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Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty

Author

Listed:
  • Saidjon Shiralievich Tavarov

    (Institute of Engineering and Technology, South Ural State University, 76 Lenin Prospekt, Chelybinsk 454080, Russia)

  • Alexander Sidorov

    (Institute of Engineering and Technology, South Ural State University, 76 Lenin Prospekt, Chelybinsk 454080, Russia)

  • Zsolt Čonka

    (Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, 042 00 Kosice, Slovakia)

  • Murodbek Safaraliev

    (Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg 620002, Russia)

  • Pavel Matrenin

    (Ural Power Engineering Institute, Ural Federal University, Yekaterinburg 620002, Russia
    Power Plants Department, Novosibirsk State Technical University, Novosibirsk 630073, Russia)

  • Mihail Senyuk

    (Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg 620002, Russia)

  • Svetlana Beryozkina

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Inga Zicmane

    (Faculty of Electrical and Environmental Engineering, Riga Technical University, LV-1048 Riga, Latvia)

Abstract

The article is devoted to solving the problem of managing the mode parameters of an urban electrical network in case of a discrepancy between the actual electrical load and the specific load. Such an issue leads to a deviation of the parameters, in particular, voltage asymmetry in phases due to current asymmetry. To optimize the mode parameters, it is required that the effective value of the electrical load corresponds as much as possible to the values of the specific electrical load. This depends on the following: actual power consumption, external (climatic and meteorological) factors, internal factors (structural design of residential buildings, uneven load when distributed over the phases of three-phase lines and inputs, different number of electrical receivers for consumers), and the provision of consumers with other sources of energy (both gas and heat supply, and hot water supply). To establish the influencing factors on the actual power consumption, it is proposed to generalize the uncertainty accounting coefficient which generalizes both more well-known and less considered factors. Therefore, the authors propose models for determining the electrical loads based on the possibility of assessing the mode parameters of the electrical network by electrical loads. The accuracy of the proposed models is based on the use of the proposed forecasting method considering the actual power consumption and the generalized uncertainty coefficient. Applying the obtained data based on models of electrical loads to the constructed model of a part of a distribution electrical network with real parameters of the electrical network in the MathWorks Simulink environment, the correspondence to the mode parameters of the distribution electrical network is determined. As a result, a device for balancing the voltage depending on the load asymmetry is proposed that is related to the discrepancy between the mode parameters allowing control of the mode parameters by bringing them to acceptable values.

Suggested Citation

  • Saidjon Shiralievich Tavarov & Alexander Sidorov & Zsolt Čonka & Murodbek Safaraliev & Pavel Matrenin & Mihail Senyuk & Svetlana Beryozkina & Inga Zicmane, 2023. "Control of Operational Modes of an Urban Distribution Grid under Conditions of Uncertainty," Energies, MDPI, vol. 16(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3497-:d:1125598
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    1. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    2. Carlo Fezzi and Luca Mosetti, 2020. "Size Matters: Estimation Sample Length and Electricity Price Forecasting Accuracy," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 231-254.
    3. 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).
    4. Fatih Issi & Orhan Kaplan, 2018. "The Determination of Load Profiles and Power Consumptions of Home Appliances," Energies, MDPI, vol. 11(3), pages 1-18, March.
    5. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    6. Chan, Felix & Pauwels, Laurent L., 2018. "Some theoretical results on forecast combinations," International Journal of Forecasting, Elsevier, vol. 34(1), pages 64-74.
    7. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.
    8. Mihail Senyuk & Murodbek Safaraliev & Aminjon Gulakhmadov & Javod Ahyoev, 2022. "Application of the Conditional Optimization Method for the Synthesis of the Law of Emergency Control of a Synchronous Generator Steam Turbine Operating in a Complex-Closed Configuration Power System," Mathematics, MDPI, vol. 10(21), pages 1-18, October.
    9. Jicheng Liu & Yu Yin, 2022. "Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China," Energies, MDPI, vol. 15(3), pages 1-23, February.
    10. Hussein Jumma Jabir & Jiashen Teh & Dahaman Ishak & Hamza Abunima, 2018. "Impacts of Demand-Side Management on Electrical Power Systems: A Review," Energies, MDPI, vol. 11(5), pages 1-19, April.
    11. Babai, M.Z. & Dallery, Y. & Boubaker, S. & Kalai, R., 2019. "A new method to forecast intermittent demand in the presence of inventory obsolescence," International Journal of Production Economics, Elsevier, vol. 209(C), pages 30-41.
    12. Konstantinos V. Blazakis & Theodoros N. Kapetanakis & George S. Stavrakakis, 2020. "Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System," Energies, MDPI, vol. 13(12), pages 1-13, June.
    13. Ashfaq Ahmad & Nadeem Javaid & Abdul Mateen & Muhammad Awais & Zahoor Ali Khan, 2019. "Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach," Energies, MDPI, vol. 12(1), pages 1-21, January.
    14. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    15. Fan, Yuantao & Nowaczyk, Sławomir & Rögnvaldsson, Thorsteinn, 2020. "Transfer learning for remaining useful life prediction based on consensus self-organizing models," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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