IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3080-d562191.html
   My bibliography  Save this article

Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study

Author

Listed:
  • Francesco Mancini

    (Department of Planning, Design and Technology of Architecture, Sapienza University of Rome, 72-00197 Rome, Italy)

  • Jacopo Cimaglia

    (Interdepartmental Center for Territory, Building, Conservation and Environment, Sapienza University of Rome, 53-00197 Rome, Italy)

  • Gianluigi Lo Basso

    (Department of Astronautics, Electrical Energy Engineering, Sapienza University of Rome, 18-00184 Rome, Italy)

  • Sabrina Romano

    (Energy Technologies Department (DTE), Italian National Agency for Technologies, Energy and Sustainable Economic Development (ENEA), 301-00123 Rome, Italy)

Abstract

This work aims to evaluate the Flexibility Potential that a residential household can effectively provide to the public grid for participating in a Demand Response activity. In detail, by using 14 dwellings electrical data collection, an algorithm to simulate the Load Shifting activity over the daytime is implemented. That algorithm is applied to different scenarios having considered the addition of several technical constraints on the end users’ devices. In such a way, more realistic demand-side management actions are implemented in order to assess the Flexibility Potential deriving from the loads shifting. Basically, by performing simulations it is possible to investigate how the household appliances real operating conditions can reduce the theoretical Flexibility Potential extent. Starting from a Flexibility Price-Market-based Strategy, this work simulates the shifting over the day and night-time of some flexible loads, i.e., the shiftable and the storable ones. Specifically, all instants where load curtailments and enhancements occur over the typical day, the flexibility strategy effectiveness in terms of percentage, the power and energy that are potentially flexible, are evaluated. All the simulations are performed only for residential consumers to evaluate the actual dwellings Flexibility Potential in the absence of any electrical storage and production systems. The outcomes of these simulations show an average Theoretical Flexibility reduction, which is calculated as the fraction of appliances’ cycles shifting over the total ones, equal to 53%, instead of 66%; in a single dwelling, a maximum variation equal to 29% has been registered. In the end, the monthly average shifted energy per dwellings decreases from 27 to 18 kWh, entailing 32.5% off.

Suggested Citation

  • Francesco Mancini & Jacopo Cimaglia & Gianluigi Lo Basso & Sabrina Romano, 2021. "Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study," Energies, MDPI, vol. 14(11), pages 1-21, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3080-:d:562191
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3080/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/11/3080/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    2. Mazzoni, Stefano & Ooi, Sean & Nastasi, Benedetto & Romagnoli, Alessandro, 2019. "Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems," Applied Energy, Elsevier, vol. 254(C).
    3. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2015. "Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving," Applied Energy, Elsevier, vol. 147(C), pages 246-257.
    4. Leehter Yao & Fazida Hanim Hashim & Chien-Chi Lai, 2020. "Dynamic Residential Energy Management for Real-Time Pricing," Energies, MDPI, vol. 13(10), pages 1-15, May.
    5. Md Mamun Ur Rashid & Fabrizio Granelli & Md. Alamgir Hossain & Md. Shafiul Alam & Fahad Saleh Al-Ismail & Ashish Kumar Karmaker & Md. Mijanur Rahaman, 2020. "Development of Home Energy Management Scheme for a Smart Grid Community," Energies, MDPI, vol. 13(17), pages 1-24, August.
    6. Feuerriegel, Stefan & Neumann, Dirk, 2016. "Integration scenarios of Demand Response into electricity markets: Load shifting, financial savings and policy implications," Energy Policy, Elsevier, vol. 96(C), pages 231-240.
    7. Osaru Agbonaye & Patrick Keatley & Ye Huang & Motasem Bani Mustafa & Neil Hewitt, 2020. "Design, Valuation and Comparison of Demand Response Strategies for Congestion Management," Energies, MDPI, vol. 13(22), pages 1-29, November.
    8. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    9. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    10. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    11. Shirazi, Elham & Jadid, Shahram, 2017. "Cost reduction and peak shaving through domestic load shifting and DERs," Energy, Elsevier, vol. 124(C), pages 146-159.
    12. Alexandre Lucas & Luca Jansen & Nikoleta Andreadou & Evangelos Kotsakis & Marcelo Masera, 2019. "Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector," Energies, MDPI, vol. 12(14), pages 1-19, July.
    13. Livio de Santoli & Gianluigi Lo Basso & Davide Astiaso Garcia & Giuseppe Piras & Giulia Spiridigliozzi, 2019. "Dynamic Simulation Model of Trans-Critical Carbon Dioxide Heat Pump Application for Boosting Low Temperature Distribution Networks in Dwellings," Energies, MDPI, vol. 12(3), pages 1-20, February.
    14. Francesco Mancini & Gianluigi Lo Basso & Livio De Santoli, 2019. "Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey," Energies, MDPI, vol. 12(11), pages 1-19, May.
    15. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.
    16. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    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. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    2. Martha N. Acosta & Francisco Gonzalez-Longatt & Juan Manuel Roldan-Fernandez & Manuel Burgos-Payan, 2021. "A Coordinated Control of Offshore Wind Power and BESS to Provide Power System Flexibility," Energies, MDPI, vol. 14(15), pages 1-17, July.

    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. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    2. Francesco Mancini & Benedetto Nastasi, 2020. "Solar Energy Data Analytics: PV Deployment and Land Use," Energies, MDPI, vol. 13(2), pages 1-18, January.
    3. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    4. Bhagya Nathali Silva & Murad Khan & Kijun Han, 2020. "Futuristic Sustainable Energy Management in Smart Environments: A Review of Peak Load Shaving and Demand Response Strategies, Challenges, and Opportunities," Sustainability, MDPI, vol. 12(14), pages 1-23, July.
    5. Lo Basso, Gianluigi & de Santoli, Livio & Paiolo, Romano & Losi, Claudio, 2021. "The potential role of trans-critical CO2 heat pumps within a solar cooling system for building services: The hybridised system energy analysis by a dynamic simulation model," Renewable Energy, Elsevier, vol. 164(C), pages 472-490.
    6. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    7. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    8. Shakeri, Mohammad & Shayestegan, Mohsen & Reza, S.M. Salim & Yahya, Iskandar & Bais, Badariah & Akhtaruzzaman, Md & Sopian, Kamaruzzaman & Amin, Nowshad, 2018. "Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source," Renewable Energy, Elsevier, vol. 125(C), pages 108-120.
    9. Meng, Qinglong & Wei, Ying'an & Fan, Jingjing & Li, Yanbo & Zhao, Fan & Lei, Yu & Sun, Hang & Jiang, Le & Yu, Lingli, 2024. "Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China," Renewable Energy, Elsevier, vol. 224(C).
    10. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    11. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    12. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    13. Yunbo Yang & Rongling Li & Tao Huang, 2020. "Smart Meter Data Analysis of a Building Cluster for Heating Load Profile Quantification and Peak Load Shifting," Energies, MDPI, vol. 13(17), pages 1-20, August.
    14. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    15. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
    16. Rajavelu Dharani & Madasamy Balasubramonian & Thanikanti Sudhakar Babu & Benedetto Nastasi, 2021. "Load Shifting and Peak Clipping for Reducing Energy Consumption in an Indian University Campus," Energies, MDPI, vol. 14(3), pages 1-16, January.
    17. Heendeniya, Charitha Buddhika & Sumper, Andreas & Eicker, Ursula, 2020. "The multi-energy system co-planning of nearly zero-energy districts – Status-quo and future research potential," Applied Energy, Elsevier, vol. 267(C).
    18. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    19. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    20. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.

    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:gam:jeners:v:14:y:2021:i:11:p:3080-:d:562191. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.