IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2021i1p109-d710044.html
   My bibliography  Save this article

Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid

Author

Listed:
  • Piotr Powroźnik

    (Institute of Metrology, Electronics and Computer Science, University of Zielona Góra, 65-516 Zielona Gora, Poland)

  • Paweł Szcześniak

    (Institute of Automatic Control, Electronics and Electrical Engineering, University of Zielona Góra, 65-516 Zielona Gora, Poland)

  • Krzysztof Piotrowski

    (IHP-Leibniz Institute for High Performance Microelectronics, 15236 Frankfurt (Oder), Germany)

Abstract

Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of Renewable Energy Sources (RES), the occurrence of the phenomenon of peak demand, as well as exceeding the voltage level above the nominal values in a smart grid makes it justifiable to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research is based on the Elastic Energy Management (EEM) algorithm for smart appliances (SA) using IoT (Internet of Things) technology. The data for the algorithm was obtained from a message broker that implements the Message Queue Telemetry Transport (MQTT) protocol. The complexity of selecting power settings for SA in the EEM algorithm required the use of a solution that is applied to the NP difficult problem class. For this purpose, the Greedy Randomized Adaptive Search Procedure (GRASP) was used in the EEM algorithm. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation.

Suggested Citation

  • Piotr Powroźnik & Paweł Szcześniak & Krzysztof Piotrowski, 2021. "Elastic Energy Management Algorithm Using IoT Technology for Devices with Smart Appliance Functionality for Applications in Smart-Grid," Energies, MDPI, vol. 15(1), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:109-:d:710044
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/1/109/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/1/109/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chu Donatus Iweh & Samuel Gyamfi & Emmanuel Tanyi & Eric Effah-Donyina, 2021. "Distributed Generation and Renewable Energy Integration into the Grid: Prerequisites, Push Factors, Practical Options, Issues and Merits," Energies, MDPI, vol. 14(17), pages 1-34, August.
    2. Fco. Javier Zarco-Soto & Pedro J. Zarco-Periñán & Jose L. Martínez-Ramos, 2021. "Centralized Control of Distribution Networks with High Penetration of Renewable Energies," Energies, MDPI, vol. 14(14), pages 1-13, July.
    3. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    Full references (including those not matched with items on IDEAS)

    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. Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    2. Chen, Long Xiang & Xie, Mei Na & Zhao, Pan Pan & Wang, Feng Xiang & Hu, Peng & Wang, Dong Xiang, 2018. "A novel isobaric adiabatic compressed air energy storage (IA-CAES) system on the base of volatile fluid," Applied Energy, Elsevier, vol. 210(C), pages 198-210.
    3. Miguel J. Prieto & Juan Á. Martínez & Rogelio Peón & Lourdes Á. Barcia & Fernando Nuño, 2017. "On the Convenience of Using Simulation Models to Optimize the Control Strategy of Molten-Salt Heat Storage Systems in Solar Thermal Power Plants," Energies, MDPI, vol. 10(7), pages 1-17, July.
    4. Wang, Longyi & Wu, Mei & Sun, Xiao & Gan, Zhihua, 2016. "A cascade pulse tube cooler capable of energy recovery," Applied Energy, Elsevier, vol. 164(C), pages 572-578.
    5. Majumder, Suman & De, Krishnarti & Kumar, Praveen & Sengupta, Bodhisattva & Biswas, Pabitra Kumar, 2021. "Techno-commercial analysis of sustainable E-bus-based public transit systems: An Indian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    6. Cheayb, Mohamad & Marin Gallego, Mylène & Tazerout, Mohand & Poncet, Sébastien, 2022. "A techno-economic analysis of small-scale trigenerative compressed air energy storage system," Energy, Elsevier, vol. 239(PA).
    7. 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.
    8. Andoni, Merlinda & Robu, Valentin & Flynn, David & Abram, Simone & Geach, Dale & Jenkins, David & McCallum, Peter & Peacock, Andrew, 2019. "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 143-174.
    9. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    10. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Canales, Fausto A. & Lin, Shaoquan & Ahmed, Salman & Zhang, Yijie, 2021. "Economic analysis and optimization of a renewable energy based power supply system with different energy storages for a remote island," Renewable Energy, Elsevier, vol. 164(C), pages 1376-1394.
    11. Qiu, Rui & Zhang, Haoran & Wang, Guotao & Liang, Yongtu & Yan, Jinyue, 2023. "Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid," Applied Energy, Elsevier, vol. 350(C).
    12. Chen, Yang & Odukomaiya, Adewale & Kassaee, Saiid & O’Connor, Patrick & Momen, Ayyoub M. & Liu, Xiaobing & Smith, Brennan T., 2019. "Preliminary analysis of market potential for a hydropneumatic ground-level integrated diverse energy storage system," Applied Energy, Elsevier, vol. 242(C), pages 1237-1247.
    13. Georgiou, Giorgos S. & Christodoulides, Paul & Kalogirou, Soteris A., 2019. "Real-time energy convex optimization, via electrical storage, in buildings – A review," Renewable Energy, Elsevier, vol. 139(C), pages 1355-1365.
    14. Muhammad Umair Safder & Mohammad J. Sanjari & Ameer Hamza & Rasoul Garmabdari & Md. Alamgir Hossain & Junwei Lu, 2023. "Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions," Energies, MDPI, vol. 16(18), pages 1-28, September.
    15. Sherif A. Zaid & Ahmed M. Kassem & Aadel M. Alatwi & Hani Albalawi & Hossam AbdelMeguid & Atef Elemary, 2023. "Optimal Control of an Autonomous Microgrid Integrated with Super Magnetic Energy Storage Using an Artificial Bee Colony Algorithm," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    16. Dib, Ghady & Haberschill, Philippe & Rullière, Romuald & Revellin, Rémi, 2021. "Modelling small-scale trigenerative advanced adiabatic compressed air energy storage for building application," Energy, Elsevier, vol. 237(C).
    17. Chen, Hao & Wang, Huanran & Li, Ruixiong & Sun, Hao & Ge, Gangqiang & Ling, Lanning, 2022. "Experimental and analytical investigation of near-isothermal pumped hydro-compressed air energy storage system," Energy, Elsevier, vol. 249(C).
    18. Guo, Cong & Xu, Yujie & Zhang, Xinjing & Guo, Huan & Zhou, Xuezhi & Liu, Chang & Qin, Wei & Li, Wen & Dou, Binlin & Chen, Haisheng, 2017. "Performance analysis of compressed air energy storage systems considering dynamic characteristics of compressed air storage," Energy, Elsevier, vol. 135(C), pages 876-888.
    19. Hanak, Dawid P. & Powell, Dante & Manovic, Vasilije, 2017. "Techno-economic analysis of oxy-combustion coal-fired power plant with cryogenic oxygen storage," Applied Energy, Elsevier, vol. 191(C), pages 193-203.
    20. Alexandru Ciocan & Cosmin Ungureanu & Alin Chitu & Elena Carcadea & George Darie, 2020. "Electrical Longboard for Everyday Urban Commuting," Sustainability, MDPI, vol. 12(19), pages 1-14, September.

    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:15:y:2021:i:1:p:109-:d:710044. 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.