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

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  • 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
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    References listed on IDEAS

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    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.
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    2. 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).
    3. 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).
    4. 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.
    5. 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).

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