IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1463-d1054926.html
   My bibliography  Save this article

Risk-Averse Stochastic Programming for Planning Hybrid Electrical Energy Systems: A Brazilian Case

Author

Listed:
  • Daniel Kitamura

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Leonardo Willer

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Bruno Dias

    (Electrical Energy Department, Federal University of Juiz de Fora, UFJF, Juiz de Fora 36036-330, Brazil)

  • Tiago Soares

    (Center for Power and Energy Systems, Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal)

Abstract

This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer’s risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse consumers. Under this condition, the PV/battery system is viable for a reduction of 87% in the battery investment cost. An important conclusion is that the risk analysis tool is essential to assist consumers in the decision-making process of investing in HEES.

Suggested Citation

  • Daniel Kitamura & Leonardo Willer & Bruno Dias & Tiago Soares, 2023. "Risk-Averse Stochastic Programming for Planning Hybrid Electrical Energy Systems: A Brazilian Case," Energies, MDPI, vol. 16(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1463-:d:1054926
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1463/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1463/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fodhil, F. & Hamidat, A. & Nadjemi, O., 2019. "Potential, optimization and sensitivity analysis of photovoltaic-diesel-battery hybrid energy system for rural electrification in Algeria," Energy, Elsevier, vol. 169(C), pages 613-624.
    2. Mengjun Ming & Rui Wang & Yabing Zha & Tao Zhang, 2017. "Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(5), pages 1-15, May.
    3. Narayan, Apurva & Ponnambalam, Kumaraswamy, 2017. "Risk-averse stochastic programming approach for microgrid planning under uncertainty," Renewable Energy, Elsevier, vol. 101(C), pages 399-408.
    4. Pouria Sheikhahmadi & Ramyar Mafakheri & Salah Bahramara & Maziar Yazdani Damavandi & João P. S. Catalão, 2018. "Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs," Energies, MDPI, vol. 11(3), pages 1-17, March.
    5. Chih-Ta Tsai & Teketay Mulu Beza & Wei-Bin Wu & Cheng-Chien Kuo, 2019. "Optimal Configuration with Capacity Analysis of a Hybrid Renewable Energy and Storage System for an Island Application," Energies, MDPI, vol. 13(1), pages 1-28, December.
    6. Botelho, D.F. & de Oliveira, L.W. & Dias, B.H. & Soares, T.A. & Moraes, C.A., 2022. "Prosumer integration into the Brazilian energy sector: An overview of innovative business models and regulatory challenges," Energy Policy, Elsevier, vol. 161(C).
    7. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, December.
    8. Yu, Jiah & Ryu, Jun-Hyung & Lee, In-beum, 2019. "A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system," Applied Energy, Elsevier, vol. 247(C), pages 212-220.
    9. Alfonso Angel Medina-Santana & Leopoldo Eduardo Cárdenas-Barrón, 2022. "Optimal Design of Hybrid Renewable Energy Systems Considering Weather Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 15(23), pages 1-28, November.
    10. Rovick Tarife & Yosuke Nakanishi & Yining Chen & Yicheng Zhou & Noel Estoperez & Anacita Tahud, 2022. "Optimization of Hybrid Renewable Energy Microgrid for Rural Agricultural Area in Southern Philippines," Energies, MDPI, vol. 15(6), pages 1-29, March.
    11. Md. Rashedul Islam & Homeyra Akter & Harun Or Rashid Howlader & Tomonobu Senjyu, 2022. "Optimal Sizing and Techno-Economic Analysis of Grid-Independent Hybrid Energy System for Sustained Rural Electrification in Developing Countries: A Case Study in Bangladesh," Energies, MDPI, vol. 15(17), pages 1-21, September.
    12. Lucas Deotti & Wanessa Guedes & Bruno Dias & Tiago Soares, 2020. "Technical and Economic Analysis of Battery Storage for Residential Solar Photovoltaic Systems in the Brazilian Regulatory Context," Energies, MDPI, vol. 13(24), pages 1-30, December.
    13. Mokhtara, Charafeddine & Negrou, Belkhir & Settou, Noureddine & Settou, Belkhir & Samy, Mohamed Mahmoud, 2021. "Design optimization of off-grid Hybrid Renewable Energy Systems considering the effects of building energy performance and climate change: Case study of Algeria," Energy, Elsevier, vol. 219(C).
    14. 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).
    15. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    Full references (including those not matched with items on IDEAS)

    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. Guodong Liu & Zhi Li & Yaosuo Xue & Kevin Tomsovic, 2022. "Microgrid Assisted Design for Remote Areas," Energies, MDPI, vol. 15(10), pages 1-23, May.
    2. Bohlayer, Markus & Bürger, Adrian & Fleschutz, Markus & Braun, Marco & Zöttl, Gregor, 2021. "Multi-period investment pathways - Modeling approaches to design distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 285(C).
    3. 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).
    4. Davoudkhani, Iraj Faraji & Dejamkhooy, Abdolmajid & Nowdeh, Saber Arabi, 2023. "A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging," Applied Energy, Elsevier, vol. 344(C).
    5. Deepika Bishnoi & Harsh Chaturvedi, 2022. "Optimal Design of a Hybrid Energy System for Economic and Environmental Sustainability of Onshore Oil and Gas Fields," Energies, MDPI, vol. 15(6), pages 1-21, March.
    6. Gbalimene Richard Ileberi & Pu Li, 2023. "Integrating Hydrokinetic Energy into Hybrid Renewable Energy System: Optimal Design and Comparative Analysis," Energies, MDPI, vol. 16(8), pages 1-28, April.
    7. Leprince, Julien & Schledorn, Amos & Guericke, Daniela & Dominkovic, Dominik Franjo & Madsen, Henrik & Zeiler, Wim, 2023. "Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities," Applied Energy, Elsevier, vol. 348(C).
    8. Perera, A.T.D. & Khayatian, F. & Eggimann, S. & Orehounig, K. & Halgamuge, Saman, 2022. "Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs," Applied Energy, Elsevier, vol. 328(C).
    9. Pickering, Bryn & Choudhary, Ruchi, 2021. "Quantifying resilience in energy systems with out-of-sample testing," Applied Energy, Elsevier, vol. 285(C).
    10. Pascasio, Jethro Daniel A. & Esparcia, Eugene A. & Castro, Michael T. & Ocon, Joey D., 2021. "Comparative assessment of solar photovoltaic-wind hybrid energy systems: A case for Philippine off-grid islands," Renewable Energy, Elsevier, vol. 179(C), pages 1589-1607.
    11. Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2023. "A framework for quantifying the value of information to mitigate risk in the optimal design of distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 350(C).
    12. Naderipour, Amirreza & Kamyab, Hesam & Klemeš, Jiří Jaromír & Ebrahimi, Reza & Chelliapan, Shreeshivadasan & Nowdeh, Saber Arabi & Abdullah, Aldrin & Hedayati Marzbali, Massoomeh, 2022. "Optimal design of hybrid grid-connected photovoltaic/wind/battery sustainable energy system improving reliability, cost and emission," Energy, Elsevier, vol. 257(C).
    13. Lauren E. Natividad & Pablo Benalcazar, 2023. "Hybrid Renewable Energy Systems for Sustainable Rural Development: Perspectives and Challenges in Energy Systems Modeling," Energies, MDPI, vol. 16(3), pages 1-15, January.
    14. Gabrielli, Paolo & Fürer, Florian & Mavromatidis, Georgios & Mazzotti, Marco, 2019. "Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis," Applied Energy, Elsevier, vol. 238(C), pages 1192-1210.
    15. Güven, Aykut Fatih & Yörükeren, Nuran & Samy, Mohamed Mahmoud, 2022. "Design optimization of a stand-alone green energy system of university campus based on Jaya-Harmony Search and Ant Colony Optimization algorithms approaches," Energy, Elsevier, vol. 253(C).
    16. Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    17. Pandžić, Hrvoje & Kuzle, Igor & Capuder, Tomislav, 2013. "Virtual power plant mid-term dispatch optimization," Applied Energy, Elsevier, vol. 101(C), pages 134-141.
    18. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    19. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    20. Christos N. Dimitriadis & Evangelos G. Tsimopoulos & Michael C. Georgiadis, 2021. "A Review on the Complementarity Modelling in Competitive Electricity Markets," Energies, MDPI, vol. 14(21), pages 1-27, November.

    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:3:p:1463-:d:1054926. 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.