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Data-Driven Distribution Network Energy-Saving Planning Based on Cluster Division and High-Penetration Renewable Energy Access

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Big Data and Security (ICBDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1563))

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Abstract

In view of the increasing proportion of renewable energy access in the distribution network, traditional distribution network planning methods have been unable to meet the needs of grid development. This paper proposes a data-driven distribution network energy-saving plan based on cluster division with high-penetration renewable energy access. This method firstly optimizes the capacity and layout of high-proportion renewable energy to realize the initial site selection and fixed capacity of renewable energy; secondly, it divides the distribution network based on data-driven clusters to realize the zoning planning of the distribution network, and finally, through a two-level iterative programming model is constructed to realize an optimized decision-making model with cost and energy-saving benefits as the goal. The calculation example uses real power grid data to analyze, and the simulation results show the effectiveness and feasibility of the model and method proposed in this paper.

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References

  1. Koutsoukis, N.C., Georgilakis, P.S., Hatziargyriou, N.D.: Multistage coordinated planning of active distribution networks. IEEE Trans. Power Syst. 33(1), 32–44 (2018)

    Article  Google Scholar 

  2. Xiang, Y., Liu, J.Y., Li, F.R., et al.: Optimal active distribution network planning: a review. Electr. Power Compon. Syst. 44(10), 1075–1094 (2016)

    Article  Google Scholar 

  3. Dorostkar-Ghamsari, M.R., Fotuhi-Firuzabad, M., Lehtonen, M.: Value of distribution network reconfiguration in presence of renewable energy resources. IEEE Trans. Power Syst. 31(3), 1879–1888 (2016)

    Article  Google Scholar 

  4. Liu, H., Wang, Z.: Research on energy storage and high proportion of renewable energy planning considering demand. IEEE Access 8, 198591–198599 (2020)

    Article  Google Scholar 

  5. Quijano, D.A., Padilha-Feltrin, A.: Optimal integration of distributed generation and conservation voltage reduction in active distribution networks. Int. J. Electr. Power Energy Syst. 113(1), 197–207 (2019)

    Article  Google Scholar 

  6. Manbachi, M., Farhangi, H., Palizban, A., et al.: Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification. Appl. Energy 174(1), 69–79 (2016)

    Article  Google Scholar 

  7. Pamshetti, V.B., Singh, S., Singh, S.P.: Reduction of energy demand via conservation voltage reduction considering network reconfiguration and soft open point. Int. Trans. Electr. Energy Syst. 30(1), e12147 (2020)

    Article  Google Scholar 

  8. Li, Y., Tian, X., et al.: Study on voltage control in distribution network with renewable energy integration. In: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. State Grid Heilongjiang Electric Power Co., Ltd., Harbin, 150090, Heilongjiang, China

    Ke Hua & Zhichao Wang

  2. School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, Jiangsu, China

    Baoyin Wu & Maosheng Fu

Authors
  1. Ke Hua
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  2. Zhichao Wang
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  3. Baoyin Wu
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  4. Maosheng Fu
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Editor information

Editors and Affiliations

  1. Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Tinghuai Ma

  3. King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

  4. Texas Tech University, Lubbock, TX, USA

    Victor S. Sheng

  5. Tianjin University, Tianjin, China

    Zhaoqing Pan

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Hua, K., Wang, Z., Wu, B., Fu, M. (2022). Data-Driven Distribution Network Energy-Saving Planning Based on Cluster Division and High-Penetration Renewable Energy Access. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_49

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  • DOI: https://doi.org/10.1007/978-981-19-0852-1_49

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0851-4

  • Online ISBN: 978-981-19-0852-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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