IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3369-d571025.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3369/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/12/3369/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bakar, Nur Najihah Abu & Bazmohammadi, Najmeh & Çimen, Halil & Uyanik, Tayfun & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Data-driven ship berthing forecasting for cold ironing in maritime transportation," Applied Energy, Elsevier, vol. 326(C).
    2. Dimitar Bozalakov & Mohannad J. Mnati & Joannes Laveyne & Jan Desmet & Lieven Vandevelde, 2019. "Battery Storage Integration in Voltage Unbalance and Overvoltage Mitigation Control Strategies and Its Impact on the Power Quality," Energies, MDPI, vol. 12(8), pages 1-26, April.
    3. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    4. Yang Shen & Xiuwu Zhang, 2022. "Study on the Impact of Environmental Tax on Industrial Green Transformation," IJERPH, MDPI, vol. 19(24), pages 1-18, December.
    5. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    6. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    7. Michel Noussan & Matteo Jarre, 2021. "Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region," Energies, MDPI, vol. 14(21), pages 1-19, November.
    8. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    9. Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
    10. Fikru, Mahelet G. & Gautier, Luis, 2023. "Consumption and production of cleaner energy by prosumers," Energy Economics, Elsevier, vol. 124(C).
    11. Maren Helen Meyer & Sandra Dullau & Pascal Scholz & Markus Andreas Meyer & Sabine Tischew, 2023. "Bee-Friendly Native Seed Mixtures for the Greening of Solar Parks," Land, MDPI, vol. 12(6), pages 1-16, June.
    12. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
    13. Min-Hwi Kim & Dong-Won Lee & Deuk-Won Kim & Young-Sub An & Jae-Ho Yun, 2021. "Energy Performance Investigation of Bi-Directional Convergence Energy Prosumers for an Energy Sharing Community," Energies, MDPI, vol. 14(17), pages 1-17, September.
    14. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    15. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    16. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    17. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    18. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    19. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    20. Jinyeong Lee & Kyungcheol Shin & Young-Min Wi, 2024. "Decentralized Operations of Industrial Complex Microgrids Considering Corporate Power Purchase Agreements for Renewable Energy 100% Initiatives in South Korea," Sustainability, MDPI, vol. 16(13), pages 1-23, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3369-:d:571025. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.