Improving the Effect of Electric Vehicle Charging on Imbalance Index in the ‎Unbalanced Distribution Network Using Demand Response Considering Data ‎Mining Techniques

Document Type : Research paper

Authors

1 Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

2 Northumbria University, Electrical Power and Control Systems Research Group, Ellison Place NE1 8ST, Newcastle ‎upon Tyne, United Kingdom

Abstract

With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25% and 75% for electric vehicles in the network, charging\discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time.

Keywords

References

  1. Singh and A. Yassine, “Big data mining of energy time series for behavioral analytics and energy consumption forecasting”, Energies, vol. 11, 2018.
  2. Ruiz et al, “A time-series clustering methodology for knowledge extraction in energy consumption data”, Expert Syst. Appl., vol. 160, p. 113731, 2020.
  3. Saas, A. Guitart, and A. Perianez, “Discovering playing patterns: Time series clustering of free-to-play game data”, IEEE Conf. Comput. Intell. Games, 2016.
  4. Räsänen and M. Kolehmainen, “Feature-based clustering for electricity use time series data”, Lecture Notes Comput. Sci., 2009.
  5. Wen et al, “Stock market trend prediction using high-order information of time series”, IEEE Access, vol. 7, pp. 28299-308, 2019.
  6. Wismüller et al., “Cluster analysis of biomedical image time-series”, Int. J. Comput. Vis., vol. 46, no. 2, pp. 103-28, 2002.
  7. Kurbalija et al, “Time-series mining in a psychological domain”, ACM Int. Conf. Proc. Series, pp. 58-63, 2012.
  8. Gopalapillai, D. Gupta, and T. Sudarshan, “Experimentation and analysis of time series data for rescue robotics”, Adv. Intell. Syst. Comput., vol. 235, pp. 443-53, 2014.
  9. Fong, “Using hierarchical time series clustering algorithm and wavelet classifier for biometric voice classification”, J. Biomed. Biotechnol., vol. 2012, 2012.
  10. Subhani et al, “Multiple gene expression profile alignment for microarray time-series data clustering”, Bioinformatics, vol. 27, no. 13, pp. 2281-88, 2011.
  11. Scotto, A. Alonso, and S. Barbosa, “Clustering time series of sea levels: Extreme value approach”, J. Waterw. Port, Coast. Ocean Eng., vol. 136, no. 4, pp. 215-25, 2010.
  12. Crozier, D. Apostolopoulou, and M. McCulloch, “Clustering of usage profiles for electric vehicle behaviour analysis”, 2018 IEEE PES Innov. Smart Grid Tech. Conf. Europe, 2018.
  13. Sfetsos and C. Siriopoulos, “Time series forecasting with a hybrid clustering scheme and pattern recognition”, IEEE Trans. Syst. Man, Cybern. Part ASystems Humans., vol. 34, no. 3, pp. 399-405, 2004.
  14. He et al, “A new method for abrupt dynamic change detection of correlated time series”, Int. J. Climatol., vol. 32, no. 10, pp. 1604-14, 2012.
  15. Yang et al., “k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement”, Energy Build., vol. 146, pp. 27-37, 2017.
  16. Penangsang, D. Putra, and T. Kurniawan, “Optimal placement and sizing of distributed generation in radial distribution system using K-means clustering method”, 2017 Int. Seminar Intell. Tech. Its Appl., 2017.
  17. Ji et al, “Clustering algorithm of power load curves in distribution network based on analysis of matrix characteristic roots”, Asia-Pacific Power Energy Eng. Conf., 2016.
  18. Liu et al., “Hybrid Features based k-means clustering algorithm for use in electricity customer load pattern analysis”, Chinese Control Conf., 2018.
  19. Kim et al, “Binary decision tree using k-means and genetic algorithm for recognizing defect patterns of cold mill strip”, pp. 341-350, 2004.
  20. Yousefi, S. Gholamian, and A. Zakariazadeh, “Distributed voltage control in distribution networks with high penetration of photovoltaic systems”, J. Oper. Autom. Power Eng., vol. 8, no. 2, pp. 164-71, 2020.
  21. Shayeghi and M. Alilou, “Multi-objective demand side management to improve economic and ‎environmental issues of a smart microgrid ‎”, J. Oper. Autom. Power Eng., vol. 9, no. 3, pp. 182-92, 2021.
  22. Shahriyari and H. Khoshkhoo, “A deep learning-based approach for comprehensive rotor angle stability ‎assessment ‎”, J. Oper. Autom. Power Eng., vol. 10, no. 2, pp. 105-12, 2022.
  23. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. Elsevier Inc., 2012.
  24. García Márquez, I. Segovia Ramírez, and A. Pliego Marugán, “Decision making using logical decision tree and binary decision diagrams: a real case study of wind turbine manufacturing”, Energies, vol. 12, no. 9, 2019.
  25. Tavakoli Bina and A. Kashefi Kaviani, “Erratum to ‘Three-phase unbalance of distribution systems: Complementary analysis and experimental case study”, Int. J. Electr. Power Energy Syst., vol. 35, no. 1, p. 201, 2012.
  26. Niromandfam, A. Yazdankhah, and R. Kazemzadeh, “Modeling demand response based on utility function considering wind profit maximization in the day-ahead market”, J. Clean. Prod., vol. 251, p. 119317, 2020.
  27. Baherifard et al., “Intelligent charging planning for electric vehicle commercial parking lots and its impact on distribution network’s imbalance indices”, Sustain. Energy, Grids Net., vol. 30, p. 100620, 2022.
Journal of Operation and Automation in Power Engineering
Volume 11, Issue 3
October 2023
Pages 182-192

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History

  • Receive Date: 12 July 2021
  • Revise Date: 03 December 2021
  • Accept Date: 02 February 2022

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