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Techno-economic assessment of university energy communities with on/off microgrid

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  • Sima, Catalina Alexandra
  • Popescu, Claudia Laurenta
  • Popescu, Mihai Octavian
  • Roscia, Mariacristina
  • Seritan, George
  • Panait, Cornel

Abstract

The integration of small-scale renewable energy sources, in particular in areas with favorable meteorological conditions and medium size power request, supported by governmental incentives and other regulatory facilities determined their aggregation towards local energy communities. In addition, the new technological developments like smart grids, smart measuring and blockchain for locally managing energy flows and monetary transactions determined the diffusion of the energy communities towards educational facilities, in particular university campuses. These campuses benefit for large open spaces, important rooftops areas and various energy consumption installations. The paper deals with the analysis of a real case study of an energy community university campus in the seaside area of Romania. A planning investment process was carried on accommodating the smart grid facilities to cover load demand minimizing energy costs, concomitant with the up-scaling design for ensuring decarbonation of the university campus. An optimization problem is formulated based on the existing renewable energy sources and storage systems such that to maximize the profit obtained by the university campus in exchanging energy flows with the upstream grid, and considering the benefits of load consumption considering the university character of the facility. The simulation results are based on the real measurements.

Suggested Citation

  • Sima, Catalina Alexandra & Popescu, Claudia Laurenta & Popescu, Mihai Octavian & Roscia, Mariacristina & Seritan, George & Panait, Cornel, 2022. "Techno-economic assessment of university energy communities with on/off microgrid," Renewable Energy, Elsevier, vol. 193(C), pages 538-553.
    • Handle: RePEc:eee:renene:v:193:y:2022:i:c:p:538-553
    DOI: 10.1016/j.renene.2022.05.047
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    Cited by:

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    2. Heilmann, Jakob & Wensaas, Marthe & Crespo del Granado, Pedro & Hashemipour, Naser, 2022. "Trading algorithms to represent the wholesale market of energy communities in Norway and England," Renewable Energy, Elsevier, vol. 200(C), pages 1426-1437.
    3. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    4. Simoiu, Mircea Stefan & Fagarasan, Ioana & Ploix, Stéphane & Calofir, Vasile, 2022. "Modeling the energy community members’ willingness to change their behaviour with multi-agent systems: A stochastic approach," Renewable Energy, Elsevier, vol. 194(C), pages 1233-1246.
    5. Tomin, Nikita & Shakirov, Vladislav & Kurbatsky, Victor & Muzychuk, Roman & Popova, Ekaterina & Sidorov, Denis & Kozlov, Alexandr & Yang, Dechang, 2022. "A multi-criteria approach to designing and managing a renewable energy community," Renewable Energy, Elsevier, vol. 199(C), pages 1153-1175.

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