A Microgrid Formation-Based Restoration Model for Resilient Distribution Systems Using Distributed Energy Resources and Demand Response Programs

Gilani, Mohammad Amin; Dashti, Reza; Ghasemi, Mostafa; Amirioun, Mohammad Hassan; Shafie‐khah, Miadreza

doi:10.2139/ssrn.4046621

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Modelling Of Price Elasticity Coefficient Based On Znumber1

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2022

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Cited by 3 publications

(1 citation statement)

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“…This paper introduces Z-number method to model the uncertainty of price elasticity coefficient t ε . Z-Number is based on linguistic logic, which can simultaneously consider the uncertainty in the process of data receiving and the reliability of data inference, and reduce the fuzzy uncertainty of data to a minimum [20].…”

Section: Modelling Of Price Elasticity Coefficient Based On Znumbermentioning

confidence: 99%

Quantifying the Capacity Credit of Idc-Based Demand Response in Smart Distribution Systems

Zhang

Zeng

et al. 2022

SSRN Journal

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Section: Modelling Of Price Elasticity Coefficient Based On Znumbermentioning

confidence: 99%

Quantifying the Capacity Credit of Idc-Based Demand Response in Smart Distribution Systems

Zhang

Zeng

et al. 2022

SSRN Journal

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A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units

Mansouri

Nematbakhsh

Ahmarinejad

et al. 2022

Renewable and Sustainable Energy Reviews

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Quantifying the Capacity Credit of IDC-based Demand Response in Smart Distribution Systems

Zhang

Zeng

et al. 2022

Preprint

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With the explosive growth of online services, the Internet Data Center (IDC) has been constantly growing, and becoming an emerging load demand in modern power systems. The power consumption of IDCs is believed suitable for demand response (DR) purposes. However, to what extent IDCs could be used as candidate DR resources to provide capacity support are not only dependent on the technical property of IDC equipment but also affected by the willingness of data end-users to participate in the DR program. To address the above issue, this paper presents a methodological framework for quantifying the potential value of IDC-based DR in smart distribution grids. To achieve this, through comprehensive consideration of operational constraints for both data center and power systems, the concept of capacity credit (CC) is introduced and extended into the IDC scenario. The proposed framework explicitly considered the impacts of both uncertainty of user participation intention and the reliability of basic information in the DR process. A novel Z-number based price elasticity uncertainty model is introduced, and then a more realistic evaluation of IDC capacity credit is obtained. Finally, the effectiveness of the proposed model and method is illustrated on a modified IEEE-33 node network, and the obtained results verify the significance of IDC-based DR in enhancing the adequacy of supply in distribution grids.

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A Microgrid Formation-Based Restoration Model for Resilient Distribution Systems Using Distributed Energy Resources and Demand Response Programs

Cited by 3 publications

References 30 publications

Quantifying the Capacity Credit of Idc-Based Demand Response in Smart Distribution Systems

Quantifying the Capacity Credit of Idc-Based Demand Response in Smart Distribution Systems

A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units

Quantifying the Capacity Credit of IDC-based Demand Response in Smart Distribution Systems

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