IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i23p7871-d1292358.html
   My bibliography  Save this article

Synthesis of Solar Production and Energy Demand Profiles Using Markov Chains for Microgrid Design

Author

Listed:
  • Hugo Radet

    (LAPLACE, Université de Toulouse, CNRS, INPT, UPS, 2 rue Camichel, 31 071 Toulouse, France)

  • Bruno Sareni

    (LAPLACE, Université de Toulouse, CNRS, INPT, UPS, 2 rue Camichel, 31 071 Toulouse, France)

  • Xavier Roboam

    (LAPLACE, Université de Toulouse, CNRS, INPT, UPS, 2 rue Camichel, 31 071 Toulouse, France)

Abstract

Uncertainties related to the energy produced and consumed in smart grids, especially in microgrids, are among the major issues for both their design and optimal management. In that context, it is essential to have representative probabilistic scenarios of these environmental uncertainties. The intensive development and massive installation of smart meters will help to better characterize local energy consumption and production in the following years. However, models representing these variables over large timescales are essential for microgrid design. In this paper, we explore a simple method based on Markov chains capable of generating a large number of probabilistic production or consumption profiles from available historical measurements. We show that the developed approach can capture the main characteristics and statistical variability of real data on both short-term and long-term scales. Moreover, the correlation between both production and demand is conserved in generated profiles with respect to historical measurements.

Suggested Citation

  • Hugo Radet & Bruno Sareni & Xavier Roboam, 2023. "Synthesis of Solar Production and Energy Demand Profiles Using Markov Chains for Microgrid Design," Energies, MDPI, vol. 16(23), pages 1-12, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7871-:d:1292358
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/23/7871/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/23/7871/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    2. Lombardi, Francesco & Balderrama, Sergio & Quoilin, Sylvain & Colombo, Emanuela, 2019. "Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model," Energy, Elsevier, vol. 177(C), pages 433-444.
    3. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    4. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    5. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    6. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    7. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    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. Fraunholz, Christoph & Miskiw, Kim K. & Kraft, Emil & Fichtner, Wolf & Weber, Christoph, 2021. "On the role of risk aversion and market design in capacity expansion planning," Working Paper Series in Production and Energy 62, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    2. Wang, Jing & Ye, Kai & Kang, Lixia & Liu, Yongzhong, 2024. "Flexible design of renewable hydrogen production systems through identifying bottlenecks under uncertainty," Energy, Elsevier, vol. 311(C).
    3. Phillips, K. & Moncada, J.A. & Ergun, H. & Delarue, E., 2023. "Spatial representation of renewable technologies in generation expansion planning models," Applied Energy, Elsevier, vol. 342(C).
    4. Wogrin, S. & Tejada-Arango, D. & Delikaraoglou, S. & Botterud, A., 2020. "Assessing the impact of inertia and reactive power constraints in generation expansion planning," Applied Energy, Elsevier, vol. 280(C).
    5. Olleik, Majd & Tarhini, Hussein & Auer, Hans, 2025. "Integrating upstream natural gas and electricity planning in times of energy transition," Applied Energy, Elsevier, vol. 377(PB).
    6. Pavičević, Matija & Kavvadias, Konstantinos & Pukšec, Tomislav & Quoilin, Sylvain, 2019. "Comparison of different model formulations for modelling future power systems with high shares of renewables – The Dispa-SET Balkans model," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Bartolini, Andrea & Comodi, Gabriele & Salvi, Danilo & Østergaard, Poul Alberg, 2020. "Renewables self-consumption potential in districts with high penetration of electric vehicles," Energy, Elsevier, vol. 213(C).
    8. Petkov, Ivalin & Gabrielli, Paolo, 2020. "Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems," Applied Energy, Elsevier, vol. 274(C).
    9. Balderrama, Sergio & Lombardi, Francesco & Stevanato, Nicolo & Peña, Gabriela & Colombo, Emanuela & Quoilin, Sylvain, 2021. "Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities," Energy, Elsevier, vol. 232(C).
    10. Chi Kong Chyong & Carmen Li & David Reiner & Fabien Roques, 2020. "A Portfolio approach to wind and solar deployment in Australia," Working Papers EPRG2022, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    11. de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Energy, Elsevier, vol. 213(C).
    12. Plain, N. & Hingray, B. & Mathy, S., 2019. "Accounting for low solar resource days to size 100% solar microgrids power systems in Africa," Renewable Energy, Elsevier, vol. 131(C), pages 448-458.
    13. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.
    14. 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).
    15. Marko Hočevar & Lovrenc Novak & Primož Drešar & Gašper Rak, 2022. "The Status Quo and Future of Hydropower in Slovenia," Energies, MDPI, vol. 15(19), pages 1-13, September.
    16. Koecklin, Manuel Tong & Longoria, Genaro & Fitiwi, Desta Z. & DeCarolis, Joseph F. & Curtis, John, 2021. "Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland," Energy Policy, Elsevier, vol. 151(C).
    17. Zheng, Li & Abbasi, Kashif Raza & Salem, Sultan & Irfan, Muhammad & Alvarado, Rafael & Lv, Kangjuan, 2022. "How technological innovation and institutional quality affect sectoral energy consumption in Pakistan? Fresh policy insights from novel econometric approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    18. Yin, Boyi & Zhu, Wenjiang & Tang, Cheng & Wang, Can & Xu, Xinhai, 2025. "Hierarchical optimal scheduling of IES considering SOFC degradation, internal and external uncertainties," Applied Energy, Elsevier, vol. 381(C).
    19. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    20. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, 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:23:p:7871-:d:1292358. 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.