPEC).

[3]  Valentin Ilea,et al.  European day-ahead electricity market coupling: Discussion, modeling, and case study , 2018 .

[4]  Yang Zhang,et al.  Overview of the Clustering Algorithms for the Formation of the Bidding Zones , 2019, 2019 54th International Universities Power Engineering Conference (UPEC).

[5]  J. Olsen,et al.  The European Commission , 2020, The European Union.

[6]  Hong Lam Le,et al.  Integrated European intra-day electricity market: Rules, modeling and analysis , 2019, Applied Energy.

[7]  Valentin Ilea,et al.  A Thorough Comparison Among Various Mathematical Approaches to Compute PUN in Italy , 2018, 2018 15th International Conference on the European Energy Market (EEM).

[8]  M. Enns,et al.  Fast Linear Contingency Analysis , 1982, IEEE Transactions on Power Apparatus and Systems.

[9]  Enrico Maria Carlini,et al.  Model-based Identification of Alternative Bidding Zone Configurations from Clustering Algorithms Applied on Locational Marginal Prices , 2020, 2020 55th International Universities Power Engineering Conference (UPEC).

[10]  Valentin Ilea,et al.  Optimal Bidding Zone Configuration: Investigation on Model-based Algorithms and their Application to the Italian Power System , 2019, 2019 AEIT International Annual Conference (AEIT).

[11]  Valentin Ilea,et al.  Review of the Mathematic Models to Calculate the Network Indicators to Define the Bidding Zones , 2019, 2019 54th International Universities Power Engineering Conference (UPEC).