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PV Hosting Capacity Analysis and Enhancement Using High Resolution Stochastic Modeling

Author

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  • Emilio J. Palacios-Garcia

    (Departamento de Arquitectura de Computadores, Electrónica y Tecnología Electrónica, Escuela Politécnica Superior, Universidad de Córdoba, Campus de Rabanales, Edificio Leonardo da Vinci, E-14071 Córdoba, Spain)

  • Antonio Moreno-Muñoz

    (Departamento de Arquitectura de Computadores, Electrónica y Tecnología Electrónica, Escuela Politécnica Superior, Universidad de Córdoba, Campus de Rabanales, Edificio Leonardo da Vinci, E-14071 Córdoba, Spain)

  • Isabel Santiago

    (Departamento de Arquitectura de Computadores, Electrónica y Tecnología Electrónica, Escuela Politécnica Superior, Universidad de Córdoba, Campus de Rabanales, Edificio Leonardo da Vinci, E-14071 Córdoba, Spain)

  • Isabel M. Moreno-Garcia

    (Departamento de Arquitectura de Computadores, Electrónica y Tecnología Electrónica, Escuela Politécnica Superior, Universidad de Córdoba, Campus de Rabanales, Edificio Leonardo da Vinci, E-14071 Córdoba, Spain)

  • María I. Milanés-Montero

    (Power Electrical and Electronic Systems Research Group, Escuela de Ingenierías Industriales, Universidad de Extremadura, Avda. de Elvas, s/n, E-06006 Badajoz, Spain)

Abstract

Reduction of CO 2 emissions is a main target in the future smart grid. This goal is boosting the installation of renewable energy resources (RES), as well as a major consumer engagement that seeks for a more efficient utilization of these resources toward the figure of ‘prosumers’. Nevertheless, these resources present an intermittent nature, which requires the presence of an energy storage system and an energy management system (EMS) to ensure an uninterrupted power supply. Moreover, network-related issues might arise due to the increasing power of renewable resources installed in the grid, the storage systems also being capable of contributing to the network stability. However, to assess these future scenarios and test the control strategies, a simulation system is needed. The aim of this paper is to analyze the interaction between residential consumers with high penetration of PV generation and distributed storage and the grid by means of a high temporal resolution simulation scenario based on a stochastic residential load model and PV production records. Results of the model are presented for different PV power rates and storage capacities, as well as a two-level charging strategy as a mechanism for increasing the hosting capacity (HC) of the network.

Suggested Citation

  • Emilio J. Palacios-Garcia & Antonio Moreno-Muñoz & Isabel Santiago & Isabel M. Moreno-Garcia & María I. Milanés-Montero, 2017. "PV Hosting Capacity Analysis and Enhancement Using High Resolution Stochastic Modeling," Energies, MDPI, vol. 10(10), pages 1-22, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1488-:d:113256
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    References listed on IDEAS

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    1. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    2. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    3. Pardo, Angel & Meneu, Vicente & Valor, Enric, 2002. "Temperature and seasonality influences on Spanish electricity load," Energy Economics, Elsevier, vol. 24(1), pages 55-70, January.
    4. Moral-Carcedo, Julian & Vicens-Otero, Jose, 2005. "Modelling the non-linear response of Spanish electricity demand to temperature variations," Energy Economics, Elsevier, vol. 27(3), pages 477-494, May.
    5. Akarslan, Emre & Hocaoglu, Fatih Onur, 2017. "A novel method based on similarity for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 112(C), pages 337-346.
    6. López-Rodríguez, M.A. & Santiago, I. & Trillo-Montero, D. & Torriti, J. & Moreno-Munoz, A., 2013. "Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption," Energy Policy, Elsevier, vol. 62(C), pages 742-751.
    7. Moreno-Munoz, A. & Bellido-Outeirino, F.J. & Siano, P. & Gomez-Nieto, M.A., 2016. "Mobile social media for smart grids customer engagement: Emerging trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1611-1616.
    8. Olaszi, Balint D. & Ladanyi, Jozsef, 2017. "Comparison of different discharge strategies of grid-connected residential PV systems with energy storage in perspective of optimal battery energy storage system sizing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 710-718.
    9. Salom, Jaume & Marszal, Anna Joanna & Widén, Joakim & Candanedo, José & Lindberg, Karen Byskov, 2014. "Analysis of load match and grid interaction indicators in net zero energy buildings with simulated and monitored data," Applied Energy, Elsevier, vol. 136(C), pages 119-131.
    10. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    11. Cao, Sunliang & Sirén, Kai, 2014. "Impact of simulation time-resolution on the matching of PV production and household electric demand," Applied Energy, Elsevier, vol. 128(C), pages 192-208.
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    4. Hwang, Hyunkyeong & Yoon, Ahyun & Yoon, Yongtae & Moon, Seungil, 2023. "Demand response of HVAC systems for hosting capacity improvement in distribution networks: A comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    5. 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.
    6. Musharraf Wajahat & Hassan Abdullah Khalid & Ghullam Mustafa Bhutto & Claus Leth Bak, 2019. "A Comparative Study into Enhancing the PV Penetration Limit of a LV CIGRE Residential Network with Distributed Grid-Tied Single-Phase PV Systems," Energies, MDPI, vol. 12(15), pages 1-17, August.
    7. Koirala, Arpan & Van Acker, Tom & D’hulst, Reinhilde & Van Hertem, Dirk, 2022. "Hosting capacity of photovoltaic systems in low voltage distribution systems: A benchmark of deterministic and stochastic approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    8. 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.
    9. Sherif M. Ismael & Shady H. E. Abdel Aleem & Almoataz Y. Abdelaziz & Ahmed F. Zobaa, 2019. "Probabilistic Hosting Capacity Enhancement in Non-Sinusoidal Power Distribution Systems Using a Hybrid PSOGSA Optimization Algorithm," Energies, MDPI, vol. 12(6), pages 1-23, March.
    10. Barbour, Edward & Parra, David & Awwad, Zeyad & González, Marta C., 2018. "Community energy storage: A smart choice for the smart grid?," Applied Energy, Elsevier, vol. 212(C), pages 489-497.
    11. Santiago, I. & Moreno-Munoz, A. & Quintero-Jiménez, P. & Garcia-Torres, F. & Gonzalez-Redondo, M.J., 2021. "Electricity demand during pandemic times: The case of the COVID-19 in Spain," Energy Policy, Elsevier, vol. 148(PA).

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