IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v208y2020ics0360544220314304.html
   My bibliography  Save this article

Decision making mechanism for a smart neighborhood fed by multi-energy systems considering demand response

Author

Listed:
  • Çiçek, Alper
  • Şengör, İbrahim
  • Erenoğlu, Ayşe Kübra
  • Erdinç, Ozan

Abstract

This study covers a decision-making model in which a multi-energy system (MES) including heat pumps (HPs), combined heat and power (CHP), community energy storage (CES), air conditioners (ACs), and renewable energy sources (RESs) meets the electrical, cooling and heating demands of end-users in a smart neighborhood (SN). The thermostat set point control mechanism (TSCM) and direct compressor control mechanism (DCCM) based thermostatically controllable loads oriented demand response (DR) approaches are also considered in order to increase the effectiveness and economy of the MES operation. The SN, including houses with different types of residential end-users, has flexible and inelastic electricity, heating, and cooling power demands. CHPs, HPs, and ACs are operated optimally to keep the room temperatures between desired temperature limits; furthermore, some end-users have electric vehicles (EVs) assumed as flexible loads. Due to the intermittent nature of RESs, stochastic modeling is used to cope with uncertainties in their production pattern. In addition, time-of-use (TOU) electricity prices and real gas price data are used to handle the test system more realistically. Various comparative case studies have been conducted to prove the effectiveness of the proposed model. According to the obtained results, it can be stated that the DR strategies provide better results than the CES, and the most effective element in MES architecture is CHP for this study. Also, another striking finding is that the reduction in cost is experienced when RESs and EVs penetrate together.

Suggested Citation

  • Çiçek, Alper & Şengör, İbrahim & Erenoğlu, Ayşe Kübra & Erdinç, Ozan, 2020. "Decision making mechanism for a smart neighborhood fed by multi-energy systems considering demand response," Energy, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:energy:v:208:y:2020:i:c:s0360544220314304
    DOI: 10.1016/j.energy.2020.118323
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220314304
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118323?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. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    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. Aghamohamadi, Mehrdad & Hajiabadi, Mohammad Ebrahim & Samadi, Mahdi, 2018. "A novel approach to multi energy system operation in response to DR programs; an application to incentive-based and time-based schemes," Energy, Elsevier, vol. 156(C), pages 534-547.
    4. Wang, Jueying & Hu, Zhijian & Xie, Shiwei, 2019. "Expansion planning model of multi-energy system with the integration of active distribution network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Ma, Tengfei & Wu, Junyong & Hao, Liangliang & Lee, Wei-Jen & Yan, Huaguang & Li, Dezhi, 2018. "The optimal structure planning and energy management strategies of smart multi energy systems," Energy, Elsevier, vol. 160(C), pages 122-141.
    6. Li, Zhengmao & Xu, Yan, 2018. "Optimal coordinated energy dispatch of a multi-energy microgrid in grid-connected and islanded modes," Applied Energy, Elsevier, vol. 210(C), pages 974-986.
    7. Li, Zhengmao & Xu, Yan, 2019. "Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties," Applied Energy, Elsevier, vol. 240(C), pages 719-729.
    8. Ata, Mustafa & Erenoğlu, Ayşe Kübra & Şengör, İbrahim & Erdinç, Ozan & Taşcıkaraoğlu, Akın & Catalão, João P.S., 2019. "Optimal operation of a multi-energy system considering renewable energy sources stochasticity and impacts of electric vehicles," Energy, Elsevier, vol. 186(C).
    9. Pan, Guangsheng & Gu, Wei & Wu, Zhi & Lu, Yuping & Lu, Shuai, 2019. "Optimal design and operation of multi-energy system with load aggregator considering nodal energy prices," Applied Energy, Elsevier, vol. 239(C), pages 280-295.
    10. Good, Nicholas & Martínez Ceseña, Eduardo A. & Heltorp, Christopher & Mancarella, Pierluigi, 2019. "A transactive energy modelling and assessment framework for demand response business cases in smart distributed multi-energy systems," Energy, Elsevier, vol. 184(C), pages 165-179.
    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. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    2. Kachirayil, Febin & Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2022. "Reviewing local and integrated energy system models: insights into flexibility and robustness challenges," Applied Energy, Elsevier, vol. 324(C).
    3. Dezhou Kong & Jianru Jing & Tingyue Gu & Xuanyue Wei & Xingning Sa & Yimin Yang & Zhiang Zhang, 2023. "Theoretical Analysis of Integrated Community Energy Systems (ICES) Considering Integrated Demand Response (IDR): A Review of the System Modelling and Optimization," Energies, MDPI, vol. 16(10), pages 1-22, May.
    4. Erdinç, Fatma Gülşen, 2023. "Rolling horizon optimization based real-time energy management of a residential neighborhood considering PV and ESS usage fairness," Applied Energy, Elsevier, vol. 344(C).
    5. Bodong, Song & Wiseong, Jin & Chengmeng, Li & Khakichi, Aroos, 2023. "Economic management and planning based on a probabilistic model in a multi-energy market in the presence of renewable energy sources with a demand-side management program," Energy, Elsevier, vol. 269(C).

    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. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    2. Rigo-Mariani, Rémy & Chea Wae, Sean Ooi & Mazzoni, Stefano & Romagnoli, Alessandro, 2020. "Comparison of optimization frameworks for the design of a multi-energy microgrid," Applied Energy, Elsevier, vol. 257(C).
    3. Pinto, Edwin S. & Gronier, Timothé & Franquet, Erwin & Serra, Luis M., 2023. "Opportunities and economic assessment for a third-party delivering electricity, heat and cold to residential buildings," Energy, Elsevier, vol. 272(C).
    4. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    5. Moser, A. & Muschick, D. & Gölles, M. & Nageler, P. & Schranzhofer, H. & Mach, T. & Ribas Tugores, C. & Leusbrock, I. & Stark, S. & Lackner, F. & Hofer, A., 2020. "A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis," Applied Energy, Elsevier, vol. 261(C).
    6. 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).
    7. Christina Papadimitriou & Marialaura Di Somma & Chrysanthos Charalambous & Martina Caliano & Valeria Palladino & Andrés Felipe Cortés Borray & Amaia González-Garrido & Nerea Ruiz & Giorgio Graditi, 2023. "A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks," Energies, MDPI, vol. 16(10), pages 1-46, May.
    8. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    9. Xie, Shiwei & Hu, Zhijian & Wang, Jueying & Chen, Yuwei, 2020. "The optimal planning of smart multi-energy systems incorporating transportation, natural gas and active distribution networks," Applied Energy, Elsevier, vol. 269(C).
    10. Qin, Xin & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "A generalized quasi-dynamic model for electric-heat coupling integrated energy system with distributed energy resources," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Maurer, Jona & Tschuch, Nicolai & Krebs, Stefan & Bhattacharya, Kankar & Cañizares, Claudio & Hohmann, Sören, 2023. "Toward transactive control of coupled electric power and district heating networks," Applied Energy, Elsevier, vol. 332(C).
    12. Xie, Shiwei & Zheng, Jieyun & Hu, Zhijian & Wang, Jueying & Chen, Yuwei, 2020. "Urban multi-energy network optimization: An enhanced model using a two-stage bound-tightening approach," Applied Energy, Elsevier, vol. 277(C).
    13. Hoseini, Naghi & Sheikholeslami, Abdolreza & Barforoushi, Taghi & Latify, Mohammad Amin, 2020. "Preventive maintenance mid-term scheduling of resources in multi-carrier energy systems," Energy, Elsevier, vol. 197(C).
    14. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan & Zhang, Xian & Wang, Guibin, 2020. "Low-carbon oriented optimal energy dispatch in coupled natural gas and electricity systems," Applied Energy, Elsevier, vol. 280(C).
    15. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).
    16. Mittelviefhaus, Moritz & Pareschi, Giacomo & Allan, James & Georges, Gil & Boulouchos, Konstantinos, 2021. "Optimal investment and scheduling of residential multi-energy systems including electric mobility: A cost-effective approach to climate change mitigation," Applied Energy, Elsevier, vol. 301(C).
    17. Jasmine Ramsebner & Reinhard Haas & Amela Ajanovic & Martin Wietschel, 2021. "The sector coupling concept: A critical review," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(4), July.
    18. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
    19. Yu Huang & Shuqin Li & Peng Ding & Yan Zhang & Kai Yang & Weiting Zhang, 2019. "Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program," Energies, MDPI, vol. 12(20), pages 1-21, October.
    20. Bartolini, Andrea & Mazzoni, Stefano & Comodi, Gabriele & Romagnoli, Alessandro, 2021. "Impact of carbon pricing on distributed energy systems planning," Applied Energy, Elsevier, vol. 301(C).

    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:energy:v:208:y:2020:i:c:s0360544220314304. 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.journals.elsevier.com/energy .

    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.