IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p85-d125044.html
   My bibliography  Save this article

Application and Comparison of Metaheuristic and New Metamodel Based Global Optimization Methods to the Optimal Operation of Active Distribution Networks

Author

Listed:
  • Hao Xiao

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Wei Pei

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Zuomin Dong

    (Department of Mechanical Engineering and Institute of Integrated Energy Systems, University of Victoria, Victoria, BC V8W2Y2, Canada)

  • Li Kong

    (Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China)

  • Dan Wang

    (Key Lab of Smart Grid of Min of Education, Tianjin University, Tianjin 300072, China)

Abstract

As an imperative part of smart grids (SG) technology, the optimal operation of active distribution networks (ADNs) is critical to the best utilization of renewable energy and minimization of network power losses. However, the increasing penetration of distributed renewable energy sources with uncertain power generation and growing demands for higher quality power distribution are turning the optimal operation scheduling of ADN into complex and global optimization problems with non-unimodal, discontinuous and computation intensive objective functions that are difficult to solve, constituting a critical obstacle to the further advance of SG and ADN technology. In this work, power generation from renewable energy sources and network load demands are estimated using probability distribution models to capture the variation trends of load fluctuation, solar radiation and wind speed, and probability scenario generation and reduction methods are introduced to capture uncertainties and to reduce computation. The Open Distribution System Simulator (OpenDSS) is used in modeling the ADNs to support quick changes to network designs and configurations. The optimal operation of the ADN, is achieved by minimizing both network voltage deviation and power loss under the probability-based varying power supplies and loads. In solving the computation intensive ADN operation scheduling optimization problem, several novel metamodel-based global optimization (MBGO) methods have been introduced and applied. A comparative study has been carried out to compare the conventional metaheuristic global optimization (GO) and MBGO methods to better understand their advantages, drawbacks and limitations, and to provide guidelines for subsequent ADN and smart grid scheduling optimizations. Simulation studies have been carried out on the modified IEEE 13, 33 and 123 node networks to represent ADN test cases. The MBGO methods were found to be more suitable for small- and medium-scale ADN optimal operation scheduling problems, while the metaheuristic GO algorithms are more effective in the optimal operation scheduling of large-scale ADNs with relatively straightforward objective functions that require limited computational time. This research provides solution for ADN optimal operations, and forms the foundation for ADN design optimization.

Suggested Citation

  • Hao Xiao & Wei Pei & Zuomin Dong & Li Kong & Dan Wang, 2018. "Application and Comparison of Metaheuristic and New Metamodel Based Global Optimization Methods to the Optimal Operation of Active Distribution Networks," Energies, MDPI, vol. 11(1), pages 1-29, January.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:85-:d:125044
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/85/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/1/85/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Youcef Ettoumi, F. & Mefti, A. & Adane, A. & Bouroubi, M.Y., 2002. "Statistical analysis of solar measurements in Algeria using beta distributions," Renewable Energy, Elsevier, vol. 26(1), pages 47-67.
    2. McDonald, Jim, 2008. "Adaptive intelligent power systems: Active distribution networks," Energy Policy, Elsevier, vol. 36(12), pages 4346-4351, December.
    3. Thomas Weise & Yuezhong Wu & Raymond Chiong & Ke Tang & Jörg Lässig, 2016. "Global versus local search: the impact of population sizes on evolutionary algorithm performance," Journal of Global Optimization, Springer, vol. 66(3), pages 511-534, November.
    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. Jian Chen & Jiaqi Li & Yicheng Zhang & Guannan Bao & Xiaohui Ge & Peng Li, 2018. "A Hierarchical Optimal Operation Strategy of Hybrid Energy Storage System in Distribution Networks with High Photovoltaic Penetration," Energies, MDPI, vol. 11(2), pages 1-20, February.
    2. Hua, Weiqi & Chen, Ying & Qadrdan, Meysam & Jiang, Jing & Sun, Hongjian & Wu, Jianzhong, 2022. "Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. Mohammed Alshehri & Jin Yang, 2024. "Voltage Optimization in Active Distribution Networks—Utilizing Analytical and Computational Approaches in High Renewable Energy Penetration Environments," Energies, MDPI, vol. 17(5), pages 1-33, March.
    4. Pourmohammadi, Pardis & Saif, Ahmed, 2023. "Robust metamodel-based simulation-optimization approaches for designing hybrid renewable energy systems," Applied Energy, Elsevier, vol. 341(C).
    5. Yih-Der Lee & Jheng-Lun Jiang & Yuan-Hsiang Ho & Wei-Chen Lin & Hsin-Ching Chih & Wei-Tzer Huang, 2020. "Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data," Energies, MDPI, vol. 13(7), pages 1-20, April.
    6. Yih-Der Lee & Wei-Chen Lin & Jheng-Lun Jiang & Jia-Hao Cai & Wei-Tzer Huang & Kai-Chao Yao, 2021. "Optimal Individual Phase Voltage Regulation Strategies in Active Distribution Networks with High PV Penetration Using the Sparrow Search Algorithm," Energies, MDPI, vol. 14(24), pages 1-22, December.
    7. Elio Chiodo & Maurizio Fantauzzi & Davide Lauria & Fabio Mottola, 2018. "A Probabilistic Approach for the Optimal Sizing of Storage Devices to Increase the Penetration of Plug-in Electric Vehicles in Direct Current Networks," Energies, MDPI, vol. 11(5), pages 1-20, May.

    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. Karthikeyan Nainar & Florin Iov, 2020. "Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids," Energies, MDPI, vol. 13(20), pages 1-18, October.
    2. Ahmad Abuelrub & Osama Saadeh & Hussein M. K. Al-Masri, 2018. "Scenario Aggregation-Based Grid-Connected Photovoltaic Plant Design," Sustainability, MDPI, vol. 10(4), pages 1-13, April.
    3. Oleksandr Miroshnyk & Oleksandr Moroz & Taras Shchur & Andrii Chepizhnyi & Mohamed Qawaqzeh & Sławomir Kocira, 2023. "Investigation of Smart Grid Operation Modes with Electrical Energy Storage System," Energies, MDPI, vol. 16(6), pages 1-13, March.
    4. Darius Corbier & Frédéric Gonand & Marie Bessec, 2015. "Impacts of decentralised power generation on distribution networks: a statistical typology of European countries," Working Papers 1509, Chaire Economie du climat.
    5. Benseddik, A. & Azzi, A. & Chellali, F. & Khanniche, R. & Allaf, k., 2018. "An analysis of meteorological parameters influencing solar drying systems in Algeria using the isopleth chart technique," Renewable Energy, Elsevier, vol. 122(C), pages 173-183.
    6. Li, Yan-Fu & Zio, Enrico, 2012. "A multi-state model for the reliability assessment of a distributed generation system via universal generating function," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 28-36.
    7. Antonio Bracale & Pierluigi Caramia & Guido Carpinelli & Anna Rita Di Fazio & Gabriella Ferruzzi, 2013. "A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control," Energies, MDPI, vol. 6(2), pages 1-15, February.
    8. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    9. de Joode, J. & Jansen, J.C. & van der Welle, A.J. & Scheepers, M.J.J., 2009. "Increasing penetration of renewable and distributed electricity generation and the need for different network regulation," Energy Policy, Elsevier, vol. 37(8), pages 2907-2915, August.
    10. Kalim Ullah & Sajjad Ali & Taimoor Ahmad Khan & Imran Khan & Sadaqat Jan & Ibrar Ali Shah & Ghulam Hafeez, 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs," Energies, MDPI, vol. 13(21), pages 1-17, November.
    11. Veldman, Else & Gibescu, Madeleine & Slootweg, Han (J.G.) & Kling, Wil L., 2013. "Scenario-based modelling of future residential electricity demands and assessing their impact on distribution grids," Energy Policy, Elsevier, vol. 56(C), pages 233-247.
    12. Fu, Xueqian & Chen, Haoyong & Cai, Runqing & Yang, Ping, 2015. "Optimal allocation and adaptive VAR control of PV-DG in distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 173-182.
    13. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.
    14. Carlo Orsi, 2022. "On the conditional noncentral beta distribution," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 164-189, May.
    15. Yuan, Jiahai & Xu, Yan & Hu, Zhaoguang, 2012. "Delivering power system transition in China," Energy Policy, Elsevier, vol. 50(C), pages 751-772.
    16. Labed, S. & Lorenzo, E., 2004. "The impact of solar radiation variability and data discrepancies on the design of PV systems," Renewable Energy, Elsevier, vol. 29(7), pages 1007-1022.
    17. Ghadi, M. Jabbari & Ghavidel, Sahand & Rajabi, Amin & Azizivahed, Ali & Li, Li & Zhang, Jiangfeng, 2019. "A review on economic and technical operation of active distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 38-53.
    18. Poppen, Silvia, 2014. "Auswirkungen dezentraler Erzeugungsanlagen auf das Stromversorgungssystem: Ausgestaltungsmöglichkeiten der Bereitstellung neuer Erzeugungsanlagen," Arbeitspapiere 146, University of Münster, Institute for Cooperatives.
    19. Gilbert Ahamer, 2022. "Why Biomass Fuels Are Principally Not Carbon Neutral," Energies, MDPI, vol. 15(24), pages 1-39, December.
    20. Ilia Shushpanov & Konstantin Suslov & Pavel Ilyushin & Denis N. Sidorov, 2021. "Towards the Flexible Distribution Networks Design Using the Reliability Performance Metric," Energies, MDPI, vol. 14(19), pages 1-24, September.

    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:11:y:2018:i:1:p:85-:d:125044. 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.