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Building Automation and Control Systems and Electrical Distribution Grids: A Study on the Effects of Loads Control Logics on Power Losses and Peaks

Author

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  • Salvatore Favuzza

    (Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, 90128 Palermo, Italy)

  • Mariano Giuseppe Ippolito

    (Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, 90128 Palermo, Italy)

  • Fabio Massaro

    (Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, 90128 Palermo, Italy)

  • Rossano Musca

    (Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, 90128 Palermo, Italy)

  • Eleonora Riva Sanseverino

    (Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, 90128 Palermo, Italy)

  • Giuseppe Schillaci

    (Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, 90128 Palermo, Italy)

  • Gaetano Zizzo

    (Department of Energy, Information Engineering and Mathematical Models, Università di Palermo, 90128 Palermo, Italy)

Abstract

Growing home comfort is causing increasing energy consumption in residential buildings and a consequent stress in urban medium and low voltage distribution networks. Therefore, distribution system operators are obliged to manage problems related to the reliability of the electricity system and, above all, they must consider investments for enhancing the electrical infrastructure. The purpose of this paper is to assess how the reduction of building electricity consumption and the modification of the building load profile, due to load automation, combined with suitable load control programs, can improve network reliability and distribution efficiency. This paper proposes an extensive study on this issue, considering various operating scenarios with four load control programs with different purposes, the presence/absence of local generation connected to the buildings and different external thermal conditions. The study also highlights how different climatic conditions can influence the effects of the load control logics.

Suggested Citation

  • Salvatore Favuzza & Mariano Giuseppe Ippolito & Fabio Massaro & Rossano Musca & Eleonora Riva Sanseverino & Giuseppe Schillaci & Gaetano Zizzo, 2018. "Building Automation and Control Systems and Electrical Distribution Grids: A Study on the Effects of Loads Control Logics on Power Losses and Peaks," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:667-:d:136512
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    References listed on IDEAS

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    1. Favuzza, S. & Galioto, G. & Ippolito, M.G. & Massaro, F. & Milazzo, F. & Pecoraro, G. & Riva Sanseverino, E. & Telaretti, E., 2015. "Real-time pricing for aggregates energy resources in the Italian energy market," Energy, Elsevier, vol. 87(C), pages 251-258.
    2. Bernhard Faessler & Michael Schuler & Markus Preißinger & Peter Kepplinger, 2017. "Battery Storage Systems as Grid-Balancing Measure in Low-Voltage Distribution Grids with Distributed Generation," Energies, MDPI, vol. 10(12), pages 1-14, December.
    3. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2017. "Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential," Applied Energy, Elsevier, vol. 191(C), pages 125-140.
    4. Faessler, B. & Kepplinger, P. & Petrasch, J., 2017. "Decentralized price-driven grid balancing via repurposed electric vehicle batteries," Energy, Elsevier, vol. 118(C), pages 446-455.
    5. Aydin, Celil & Esen, Ömer, 2018. "Does the level of energy intensity matter in the effect of energy consumption on the growth of transition economies? Evidence from dynamic panel threshold analysis," Energy Economics, Elsevier, vol. 69(C), pages 185-195.
    6. Aghaei, Jamshid & Alizadeh, Mohammad-Iman & Siano, Pierluigi & Heidari, Alireza, 2016. "Contribution of emergency demand response programs in power system reliability," Energy, Elsevier, vol. 103(C), pages 688-696.
    7. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimization framework for distributed energy systems with integrated electrical grid constraints," Applied Energy, Elsevier, vol. 171(C), pages 296-313.
    8. Aryandoust, Arsam & Lilliestam, Johan, 2017. "The potential and usefulness of demand response to provide electricity system services," Applied Energy, Elsevier, vol. 204(C), pages 749-766.
    9. Wang, Yubo & Shi, Wenbo & Wang, Bin & Chu, Chi-Cheng & Gadh, Rajit, 2017. "Optimal operation of stationary and mobile batteries in distribution grids," Applied Energy, Elsevier, vol. 190(C), pages 1289-1301.
    10. Omowunmi Mary Longe & Khmaies Ouahada & Suvendi Rimer & Ashot N. Harutyunyan & Hendrik C. Ferreira, 2017. "Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling," Sustainability, MDPI, vol. 9(1), pages 1-13, January.
    11. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    12. Graditi, G. & Ippolito, M.G. & Telaretti, E. & Zizzo, G., 2016. "Technical and economical assessment of distributed electrochemical storages for load shifting applications: An Italian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 515-523.
    13. Muruganantham, B. & Gnanadass, R. & Padhy, N.P., 2017. "Challenges with renewable energy sources and storage in practical distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 125-134.
    14. Omowunmi Mary Longe & Khmaies Ouahada & Suvendi Rimer & Hendrik C. Ferreira & A. J. Han Vinck, 2017. "Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
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    Cited by:

    1. Di Silvestre, Maria Luisa & Ippolito, Mariano Giuseppe & Sanseverino, Eleonora Riva & Sciumè, Giuseppe & Vasile, Antony, 2021. "Energy self-consumers and renewable energy communities in Italy: New actors of the electric power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Aniela Kaminska & Andrzej Ożadowicz, 2018. "Lighting Control Including Daylight and Energy Efficiency Improvements Analysis," Energies, MDPI, vol. 11(8), pages 1-18, August.
    3. Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.
    4. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.

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