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Voltage Optimization in MV Network with Distributed Generation Using Power Consumption Control in Electrolysis Installations

Author

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  • Paweł Pijarski

    (Department of Power Engineering, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka St. 38D, 20-618 Lublin, Poland)

  • Piotr Kacejko

    (Department of Power Engineering, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka St. 38D, 20-618 Lublin, Poland)

Abstract

Connecting a large number of distributed sources to the medium and low voltage grid poses many problems. The most important of these are the voltage changes inside the network, what can be observed when the power flow from these sources towards the HV/MV (High Voltage/Medium Voltage) transformer station. In particular, if the power consumption in nodes of the MV network is small and the distance between the place of installation of the source and the substation is large, increases and changes in voltage may be dangerous for the insulation of the network and burdensome for the consumers connected to it. The solution most frequently used to control voltage increases is the appropriate setting of the controller that affects the on-load tap changer of the MV/HV or even MV/LV (Medium Voltage/Low Voltage) transformer. It is also possible to regulate the reactive power of the sources and, of course, to limit their generated active power (curtailment of generation). The development of energy storage technology has made it possible to introduce consumers into the network, whose power can be controlled in a wide range. The article proposes the concept of an innovative voltage control system in the MV network, whose output values are three groups of parameters: HV/MV transformer ratio, reactive power of sources and active power of consumers connected in generation nodes. In the technological sense, it has been assumed that the loads are installations of electrolyzers used to produce “green hydrogen”, according to the P2G (Power to Gas) formula. The tests consisting in the execution of several hundred calculation cycles for the IEEE 37 test network, using the Monte Carlo simulation, have shown that the subordination of the hydrogen production process to the objectives of voltage control in the MV network clearly contributes to stabilizing its value, while meeting the technological requirements. The control variables of the proposed control system are the result of the optimization algorithm described in the article, the function of which is the quality of network voltage.

Suggested Citation

  • Paweł Pijarski & Piotr Kacejko, 2021. "Voltage Optimization in MV Network with Distributed Generation Using Power Consumption Control in Electrolysis Installations," Energies, MDPI, vol. 14(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:993-:d:499149
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    References listed on IDEAS

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    2. Karol Sidor & Piotr Miller & Robert Małkowski & Michał Izdebski, 2024. "Optimization of Division and Reconfiguration Locations of the Medium-Voltage Power Grid Based on Forecasting the Level of Load and Generation from Renewable Energy Sources," Energies, MDPI, vol. 17(19), pages 1-21, October.
    3. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    4. Tiago P. Abud & Andre A. Augusto & Marcio Z. Fortes & Renan S. Maciel & Bruno S. M. C. Borba, 2022. "State of the Art Monte Carlo Method Applied to Power System Analysis with Distributed Generation," Energies, MDPI, vol. 16(1), pages 1-24, December.
    5. Paweł Pijarski & Adrian Belowski, 2024. "Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 17(2), pages 1-42, January.
    6. Paweł Pijarski & Piotr Kacejko & Marek Wancerz, 2022. "Voltage Control in MV Network with Distributed Generation—Possibilities of Real Quality Enhancement," Energies, MDPI, vol. 15(6), pages 1-22, March.
    7. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
    8. Evgeny Solomin & Shanmuga Priya Selvanathan & Sudhakar Kumarasamy & Anton Kovalyov & Ramyashree Maddappa Srinivasa, 2021. "The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations," Energies, MDPI, vol. 14(9), pages 1-18, May.
    9. Paweł Pijarski & Candra Saigustia & Piotr Kacejko & Adrian Belowski & Piotr Miller, 2024. "Optimal Network Reconfiguration and Power Curtailment of Renewable Energy Sources to Eliminate Overloads of Power Lines," Energies, MDPI, vol. 17(12), pages 1-22, June.
    10. Bartłomiej Mroczek & Paweł Pijarski, 2021. "DSO Strategies Proposal for the LV Grid of the Future," Energies, MDPI, vol. 14(19), pages 1-19, October.
    11. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.

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