IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v254y2019ics0306261919313571.html
   My bibliography  Save this article

Application of differential evolution-based constrained optimization methods to district energy optimization and comparison with dynamic programming

Author

Listed:
  • Ikeda, Shintaro
  • Ooka, Ryozo

Abstract

Metaheuristic optimization methods, as model-free approaches, are expected to be applicable to practical issues (e.g., engineering problems). Although optimization methods have been proposed or improved through many different theoretical studies, they should be tested using not only certain benchmark functions, but also other models representing practical situations, such as those involving discrete control variables and equality or inequality constraints. Hence, in this study, differential evolution based constrained optimization methods were applied to district energy optimization. To obtain theoretical results, several different types of proposed methods were compared with dynamic programming and genetic algorithm. In addition, a parametric study was conducted to evaluate the effects of the population size, mutation rate, and random jumping rate. The proposed method, namely, ε-constrained differential evolution with random jumping II, proved capable of producing results that differ from the theoretical results by only 2.1% within a computation time 1/457 of that required by dynamic programming. In addition, the method was superior to genetic algorithm which had been often adopted as a metaheuristic method in engineering problems because the result of the proposed method was 460,417 yen/day and that of genetic algorithm was 660,424 yen/day. Therefore, the proposed method has high potential to provide comprehensive district energy optimization within a realistic computational time.

Suggested Citation

  • Ikeda, Shintaro & Ooka, Ryozo, 2019. "Application of differential evolution-based constrained optimization methods to district energy optimization and comparison with dynamic programming," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919313571
    DOI: 10.1016/j.apenergy.2019.113670
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919313571
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113670?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. Garshasbi, Samira & Kurnitski, Jarek & Mohammadi, Yousef, 2016. "A hybrid Genetic Algorithm and Monte Carlo simulation approach to predict hourly energy consumption and generation by a cluster of Net Zero Energy Buildings," Applied Energy, Elsevier, vol. 179(C), pages 626-637.
    2. Essiet, Ima O. & Sun, Yanxia & Wang, Zenghui, 2019. "Optimized energy consumption model for smart home using improved differential evolution algorithm," Energy, Elsevier, vol. 172(C), pages 354-365.
    3. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    4. Ikeda, Shintaro & Choi, Wonjun & Ooka, Ryozo, 2017. "Optimization method for multiple heat source operation including ground source heat pump considering dynamic variation in ground temperature," Applied Energy, Elsevier, vol. 193(C), pages 466-478.
    5. Wallerand, Anna S. & Kermani, Maziar & Kantor, Ivan & Maréchal, François, 2018. "Optimal heat pump integration in industrial processes," Applied Energy, Elsevier, vol. 219(C), pages 68-92.
    6. 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.
    7. Mohan, Vivek & Singh, Jai Govind & Ongsakul, Weerakorn, 2015. "An efficient two stage stochastic optimal energy and reserve management in a microgrid," Applied Energy, Elsevier, vol. 160(C), pages 28-38.
    8. Abedi, S. & Alimardani, A. & Gharehpetian, G.B. & Riahy, G.H. & Hosseinian, S.H., 2012. "A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1577-1587.
    9. Ikeda, Shintaro & Ooka, Ryozo, 2015. "Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system," Applied Energy, Elsevier, vol. 151(C), pages 192-205.
    10. Omu, Akomeno & Choudhary, Ruchi & Boies, Adam, 2013. "Distributed energy resource system optimisation using mixed integer linear programming," Energy Policy, Elsevier, vol. 61(C), pages 249-266.
    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. 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).
    2. Mauro Jurado & Eduardo Salazar & Mauricio Samper & Rodolfo Rosés & Diego Ojeda Esteybar, 2023. "Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control," Energies, MDPI, vol. 16(20), pages 1-20, October.
    3. Muhammad Shahzad Nazir & Zhang Chu & Ahmad N. Abdalla & Hong Ki An & Sayed M. Eldin & Ahmed Sayed M. Metwally & Patrizia Bocchetta & Muhammad Sufyan Javed, 2022. "Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm," Sustainability, MDPI, vol. 14(23), pages 1-18, December.
    4. Shakeri, Alireza & Asadbagi, Poorya & Babamiri Naamrudi, Arash, 2024. "Techno-economic, techno-environmental assessments, and deep learning optimization of an integrated system for CO2 capturing from a gas turbine: Tehran case study," Energy, Elsevier, vol. 306(C).
    5. Gjorgievski, Vladimir Z. & Cundeva, Snezana & Georghiou, George E., 2021. "Social arrangements, technical designs and impacts of energy communities: A review," Renewable Energy, Elsevier, vol. 169(C), pages 1138-1156.
    6. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(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. Li, Jianwei & Xiong, Rui & Mu, Hao & Cornélusse, Bertrand & Vanderbemden, Philippe & Ernst, Damien & Yuan, Weijia, 2018. "Design and real-time test of a hybrid energy storage system in the microgrid with the benefit of improving the battery lifetime," Applied Energy, Elsevier, vol. 218(C), pages 470-478.
    2. Han, Yongming & Wu, Hao & Geng, Zhiqiang & Zhu, Qunxiong & Gu, Xiangbai & Yu, Bin, 2020. "Review: Energy efficiency evaluation of complex petrochemical industries," Energy, Elsevier, vol. 203(C).
    3. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    4. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    5. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    6. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    7. 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).
    8. Jiang, Tao & Wu, Chenghao & Huang, Tao & Zhang, Rufeng & Li, Xue, 2024. "Optimal market participation of VPPs in TSO-DSO coordinated energy and flexibility markets," Applied Energy, Elsevier, vol. 360(C).
    9. Chen, Houhe & Wang, Di & Zhang, Rufeng & Jiang, Tao & Li, Xue, 2022. "Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties," Applied Energy, Elsevier, vol. 308(C).
    10. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    11. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    12. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    13. Sharafi, Masoud & ELMekkawy, Tarek Y., 2014. "Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach," Renewable Energy, Elsevier, vol. 68(C), pages 67-79.
    14. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    15. Clarke, Fiona & Dorneanu, Bogdan & Mechleri, Evgenia & Arellano-Garcia, Harvey, 2021. "Optimal design of heating and cooling pipeline networks for residential distributed energy resource systems," Energy, Elsevier, vol. 235(C).
    16. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    17. Jin, Hongyang & Li, Zhengshuo & Sun, Hongbin & Guo, Qinglai & Chen, Runze & Wang, Bin, 2017. "A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment," Applied Energy, Elsevier, vol. 206(C), pages 1364-1378.
    18. Bhowmik, Chiranjib & Bhowmik, Sumit & Ray, Amitava & Pandey, Krishna Murari, 2017. "Optimal green energy planning for sustainable development: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 796-813.
    19. Liu, Hua & Zhao, Baiyang & Zhang, Zhiping & Li, Hongbo & Hu, Bin & Wang, R.Z., 2020. "Experimental validation of an advanced heat pump system with high-efficiency centrifugal compressor," Energy, Elsevier, vol. 213(C).
    20. Mughees, Neelam & Jaffery, Mujtaba Hussain & Mughees, Anam & Ansari, Ejaz Ahmad & Mughees, Abdullah, 2023. "Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids," Applied Energy, Elsevier, vol. 342(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:254:y:2019:i:c:s0306261919313571. 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.