IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i15p5717-d881693.html
   My bibliography  Save this article

Planning of Multi-Vector Energy Systems with High Penetration of Renewable Energy Source: A Comprehensive Review

Author

Listed:
  • Patrick Sunday Onen

    (Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK)

  • Geev Mokryani

    (Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK)

  • Rana H. A. Zubo

    (Technical Engineering College Kirkuk, Northern Technical University, Kirkuk 36001, Iraq)

Abstract

The increasing use of high shares of renewable energy sources (RESs) in the current electricity network introduces challenges to the design and management of the electricity network due to the variation and uncertainty nature of the RESs. Some existing energy infrastructures, such as heat, gas, and transport, all have some level of inbuilt storage capacity and demand response (DR) potentials that can be exploited in an energy system integration to give the electricity network some level of flexibility and promote an efficient transition to a low-carbon, resilient, and robust energy system. The process of integrating different energy infrastructure is known as multi-vector energy systems (MESs). This paper reviews different studies on the planning of MESs using the energy hubs (EHs) approach. The EHs model used in this paper links different energy vectors such as gas, electricity, and heat energy vectors in its planning model, as opposed to planning each energy vector independently, in order to provide more flexibility in the system, minimise total planning cost, and encourage high penetration of renewable energy source for future energy demands. In addition, different uncertainty modelling and optimization methods that have been used in past studies in planning of EH are classified and reviewed to ascertain the appropriate techniques for addressing RESs uncertainty when planning future EH. Numerical results show 12% reduction in the planning cost in the case of integrated planning with other energy vectors compared to independent planning.

Suggested Citation

  • Patrick Sunday Onen & Geev Mokryani & Rana H. A. Zubo, 2022. "Planning of Multi-Vector Energy Systems with High Penetration of Renewable Energy Source: A Comprehensive Review," Energies, MDPI, vol. 15(15), pages 1-25, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5717-:d:881693
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/15/5717/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/15/5717/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Liu, Xuezhi & Wu, Jianzhong & Jenkins, Nick & Bagdanavicius, Audrius, 2016. "Combined analysis of electricity and heat networks," Applied Energy, Elsevier, vol. 162(C), pages 1238-1250.
    3. Xianzheng Zhou & Chuangxin Guo & Yifei Wang & Wanqi Li, 2017. "Optimal Expansion Co-Planning of Reconfigurable Electricity and Natural Gas Distribution Systems Incorporating Energy Hubs," Energies, MDPI, vol. 10(1), pages 1-22, January.
    4. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    5. Wang, Yi & Zhang, Ning & Zhuo, Zhenyu & Kang, Chongqing & Kirschen, Daniel, 2018. "Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch," Applied Energy, Elsevier, vol. 210(C), pages 1141-1150.
    6. Maroufmashat, Azadeh & Elkamel, Ali & Fowler, Michael & Sattari, Sourena & Roshandel, Ramin & Hajimiragha, Amir & Walker, Sean & Entchev, Evgueniy, 2015. "Modeling and optimization of a network of energy hubs to improve economic and emission considerations," Energy, Elsevier, vol. 93(P2), pages 2546-2558.
    7. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    8. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    9. Neumann, Scott & Sioshansi, Fereidoon & Vojdani, Ali & Yee, Gaymond, 2006. "How to Get More Response from Demand Response," The Electricity Journal, Elsevier, vol. 19(8), pages 24-31, October.
    10. Eid, Cherrelle & Codani, Paul & Perez, Yannick & Reneses, Javier & Hakvoort, Rudi, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 237-247.
    11. Zubo, Rana.H.A. & Mokryani, Geev & Rajamani, Haile-Selassie & Aghaei, Jamshid & Niknam, Taher & Pillai, Prashant, 2017. "Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1177-1198.
    12. Cherrelle Eid & Paul Codani & Yannick Perez & Javier Reneses & Rudi Hakvoort, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Post-Print hal-01792419, HAL.
    13. Bracco, Stefano & Dentici, Gabriele & Siri, Silvia, 2013. "Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area," Energy, Elsevier, vol. 55(C), pages 1014-1024.
    14. Datta, Manoj & Senjyu, Tomonobu & Yona, Atsushi & Funabashi, Toshihisa, 2011. "A fuzzy based method for leveling output power fluctuations of photovoltaic-diesel hybrid power system," Renewable Energy, Elsevier, vol. 36(6), pages 1693-1703.
    15. Liu, Xuezhi & Mancarella, Pierluigi, 2016. "Modelling, assessment and Sankey diagrams of integrated electricity-heat-gas networks in multi-vector district energy systems," Applied Energy, Elsevier, vol. 167(C), pages 336-352.
    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. Heendeniya, Charitha Buddhika & Sumper, Andreas & Eicker, Ursula, 2020. "The multi-energy system co-planning of nearly zero-energy districts – Status-quo and future research potential," Applied Energy, Elsevier, vol. 267(C).
    2. Andoni, Merlinda & Robu, Valentin & Flynn, David & Abram, Simone & Geach, Dale & Jenkins, David & McCallum, Peter & Peacock, Andrew, 2019. "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 143-174.
    3. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    4. Yu Huang & Weiting Zhang & Kai Yang & Weizhen Hou & Yiran Huang, 2019. "An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory," Energies, MDPI, vol. 12(12), pages 1-20, June.
    5. Xianzheng Zhou & Chuangxin Guo & Yifei Wang & Wanqi Li, 2017. "Optimal Expansion Co-Planning of Reconfigurable Electricity and Natural Gas Distribution Systems Incorporating Energy Hubs," Energies, MDPI, vol. 10(1), pages 1-22, January.
    6. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    7. Li, Qi & Xiao, Xukang & Pu, Yuchen & Luo, Shuyu & Liu, Hong & Chen, Weirong, 2023. "Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy," Applied Energy, Elsevier, vol. 349(C).
    8. Lun Yang & Xia Zhao & Xinyi Li & Wei Yan, 2018. "Probabilistic Steady-State Operation and Interaction Analysis of Integrated Electricity, Gas and Heating Systems," Energies, MDPI, vol. 11(4), pages 1-21, April.
    9. 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.
    10. Carvallo, Claudio & Jalil-Vega, Francisca & Moreno, Rodrigo, 2023. "A multi-energy multi-microgrid system planning model for decarbonisation and decontamination of isolated systems," Applied Energy, Elsevier, vol. 343(C).
    11. Oskar Juszczyk & Khuram Shahzad, 2022. "Blockchain Technology for Renewable Energy: Principles, Applications and Prospects," Energies, MDPI, vol. 15(13), pages 1-24, June.
    12. Frölke, Linde & Sousa, Tiago & Pinson, Pierre, 2022. "A network-aware market mechanism for decentralized district heating systems," Applied Energy, Elsevier, vol. 306(PA).
    13. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    14. Tsaousoglou, Georgios & Giraldo, Juan S. & Paterakis, Nikolaos G., 2022. "Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    15. Markensteijn, A.S. & Romate, J.E. & Vuik, C., 2020. "A graph-based model framework for steady-state load flow problems of general multi-carrier energy systems," Applied Energy, Elsevier, vol. 280(C).
    16. Rae, Callum & Kerr, Sandy & Maroto-Valer, M. Mercedes, 2020. "Upscaling smart local energy systems: A review of technical barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. 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.
    18. Gabrielli, Paolo & Gazzani, Matteo & Mazzotti, Marco, 2018. "Electrochemical conversion technologies for optimal design of decentralized multi-energy systems: Modeling framework and technology assessment," Applied Energy, Elsevier, vol. 221(C), pages 557-575.
    19. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    20. Liu, Tianhao & Tian, Jun & Zhu, Hongyu & Goh, Hui Hwang & Liu, Hui & Wu, Thomas & Zhang, Dongdong, 2023. "Key technologies and developments of multi-energy system: Three-layer framework, modelling and optimisation," Energy, Elsevier, vol. 277(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:gam:jeners:v:15:y:2022:i:15:p:5717-:d:881693. 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.