Abstract
With the recent liberalization of electricity markets, market players need to decide whether to and how to participate in each electricity market type that is available to them. The search for the best opportunities to sell or buy the required energy is, however, not an easy task. Moreover, the changes that electricity markets are constantly suffering make this an highly dynamic environment, with huge associated unpredictability. Decision support tools become, therefore, essential for market players to be able to take the best advantage from market participation. This paper proposes a methodology to estimate the expected prices of bilateral contracts based on the analysis of contracts’ historic log. The proposed method is based on the application of a clustering methodology that groups the historic contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts log.
This work is supported by FEDER Funds through COMPETE program and by National Funds through FCT under the projects FCOMP-01-0124-FEDER: PEst-OE/EEI/UI0760/2015, PTDC/EEA-EEL/122988/2010 and SFRH/BD/80632/2011 (Tiago Pinto PhD).
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References
Shahidehpour, M., et al.: Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, pp. 233–274. Wiley-IEEE Press, New York (2002)
Meeus, L., et al.: Development of the internal electricity market in Europe. Electr. J. 18(6), 25–35 (2005)
Li, H., Tesfatsion, L.: Development of open source software for power market research: the AMES test bed. J. Energy Markets 2(2), 111–128 (2009)
Koritarov, V.: Real-world market representation with agents: modeling the electricity market as a complex adaptive system with an agent-based approach. IEEE Power Energy Mag. 2, 39–46 (2004)
Praça, I., et al.: MASCEM: a multi-agent system that simulates competitive electricity markets. IEEE Intell. Syst. 18(6), 54–60 (2003)
Pinto, T., et al.: Adaptive learning in agents behaviour: a framework for electricity markets simulation. Integr. Comput. Aided Eng. 21(4), 399–415 (2014)
Pinto, T., Vale, Z., Sousa, T.M., Sousa, T., Morais, H., Praça, I.: Particle swarm optimization of electricity market negotiating players portfolio. In: Corchado, J.M., et al. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 273–284. Springer, Heidelberg (2014)
Pinto, T.: Bid definition method for electricity markets based on an adaptive multiagent system. In: Demazeau, Y., Pěchoucěk, M., Corchado, J.M., Pérez, J.B. (eds.) PAAMS 2014. AISC, vol. 88, pp. 309–316. Springer, Heidelberg (2011)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31(8), 651–666 (2010)
Jiawei, H., Kamber, M.: Data Mining: Concepts and Techniques, p. 800. Morgan Kaufmann, San Francisco (2006)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Dent, I., Craig, T., Aickelin, U., Rodden, T.: Variability of behaviour in electricity load profile clustering; who does things at the same time each day? In: Perner, P. (ed.) ICDM 2014. LNCS, vol. 8557, pp. 70–84. Springer, Heidelberg (2014)
Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1–27 (1974)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 1(2), 224–227 (1979)
Kaufman, L., Rouseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Hoboken (1990)
Dianhui, W., Tapan, S.: A robust elicitation algorithm for discovering DNA motifs using fuzzy self-organizing maps. IEEE Trans. Neural Netw. Learn. Sys. 24(10), 1677–1688 (2013)
Gladun, V., Yadid-pecht, O.: Information technologies and knowledge. Int. J. 5(1), 2 (2011)
Hu, X.B., Wang, M., Di Paolo, E.: Calculating complete and exact pareto front for multiobjective optimization: a new deterministic approach for discrete problems. IEEE Trans. Cybern. 43(3), 1088–1101 (2013)
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Faia, R., Pinto, T., Vale, Z. (2015). Dynamic Fuzzy Estimation of Contracts Historic Information Using an Automatic Clustering Methodology. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection. PAAMS 2015. Communications in Computer and Information Science, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-19033-4_23
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DOI: https://doi.org/10.1007/978-3-319-19033-4_23
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