IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v239y2019icp1066-1076.html
   My bibliography  Save this article

Interchange objective value method for distributed multi-objective optimization: Theory, application, implementation

Author

Listed:
  • Yin, Linfei
  • Wang, Tao
  • Wang, Senlin
  • Zheng, Baomin

Abstract

An analytic distributed multi-objective optimization algorithm, which is named as interchange objective value method, is proposed for distributed multi-objective optimization problems. The interchange objective value method can be implemented with two types of methods, i.e., interchange unilateral objective value method and interchange bilateral objective value method. To verify the feasibility and effectiveness of the proposed analytic interchange objective value method, these two interchange objective value methods are applied in the distributed multi-objective optimal power flow of distributed multi-area interconnected power systems. The global optimal power flow problem of distributed multi-area interconnected power systems is divided to multiple subsidiary distributed multi-objective optimal power flow problems with interchanging objective values and boundary variables in security. The proposed interchange objective value methods are developed in IEEE 118-bus two-area power system, IEEE 300-bus two-area power system, and Xixiangtang district 22-bus power system. The numerical simulation results verify the feasibility and effectiveness of the proposed security analytic interchange objective value method for the distributed multi-objective optimal power flow of distributed multi-area interconnected power systems.

Suggested Citation

  • Yin, Linfei & Wang, Tao & Wang, Senlin & Zheng, Baomin, 2019. "Interchange objective value method for distributed multi-objective optimization: Theory, application, implementation," Applied Energy, Elsevier, vol. 239(C), pages 1066-1076.
    • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:1066-1076
    DOI: 10.1016/j.apenergy.2019.01.149
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919301552
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.149?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. Yuan, Xiaohui & Zhang, Binqiao & Wang, Pengtao & Liang, Ji & Yuan, Yanbin & Huang, Yuehua & Lei, Xiaohui, 2017. "Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm," Energy, Elsevier, vol. 122(C), pages 70-82.
    2. Robledo, Carla B. & Oldenbroek, Vincent & Abbruzzese, Francesca & van Wijk, Ad J.M., 2018. "Integrating a hydrogen fuel cell electric vehicle with vehicle-to-grid technology, photovoltaic power and a residential building," Applied Energy, Elsevier, vol. 215(C), pages 615-629.
    3. Zhang, Xiaoshun & Chen, Yixuan & Yu, Tao & Yang, Bo & Qu, Kaiping & Mao, Senmao, 2017. "Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems," Applied Energy, Elsevier, vol. 189(C), pages 157-176.
    4. Zhang, Shenxi & Cheng, Haozhong & Li, Ke & Tai, Nengling & Wang, Dan & Li, Furong, 2018. "Multi-objective distributed generation planning in distribution network considering correlations among uncertainties," Applied Energy, Elsevier, vol. 226(C), pages 743-755.
    5. Verma, Om Prakash & Mohammed, Toufiq Haji & Mangal, Shubham & Manik, Gaurav, 2017. "Minimization of energy consumption in multi-stage evaporator system of Kraft recovery process using Interior-Point Method," Energy, Elsevier, vol. 129(C), pages 148-157.
    6. Lai, Kexing & Illindala, Mahesh S., 2018. "A distributed energy management strategy for resilient shipboard power system," Applied Energy, Elsevier, vol. 228(C), pages 821-832.
    7. Fang, Xin & Hodge, Bri-Mathias & Du, Ershun & Zhang, Ning & Li, Fangxing, 2018. "Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach," Applied Energy, Elsevier, vol. 230(C), pages 531-539.
    8. Qu, Kaiping & Yu, Tao & Huang, Linni & Yang, Bo & Zhang, Xiaoshun, 2018. "Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market," Energy, Elsevier, vol. 149(C), pages 779-791.
    9. Ma, Qiuzhuo & Paudel, Krishna P. & Cui, Luqi, 2018. "A multi-objective optimization problem for using poultry litter in electricity production," Applied Energy, Elsevier, vol. 228(C), pages 1220-1242.
    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. Yin, Linfei & Wang, Tao & Zheng, Baomin, 2021. "Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems," Energy, Elsevier, vol. 216(C).
    2. Han, Kunlun & Yang, Kai & Yin, Linfei, 2022. "Lightweight actor-critic generative adversarial networks for real-time smart generation control of microgrids," Applied Energy, Elsevier, vol. 317(C).
    3. Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(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. Yin, Linfei & Wang, Tao & Zheng, Baomin, 2021. "Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems," Energy, Elsevier, vol. 216(C).
    2. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    4. Hossam A. Gabbar, 2021. "Resiliency Analysis of Hybrid Energy Systems within Interconnected Infrastructures," Energies, MDPI, vol. 14(22), pages 1-12, November.
    5. Mohammad Zohrul Islam & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy & Hashim Hizam & Nashiren Farzilah Mailah & Josep M. Guerrero & Mohamad Nasrun Mohd Nasir, 2020. "A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission," Sustainability, MDPI, vol. 12(13), pages 1-25, June.
    6. Li, Yinxiao & Wang, Yi & Chen, Qixin, 2020. "Study on the impacts of meteorological factors on distributed photovoltaic accommodation considering dynamic line parameters," Applied Energy, Elsevier, vol. 259(C).
    7. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    9. Quan, Hao & Lv, Junjie & Guo, Jian & Zhang, Wenjie, 2022. "Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study," Applied Energy, Elsevier, vol. 316(C).
    10. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    11. Amr Khaled Khamees & Almoataz Y. Abdelaziz & Makram R. Eskaros & Mahmoud A. Attia & Mariam A. Sameh, 2022. "Optimal Power Flow with Stochastic Renewable Energy Using Three Mixture Component Distribution Functions," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    12. Fan Li & Jingxi Su & Bo Sun, 2023. "An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm," Energies, MDPI, vol. 16(9), pages 1-22, April.
    13. Cao, Sunliang, 2019. "The impact of electric vehicles and mobile boundary expansions on the realization of zero-emission office buildings," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    14. Liu, Jia & Zeng, Peter Pingliang & Xing, Hao & Li, Yalou & Wu, Qiuwei, 2020. "Hierarchical duality-based planning of transmission networks coordinating active distribution network operation," Energy, Elsevier, vol. 213(C).
    15. Qu, Kaiping & Shi, Shouyuan & Yu, Tao & Wang, Wenrui, 2019. "A convex decentralized optimization for environmental-economic power and gas system considering diversified emission control," Applied Energy, Elsevier, vol. 240(C), pages 630-645.
    16. Zhang, Jingrui & Li, Zhuoyun & Wang, Beibei, 2021. "Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing," Energy, Elsevier, vol. 223(C).
    17. Sarjiya, & Budi, Rizki Firmansyah Setya & Hadi, Sasongko Pramono, 2019. "Game theory for multi-objective and multi-period framework generation expansion planning in deregulated markets," Energy, Elsevier, vol. 174(C), pages 323-330.
    18. Fang, Xin & Cui, Hantao & Du, Ershun & Li, Fangxing & Kang, Chongqing, 2021. "Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands," Applied Energy, Elsevier, vol. 282(PA).
    19. Luo, Lizi & Gu, Wei & Wu, Zhi & Zhou, Suyang, 2019. "Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation," Applied Energy, Elsevier, vol. 242(C), pages 1274-1284.
    20. Haoxiang Yang & David P. Morton & Chaithanya Bandi & Krishnamurthy Dvijotham, 2021. "Robust Optimization for Electricity Generation," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 336-351, January.

    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:239:y:2019:i:c:p:1066-1076. 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.