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Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

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  • Moreno Jaramillo, Andres F.
  • Laverty, David M.
  • Morrow, D. John
  • Martinez del Rincon, Jesús
  • Foley, Aoife M.

Abstract

In many countries distributed energy resources (DER) (e.g. photovoltaics, batteries, wind turbines, electric vehicles, electric heat pumps, air-conditioning units and smart domestic appliances) are part of the ‘Green Deal’ to deliver a climate neutral society. Policy roadmaps, despite providing a framework and penetration targets for DER, often lack the network planning strategies needed to transition from passive to active distribution networks. Currently, DER's dynamic performance parameters and location identification techniques are not fully standardised. In fact, it can be very ad hoc. Standardised distributed load modelling and non-intrusive load monitoring (NILM) for equipment manufacturers, installers and network operators is critical to low and medium voltage network management in order to facilitate better balancing, flexibility and electricity trading across and within the power system for mass DER deployment. The aim of this paper is to fill this load modelling and NILM knowledge gap for DERto inform the ‘Green Deal’ transition and support standardisation. In the paper, existing load modelling techniques and NILM methodologies are critically examined to inform and guide research activity, equipment development and regulator thinking, as well as network operators. Seven key findings that need urgent attention are identified to support a smooth power system reconfiguration.

Suggested Citation

  • Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:445-466
    DOI: 10.1016/j.renene.2021.07.056
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    as
    1. Stephen Adams & Steven Greenspan & Maria Velez-Rojas & Serge Mankovski & Peter A. Beling, 2019. "Data-driven simulation for energy consumption estimation in a smart home," Environment Systems and Decisions, Springer, vol. 39(3), pages 281-294, September.
    2. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
    3. Dinesh, Chinthaka & Welikala, Shirantha & Liyanage, Yasitha & Ekanayake, Mervyn Parakrama B. & Godaliyadda, Roshan Indika & Ekanayake, Janaka, 2017. "Non-intrusive load monitoring under residential solar power influx," Applied Energy, Elsevier, vol. 205(C), pages 1068-1080.
    4. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    5. Liu, Bo & Luan, Wenpeng & Yu, Yixin, 2017. "Dynamic time warping based non-intrusive load transient identification," Applied Energy, Elsevier, vol. 195(C), pages 634-645.
    6. Hereher, Mohamed & El Kenawy, Ahmed M., 2020. "Exploring the potential of solar, tidal, and wind energy resources in Oman using an integrated climatic-socioeconomic approach," Renewable Energy, Elsevier, vol. 161(C), pages 662-675.
    7. Harris, Chioke B. & Webber, Michael E., 2014. "An empirically-validated methodology to simulate electricity demand for electric vehicle charging," Applied Energy, Elsevier, vol. 126(C), pages 172-181.
    8. Ehsan, Ali & Yang, Qiang, 2019. "State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review," Applied Energy, Elsevier, vol. 239(C), pages 1509-1523.
    9. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    10. Gebbran, Daniel & Mhanna, Sleiman & Ma, Yiju & Chapman, Archie C. & Verbič, Gregor, 2021. "Fair coordination of distributed energy resources with Volt-Var control and PV curtailment," Applied Energy, Elsevier, vol. 286(C).
    11. Johansson, Petter & Vendel, Martin & Nuur, Cali, 2020. "Integrating distributed energy resources in electricity distribution systems: An explorative study of challenges facing DSOs in Sweden," Utilities Policy, Elsevier, vol. 67(C).
    12. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    13. Abubakar, I. & Khalid, S.N. & Mustafa, M.W. & Shareef, Hussain & Mustapha, M., 2017. "Application of load monitoring in appliances’ energy management – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 235-245.
    14. Bayer, Benjamin & Matschoss, Patrick & Thomas, Heiko & Marian, Adela, 2018. "The German experience with integrating photovoltaic systems into the low-voltage grids," Renewable Energy, Elsevier, vol. 119(C), pages 129-141.
    15. Mehrjerdi, Hasan & Hemmati, Reza, 2020. "Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing building," Renewable Energy, Elsevier, vol. 146(C), pages 568-579.
    16. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    17. Juncheng Zhu & Zhile Yang & Monjur Mourshed & Yuanjun Guo & Yimin Zhou & Yan Chang & Yanjie Wei & Shengzhong Feng, 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches," Energies, MDPI, vol. 12(14), pages 1-19, July.
    18. Hosseini, Sayed Saeed & Agbossou, Kodjo & Kelouwani, Sousso & Cardenas, Alben, 2017. "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1266-1274.
    19. Xu, Xu & Li, Jiayong & Xu, Zhao & Zhao, Jian & Lai, Chun Sing, 2019. "Enhancing photovoltaic hosting capacity—A stochastic approach to optimal planning of static var compensator devices in distribution networks," Applied Energy, Elsevier, vol. 238(C), pages 952-962.
    20. Abdmouleh, Zeineb & Gastli, Adel & Ben-Brahim, Lazhar & Haouari, Mohamed & Al-Emadi, Nasser Ahmed, 2017. "Review of optimization techniques applied for the integration of distributed generation from renewable energy sources," Renewable Energy, Elsevier, vol. 113(C), pages 266-280.
    21. Hui, Hongxun & Ding, Yi & Song, Yonghua & Rahman, Saifur, 2019. "Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error," Applied Energy, Elsevier, vol. 250(C), pages 161-174.
    22. Wu, Xin & Jiao, Dian & Liang, Kaixin & Han, Xiao, 2019. "A fast online load identification algorithm based on V-I characteristics of high-frequency data under user operational constraints," Energy, Elsevier, vol. 188(C).
    23. Depuru, Soma Shekara Sreenadh Reddy & Wang, Lingfeng & Devabhaktuni, Vijay, 2011. "Smart meters for power grid: Challenges, issues, advantages and status," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2736-2742, August.
    24. Tong, Xing & Li, Ran & Li, Furong & Kang, Chongqing, 2016. "Cross-domain feature selection and coding for household energy behavior," Energy, Elsevier, vol. 107(C), pages 9-16.
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    2. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
    3. Toro-Cárdenas, Mateo & Moreira, Inês & Morais, Hugo & Carvalho, Pedro M.S. & Ferreira, Luis A.F.M., 2023. "Net load disaggregation at secondary substation level," Renewable Energy, Elsevier, vol. 207(C), pages 765-771.
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    5. İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.

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