IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i1p562-d1024110.html
   My bibliography  Save this article

Optimal Integration of Photovoltaic Systems in Distribution Networks from a Technical, Financial, and Environmental Perspective

Author

Listed:
  • Jhony Guzman-Henao

    (Facultad de Ingenierías, Instituto Tecnológico Metropolitano, Medellín 050036, Colombia
    These authors contributed equally to this work.)

  • Luis Fernando Grisales-Noreña

    (Department of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile
    These authors contributed equally to this work.)

  • Bonie Johana Restrepo-Cuestas

    (Facultad de Ingenierías, Instituto Tecnológico Metropolitano, Medellín 050036, Colombia
    These authors contributed equally to this work.)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
    Laboratorio Inteligente de Energía, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia
    These authors contributed equally to this work.)

Abstract

Due to the increasing demand for electricity around the world, different technologies have been developed to ensure the sustainability of each and every process involved in its production, transmission, and consumption. In addition to ensuring energy sustainability, these technologies seek to improve some of the characteristics of power systems and, in doing so, make them efficient from a financial, technical, and environmental perspective. In particular, solar photovoltaic (PV) technology is one of the power generation technologies that has had the most influence and development in recent years due to its easy implementation and low maintenance costs. Additionally, since PV systems can be located close to the load, power losses during distribution and transmission can be significantly reduced. However, in order to maximize the financial, technical, and environmental variables involved in the operation of an electrical system, a PV power generation project must guarantee the proper location and sizing of the generation sources. In the specialized literature, different studies have employed mathematical methods to determine the optimal location and size of generation sources. These methods model the operation of electrical systems and provide potential analysis scenarios following the deployment of solar PV units. The majority of such studies, however, do not assess the quality and repeatability of the solutions in short processing times. In light of this, the purpose of this study is to review the literature and contributions made in the field.

Suggested Citation

  • Jhony Guzman-Henao & Luis Fernando Grisales-Noreña & Bonie Johana Restrepo-Cuestas & Oscar Danilo Montoya, 2023. "Optimal Integration of Photovoltaic Systems in Distribution Networks from a Technical, Financial, and Environmental Perspective," Energies, MDPI, vol. 16(1), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:562-:d:1024110
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/562/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/562/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kang, Sang Hoon & Islam, Faridul & Kumar Tiwari, Aviral, 2019. "The dynamic relationships among CO2 emissions, renewable and non-renewable energy sources, and economic growth in India: Evidence from time-varying Bayesian VAR model," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 90-101.
    2. David Barbosa de Alencar & Carolina De Mattos Affonso & Roberto Célio Limão de Oliveira & Jorge Laureano Moya Rodríguez & Jandecy Cabral Leite & José Carlos Reston Filho, 2017. "Different Models for Forecasting Wind Power Generation: Case Study," Energies, MDPI, vol. 10(12), pages 1-27, November.
    3. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    4. Wu, Di & Ma, Xu & Huang, Sen & Fu, Tao & Balducci, Patrick, 2020. "Stochastic optimal sizing of distributed energy resources for a cost-effective and resilient Microgrid," Energy, Elsevier, vol. 198(C).
    5. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    6. Schaube, P. & Ise, A. & Clementi, L., 2022. "Distributed photovoltaic generation in Argentina: An analysis based on the technical innovation system framework," Technology in Society, Elsevier, vol. 68(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Naamane Debdouche & Brahim Deffaf & Habib Benbouhenni & Zarour Laid & Mohamed I. Mosaad, 2023. "Direct Power Control for Three-Level Multifunctional Voltage Source Inverter of PV Systems Using a Simplified Super-Twisting Algorithm," Energies, MDPI, vol. 16(10), pages 1-32, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luis Fernando Grisales-Noreña & Bonie Johana Restrepo-Cuestas & Brandon Cortés-Caicedo & Jhon Montano & Andrés Alfonso Rosales-Muñoz & Marco Rivera, 2022. "Optimal Location and Sizing of Distributed Generators and Energy Storage Systems in Microgrids: A Review," Energies, MDPI, vol. 16(1), pages 1-30, December.
    2. Huang, Yun-Hsun & Wu, Jung-Hua & Hsu, Yu-Ju, 2016. "Two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty," Energy, Elsevier, vol. 116(P1), pages 1145-1157.
    3. Zhang, Xuehan & Son, Yongju & Cheong, Taesu & Choi, Sungyun, 2022. "Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation," Energy, Elsevier, vol. 242(C).
    4. Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    5. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    7. Abdul Rehman & Hengyun Ma & Magdalena Radulescu & Crenguta Ileana Sinisi & Zahid Yousaf, 2021. "Energy Crisis in Pakistan and Economic Progress: Decoupling the Impact of Coal Energy Consumption in Power and Brick Kilns," Mathematics, MDPI, vol. 9(17), pages 1-15, August.
    8. A. Azadeh & M. Saberi & A. Gitiforouz, 2013. "An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2163-2176, June.
    9. Yanfeng Liu & Yaxing Wang & Xi Luo, 2020. "Design and Operation Optimization of Distributed Solar Energy System Based on Dynamic Operation Strategy," Energies, MDPI, vol. 14(1), pages 1-26, December.
    10. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    11. Hanif, Sarmad & Alam, M.J.E. & Roshan, Kini & Bhatti, Bilal A. & Bedoya, Juan C., 2022. "Multi-service battery energy storage system optimization and control," Applied Energy, Elsevier, vol. 311(C).
    12. Vitale, F. & Rispoli, N. & Sorrentino, M. & Rosen, M.A. & Pianese, C., 2021. "On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids," Energy, Elsevier, vol. 225(C).
    13. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    14. Cheng, Min-Yuan & Vu, Quoc-Tuan, 2024. "Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management," Energy, Elsevier, vol. 302(C).
    15. Yang, Yang & Xue, Dingyü, 2016. "Continuous fractional-order grey model and electricity prediction research based on the observation error feedback," Energy, Elsevier, vol. 115(P1), pages 722-733.
    16. Yu Liu & Shan Gao & Xin Zhao & Chao Zhang & Ningyu Zhang, 2017. "Coordinated Operation and Control of Combined Electricity and Natural Gas Systems with Thermal Storage," Energies, MDPI, vol. 10(7), pages 1-25, July.
    17. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    18. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    19. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    20. Abdul Rehman & Laura Mariana Cismas & Maria Daniela Otil, 2022. "Electrical Energy Dilemma and CO 2 Emission in Pakistan: Decomposing the Positive and Negative Shocks by Using an Asymmetric Technique," Sustainability, MDPI, vol. 14(14), pages 1-16, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:562-:d:1024110. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.