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Simple electric utility platform: A hardware/software solution for operating emergent microgrids

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  • Manur, Ashray
  • Venkataramanan, Giri
  • Sehloff, David

Abstract

Along with the technical feasibility of microgrids, the importance of supervisory communications, computing, and control (CCC) systems that are necessary to operate a microgrid in a stable and economically feasible manner have been firmly established in the literature. While several investigators have proposed and demonstrated algorithms and applications of CCC, the functional entities and responsibility centers for realizing CCC within microgrids in the context of multiple independent customers and distribution system entities have not been situated and/or studied. In this context, the term emergent microgrid may be used to define an electrical network that consists of local generation, loads, and storage. Such a grid has the potential to be clustered as a microgrid, but does not have all the CCC features to become a full-fledged microgrid. In this paper, a simple electric utility platform (SEUP) that is an end-to-end solution with all the hardware/software interfaces and components necessary for augmenting emergent microgrids with CCC into full-fledged microgrids is presented. SEUP abstracts the details of sensing and CCC for the microgrid developers, installers, operators and end-users to provide a seamless system. SEUP is distinctive from other CCC platforms in that it is entirely based on publicly available infrastructure resources (wireless cellular networks, internet and cloud computing) without any intellectual property restrictions. The paper introduces the functional roles, main architecture, components and security features of SEUP along with the results from a sample application case of a laboratory-scale power system.

Suggested Citation

  • Manur, Ashray & Venkataramanan, Giri & Sehloff, David, 2018. "Simple electric utility platform: A hardware/software solution for operating emergent microgrids," Applied Energy, Elsevier, vol. 210(C), pages 748-763.
    • Handle: RePEc:eee:appene:v:210:y:2018:i:c:p:748-763
    DOI: 10.1016/j.apenergy.2017.07.073
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    2. Razavi, Rouzbeh & Gharipour, Amin & Fleury, Martin & Akpan, Ikpe Justice, 2019. "A practical feature-engineering framework for electricity theft detection in smart grids," Applied Energy, Elsevier, vol. 238(C), pages 481-494.
    3. Dutton, Spencer & Marnay, Chris & Feng, Wei & Robinson, Matthew & Mammoli, Andrea, 2019. "Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service," Applied Energy, Elsevier, vol. 238(C), pages 1126-1137.
    4. Byeong-Cheol Jeong & Dong-Hwan Shin & Jae-Beom Im & Jae-Young Park & Young-Jin Kim, 2019. "Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid," Energies, MDPI, vol. 12(6), pages 1-20, March.
    5. Zeeshan Aslam & Nadeem Javaid & Ashfaq Ahmad & Abrar Ahmed & Sardar Muhammad Gulfam, 2020. "A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-24, October.
    6. Sahoo, Subham & Pullaguram, Deepak & Mishra, Sukumar & Wu, Jianzhong & Senroy, Nilanjan, 2018. "A containment based distributed finite-time controller for bounded voltage regulation & proportionate current sharing in DC microgrids," Applied Energy, Elsevier, vol. 228(C), pages 2526-2538.

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