IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v210y2018icp944-963.html
   My bibliography  Save this article

Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming

Author

Listed:
  • Nemati, Mohsen
  • Braun, Martin
  • Tenbohlen, Stefan

Abstract

Energy Management System (EMS) applications of modern power networks like microgrids have to respond to a number of stringent challenges due to current energy revolution. Optimal resource dispatch tasks must be handled with specific regard to the addition of new resource types and the adoption of novel modeling considerations. In addition, due to the comprehensive changes concerning the multi cell grid structure, new policies should be fulfilled via microgrids’ EMS. At the same time achieving a variety of (conflicting) goals in different microgrids requires a universal and a multi criteria optimization tool. Few of recent works in this area have considered the different perspectives of network operation with high amount of constraints and decision criteria. In this paper two dispatch-optimizers for a centralized EMS (CEMS) as a universal tool are introduced. An improved real-coded genetic algorithm and an enhanced mixed integer linear programming (MILP) based method have been developed to schedule the unit commitment and economic dispatch of microgrid units. In the proposed methods, network restrictions like voltages and equipment loadings and unit constraints have been considered. The adopted genetic algorithm features a highly flexible set of sub-functions, intelligent convergence behavior, as well as diversified searching approaches and penalty methods for constraint violations. Moreover, a novel method has been introduced to deal with the limitations of the MILP algorithm for handling the non-linear network topology constraints. A new aging model of a Lithium-Ion battery based on an event-driven aging behavior has been introduced. Ultimately, the developed GA-based and MILP-based optimizers have been applied to a test microgrid model under different operation policies, and the functionality of each method has been evaluated and compared together.

Suggested Citation

  • Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    • Handle: RePEc:eee:appene:v:210:y:2018:i:c:p:944-963
    DOI: 10.1016/j.apenergy.2017.07.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917308723
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.07.007?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. Soshinskaya, Mariya & Crijns-Graus, Wina H.J. & Guerrero, Josep M. & Vasquez, Juan C., 2014. "Microgrids: Experiences, barriers and success factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 659-672.
    2. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Jiang, Tao & Yu, Xiaodan, 2017. "Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system," Applied Energy, Elsevier, vol. 194(C), pages 386-398.
    3. Li, Xuping, 2012. "Understanding the Design and Performance of Distributed Tri-Generation Systems for Home and Neighborhood Refueling," Institute of Transportation Studies, Working Paper Series qt0h87d4sm, Institute of Transportation Studies, UC Davis.
    4. Soares, Ana & Antunes, Carlos Henggeler & Oliveira, Carlos & Gomes, Álvaro, 2014. "A multi-objective genetic approach to domestic load scheduling in an energy management system," Energy, Elsevier, vol. 77(C), pages 144-152.
    5. Wang, Luhao & Li, Qiqiang & Ding, Ran & Sun, Mingshun & Wang, Guirong, 2017. "Integrated scheduling of energy supply and demand in microgrids under uncertainty: A robust multi-objective optimization approach," Energy, Elsevier, vol. 130(C), pages 1-14.
    6. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.
    7. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    8. Li, Bei & Roche, Robin & Miraoui, Abdellatif, 2017. "Microgrid sizing with combined evolutionary algorithm and MILP unit commitment," Applied Energy, Elsevier, vol. 188(C), pages 547-562.
    9. Deshmukh, M.K. & Deshmukh, S.S., 2008. "Modeling of hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 235-249, January.
    10. Rigo-Mariani, Rémy & Sareni, Bruno & Roboam, Xavier & Turpin, Christophe, 2014. "Optimal power dispatching strategies in smart-microgrids with storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 649-658.
    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. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    2. Avilés A., Camilo & Oliva H., Sebastian & Watts, David, 2019. "Single-dwelling and community renewable microgrids: Optimal sizing and energy management for new business models," Applied Energy, Elsevier, vol. 254(C).
    3. Thomas Sachs & Anna Gründler & Milos Rusic & Gilbert Fridgen, 2019. "Framing Microgrid Design from a Business and Information Systems Engineering Perspective," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(6), pages 729-744, December.
    4. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    5. Kia, M. & Shafiekhani, M. & Arasteh, H. & Hashemi, S.M. & Shafie-khah, M. & Catalão, J.P.S., 2020. "Short-term operation of microgrids with thermal and electrical loads under different uncertainties using information gap decision theory," Energy, Elsevier, vol. 208(C).
    6. Makbul A.M. Ramli & H.R.E.H. Bouchekara & Abdulsalam S. Alghamdi, 2019. "Efficient Energy Management in a Microgrid with Intermittent Renewable Energy and Storage Sources," Sustainability, MDPI, vol. 11(14), pages 1-28, July.
    7. Chmielewski, Adrian & Gumiński, Robert & Mączak, Jędrzej & Radkowski, Stanisław & Szulim, Przemysław, 2016. "Aspects of balanced development of RES and distributed micro-cogeneration use in Poland: Case study of a µCHP with Stirling engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 930-952.
    8. Carlos Adrian Correa-Florez & Andrea Michiorri & Georges Kariniotakis, 2019. "Comparative Analysis of Adjustable Robust Optimization Alternatives for the Participation of Aggregated Residential Prosumers in Electricity Markets," Energies, MDPI, vol. 12(6), pages 1-27, March.
    9. L. Alvarado-Barrios & A. Rodríguez del Nozal & A. Tapia & J. L. Martínez-Ramos & D. G. Reina, 2019. "An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes," Energies, MDPI, vol. 12(11), pages 1-23, June.
    10. Carpinelli, G. & Mottola, F. & Proto, D. & Varilone, P., 2017. "Minimizing unbalances in low-voltage microgrids: Optimal scheduling of distributed resources," Applied Energy, Elsevier, vol. 191(C), pages 170-182.
    11. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    12. Liu, Zifa & Chen, Yixiao & Zhuo, Ranqun & Jia, Hongjie, 2018. "Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling," Applied Energy, Elsevier, vol. 210(C), pages 1113-1125.
    13. Sarshar, Javad & Moosapour, Seyyed Sajjad & Joorabian, Mahmood, 2017. "Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting," Energy, Elsevier, vol. 139(C), pages 680-693.
    14. Soheil Mohseni & Alan C. Brent, 2022. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    15. Domenech, B. & Ferrer-Martí, L. & Pastor, R., 2015. "Including management and security of supply constraints for designing stand-alone electrification systems in developing countries," Renewable Energy, Elsevier, vol. 80(C), pages 359-369.
    16. Iolanda Saviuc & Herbert Peremans & Steven Van Passel & Kevin Milis, 2019. "Economic Performance of Using Batteries in European Residential Microgrids under the Net-Metering Scheme," Energies, MDPI, vol. 12(1), pages 1-28, January.
    17. Koo, Jamin & Park, Kyungtae & Shin, Dongil & Yoon, En Sup, 2011. "Economic evaluation of renewable energy systems under varying scenarios and its implications to Korea's renewable energy plan," Applied Energy, Elsevier, vol. 88(6), pages 2254-2260, June.
    18. Zhao, Bo & Chen, Jian & Zhang, Leiqi & Zhang, Xuesong & Qin, Ruwen & Lin, Xiangning, 2018. "Three representative island microgrids in the East China Sea: Key technologies and experiences," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 262-274.
    19. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    20. Bui, Duong Minh & Chen, Shi-Lin & Lien, Keng-Yu & Chang, Yung-Ruei & Lee, Yih-Der & Jiang, Jheng-Lun, 2017. "Investigation on transient behaviours of a uni-grounded low-voltage AC microgrid and evaluation on its available fault protection methods: Review and proposals," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1417-1452.

    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:210:y:2018:i:c:p:944-963. 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.