IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v132y2017icp126-135.html
   My bibliography  Save this article

An optimisation algorithm for distributed energy resources management in micro-scale energy hubs

Author

Listed:
  • Roldán-Blay, Carlos
  • Escrivá-Escrivá, Guillermo
  • Roldán-Porta, Carlos
  • Álvarez-Bel, Carlos

Abstract

In this paper, a new algorithm for optimal management of distributed energy resources in facilities with distributed generation, energy storage systems and specific loads – energy hubs – is shown. This method consists of an iterative algorithm that manages optimal energy flows to obtain the minimum energy cost based on availability of each resource, prices and expected demand. A simulation tool has been developed to run the algorithm under different scenarios. Eight different scenarios of an energy hub have been simulated to illustrate the operation of this method. These scenarios consist of a demand curve under different conditions related to the existence or absence of renewable energy sources and energy storage systems and different electricity tariffs for grid supply. Partial results in the iterative process of the developed algorithm are shown and the results of these simulations are analysed. Results show a good level of optimisation of energy resources by means of optimal use of renewable energy sources and optimal management of energy storage systems. Moreover, the impact of this optimised management on carbon dioxide emissions is analysed.

Suggested Citation

  • Roldán-Blay, Carlos & Escrivá-Escrivá, Guillermo & Roldán-Porta, Carlos & Álvarez-Bel, Carlos, 2017. "An optimisation algorithm for distributed energy resources management in micro-scale energy hubs," Energy, Elsevier, vol. 132(C), pages 126-135.
    • Handle: RePEc:eee:energy:v:132:y:2017:i:c:p:126-135
    DOI: 10.1016/j.energy.2017.05.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217307958
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.05.038?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. Abdmouleh, Zeineb & Alammari, Rashid A.M. & Gastli, Adel, 2015. "Review of policies encouraging renewable energy integration & best practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 249-262.
    2. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "An MILP (mixed integer linear programming) model for optimal design of district-scale distributed energy resource systems," Energy, Elsevier, vol. 90(P2), pages 1901-1915.
    3. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    4. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    5. Pérez-Navarro, A. & Alfonso, D. & Ariza, H.E. & Cárcel, J. & Correcher, A. & Escrivá-Escrivá, G. & Hurtado, E. & Ibáñez, F. & Peñalvo, E. & Roig, R. & Roldán, C. & Sánchez, C. & Segura, I. & Vargas, C, 2016. "Experimental verification of hybrid renewable systems as feasible energy sources," Renewable Energy, Elsevier, vol. 86(C), pages 384-391.
    6. Lv, Tianguang & Ai, Qian, 2016. "Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources," Applied Energy, Elsevier, vol. 163(C), pages 408-422.
    7. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "Optimal design of distributed energy resource systems coupled with energy distribution networks," Energy, Elsevier, vol. 85(C), pages 433-448.
    8. Izadbakhsh, Maziar & Gandomkar, Majid & Rezvani, Alireza & Ahmadi, Abdollah, 2015. "Short-term resource scheduling of a renewable energy based micro grid," Renewable Energy, Elsevier, vol. 75(C), pages 598-606.
    9. Niknam, Taher & Meymand, Hamed Zeinoddini & Mojarrad, Hasan Doagou, 2011. "A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plants," Renewable Energy, Elsevier, vol. 36(5), pages 1529-1544.
    10. DeForest, Nicholas & Mendes, Gonçalo & Stadler, Michael & Feng, Wei & Lai, Judy & Marnay, Chris, 2014. "Optimal deployment of thermal energy storage under diverse economic and climate conditions," Applied Energy, Elsevier, vol. 119(C), pages 488-496.
    11. Wouters, Carmen & Fraga, Eric S. & James, Adrian M., 2015. "An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study," Energy, Elsevier, vol. 85(C), pages 30-44.
    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. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
    2. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.

    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. Yokoyama, Ryohei & Shinano, Yuji & Taniguchi, Syusuke & Wakui, Tetsuya, 2019. "Search for K-best solutions in optimal design of energy supply systems by an extended MILP hierarchical branch and bound method," Energy, Elsevier, vol. 184(C), pages 45-57.
    2. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    3. 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.
    4. Gou, Xing & Chen, Qun & Sun, Yong & Ma, Huan & Li, Bao-Ju, 2021. "Holistic analysis and optimization of distributed energy system considering different transport characteristics of multi-energy and component efficiency variation," Energy, Elsevier, vol. 228(C).
    5. Yokoyama, Ryohei & Tokunaga, Akira & Wakui, Tetsuya, 2018. "Robust optimal design of energy supply systems under uncertain energy demands based on a mixed-integer linear model," Energy, Elsevier, vol. 153(C), pages 159-169.
    6. Xiang, Yue & Cai, Hanhu & Liu, Junyong & Zhang, Xin, 2021. "Techno-economic design of energy systems for airport electrification: A hydrogen-solar-storage integrated microgrid solution," Applied Energy, Elsevier, vol. 283(C).
    7. Wirtz, Marco & Kivilip, Lukas & Remmen, Peter & Müller, Dirk, 2020. "5th Generation District Heating: A novel design approach based on mathematical optimization," Applied Energy, Elsevier, vol. 260(C).
    8. 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.
    9. Rigo-Mariani, Rémy & Chea Wae, Sean Ooi & Mazzoni, Stefano & Romagnoli, Alessandro, 2020. "Comparison of optimization frameworks for the design of a multi-energy microgrid," Applied Energy, Elsevier, vol. 257(C).
    10. Schütz, Thomas & Schraven, Markus Hans & Fuchs, Marcus & Remmen, Peter & Müller, Dirk, 2018. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis," Renewable Energy, Elsevier, vol. 129(PA), pages 570-582.
    11. Mohammad Ali Taghikhani & Behnam Zangeneh, 2022. "Optimal energy scheduling of micro-grids considering the uncertainty of solar and wind renewable resources," Journal of Scheduling, Springer, vol. 25(5), pages 567-576, October.
    12. Xiang, Yue & Cai, Hanhu & Gu, Chenghong & Shen, Xiaodong, 2020. "Cost-benefit analysis of integrated energy system planning considering demand response," Energy, Elsevier, vol. 192(C).
    13. Wang, Yongli & Wang, Yudong & Huang, Yujing & Li, Fang & Zeng, Ming & Li, Jiapu & Wang, Xiaohai & Zhang, Fuwei, 2019. "Planning and operation method of the regional integrated energy system considering economy and environment," Energy, Elsevier, vol. 171(C), pages 731-750.
    14. Afzali, Sayyed Faridoddin & Cotton, James S. & Mahalec, Vladimir, 2020. "Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions," Applied Energy, Elsevier, vol. 259(C).
    15. Alqahtani, Mohammed & Hu, Mengqi, 2020. "Integrated energy scheduling and routing for a network of mobile prosumers," Energy, Elsevier, vol. 200(C).
    16. Ma, Tengfei & Wu, Junyong & Hao, Liangliang & Lee, Wei-Jen & Yan, Huaguang & Li, Dezhi, 2018. "The optimal structure planning and energy management strategies of smart multi energy systems," Energy, Elsevier, vol. 160(C), pages 122-141.
    17. Oluwaseun Olanrewaju Akinte & Boonyang Plangklang & Boonrit Prasartkaew & Taiwo Samuel Aina, 2023. "Energy Storage Management of a Solar Photovoltaic–Biomass Hybrid Power System," Energies, MDPI, vol. 16(13), pages 1-31, July.
    18. Guo, Jiacheng & Liu, Zhijian & Li, Ying & Wu, Di & Liu, Xuan & Zhang, Shicong & Yang, Xinyan & Ge, Hua & Zhang, Peiwen, 2022. "Thermodynamic performance analyses and optimization design method of a novel distributed energy system coupled with hybrid-energy storage," Renewable Energy, Elsevier, vol. 182(C), pages 1182-1200.
    19. José Claudio Isaias & Pedro Paulo Balestrassi & Guilherme Augusto Barucke Marcondes & Wesley Vieira da Silva & Carlos Henrique Pereira Mello & Claudimar Pereira da Veiga, 2021. "Project Portfolio Selection of Solar Energy by Photovoltaic Generation Using Gini-CAPM Multi-Criteria and Considering ROI Covariations," Energies, MDPI, vol. 14(24), pages 1-21, December.
    20. Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.

    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:energy:v:132:y:2017:i:c:p:126-135. 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.journals.elsevier.com/energy .

    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.