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A novel approach to multi energy system operation in response to DR programs; an application to incentive-based and time-based schemes

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  • Aghamohamadi, Mehrdad
  • Hajiabadi, Mohammad Ebrahim
  • Samadi, Mahdi

Abstract

Today, energy hubs (EH) are widely used as the new power delivery methods which can interact as a key role to response the conventional DR programs, even for inelastic loads by integrating electricity, natural gas and other forms of energy. This paper presents an EH operation optimization problem in response to both the time-based DR (TBDR) and incentive-based DR (IBDR) programs. To this end, an integrated responsive load model is employed to illustrate the load modifications due to price changes (pertaining to TBDR programs) and incentives/penalties (pertaining to IBDR programs). The proposed model, tends to maximize the customer's benefit, while, minimizing the EH operation cost by optimal deciding on the purchased energy carriers as well as the converter/storage systems' schedule with respect to the modified purchased energy patterns, satisfying DR schemes. Further, to evaluate the performance of the proposed model, available DR programs for both TBDR and IBDR programs are examined through a comprehensive case study. Results show that, the responsive load demand is curtailed or shifted as a response to DR schemes from the upstream network perspective (considering the modified purchased energy by the EH), while the actual energy consumption remains unchanged from the customers' perspective.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:156:y:2018:i:c:p:534-547
    DOI: 10.1016/j.energy.2018.05.034
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    Citations

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    Cited by:

    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. Yang, Xiaohui & Chen, Zaixing & Huang, Xin & Li, Ruixin & Xu, Shaoping & Yang, Chunsheng, 2021. "Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort," Energy, Elsevier, vol. 221(C).
    3. Aghamohamadi, Mehrdad & Mahmoudi, Amin, 2019. "From bidding strategy in smart grid toward integrated bidding strategy in smart multi-energy systems, an adaptive robust solution approach," Energy, Elsevier, vol. 183(C), pages 75-91.
    4. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    5. 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.
    6. Amin, Amin & Kem, Oudom & Gallegos, Pablo & Chervet, Philipp & Ksontini, Feirouz & Mourshed, Monjur, 2022. "Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation," Applied Energy, Elsevier, vol. 305(C).
    7. 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).

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