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Time-Series PV Hosting Capacity Assessment with Storage Deployment

Author

Listed:
  • Magdalena Bartecka

    (Institute of Electrical Power Engineering, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland)

  • Grazia Barchi

    (Institute for Renewable Energy, Eurac Research, viale Druso 1, 39100 Bolzano, Italy)

  • Józef Paska

    (Institute of Electrical Power Engineering, Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland)

Abstract

Europe aims to diversify energy sources and reduce greenhouse gas emissions. On this field, large PV power growth is observed that may cause problems in existing networks. This paper examines the impact of distributed PV systems on voltage quality in a low voltage feeder in terms of the European standard EN 50160. As the standard defines allowable percentage of violation during one week period, time-series analyses are done to assess PV hosting capacity. The simulations are conducted with 10-minute step and comprise variable load profiles based on Gaussian Mixture Model and PV profiles based on a distribution with experimentally obtained parameters. In addition, the outcomes are compared with “snapshot” simulations. Next, it is examined how energy storage utilization affects the hosting capacity. Several deployments of energy storages are presented with different number and capacity. In particular, a greedy algorithm is proposed to determine the sub-optimal energy storage deployment based on the voltage deviation minimization. The simulations show that time-series analyses in comparison with snapshot analyses give completely different results and change the level of PV hosting capacity. Moreover, incorrect energy storage capacity selection and location may cause even deterioration of power quality in electrical systems with high RES penetration.

Suggested Citation

  • Magdalena Bartecka & Grazia Barchi & Józef Paska, 2020. "Time-Series PV Hosting Capacity Assessment with Storage Deployment," Energies, MDPI, vol. 13(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2524-:d:358862
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    References listed on IDEAS

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    1. Miha Grabner & Andrej Souvent & Nermin Suljanović & Andrej Košir & Boštjan Blažič, 2019. "Probabilistic Methodology for Calculating PV Hosting Capacity in LV Networks Using Actual Building Roof Data," Energies, MDPI, vol. 12(21), pages 1-15, October.
    2. Nathphol Khaboot & Chitchai Srithapon & Apirat Siritaratiwat & Pirat Khunkitti, 2019. "Increasing Benefits in High PV Penetration Distribution System by Using Battery Enegy Storage and Capacitor Placement Based on Salp Swarm Algorithm," Energies, MDPI, vol. 12(24), pages 1-20, December.
    3. Tiago Elias Castelo de Oliveira & Math Bollen & Paulo Fernando Ribeiro & Pedro M. S. de Carvalho & Antônio C. Zambroni & Benedito D. Bonatto, 2019. "The Concept of Dynamic Hosting Capacity for Distributed Energy Resources: Analytics and Practical Considerations," Energies, MDPI, vol. 12(13), pages 1-18, July.
    4. Ismael, Sherif M. & Abdel Aleem, Shady H.E. & Abdelaziz, Almoataz Y. & Zobaa, Ahmed F., 2019. "State-of-the-art of hosting capacity in modern power systems with distributed generation," Renewable Energy, Elsevier, vol. 130(C), pages 1002-1020.
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    Cited by:

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    2. Jakub Jurasz & Jerzy Mikulik & Paweł B. Dąbek & Mohammed Guezgouz & Bartosz Kaźmierczak, 2021. "Complementarity and ‘Resource Droughts’ of Solar and Wind Energy in Poland: An ERA5-Based Analysis," Energies, MDPI, vol. 14(4), pages 1-24, February.
    3. Mohsen Kandidayeni & Alvaro Macias & Loïc Boulon & João Pedro F. Trovão, 2020. "Online Modeling of a Fuel Cell System for an Energy Management Strategy Design," Energies, MDPI, vol. 13(14), pages 1-17, July.
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    5. Karmaker, Ashish Kumar & Prakash, Krishneel & Siddique, Md Nazrul Islam & Hossain, Md Alamgir & Pota, Hemanshu, 2024. "Electric vehicle hosting capacity analysis: Challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    6. Marco Pasetti, 2021. "Assessing the Effectiveness of the Energy Storage Rule-Based Control in Reducing the Power Flow Uncertainties Caused by Distributed Photovoltaic Systems," Energies, MDPI, vol. 14(8), pages 1-25, April.

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