Abstract
The future of the industry foresees the automation and allocation of more intelligence to processes. A revolution in relation to the present. With this, new challenges and consequently more complexity is added to the management of the sectors. In the electric sector is introduced the theme of the Smart grids and so all the concepts aggregated with it. The possibility of the existence of demand response programs and the expansion of the distributed generation units for small players are key concepts and with enormous influence in the management of the markets belonging to this sector. Thus, a method is proposed that would help manage these resources through their aggregation, opening a new port for business models based on this idea. The benefit will be to take advantage of a more effective and efficient way the energy potential present in each group that is formed. Thus, in this paper will be explored the potential of clustering methods for the aggregation of resources.
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References
Alahakoon, D., Yu, X.: Smart electricity meter data intelligence for future energy systems: a survey. IEEE Trans. Ind. Inform. 12(1), 425–436 (2016)
Faria, P., Vale, Z., Baptista, J.: Demand response programs design and use considering intensive penetration of distributed generation. Energies 8(6), 6230–6246 (2015)
Silva, C., Faria, P., Vale, Z.: Assessment of distributed generation units remuneration using different clustering methods for aggregation. In: IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, Aalborg, Denmark (2018)
Clastres, C.: Smart grids: another step towards competition, energy security and climate change objectives. Energy Policy 39(9), 5399–5408 (2011)
Deng, R., Yang, Z., Chow, M.-Y., Chen, J.: A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans. Ind. Inform. 11(3), 1 (2015)
Lin, S., Li, F., Tian, E., Fu, Y., Li, D.: Clustering load profiles for demand response applications. IEEE Trans. Smart Grid 3053(c), 1–9 (2017)
Faria, P., Spínola, J., Vale, Z.: Aggregation and remuneration of electricity consumers and producers for the definition of demand-response programs. IEEE Trans. Ind. Inform. 12(3), 952–961 (2016)
Zupancic, J., et al.: Market-based business model for flexible energy aggregators in distribution networks. In: International Conference European Energy Market, EEM (2017)
Rezania, R., Prüggler, W.: Business models for the integration of electric vehicles into the Austrian energy system. In: 9th International Conference European Energy Market, EEM 2012, pp. 1–8 (2012)
Ilieva, I., Rajasekharan, J.: Energy storage as a trigger for business model innovation in the energy sector. In: 2018 IEEE International Energy Conference ENERGYCON 2018, pp. 1–6 (2018)
Zhang, Q., Zhang, J., Han, X., Jin, X., Fu, H., Zang, T.: Business model status of distributed power supply and its concerns in future development. In: 2017 IEEE Conference on Energy Internet and Energy System Integration, EI2 2017 - Proceedings, vol. 2018, pp. 1–5 (2018)
Silva, C., Faria, P., Vale, Z.: Classification Approaches to Foster the Use of Distributed Generation with Improved Remuneration (2013)
Morissette, L., Chartier, S.: The k-means clustering technique: general considerations and implementation in Mathematica. Tutor. Quant. Methods Psychol. 9(1), 15–24 (2013)
Kassambara, A.: Practical Guide to Cluster Analysis in R, 1st ed
Saket, S.J., Pandya, S.: An overview of partitioning algorithms in clustering techniques. Int. J. Adv. Res. Comput. Eng. Technol. 5(6) (2016)
Jyoti Bora, D., Kumar Gupta, A.: A comparative study between fuzzy clustering algorithm and hard clustering algorithm. Int. J. Comput. Trends Technol. 10(2), 108–113 (2014)
Wirasanti, P., Ortjohann, E., Schmelter, A., Morton, D.: Clustering power systems strategy the future of distributed generation. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 679–683 (2012)
Acknowledgments
The present work was done and funded in the scope of the following projects: CONTEST Project (P2020 - 23575), and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.
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Silva, C., Faria, P., Vale, Z. (2020). Clustering Support for an Aggregator in a Smart Grid Context. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_16
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DOI: https://doi.org/10.1007/978-3-030-14347-3_16
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