IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v72y2017icp1208-1232.html
   My bibliography  Save this article

A survey on behind the meter energy management systems in smart grid

Author

Listed:
  • Bayram, Islam Safak
  • Ustun, Taha Selim

Abstract

Over the last few years, the fast-growing energy needs across the world have intensified a central challenge: how to reduce the generation and operation costs in power systems and, in parallel, to minimize the hydrocarbon emissions. Moreover, one-quarter of world's population still lacks access to electricity, as the cost of building conventional power grids is not affordable by third world countries. On the other hand, behind-the-meter (BTM) energy systems offer cost-effective solutions to aforementioned challenges, as they enable end-users to satisfy their energy needs with distributed energy generation and storage technologies. To that end, this paper presents a detailed survey of BTM energy management systems. The paper starts with the classification of the electrical loads with respect to their physical properties, priority ranking, and sizes. Next, the literature on BTM energy management systems is systematically classified into three main categories: technology layer, economic layer, and social layer. The technology layer spans the studies related to power systems including distributed generation and storage technologies, whereas the economic layer shows how economic incentives along with optimization and scheduling techniques are employed to shape the energy consumption. The social layer, on the other hand, presents the recent studies on how to employ social sciences to reduce the energy consumption without requiring any technological upgrades. This paper also provides an overview of the enabling technologies and standards for communication, sensing, and monitoring purposes. In the final part, a case study is provided to illustrate an implementation of the system.

Suggested Citation

  • Bayram, Islam Safak & Ustun, Taha Selim, 2017. "A survey on behind the meter energy management systems in smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1208-1232.
  • Handle: RePEc:eee:rensus:v:72:y:2017:i:c:p:1208-1232
    DOI: 10.1016/j.rser.2016.10.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032116306852
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2016.10.034?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. Clastres, C. & Ha Pham, T.T. & Wurtz, F. & Bacha, S., 2010. "Ancillary services and optimal household energy management with photovoltaic production," Energy, Elsevier, vol. 35(1), pages 55-64.
    2. Wang, Qiang & Li, Rongrong, 2015. "Cheaper oil: A turning point in Paris climate talk?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1186-1192.
    3. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    4. Faruqui, Ahmad & Hledik, Ryan & Tsoukalis, John, 2009. "The Power of Dynamic Pricing," The Electricity Journal, Elsevier, vol. 22(3), pages 42-56, April.
    5. Brounen, Dirk & Kok, Nils & Quigley, John M., 2012. "Residential energy use and conservation: Economics and demographics," European Economic Review, Elsevier, vol. 56(5), pages 931-945.
    6. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    7. Narayanan, Shankar & Li, Xiansen & Yang, Sungwoo & Kim, Hyunho & Umans, Ari & McKay, Ian S. & Wang, Evelyn N., 2015. "Thermal battery for portable climate control," Applied Energy, Elsevier, vol. 149(C), pages 104-116.
    8. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    9. John A. Mathews & Hao Tan, 2014. "Economics: Manufacture renewables to build energy security," Nature, Nature, vol. 513(7517), pages 166-168, September.
    10. Bartusch, Cajsa & Odlare, Monica & Wallin, Fredrik & Wester, Lars, 2012. "Exploring variance in residential electricity consumption: Household features and building properties," Applied Energy, Elsevier, vol. 92(C), pages 637-643.
    11. Ulyanchenko Yu. O. & Kosenko A. V., 2015. "Problems that form investment climate of Ukraine," Visnyk of National University of Civil Protection of Ukraine. Public Administration series., National University of Civil Protection of Ukraine, vol. 3(2), pages 42-49, July.
    12. Sanquist, Thomas F. & Orr, Heather & Shui, Bin & Bittner, Alvah C., 2012. "Lifestyle factors in U.S. residential electricity consumption," Energy Policy, Elsevier, vol. 42(C), pages 354-364.
    13. Lin, Boqiang & Lei, Xiaojing, 2015. "Carbon emissions reduction in China's food industry," Energy Policy, Elsevier, vol. 86(C), pages 483-492.
    14. Rosario Miceli, 2013. "Energy Management and Smart Grids," Energies, MDPI, vol. 6(4), pages 1-29, April.
    15. Li, Chengzheng & Xiang, Xunyong & Gu, Haiying, 2015. "Climate shocks and international trade: Evidence from China," Economics Letters, Elsevier, vol. 135(C), pages 55-57.
    16. Vringer, Kees & Aalbers, Theo & Blok, Kornelis, 2007. "Household energy requirement and value patterns," Energy Policy, Elsevier, vol. 35(1), pages 553-566, January.
    17. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
    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. Omar Alrawi & I. Safak Bayram & Sami G. Al-Ghamdi & Muammer Koc, 2019. "High-Resolution Household Load Profiling and Evaluation of Rooftop PV Systems in Selected Houses in Qatar," Energies, MDPI, vol. 12(20), pages 1-25, October.
    2. Ayu Washizu & Satoshi Nakano & Hideo Ishii & Yasuhiro Hayashi, 2019. "Willingness to Pay for Home Energy Management Systems: A Survey in New York and Tokyo," Sustainability, MDPI, vol. 11(17), pages 1-20, September.
    3. Eduardo Viciana & Alfredo Alcayde & Francisco G. Montoya & Raul Baños & Francisco M. Arrabal-Campos & Antonio Zapata-Sierra & Francisco Manzano-Agugliaro, 2018. "OpenZmeter: An Efficient Low-Cost Energy Smart Meter and Power Quality Analyzer," Sustainability, MDPI, vol. 10(11), pages 1-13, November.
    4. S. M. Mahfuz Alam & Mohd. Hasan Ali, 2020. "Equation Based New Methods for Residential Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-22, December.
    5. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    6. Touzani, Samir & Prakash, Anand Krishnan & Wang, Zhe & Agarwal, Shreya & Pritoni, Marco & Kiran, Mariam & Brown, Richard & Granderson, Jessica, 2021. "Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency," Applied Energy, Elsevier, vol. 304(C).
    7. van de Kaa, G. & Fens, T. & Rezaei, J. & Kaynak, D. & Hatun, Z. & Tsilimeni-Archangelidi, A., 2019. "Realizing smart meter connectivity: Analyzing the competing technologies Power line communication, mobile telephony, and radio frequency using the best worst method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 320-327.
    8. Edwin Chukwuemeka Idoko & Chukwunonso Oraedu & Christian Chidera Ugwuanyi & Stephen Ikechukwu Ukenna, 2021. "Determinants of Smart Meter on Sustainable Energy Consumption Behavior: A Developing Country Perspective," SAGE Open, , vol. 11(3), pages 21582440211, July.
    9. Liu, Chao Charles & Chen, Hongkun & Shi, Jing & Chen, Lei, 2022. "Self-supervised learning method for consumer-level behind-the-meter PV estimation," Applied Energy, Elsevier, vol. 326(C).
    10. Bhaskar P. Rimal & Cuiyu Kong & Bikrant Poudel & Yong Wang & Pratima Shahi, 2022. "Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues," Energies, MDPI, vol. 15(5), pages 1-24, March.
    11. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    12. Walmsley, Timothy Gordon & Philipp, Matthias & Picón-Núñez, Martín & Meschede, Henning & Taylor, Matthew Thomas & Schlosser, Florian & Atkins, Martin John, 2023. "Hybrid renewable energy utility systems for industrial sites: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    13. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    14. Gordon Rausser & Wadim Strielkowski & Dalia Å treimikienÄ—, 2018. "Smart meters and household electricity consumption: A case study in Ireland," Energy & Environment, , vol. 29(1), pages 131-146, February.

    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. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    2. Tilov, Ivan & Farsi, Mehdi & Volland, Benjamin, 2020. "From frugal Jane to wasteful John: A quantile regression analysis of Swiss households’ electricity demand," Energy Policy, Elsevier, vol. 138(C).
    3. Huebner, Gesche & Shipworth, David & Hamilton, Ian & Chalabi, Zaid & Oreszczyn, Tadj, 2016. "Understanding electricity consumption: A comparative contribution of building factors, socio-demographics, appliances, behaviours and attitudes," Applied Energy, Elsevier, vol. 177(C), pages 692-702.
    4. Fournier, Eric D. & Federico, Felicia & Porse, Erik & Pincetl, Stephanie, 2019. "Effects of building size growth on residential energy efficiency and conservation in California," Applied Energy, Elsevier, vol. 240(C), pages 446-452.
    5. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
    6. Kettani, Maryème & Sanin, Maria Eugenia, 2024. "Energy consumption and energy poverty in Morocco," Energy Policy, Elsevier, vol. 185(C).
    7. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    8. Wallis, Hannah & Nachreiner, Malte & Matthies, Ellen, 2016. "Adolescents and electricity consumption; Investigating sociodemographic, economic, and behavioural influences on electricity consumption in households," Energy Policy, Elsevier, vol. 94(C), pages 224-234.
    9. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    10. Dimitra Kotsila & Persefoni Polychronidou, 2021. "Determinants of household electricity consumption in Greece: a statistical analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-20, December.
    11. Roberts, Mike B. & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2019. "Characterisation of Australian apartment electricity demand and its implications for low-carbon cities," Energy, Elsevier, vol. 180(C), pages 242-257.
    12. Khosrowpour, Ardalan & Xie, Yimeng & Taylor, John E. & Hong, Yili, 2016. "One size does not fit all: Establishing the need for targeted eco-feedback," Applied Energy, Elsevier, vol. 184(C), pages 523-530.
    13. Jieyi Kang & David Reiner, 2021. "Machine Learning on residential electricity consumption: Which households are more responsive to weather?," Working Papers EPRG2113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    14. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    15. Jaebin Lim & Myounggu Kang, 2022. "The relationship between site planning and electricity consumption: An empirical analysis of multi-unit residential complexes in Seoul, Korea," Environment and Planning B, , vol. 49(3), pages 971-986, March.
    16. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    17. del Río, Pablo, 2017. "Why does the combination of the European Union Emissions Trading Scheme and a renewable energy target makes economic sense?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 824-834.
    18. Khosrowpour, Ardalan & Jain, Rishee K. & Taylor, John E. & Peschiera, Gabriel & Chen, Jiayu & Gulbinas, Rimas, 2018. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation," Applied Energy, Elsevier, vol. 218(C), pages 304-316.
    19. Hoai-Son Nguyen & Minh Ha-Duong, 2018. "Family size, Increasing block tariff and Economies of scale of household electricity consumption in Vietnam from 2010 to 2014," CIRED Working Papers hal-01714899, HAL.
    20. Mark van de Logt, 2016. "?The Most Dangerous Man on the Planet\," Proceedings of International Academic Conferences 3505987, International Institute of Social and Economic Sciences.

    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:rensus:v:72:y:2017:i:c:p:1208-1232. 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/600126/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.