IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v356y2024ics0306261923013661.html
   My bibliography  Save this article

Optimal design of aggregated energy systems with (N-1) reliability: MILP models and decomposition algorithms

Author

Listed:
  • Castelli, Alessandro Francesco
  • Pilotti, Lorenzo
  • Monchieri, Alessandro
  • Martelli, Emanuele

Abstract

This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1)-reliability requirements. The problem is formulated as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs.

Suggested Citation

  • Castelli, Alessandro Francesco & Pilotti, Lorenzo & Monchieri, Alessandro & Martelli, Emanuele, 2024. "Optimal design of aggregated energy systems with (N-1) reliability: MILP models and decomposition algorithms," Applied Energy, Elsevier, vol. 356(C).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s0306261923013661
    DOI: 10.1016/j.apenergy.2023.122002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923013661
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    2. Elsido, Cristina & Bischi, Aldo & Silva, Paolo & Martelli, Emanuele, 2017. "Two-stage MINLP algorithm for the optimal synthesis and design of networks of CHP units," Energy, Elsevier, vol. 121(C), pages 403-426.
    3. L. Escudero & M. Garín & G. Pérez & A. Unzueta, 2012. "Lagrangian Decomposition for large-scale two-stage stochastic mixed 0-1 problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 347-374, July.
    4. Bernard Knueven & James Ostrowski & Jean-Paul Watson, 2020. "On Mixed-Integer Programming Formulations for the Unit Commitment Problem," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 857-876, October.
    5. Mehleri, Eugenia D. & Sarimveis, Haralambos & Markatos, Nikolaos C. & Papageorgiou, Lazaros G., 2012. "A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level," Energy, Elsevier, vol. 44(1), pages 96-104.
    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. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    2. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    3. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2021. "Structural design of distributed energy networks by a hierarchical combination of variable- and constraint-based decomposition methods," Energy, Elsevier, vol. 224(C).
    4. Xiaojing Hu & Haoling Min & Sai Dai & Zhi Cai & Xiaonan Yang & Qiang Ding & Zhanyong Yang & Feng Xiao, 2022. "Research on Maintenance Strategies for Different Transmission Sections to Improve the Consumption Rate Based on a Renewable Energy Production Simulation," Energies, MDPI, vol. 15(24), pages 1-11, December.
    5. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    6. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    7. Kwag, Kyuhyeong & Shin, Hansol & Oh, Hyobin & Yun, Sangmin & Kim, Tae Hyun & Hwang, Pyeong-Ik & Kim, Wook, 2023. "Bilevel programming approach for the quantitative analysis of renewable portfolio standards considering the electricity market," Energy, Elsevier, vol. 263(PD).
    8. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    9. Entchev, E. & Yang, L. & Ghorab, M. & Lee, E.J., 2013. "Simulation of hybrid renewable microgeneration systems in load sharing applications," Energy, Elsevier, vol. 50(C), pages 252-261.
    10. Li, Ruonan & Mahalec, Vladimir, 2022. "Integrated design and operation of energy systems for residential buildings, commercial buildings, and light industries," Applied Energy, Elsevier, vol. 305(C).
    11. Martelli, Emanuele & Freschini, Marco & Zatti, Matteo, 2020. "Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming," Applied Energy, Elsevier, vol. 267(C).
    12. Xiaofeng Liu & Shijun Wang & Jiawen Sun, 2018. "Energy Management for Community Energy Network with CHP Based on Cooperative Game," Energies, MDPI, vol. 11(5), pages 1-18, April.
    13. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    14. Luca Urbanucci & Francesco D’Ettorre & Daniele Testi, 2019. "A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage," Energies, MDPI, vol. 12(5), pages 1-17, March.
    15. Sameti, Mohammad & Haghighat, Fariborz, 2018. "Integration of distributed energy storage into net-zero energy district systems: Optimum design and operation," Energy, Elsevier, vol. 153(C), pages 575-591.
    16. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    17. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    18. 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.
    19. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    20. Reveron Baecker, Beneharo & Candas, Soner, 2022. "Co-optimizing transmission and active distribution grids to assess demand-side flexibilities of a carbon-neutral German energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).

    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:eee:appene:v:356:y:2024:i:c:s0306261923013661. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.