IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v234y2021ics0360544221014535.html
   My bibliography  Save this article

Collaborative planning of spatial layouts of distributed energy stations and networks: A case study

Author

Listed:
  • Zhou, Yuan
  • Ma, Yanpeng
  • Wang, Jiangjiang
  • Lu, Shuaikang

Abstract

The spatial layout of energy stations and networks is important for the implementation of regional distributed energy systems (RDES). The existing literatures mainly employed the shortest path algorithm to find the optimal layouts, which cannot fully consider the difference and complementarity between energy users. This paper employs graph theory to transform the spatial optimization into an optimization problem of searching the optimal sub-graph from the abstract graph in the studied region and solved by genetic algorithm (GA), in which the uncertainties of users loads are integrated to the optimization model. Besides, hybrid coding is introduced in GA to simultaneously optimizes the siting of energy stations and network distributions. Therefore, collaborative optimization is realized to transform such traditional two-stage optimization to single-stage optimization. The characteristics of energy consumers, geography, and network distance between consumers and stations are appropriately handled by combining collaborative optimizing and graphic theory, leading this work to high practicability and helping investors put RDES into practice. Different constraints in the case study are compared to verify the feasibility and reliability of the proposed method. The influences of load budget uncertainty on the optimization results are discussed. The results demonstrate that the proposed optimization method reduces the total annual cost and network cost by 12.3% and 4.3% compared to the shortest path algorithm, respectively.

Suggested Citation

  • Zhou, Yuan & Ma, Yanpeng & Wang, Jiangjiang & Lu, Shuaikang, 2021. "Collaborative planning of spatial layouts of distributed energy stations and networks: A case study," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014535
    DOI: 10.1016/j.energy.2021.121205
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221014535
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.121205?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. Harney, Patrick & Gartland, Donna & Murphy, Fionnuala, 2020. "Determining the optimum low-temperature district heating network design for a secondary network supplying a low-energy-use apartment block in Ireland," Energy, Elsevier, vol. 192(C).
    2. Tol, H.İ. & Svendsen, S., 2012. "Improving the dimensioning of piping networks and network layouts in low-energy district heating systems connected to low-energy buildings: A case study in Roskilde, Denmark," Energy, Elsevier, vol. 38(1), pages 276-290.
    3. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    4. Salehimaleh, Mohammad & Akbarimajd, Adel & Valipour, Khalil & Dejamkhooy, Abdolmajid, 2018. "Generalized modeling and optimal management of energy hub based electricity, heat and cooling demands," Energy, Elsevier, vol. 159(C), pages 669-685.
    5. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Particle swarm optimization for redundant building cooling heating and power system," Applied Energy, Elsevier, vol. 87(12), pages 3668-3679, December.
    6. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa, 2010. "Optimization of capacity and operation for CCHP system by genetic algorithm," Applied Energy, Elsevier, vol. 87(4), pages 1325-1335, April.
    7. Zhu, Xingyi & Zhan, Xiangyan & Liang, Hao & Zheng, Xuyue & Qiu, Yuwei & Lin, Jian & Chen, Jincan & Meng, Chao & Zhao, Yingru, 2020. "The optimal design and operation strategy of renewable energy-CCHP coupled system applied in five building objects," Renewable Energy, Elsevier, vol. 146(C), pages 2700-2715.
    8. Kouhia, Mikko & Laukkanen, Timo & Holmberg, Henrik & Ahtila, Pekka, 2019. "District heat network as a short-term energy storage," Energy, Elsevier, vol. 177(C), pages 293-303.
    9. Colak, Ilhami & Sagiroglu, Seref & Fulli, Gianluca & Yesilbudak, Mehmet & Covrig, Catalin-Felix, 2016. "A survey on the critical issues in smart grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 396-405.
    10. Wang, Jiangjiang & Han, Zepeng & Guan, Zhimin, 2020. "Hybrid solar-assisted combined cooling, heating, and power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    11. Fransson, Victor & Bagge, Hans & Johansson, Dennis, 2019. "Impact of variations in residential use of household electricity on the energy and power demand for space heating – Variations from measurements in 1000 apartments," Applied Energy, Elsevier, vol. 254(C).
    12. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout," Energy, Elsevier, vol. 116(P1), pages 619-636.
    13. Parra, David & Norman, Stuart A. & Walker, Gavin S. & Gillott, Mark, 2017. "Optimum community energy storage for renewable energy and demand load management," Applied Energy, Elsevier, vol. 200(C), pages 358-369.
    14. Vahidinasab, Vahid, 2014. "Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design," Renewable Energy, Elsevier, vol. 66(C), pages 354-363.
    15. Dominković, Dominik Franjo & Junker, Rune Grønborg & Lindberg, Karen Byskov & Madsen, Henrik, 2020. "Implementing flexibility into energy planning models: Soft-linking of a high-level energy planning model and a short-term operational model," Applied Energy, Elsevier, vol. 260(C).
    16. Das, Choton K. & Bass, Octavian & Mahmoud, Thair S. & Kothapalli, Ganesh & Mousavi, Navid & Habibi, Daryoush & Masoum, Mohammad A.S., 2019. "Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    17. Ahn, Jonghoon & Chung, Dae Hun & Cho, Soolyeon, 2018. "Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model," Energy, Elsevier, vol. 151(C), pages 11-25.
    18. Wang, Jiangjiang & Lu, Zherui & Li, Meng & Lior, Noam & Li, Weihua, 2019. "Energy, exergy, exergoeconomic and environmental (4E) analysis of a distributed generation solar-assisted CCHP (combined cooling, heating and power) gas turbine system," Energy, Elsevier, vol. 175(C), pages 1246-1258.
    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. Ding, Yan & Wang, Qiaochu & Tian, Zhe & Lyu, Yacong & Li, Feng & Yan, Zhe & Xia, Xi, 2023. "A graph-theory-based dynamic programming planning method for distributed energy system planning: Campus area as a case study," Applied Energy, Elsevier, vol. 329(C).
    2. Liu, Yanfeng & Tang, Huanlong & Chen, Yaowen & Wang, Dengjia & Song, Cong, 2022. "Optimization of layout and diameter for distributed solar heating network with multi-source and multi-sink," Energy, Elsevier, vol. 258(C).
    3. Zhou, Yuan & Wang, Jiangjiang & Li, Yuxin & Wei, Changqi, 2023. "A collaborative management strategy for multi-objective optimization of sustainable distributed energy system considering cloud energy storage," Energy, Elsevier, vol. 280(C).
    4. Du, Sipeng & Wu, Di & Dai, Zhong & Li, Guiqiang & Lahaxibai, Shala, 2023. "Regional collaborative planning equipped with shared energy storage under multi-time scale rolling optimisation method," Energy, Elsevier, vol. 277(C).

    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. Zhang, Guoqing & Wang, Jiangjiang & Ren, Fukang & Liu, Yi & Dong, Fuxiang, 2021. "Collaborative optimization for multiple energy stations in distributed energy network based on electricity and heat interchanges," Energy, Elsevier, vol. 222(C).
    2. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    3. Wang, Jiangjiang & Liu, Yi & Ren, Fukang & Lu, Shuaikang, 2020. "Multi-objective optimization and selection of hybrid combined cooling, heating and power systems considering operational flexibility," Energy, Elsevier, vol. 197(C).
    4. Ren, Fukang & Lin, Xiaozhen & Wei, Ziqing & Zhai, Xiaoqiang & Yang, Jianrong, 2022. "A novel planning method for design and dispatch of hybrid energy systems," Applied Energy, Elsevier, vol. 321(C).
    5. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2022. "A review on the integration and optimization of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    6. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Piacentino, Antonio & Barbaro, Chiara & Cardona, Fabio & Gallea, Roberto & Cardona, Ennio, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part I: Description of the method," Applied Energy, Elsevier, vol. 111(C), pages 1204-1221.
    8. Jie, Pengfei & Yan, Fuchun & Li, Jing & Zhang, Yumei & Wen, Zhimei, 2019. "Optimizing the insulation thickness of walls of existing buildings with CHP-based district heating systems," Energy, Elsevier, vol. 189(C).
    9. Bostan, Alireza & Nazar, Mehrdad Setayesh & Shafie-khah, Miadreza & Catalão, João P.S., 2020. "Optimal scheduling of distribution systems considering multiple downward energy hubs and demand response programs," Energy, Elsevier, vol. 190(C).
    10. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    11. Yuan, Yu & Bai, Zhang & Zhou, Shengdong & Zheng, Bo & Hu, Wenxin, 2022. "Potential of applying the thermochemical recuperation in combined cooling, heating and power generation: Flexible demand response characteristics," Applied Energy, Elsevier, vol. 325(C).
    12. Wang, Jiangjiang & Sui, Jun & Jin, Hongguang, 2015. "An improved operation strategy of combined cooling heating and power system following electrical load," Energy, Elsevier, vol. 85(C), pages 654-666.
    13. Barber, Kyle A. & Krarti, Moncef, 2022. "A review of optimization based tools for design and control of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    14. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    15. Han, Jie & Ouyang, Leixin & Xu, Yuzhen & Zeng, Rong & Kang, Shushuo & Zhang, Guoqiang, 2016. "Current status of distributed energy system in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 288-297.
    16. Saletti, Costanza & Zimmerman, Nathan & Morini, Mirko & Kyprianidis, Konstantinos & Gambarotta, Agostino, 2021. "Enabling smart control by optimally managing the State of Charge of district heating networks," Applied Energy, Elsevier, vol. 283(C).
    17. Deng, Yan & Liu, Yicai & Zeng, Rong & Wang, Qianxu & Li, Zheng & Zhang, Yu & Liang, Heng, 2021. "A novel operation strategy based on black hole algorithm to optimize combined cooling, heating, and power-ground source heat pump system," Energy, Elsevier, vol. 229(C).
    18. Liu, Mingxi & Shi, Yang & Fang, Fang, 2012. "A new operation strategy for CCHP systems with hybrid chillers," Applied Energy, Elsevier, vol. 95(C), pages 164-173.
    19. Jradi, M. & Riffat, S., 2014. "Tri-generation systems: Energy policies, prime movers, cooling technologies, configurations and operation strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 396-415.
    20. Namuli, R. & Pillay, P. & Jaumard, B. & Laflamme, C.B., 2013. "Threshold herd size for commercial viability of biomass waste to energy conversion systems on rural farms," Applied Energy, Elsevier, vol. 108(C), pages 308-322.

    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:234:y:2021:i:c:s0360544221014535. 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.