Overview of the Clustering Algorithms for the Formation of the Bidding Zones

This paper presents an overview of the concepts, features and methods used in model-based approaches for the determination of the bidding zones in multi-regional interconnected networks. The main aspects are discussed on the basis of a set of selected articles taken from the scientific literature. The solution schemes are mainly based on clustering algorithms. The main conclusions are that no prevailing solution emerges, and that further insights are needed, also with the incorporation of specific knowledge taken from the nature of the problem in the solution methods and indicators.

[1]  Rubén J. Sánchez-García,et al.  Hierarchical Spectral Clustering of Power Grids , 2014, IEEE Transactions on Power Systems.

[2]  Eduardo Cotilla-Sanchez,et al.  Multi-Attribute Partitioning of Power Networks Based on Electrical Distance , 2013, IEEE Transactions on Power Systems.

[3]  Hongming Yang,et al.  A RBFN hierarchical clustering based network partitioning method for zonal pricing , 2005, 2005 2nd International Conference on Electrical and Electronics Engineering.

[4]  Chongqing Kang,et al.  Zonal marginal pricing approach based on sequential network partition and congestion contribution identification , 2013 .

[5]  Albert Moser,et al.  Determination of alternative bidding areas based on a full nodal pricing approach , 2013, 2013 IEEE Power & Energy Society General Meeting.

[6]  Benjamin F. Hobbs,et al.  ATC-Based System Reduction for Planning Power Systems With Correlated Wind and Loads , 2015, IEEE Transactions on Power Systems.

[7]  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).

[8]  Sergio Ramos,et al.  A Data-Mining-Based Methodology for Transmission Expansion Planning , 2011, IEEE Intelligent Systems.

[9]  Marc Andreewsky,et al.  NETWORK MODELLING FOR CONGESTION MANAGEMENT : ZONAL REPRESENTATION VERSUS NODAL REPRESENTATION , 2005 .

[10]  M. Ilić,et al.  Implementation of Cluster-based Congestion Management Systems , 2000 .

[11]  Jin-Ho Kim,et al.  Zone Clustering LMP with Location information using an Improved Fuzzy C-Mean , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

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

[14]  Y.-y. Hong,et al.  Locational marginal price forecasting in deregulated electricity markets using artificial intelligence , 2002 .

[15]  Karol Wawrzyniak,et al.  The Scheme of a Novel Methodology for Zonal Division Based on Power Transfer Distribution Factors , 2014 .

[16]  Hongming Yang,et al.  Monte Carlo Simulation Based Price Zone Partitioning Considering Market Uncertainty , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[17]  B. Burstedde,et al.  From nodal to zonal pricing: A bottom-up approach to the second-best , 2012, 2012 9th International Conference on the European Energy Market.

[18]  Hongming Yang.  A NEW CLUSTERING METHOD FOR PARTITIONING PRICE ZONE IN POWER MARKET ENVIRONMENT , 2005 .

[19]  J.W. Bialek,et al.  Effectiveness of zonal congestion management in the European electricity market , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[20]  Karol Wawrzyniak,et al.  Division of the energy market into zones in variable weather conditions using Locational Marginal Prices , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[21]  Liu Kun,et al.  Dynamic locational marginal prices based zonal division in large-scale regional electricity markets , 2016 .

[22]  Karol Wawrzyniak,et al.  Are Locational Marginal Prices a Good Heuristic to Divide Energy Market into Bidding Zones? , 2015, 2015 12th International Conference on the European Energy Market (EEM).

[23]  Karol Wawrzyniak,et al.  The scheme of a novel methodology for zonal division based on power transfer distribution factors , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[24]  Zita Vale,et al.  Zonal prices analysis supported by a data mining based methodology , 2010, IEEE PES General Meeting.

[25]  Abouzar Estebsari,et al.  Creation of a computational framework for the European transmission grid with Power-to-Gas , 2019, 2019 54th International Universities Power Engineering Conference (UPEC).

[26]  Karl-Kiên Cao,et al.  Incorporating Power Transmission Bottlenecks into Aggregated Energy System Models , 2018, Sustainability.

[27]  Fengyu Wang,et al.  Reserve zone determination based on statistical clustering methods , 2012, 2012 North American Power Symposium (NAPS).

[28]  Albert Moser,et al.  Optimized bidding area delimitations and their impact on electricity markets and congestion management , 2014, 11th International Conference on the European Energy Market (EEM14).

[29]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..

[30]  Chin-Teng Lin,et al.  A review of clustering techniques and developments , 2017, Neurocomputing.

[31]  Christoph Weber,et al.  Consistent and robust delimitation of price zones under uncertainty with an application to Central Western Europe , 2018, Energy Economics.

[32]  Martin Hennebel,et al.  Redispatch index for assessing bidding zone delineation , 2017 .

[33]  Patrick Panciatici,et al.  Network partition based on critical branches for large-scale transmission expansion planning , 2015, 2015 IEEE Eindhoven PowerTech.

[34]  Matti Lehtonen,et al.  Clustering of Connection Points and Load Modeling in Distribution Systems , 2013, IEEE Transactions on Power Systems.

[35]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[36]  Dmitry V. Volodin,et al.  Clustering approach for determination of congestion zones on nodal electricity markets in long term periods , 2015, 2015 IEEE Eindhoven PowerTech.

[37]  Christoph Weber,et al.  Identifying price zones using nodal prices and supply & demand weighted nodes , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[38]  Karol Wawrzyniak,et al.  Decomposition of Power Flow Used for Optimizing Zonal Configurations of Energy Market , 2015, 2015 12th International Conference on the European Energy Market (EEM).

[39]  B. K. Panigrahi,et al.  Zonal price based clustering of bidding zones , 2016, 2016 IEEE 6th International Conference on Power Systems (ICPS).