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Active power management in low voltage networks with high photovoltaics penetration based on prosumers’ self-consumption

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  • Nousdilis, Angelos I.
  • Christoforidis, Georgios C.
  • Papagiannis, Grigoris K.

Abstract

Increase of photovoltaic (PV) systems penetration in distribution networks is being challenged by technical barriers, especially in low voltage (LV) networks, such as over-voltages caused by reverse power flows and high PV power injection to the grid. Among other solutions for over-voltage mitigation, active power management can be a highly effective method in LV feeders. However, such methods usually curtail the excess power resulting in a loss of clean and renewable energy and do not take into account the interaction of a prosumer with the grid. To deal with that, this paper proposes a novel active power management methodology for over-voltage mitigation in active LV networks. The methodology calculates the maximum allowed amount of injected power to the grid at each time instant of the day and generates an active power management schedule for the prosumers based on their self-consumption ratio (SCR). This schedule allows prosumers to choose whether to employ either controllable loads or storage systems to manage the generated energy. In this way, injected power to the grid is efficiently handled and over-voltage mitigation is ensured, while the permissible level of PV penetration is increased without requiring large investments by the network operator. The proposed methodology is examined on a LV test network and is compared to other existing techniques for feeder voltage support. The results show that the application of the methodology increases SCR of installations, treating at the same time prosumers in a fairer way compared to existing methods.

Suggested Citation

  • Nousdilis, Angelos I. & Christoforidis, Georgios C. & Papagiannis, Grigoris K., 2018. "Active power management in low voltage networks with high photovoltaics penetration based on prosumers’ self-consumption," Applied Energy, Elsevier, vol. 229(C), pages 614-624.
  • Handle: RePEc:eee:appene:v:229:y:2018:i:c:p:614-624
    DOI: 10.1016/j.apenergy.2018.08.032
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    4. Mustika, Alyssa Diva & Rigo-Mariani, Rémy & Debusschere, Vincent & Pachurka, Amaury, 2022. "A two-stage management strategy for the optimal operation and billing in an energy community with collective self-consumption," Applied Energy, Elsevier, vol. 310(C).
    5. Li, Yinxiao & Wang, Yi & Chen, Qixin, 2020. "Study on the impacts of meteorological factors on distributed photovoltaic accommodation considering dynamic line parameters," Applied Energy, Elsevier, vol. 259(C).
    6. Barzegkar-Ntovom, Georgios A. & Chatzigeorgiou, Nikolas G. & Nousdilis, Angelos I. & Vomva, Styliani A. & Kryonidis, Georgios C. & Kontis, Eleftherios O. & Georghiou, George E. & Christoforidis, Georg, 2020. "Assessing the viability of battery energy storage systems coupled with photovoltaics under a pure self-consumption scheme," Renewable Energy, Elsevier, vol. 152(C), pages 1302-1309.
    7. Wang, Licheng & Yan, Ruifeng & Saha, Tapan Kumar, 2019. "Voltage regulation challenges with unbalanced PV integration in low voltage distribution systems and the corresponding solution," Applied Energy, Elsevier, vol. 256(C).
    8. Huy, Phung Dang & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao & Ekanayake, Janaka B., 2020. "Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage," Energy, Elsevier, vol. 195(C).
    9. Ovidiu Ivanov & Bogdan-Constantin Neagu & Gheorghe Grigoras & Florina Scarlatache & Mihai Gavrilas, 2021. "A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids," Mathematics, MDPI, vol. 9(19), pages 1-17, September.
    10. Anna-Lena Lane & Magdalena Boork & Patrik Thollander, 2019. "Barriers, Driving Forces and Non-Energy Benefits for Battery Storage in Photovoltaic (PV) Systems in Modern Agriculture," Energies, MDPI, vol. 12(18), pages 1-17, September.
    11. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    12. Alessandro Burgio & Daniele Menniti & Nicola Sorrentino & Anna Pinnarelli & Zbigniew Leonowicz, 2020. "Influence and Impact of Data Averaging and Temporal Resolution on the Assessment of Energetic, Economic and Technical Issues of Hybrid Photovoltaic-Battery Systems," Energies, MDPI, vol. 13(2), pages 1-26, January.
    13. Wanessa Guedes & Lucas Deotti & Bruno Dias & Tiago Soares & Leonardo Willer de Oliveira, 2022. "Community Energy Markets with Battery Energy Storage Systems: A General Modeling with Applications," Energies, MDPI, vol. 15(20), pages 1-22, October.

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