IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i3p860-d136864.html
   My bibliography  Save this article

Comprehensive Evaluation for Operating Efficiency of Electricity Retail Companies Based on the Improved TOPSIS Method and LSSVM Optimized by Modified Ant Colony Algorithm from the View of Sustainable Development

Author

Listed:
  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

  • Si Li

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

  • Shuyu Dai

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing 102206, China)

Abstract

The electricity market of China is currently in the process of a new institutional reform. Diversified electricity retail entities are gradually being established with the opening of the marketing electricity side. In the face of a complex market environment and fierce competition, the operating efficiency can directly reflect the current market position and development of electricity retail companies. TOPSIS method can make full use of the information of original data, calculate the distance between evaluated objects and the ideal solutions and get the relative proximity, which is generally used in the overall department and comprehensive evaluation of the benefits. Least squares support vector machine (LSSVM), with high convergence precision, helps save the training time of algorithm by solving linear equations and is used to predict the comprehensive evaluation value. Considering the ultimate goal of sustainable development, a comprehensive evaluation model on operating efficiency of electricity retail companies based on the improved TOPSIS method and LSSVM optimized by modified ant colony algorithm is proposed in this paper. Firstly, from the view of sustainable development, an operating efficiency evaluation indicator system is constructed. Secondly, the entropy weight method is applied to empower the indicators objectively. After that, based on the improved TOPSIS method, the reverse problem in the evaluation process is eliminated. According to the relative proximity between the evaluated objects and the absolute ideal solutions, the scores of comprehensive evaluation for operating efficiency can then be ranked. Finally, the LSSVM optimized by modified ant colony algorithm is introduced to realize the simplified expert scoring process and fast calculation in the comprehensive evaluation process, and its improved learning and generalization ability can be used in the comprehensive evaluation of similar projects. The example analysis proves that the comprehensive evaluation model proposed in this paper can provide scientific and effective evaluation results of the operating efficiency of electricity retail companies.

Suggested Citation

  • Dongxiao Niu & Si Li & Shuyu Dai, 2018. "Comprehensive Evaluation for Operating Efficiency of Electricity Retail Companies Based on the Improved TOPSIS Method and LSSVM Optimized by Modified Ant Colony Algorithm from the View of Sustainable ," Sustainability, MDPI, vol. 10(3), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:3:p:860-:d:136864
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/3/860/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/3/860/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Elisa Omodei & Alex Arenas, 2018. "A network approach to decentralized coordination of energy production-consumption grids," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-11, January.
    2. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    3. Lucas Cuadra & Sancho Salcedo-Sanz & Javier Del Ser & Silvia Jiménez-Fernández & Zong Woo Geem, 2015. "A Critical Review of Robustness in Power Grids Using Complex Networks Concepts," Energies, MDPI, vol. 8(9), pages 1-55, August.
    4. Pagani, Giuliano Andrea & Aiello, Marco, 2016. "From the grid to the smart grid, topologically," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 160-175.
    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. Enci Liu & Jie Li & Anni Zheng & Haoran Liu & Tao Jiang, 2022. "Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    2. Ming Meng & Shucheng Wu & Jin Zhou & Xinfang Wang, 2019. "What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    3. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    4. Jun Dong & Dongxue Wang & Dongran Liu & Palidan Ainiwaer & Linpeng Nie, 2019. "Operation Health Assessment of Power Market Based on Improved Matter-Element Extension Cloud Model," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    5. Guangqi Liang & Dongxiao Niu & Yi Liang, 2020. "Core Competitiveness Evaluation of Clean Energy Incubators Based on Matter-Element Extension Combined with TOPSIS and KPCA-NSGA-II-LSSVM," Sustainability, MDPI, vol. 12(22), pages 1-26, November.
    6. Zhen Li & Yun Li & Yanbin Li, 2019. "Performance Evaluation of Energy Transition Based on the Technique for Order Preference by a Similar to Ideal Solution and Support Vector Machine Optimized by an Improved Artificial Bee Colony Algorit," Energies, MDPI, vol. 12(16), pages 1-21, August.

    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. Tio, Adonis E. & Hill, David J. & Ma, Jin, 2020. "Can graph properties determine future grid adequacy for power injection diversity?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    2. Zou, Yanli & Wang, Ruirui & Gao, Zheng, 2020. "Improve synchronizability of a power grid through power allocation and topology adjustment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    3. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
    4. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    5. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    6. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    7. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    8. Panyam, Varuneswara & Huang, Hao & Davis, Katherine & Layton, Astrid, 2019. "Bio-inspired design for robust power grid networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Gianluca Fulli & Marcelo Masera & Catalin Felix Covrig & Francesco Profumo & Ettore Bompard & Tao Huang, 2017. "The EU Electricity Security Decision-Analytic Framework: Status and Perspective Developments," Energies, MDPI, vol. 10(4), pages 1-20, March.
    10. Tornyeviadzi, Hoese Michel & Owusu-Ansah, Emmanuel & Mohammed, Hadi & Seidu, Razak, 2022. "A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    11. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    12. Sperstad, Iver Bakken & Kjølle, Gerd H. & Gjerde, Oddbjørn, 2020. "A comprehensive framework for vulnerability analysis of extraordinary events in power systems," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    13. Fan, Dongming & Ren, Yi & Feng, Qiang & Liu, Yiliu & Wang, Zili & Lin, Jing, 2021. "Restoration of smart grids: Current status, challenges, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    14. Guo, Hengdao & Zheng, Ciyan & Iu, Herbert Ho-Ching & Fernando, Tyrone, 2017. "A critical review of cascading failure analysis and modeling of power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 9-22.
    15. Senderov, Sergey M. & Vorobev, Sergey V. & Smirnova, Elena M., 2022. "Peak underground gas storage efficiency in reducing the vulnerability of gas supply to consumers in an extensive gas transmission system," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    16. Hang Thi-Thuy Le & Eleonora Riva Sanseverino & Ninh Quang Nguyen & Maria Luisa Di Silvestre & Salvatore Favuzza & Binh Doan Van & Rossano Musca, 2023. "Critical Assessments of the Potential for Integrating Renewable Energy into Isolated Grids on Vietnamese Islands: The Case of the An-Binh Grid," Energies, MDPI, vol. 16(5), pages 1-23, March.
    17. Enrique Personal & Antonio García & Antonio Parejo & Diego Francisco Larios & Félix Biscarri & Carlos León, 2016. "A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems," Energies, MDPI, vol. 9(12), pages 1-30, December.
    18. Espejo, Rafael & Lumbreras, Sara & Ramos, Andres, 2018. "Analysis of transmission-power-grid topology and scalability, the European case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 383-395.
    19. Rishang Long & Jianhua Zhang, 2016. "Risk Assessment Method of UHV AC/DC Power System under Serious Disasters," Energies, MDPI, vol. 10(1), pages 1-13, December.
    20. Zarghami, Seyed Ashkan & Gunawan, Indra & Schultmann, Frank, 2018. "Integrating entropy theory and cospanning tree technique for redundancy analysis of water distribution networks," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 102-112.

    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:jsusta:v:10:y:2018:i:3:p:860-:d:136864. 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.