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Model Predictive Control for the Energy Management in a District of Buildings Equipped with Building Integrated Photovoltaic Systems and Batteries

Author

Listed:
  • Maria C. Fotopoulou

    (Chemical Process and Energy Resources Institute, Center for Research and Technology Hellas, 6th km Charilaou-Thermi Road, GR-57001 Thermi, Thessaloniki, Greece)

  • Panagiotis Drosatos

    (Chemical Process and Energy Resources Institute, Center for Research and Technology Hellas, 6th km Charilaou-Thermi Road, GR-57001 Thermi, Thessaloniki, Greece)

  • Stefanos Petridis

    (Chemical Process and Energy Resources Institute, Center for Research and Technology Hellas, 6th km Charilaou-Thermi Road, GR-57001 Thermi, Thessaloniki, Greece)

  • Dimitrios Rakopoulos

    (Chemical Process and Energy Resources Institute, Center for Research and Technology Hellas, 6th km Charilaou-Thermi Road, GR-57001 Thermi, Thessaloniki, Greece)

  • Fotis Stergiopoulos

    (Chemical Process and Energy Resources Institute, Center for Research and Technology Hellas, 6th km Charilaou-Thermi Road, GR-57001 Thermi, Thessaloniki, Greece)

  • Nikolaos Nikolopoulos

    (Chemical Process and Energy Resources Institute, Center for Research and Technology Hellas, 6th km Charilaou-Thermi Road, GR-57001 Thermi, Thessaloniki, Greece)

Abstract

This paper introduces a Model Predictive Control (MPC) strategy for the optimal energy management of a district whose buildings are equipped with vertically placed Building Integrated Photovoltaic (BIPV) systems and Battery Energy Storage Systems (BESS). The vertically placed BIPV systems are able to cover larger areas of buildings’ surfaces, as compared with conventional rooftop PV systems, and reach their peak of production during winter and spring, which renders them suitable for energy harvesting especially in urban areas. Driven by both these relative advantages, the proposed strategy aims to maximize the district’s autonomy from the external grid, which is achieved through the cooperation of interactive buildings. Therefore, the major contribution of this study is the management and optimal cooperation of a group of buildings, each of which is equipped with its own system of vertical BIPV panels and BESS, carried out by an MPC strategy. The proposed control scheme consists of three main components, i.e., the forecaster, the optimizer and the district, which interact periodically with each other. In order to quantitatively evaluate the benefits of the proposed MPC strategy and the implementation of vertical BIPV and BESS, a hypothetical five-node distribution network located in Greece for four representative days of the year was examined, followed by a sensitivity analysis to examine the effect of the system configuration on its performance.

Suggested Citation

  • Maria C. Fotopoulou & Panagiotis Drosatos & Stefanos Petridis & Dimitrios Rakopoulos & Fotis Stergiopoulos & Nikolaos Nikolopoulos, 2021. "Model Predictive Control for the Energy Management in a District of Buildings Equipped with Building Integrated Photovoltaic Systems and Batteries," Energies, MDPI, vol. 14(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3369-:d:571025
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    References listed on IDEAS

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    1. Bartolini, Andrea & Comodi, Gabriele & Salvi, Danilo & Østergaard, Poul Alberg, 2020. "Renewables self-consumption potential in districts with high penetration of electric vehicles," Energy, Elsevier, vol. 213(C).
    2. Iris, Çağatay & Lam, Jasmine Siu Lee, 2021. "Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty," Omega, Elsevier, vol. 103(C).
    3. Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    4. Carlos Toledo & Ana Maria Gracia Amillo & Giorgio Bardizza & Jose Abad & Antonio Urbina, 2020. "Evaluation of Solar Radiation Transposition Models for Passive Energy Management and Building Integrated Photovoltaics," Energies, MDPI, vol. 13(3), pages 1-24, February.
    5. Gasparatos, Alexandros & Doll, Christopher N.H. & Esteban, Miguel & Ahmed, Abubakari & Olang, Tabitha A., 2017. "Renewable energy and biodiversity: Implications for transitioning to a Green Economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 161-184.
    6. Ipsakis, Dimitris & Voutetakis, Spyros & Seferlis, Panos & Stergiopoulos, Fotis & Papadopoulou, Simira & Elmasides, Costas, 2008. "The effect of the hysteresis band on power management strategies in a stand-alone power system," Energy, Elsevier, vol. 33(10), pages 1537-1550.
    7. Li, Jiaming, 2019. "Optimal sizing of grid-connected photovoltaic battery systems for residential houses in Australia," Renewable Energy, Elsevier, vol. 136(C), pages 1245-1254.
    8. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    9. Nguyen, Su & Peng, Wei & Sokolowski, Peter & Alahakoon, Damminda & Yu, Xinghuo, 2018. "Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 228(C), pages 2567-2580.
    10. McClain, John O., 1988. "Dominant tracking signals," International Journal of Forecasting, Elsevier, vol. 4(4), pages 563-572.
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    Cited by:

    1. Panagiotis Michailidis & Iakovos Michailidis & Elias Kosmatopoulos, 2024. "Review and Evaluation of Multi-Agent Control Applications for Energy Management in Buildings," Energies, MDPI, vol. 17(19), pages 1-38, September.
    2. Elnour, Mariam & Himeur, Yassine & Fadli, Fodil & Mohammedsherif, Hamdi & Meskin, Nader & Ahmad, Ahmad M. & Petri, Ioan & Rezgui, Yacine & Hodorog, Andrei, 2022. "Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities," Applied Energy, Elsevier, vol. 318(C).
    3. Piotr Powroźnik & Paweł Szcześniak & Krzysztof Turchan & Miłosz Krysik & Igor Koropiecki & Krzysztof Piotrowski, 2022. "An Elastic Energy Management Algorithm in a Hierarchical Control System with Distributed Control Devices," Energies, MDPI, vol. 15(13), pages 1-24, June.
    4. Panagiotis Michailidis & Iakovos Michailidis & Socratis Gkelios & Elias Kosmatopoulos, 2024. "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, MDPI, vol. 17(3), pages 1-47, January.
    5. Apostolopoulos, Vasilis & Mamounakis, Ioannis & Seitaridis, Andreas & Tagkoulis, Nikolas & Kourkoumpas, Dimitrios-Sotirios & Iliadis, Petros & Angelakoglou, Komninos & Nikolopoulos, Nikolaos, 2023. "Αn integrated life cycle assessment and life cycle costing approach towards sustainable building renovation via a dynamic online tool," Applied Energy, Elsevier, vol. 334(C).

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