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A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid

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  • Damilola A. Asaleye

    (Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Co. Cork T12 P928, Ireland)

  • Michael Breen

    (Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Co. Cork T12 P928, Ireland)

  • Michael D. Murphy

    (Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Co. Cork T12 P928, Ireland)

Abstract

The objective of this study was to create a tool that will enable renewable energy microgrid (REμG) facility users to make informed decisions on the utilization of electrical power output from a building integrated REμG connected to a smart grid. A decision support tool for renewable energy microgrids (DSTREM) capable of predicting photovoltaic array and wind turbine power outputs was developed. The tool simulated users’ daily electricity consumption costs, avoided CO 2 emissions and incurred monetary income relative to the usage of the building integrated REμG connected to the national electricity smart grid. DSTREM forecasted climate variables, which were used to predict REμG power output over a period of seven days. Control logic was used to prioritize supply of electricity to consumers from the renewable energy sources and the national smart grid. Across the evaluated REμG electricity supply options and during working days, electricity exported by the REμG to the national smart grid ranged from 0% to 61% of total daily generation. The results demonstrated that both monetary saving and CO 2 offsets can be substantially improved through the application of DSTREM to a REμG connected to a building.

Suggested Citation

  • Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1-29, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1765-:d:117386
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    3. Michael D. Murphy & Paul D. O’Sullivan & Guilherme Carrilho da Graça & Adam O’Donovan, 2021. "Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods," Energies, MDPI, vol. 14(4), pages 1-24, February.
    4. Kamal Chapagain & Somsak Kittipiyakul, 2018. "Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables," Energies, MDPI, vol. 11(4), pages 1-34, April.
    5. Ruifeng Shi & Shaopeng Li & Changhao Sun & Kwang Y. Lee, 2018. "Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-22, August.
    6. Daniele Testi & Paolo Conti & Eva Schito & Luca Urbanucci & Francesco D’Ettorre, 2019. "Synthesis and Optimal Operation of Smart Microgrids Serving a Cluster of Buildings on a Campus with Centralized and Distributed Hybrid Renewable Energy Units," Energies, MDPI, vol. 12(4), pages 1-17, February.
    7. O' Donovan, Adam & O' Sullivan, Paul D. & Murphy, Michael D., 2019. "Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches," Applied Energy, Elsevier, vol. 250(C), pages 991-1010.
    8. Masoumeh Javadi & Mousa Marzband & Mudathir Funsho Akorede & Radu Godina & Ameena Saad Al-Sumaiti & Edris Pouresmaeil, 2018. "A Centralized Smart Decision-Making Hierarchical Interactive Architecture for Multiple Home Microgrids in Retail Electricity Market," Energies, MDPI, vol. 11(11), pages 1-22, November.
    9. Yong Long & Chengrong Pan & Yu Wang, 2018. "Research on a Microgrid Subsidy Strategy Based on Operational Efficiency of the Industry Chain," Sustainability, MDPI, vol. 10(5), pages 1-26, May.
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