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2020 | OriginalPaper | Buchkapitel

4. Forecasting Available Demand-Side Flexibility

verfasst von : Roya Ahmadiahangar, Argo Rosin, Ivo Palu, Aydin Azizi

Erschienen in: Demand-side Flexibility in Smart Grid

Verlag: Springer Singapore

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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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Literatur
1.
Zurück zum Zitat 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)CrossRef 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)CrossRef
2.
Zurück zum Zitat 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)CrossRef 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)CrossRef
3.
Zurück zum Zitat 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)CrossRef 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)CrossRef
4.
Zurück zum Zitat 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)CrossRef 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)CrossRef
5.
Zurück zum Zitat 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) 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)
6.
Zurück zum Zitat 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) 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)
7.
Zurück zum Zitat 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)CrossRef 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)CrossRef
8.
Zurück zum Zitat M. Afzalan, F. Jazizadeh, Residential loads flexibility potential for demand response using energy consumption patterns and user segments. Appl. Energy 254, 113693 (2019)CrossRef M. Afzalan, F. Jazizadeh, Residential loads flexibility potential for demand response using energy consumption patterns and user segments. Appl. Energy 254, 113693 (2019)CrossRef
9.
Zurück zum Zitat 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)CrossRef 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)CrossRef
10.
Zurück zum Zitat 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) 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)
11.
Zurück zum Zitat Brian Drysdale, Wu Jianzhong, Nick Jenkins, Flexible demand in the GB domestic electricity sector in 2030. Appl. Energy 139, 281–290 (2015)CrossRef Brian Drysdale, Wu Jianzhong, Nick Jenkins, Flexible demand in the GB domestic electricity sector in 2030. Appl. Energy 139, 281–290 (2015)CrossRef
12.
Zurück zum Zitat R.G. Junker et al., Characterizing the energy flexibility of buildings and districts. Appl. Energy 225, 175–182 (2018)CrossRef R.G. Junker et al., Characterizing the energy flexibility of buildings and districts. Appl. Energy 225, 175–182 (2018)CrossRef
13.
Zurück zum Zitat R. Li, et al., in Energy Flexibility of Building Cluster–Part I: Occupancy Modelling (2018) R. Li, et al., in Energy Flexibility of Building Cluster–Part I: Occupancy Modelling (2018)
14.
Zurück zum Zitat 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)CrossRef 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)CrossRef
15.
Zurück zum Zitat Kaveh Paridari, Lars Nordström, Flexibility prediction, scheduling and control of aggregated TCLs. Electr. Power Syst. Res. 178, 106004 (2020)CrossRef Kaveh Paridari, Lars Nordström, Flexibility prediction, scheduling and control of aggregated TCLs. Electr. Power Syst. Res. 178, 106004 (2020)CrossRef
16.
Zurück zum Zitat 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)CrossRef 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)CrossRef
17.
Zurück zum Zitat 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)CrossRef 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)CrossRef
18.
Zurück zum Zitat K. Kouzelis et al., Estimation of residential heat pump consumption for flexibility market applications. IEEE Trans. Smart Grid 6(4), 1852–1864 (2015)CrossRef K. Kouzelis et al., Estimation of residential heat pump consumption for flexibility market applications. IEEE Trans. Smart Grid 6(4), 1852–1864 (2015)CrossRef
19.
Zurück zum Zitat A. Alirezazadeh et al., A new flexible model for generation scheduling in a smart grid. Energy 191, 116438 (2020)CrossRef A. Alirezazadeh et al., A new flexible model for generation scheduling in a smart grid. Energy 191, 116438 (2020)CrossRef
20.
Zurück zum Zitat 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) 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)
21.
Zurück zum Zitat M. Sun et al., Clustering-based residential baseline estimation: a probabilistic perspective. IEEE Trans. Smart Grid 10(6), 6014–6028 (2019)CrossRef M. Sun et al., Clustering-based residential baseline estimation: a probabilistic perspective. IEEE Trans. Smart Grid 10(6), 6014–6028 (2019)CrossRef
22.
Zurück zum Zitat 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)CrossRef 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)CrossRef
23.
Zurück zum Zitat C. Lv et al., Model predictive control based robust scheduling of community integrated energy system with operational flexibility. Appl. Energy 243, 250–265 (2019)CrossRef C. Lv et al., Model predictive control based robust scheduling of community integrated energy system with operational flexibility. Appl. Energy 243, 250–265 (2019)CrossRef
24.
Zurück zum Zitat 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) 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)
25.
Zurück zum Zitat 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) 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)
26.
Zurück zum Zitat 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)CrossRef 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)CrossRef
27.
Zurück zum Zitat 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) 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)
28.
Zurück zum Zitat 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) 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)
29.
Zurück zum Zitat 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) 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)
30.
Zurück zum Zitat 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) 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)
31.
Zurück zum Zitat 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) 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)
32.
Zurück zum Zitat 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) 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)
33.
Zurück zum Zitat 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) 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)
Metadaten
Titel
Forecasting Available Demand-Side Flexibility
verfasst von
Roya Ahmadiahangar
Argo Rosin
Ivo Palu
Aydin Azizi
Copyright-Jahr
2020
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-4627-3_4