IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v303y2021ics0306261921010497.html
   My bibliography  Save this article

Toward smart distributed renewable generation via multi-uncertainty featured non-intrusive interactive energy monitoring

Author

Listed:
  • Liu, Yu
  • Liu, Congxiao
  • Ling, Qicheng
  • Zhao, Xin
  • Gao, Shan
  • Huang, Xueliang

Abstract

Quantities of advanced technologies have been utilized to realize the carbon neutrality, among which the smart implementations at energy user side are highlighted due to the extensive coverage. Supported by multiple energy management technologies, the integration of distributed renewable energy is prominent, but the compatibility problem of diverse technologies still exists especially considering the newly-developed features. In this paper, an interactive energy monitoring via non-intrusive way is allocated to analyze the roof-top photovoltaic generation and individual appliance consumption from integral energy measurements, and a complete solution is proposed to tackle the distributed generation intelligence with smart voltage maintenance. At first stage, aiming at the domestic reactive power management of photovoltaic inverters, the framework and strategy of coordination between smart meter and intelligent photovoltaic generation are proposed, and then the unified energy signature formulation of photovoltaic generation is presented. Afterwards, the energy disaggregation model is formularized by modified dictionary learning scheme with hybrid validation strategy. Lastly, a sparse coding and auxiliary optimization-based solution is proposed to solve the problem, achieving the robustness under real time architecture. Both the specific low voltage network simulator platform and the practical field measurement scenarios are utilized to verify the effectiveness of the proposed study, and results demonstrate that the proposed study provides an efficient monitoring solution to the intelligent photovoltaic generation, which helps to move toward the smart energy user implementation.

Suggested Citation

  • Liu, Yu & Liu, Congxiao & Ling, Qicheng & Zhao, Xin & Gao, Shan & Huang, Xueliang, 2021. "Toward smart distributed renewable generation via multi-uncertainty featured non-intrusive interactive energy monitoring," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921010497
    DOI: 10.1016/j.apenergy.2021.117689
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921010497
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117689?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Yunsong Jia & Xiang Li, 2021. "Complex Event Processing Methods for Greenhouse Control," Agriculture, MDPI, vol. 11(9), pages 1-24, August.
    3. ., 2021. "Structural and process dimension in innovation orientation," Chapters, in: Innovation Orientation in Business Services, chapter 6, pages 90-108, Edward Elgar Publishing.
    4. 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.
    5. Liu, Yu & Liu, Wei & Shen, Yiwen & Zhao, Xin & Gao, Shan, 2021. "Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations," Applied Energy, Elsevier, vol. 287(C).
    6. Zhao, Bochao & Ye, Minxiang & Stankovic, Lina & Stankovic, Vladimir, 2020. "Non-intrusive load disaggregation solutions for very low-rate smart meter data," Applied Energy, Elsevier, vol. 268(C).
    7. Selosse, Sandrine & Garabedian, Sabine & Ricci, Olivia & Maïzi, Nadia, 2018. "The renewable energy revolution of reunion island," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 99-105.
    8. Salvia, Monica & Reckien, Diana & Pietrapertosa, Filomena & Eckersley, Peter & Spyridaki, Niki-Artemis & Krook-Riekkola, Anna & Olazabal, Marta & De Gregorio Hurtado, Sonia & Simoes, Sofia G. & Genele, 2021. "Will climate mitigation ambitions lead to carbon neutrality? An analysis of the local-level plans of 327 cities in the EU," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. 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.
    10. 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.
    11. Allan, Grant & Eromenko, Igor & Gilmartin, Michelle & Kockar, Ivana & McGregor, Peter, 2015. "The economics of distributed energy generation: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 543-556.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yini Ni & Yanghong Xia & Zichen Li & Qifan Feng, 2023. "A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    2. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Liu, Yu & Liu, Wei & Shen, Yiwen & Zhao, Xin & Gao, Shan, 2021. "Toward smart energy user: Real time non-intrusive load monitoring with simultaneous switching operations," Applied Energy, Elsevier, vol. 287(C).
    3. Tomasz Jasiński, 2020. "Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)," Energies, MDPI, vol. 13(5), pages 1-16, March.
    4. Shi, Xin & Ming, Hao & Shakkottai, Srinivas & Xie, Le & Yao, Jianguo, 2019. "Nonintrusive load monitoring in residential households with low-resolution data," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    6. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    7. Antonio Ruano & Alvaro Hernandez & Jesus Ureña & Maria Ruano & Juan Garcia, 2019. "NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review," Energies, MDPI, vol. 12(11), pages 1-29, June.
    8. Younghoon Kwak & Jihyun Hwang & Taewon Lee, 2018. "Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building," Energies, MDPI, vol. 11(4), pages 1-22, April.
    9. Wang, Shuangyuan & Li, Ran & Evans, Adrian & Li, Furong, 2020. "Regional nonintrusive load monitoring for low voltage substations and distributed energy resources," Applied Energy, Elsevier, vol. 260(C).
    10. Ce Peng & Guoying Lin & Shaopeng Zhai & Yi Ding & Guangyu He, 2020. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model," Energies, MDPI, vol. 13(21), pages 1-19, October.
    11. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
    12. Yini Ni & Yanghong Xia & Zichen Li & Qifan Feng, 2023. "A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    13. Chia-Nan Wang & Tran Quynh Le & Ching-Hua Yu & Hsiao-Chi Ling & Thanh-Tuan Dang, 2022. "Strategic Environmental Assessment of Land Transportation: An Application of DEA with Undesirable Output Approach," Sustainability, MDPI, vol. 14(2), pages 1-16, January.
    14. Çimen, Halil & Bazmohammadi, Najmeh & Lashab, Abderezak & Terriche, Yacine & Vasquez, Juan C. & Guerrero, Josep M., 2022. "An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring," Applied Energy, Elsevier, vol. 307(C).
    15. Zhuang Zheng & Hainan Chen & Xiaowei Luo, 2018. "A Supervised Event-Based Non-Intrusive Load Monitoring for Non-Linear Appliances," Sustainability, MDPI, vol. 10(4), pages 1-28, March.
    16. Yan, Lei & Tian, Wei & Han, Jiayu & Li, Zuy, 2022. "Event-driven two-stage solution to non-intrusive load monitoring," Applied Energy, Elsevier, vol. 311(C).
    17. Luan, Wenpeng & Tian, Longfei & Zhao, Bochao, 2023. "Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design," Applied Energy, Elsevier, vol. 342(C).
    18. Welikala, Shirantha & Thelasingha, Neelanga & Akram, Muhammed & Ekanayake, Parakrama B. & Godaliyadda, Roshan I. & Ekanayake, Janaka B., 2019. "Implementation of a robust real-time non-intrusive load monitoring solution," Applied Energy, Elsevier, vol. 238(C), pages 1519-1529.
    19. Juhlin, Rasmus & Assadi, Mohsen, 2023. "Investigations of the thermodynamic efficiency limits of a novel subsea energy storage concept," Applied Energy, Elsevier, vol. 330(PA).
    20. Huijuan Wang & Wenrong Yang & Tingyu Chen & Qingxin Yang, 2019. "An Optimal Load Disaggregation Method Based on Power Consumption Pattern for Low Sampling Data," Sustainability, MDPI, vol. 11(1), pages 1-16, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921010497. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.