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Forecasting Available Demand-Side Flexibility

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Demand-side Flexibility in Smart Grid

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Abstract

The role of flexibility in increasing the efficiency and stability of the grid is an undeniable fact. In flexibility utilization, first step is known to be characterisation, meaning detrermining metrics and indices capable of describing and quantifying flexibility, next step would be forecasting available flexibility. Forecasting Demand-side flexibility refers to the actions which forecast the portion of demand in the system that is changeable or shiftable in response to the signals provided by different entities (e.g., HEMS, aggregator, system operator, etc).

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References

  1. H. Cai et al., Predicting the energy consumption of residential buildings for regional electricity supply-side and demand-side management. IEEE Access 7, 30386–30397 (2019)

    Article  Google Scholar 

  2. M. Ayar et al., A distributed control approach for enhancing smart grid transient stability and resilience. IEEE Trans. Smart Grid 8(6), 3035–3044 (2017)

    Article  Google Scholar 

  3. J. Silva et al., Estimating the active and reactive power flexibility area at the TSO-DSO interface. IEEE Trans. Power Syst. 33(5), 4741–4750 (2018)

    Article  Google Scholar 

  4. P. Kohlhepp et al., Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: a review of international field studies. Renew. Sustain. Energy Rev. 101, 527–547 (2019)

    Article  Google Scholar 

  5. Zongxiang Lu, Haibo Li, Ying Qiao, Power system flexibility planning and challenges considering high proportion of renewable energy. Autom. Electr. Power Syst. 40(13), 147–158 (2016)

    Google Scholar 

  6. S. Stinner, D. Müller, P. Heiselberg: Quantifying and aggregating the flexibility of building energy systems. No. RWTH-2018-224242. E. ON Energy Research Center (2018)

    Google Scholar 

  7. A. Wang, R. Li, S. You, Development of a data driven approach to explore the energy flexibility potential of building clusters. Appl. Energy 232, 89–100 (2018)

    Article  Google Scholar 

  8. M. Afzalan, F. Jazizadeh, Residential loads flexibility potential for demand response using energy consumption patterns and user segments. Appl. Energy 254, 113693 (2019)

    Article  Google Scholar 

  9. M. Liu, P. Heiselberg, Energy flexibility of a nearly zero-energy building with weather predictive control on a convective building energy system and evaluated with different metrics. Appl. Energy 233, 764–775 (2019)

    Article  Google Scholar 

  10. N. Ludwig, et al. Industrial demand-side flexibility: a benchmark data set, in Proceedings of the Tenth ACM International Conference on Future Energy Systems (2019)

    Google Scholar 

  11. Brian Drysdale, Wu Jianzhong, Nick Jenkins, Flexible demand in the GB domestic electricity sector in 2030. Appl. Energy 139, 281–290 (2015)

    Article  Google Scholar 

  12. R.G. Junker et al., Characterizing the energy flexibility of buildings and districts. Appl. Energy 225, 175–182 (2018)

    Article  Google Scholar 

  13. R. Li, et al., in Energy Flexibility of Building Cluster–Part I: Occupancy Modelling (2018)

    Google Scholar 

  14. Rafał Weron, Electricity price forecasting: A review of the state-of-the-art with a look into the future. Int. J. Forecast. 30(4), 1030–1081 (2014)

    Article  Google Scholar 

  15. Kaveh Paridari, Lars Nordström, Flexibility prediction, scheduling and control of aggregated TCLs. Electr. Power Syst. Res. 178, 106004 (2020)

    Article  Google Scholar 

  16. E. Azizi et al., Application of comparative strainer clustering as a novel method of high volume of data clustering to optimal power flow problem. Int. J. Electr. Power Energy Syst. 113, 362–371 (2019)

    Article  Google Scholar 

  17. D. Patteeuw et al., Clustering a building stock towards representative buildings in the context of air-conditioning electricity demand flexibility. J. Build. Perform. Simul. 12(1), 56–67 (2019)

    Article  Google Scholar 

  18. K. Kouzelis et al., Estimation of residential heat pump consumption for flexibility market applications. IEEE Trans. Smart Grid 6(4), 1852–1864 (2015)

    Article  Google Scholar 

  19. A. Alirezazadeh et al., A new flexible model for generation scheduling in a smart grid. Energy 191, 116438 (2020)

    Article  Google Scholar 

  20. S. RongQi, et al., Research of flexible load analysis of distribution network based on big data, 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (IEEE, 2019)

    Google Scholar 

  21. M. Sun et al., Clustering-based residential baseline estimation: a probabilistic perspective. IEEE Trans. Smart Grid 10(6), 6014–6028 (2019)

    Article  Google Scholar 

  22. T.Q. Péan, S. Jaume, R. Costa-Castelló, Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings. J. Process Control 74, 35–49 (2019)

    Article  Google Scholar 

  23. C. Lv et al., Model predictive control based robust scheduling of community integrated energy system with operational flexibility. Appl. Energy 243, 250–265 (2019)

    Article  Google Scholar 

  24. T. Péan, J. Salom, R. Costa-Castelló, Configurations of model predictive control to exploit energy flexibility in building thermal loads, in 2018 IEEE Conference on Decision and Control (CDC) (IEEE, 2018)

    Google Scholar 

  25. G. Chen, D. Liu, Y. Lixia, Predictive control of regional flexible load cluster based on mixed logical dynamic method, in 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia) (IEEE, 2019)

    Google Scholar 

  26. R. Ahmadiahangar, A. Rosin, A. NabaviNiaki, I. Palu, T. Korõtko, A review on real-time simulation and analysis methods of microgrids. Int. Trans. Electr. Energy Syst. 29(11), e12106 (2019)

    Article  Google Scholar 

  27. R. Ahmadiahangar, T. Häring, A. Rosin, T. Korõtko, J. Martins, Residential load forecasting for flexibility prediction using machine learning-based regression model, in 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 11 June 2019, pp. 1–4 (IEEE)

    Google Scholar 

  28. K. Peterson, R. Ahmadiahangar, N. Shabbir, T. Vinnal, Analysis of microgrid configuration effects on energy efficiency, in 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 7 Oct 2019, pp. 1–6 (IEEE)

    Google Scholar 

  29. M. Mahmudizad, R.A. Ahangar, Improving load frequency control of multi-area power system by considering uncertainty by using optimized type 2 fuzzy pid controller with the harmony search algorithm. World Acad. Sci. Eng. Technol. Int. J. Electr. Comput. Energ. Electron. Commun. Eng. 10(8), 1051–1061 (2016)

    Google Scholar 

  30. T. Häring, R. Ahmadiahangar, A. Rosin, H. Biechl, Impact of load matching algorithms on the battery capacity with different household occupancies, in IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society (Lisbon, 2019)

    Google Scholar 

  31. N. Shabbir, R. Ahmadiahangar, L. Kütt, A. Rosin, Comparison of machine learning based methods for residential load forecasting, in 2019 Electric Power Quality and Supply Reliability Conference (PQ) & 2019 Symposium on Electrical Engineering and Mechatronics (SEEM), 12 June 2019, pp. 1–4 (IEEE)

    Google Scholar 

  32. R. Ahmadi, A. Sheikholeslami, A. Nabavi Niaki, A. Ranjbar, Dynamic participation of doubly fed induction generators in multi-control area load frequency control. Int. Trans. Electr. Energy Syst. 25(7), 1130–1147 (2015)

    Google Scholar 

  33. R. Ahmadi, F. Ghardashi, D. Kabiri, A. Sheykholeslami, H. Haeri, Voltage and frequency control in smart distribution systems in presence of DER using flywheel energy storage system, IET Digital Library (2013)

    Google Scholar 

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Acknowledgements

This work has been supported by the European Commission through the H2020 project Finest Twins (grant No. 856602).

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Correspondence to Aydin Azizi .

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Ahmadiahangar, R., Rosin, A., Palu, I., Azizi, A. (2020). Forecasting Available Demand-Side Flexibility. In: Demand-side Flexibility in Smart Grid. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-15-4627-3_4

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  • DOI: https://doi.org/10.1007/978-981-15-4627-3_4

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  • Print ISBN: 978-981-15-4626-6

  • Online ISBN: 978-981-15-4627-3

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