A proposed model for determing bidding zones in the electricity market using location marginal prices and clustering algorithms
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Currently, electricity demand in Vietnam is rising rapidly due to industrialization, modernization, and population growth, creating risks of shortages, especially in the dry season when hydropower declines. To ensure supply, coal and oil generation is often dispatched, increasing costs and pollution. Renewable energy is expanding but faces issues of price volatility and supply reliability. A zonal electricity market design is therefore seen as a promising solution to optimize pricing, attract investment, and promote clean energy development, contributing to carbon reduction. This paper proposes a two-step zonal market model: first, node indices are computed under different scenarios using DC Optimal Power Flow (DC-OPF); second, nodes are grouped into bidding zones via Spectral Clustering. The model is tested on a 118-bus system with solar and rainy-day datasets to identify efficient and practical zonal configurations.
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[1] G. Chicco et al., "Overview of the Clustering Algorithms for the Formation of the Bidding Zones”. 2019 54th International Universities Power Engineering Conference (UPEC). IEEE, 2019.
[2] F. C. Schweppe, M. C. Caramanis, R. D. Tabors, R. E. Bohn, Spot Pricing of Electricity, Kluwer Academic Publishers, 1988.
[3] L. Michi et al., “Optimal Bidding Zone Configuration: Investigation on Model-based Algorithms and their Application to the Italian Power System”, Proceedings of the 111th Annual AEIT International Conference, Florence, Italy, 18–20 Sept. 2019.
[4] C. W. Tan, D. W. H. Cai, and Xin Lou, "DC optimal power flow: Uniqueness and algorithms", 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), Tainan, 2012, pp. 641–646, doi: 10.1109/SmartGridComm.2012.6486058.
[5] B. Chong, "K-means clustering algorithm: a brief review", Data Science and Big Data Technology, Shanxi University of Finance and Economics, Taiyuan, Shanxi, ISSN 2616-5775, vol. 4, issue 5, pp. 37–40, DOI: 10.25236/AJCIS.2021.040506.
[6] C. Bovo, V. Ilea, E. M. Carlini, M. Caprabianca, F. Quaglia, L. Luzi, and G. Nuzzo, “Optimal computation of Network indicators for Electricity Market Bidding Zones configuration”, Proceedings of the 2020 55th International Universities Power Engineering Conference (UPEC), Torino, Italy, 1–9 September 2020, pp. 1–6.
[7] C. Bovo, V. Ilea, E. M. Carlini, M. Caprabianca, F. Quaglia, L. Luzi, and G. Nuzzo, “Optimal Computation of Network Indicators for Electricity Market Bidding Zones Configuration Considering Explicit N-1 Security Constraints”, Energies, vol. 14, art. 4267, 2021.
[8] L. H. Lam and V. D. T. An, “A Strategy to Identify Congestions of Transmission Networks in N-1 Contingency: Khanh Hoa Case Study”, The University of Danang - Journal of Science and Technology, vol. 21, no. 7, pp. 1–6, 2023.
[9] Y. M. Al-Abdullah and M. Sahraei-Ardakani, "Differences in locational marginal prices: Deterministic vs. stochastic market formulations", 2018 5th International Conference on Renewable Energy: Generation and Applications (ICREGA), Al Ain, United Arab Emirates, 2018, pp. 268–272, doi: 10.1109/ICREGA.2018.8337598.
[10] U. V. Luxburg, "A tutorial on spectral clustering", Stat Comput, vol. 17, pp. 395–416, 2007. https://doi.org/10.1007/s11222-007-9033-z.
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