Cluster voltage control method for “Whole County” distributed photovoltaics based on improved differential evolution algorithm

Jing ZHANG, Tonghe WANG, Jiongcong CHEN, Zhuoying LIAO, Jie SHU

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Front. Energy ›› 2023, Vol. 17 ›› Issue (6) : 782-795. DOI: 10.1007/s11708-023-0905-8
RESEARCH ARTICLE
RESEARCH ARTICLE

Cluster voltage control method for “Whole County” distributed photovoltaics based on improved differential evolution algorithm

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Abstract

China is vigorously promoting the “whole county promotion” of distributed photovoltaics (DPVs). However, the high penetration rate of DPVs has brought problems such as voltage violation and power quality degradation to the distribution network, seriously affecting the safety and reliability of the power system. The traditional centralized control method of the distribution network has the problem of low efficiency, which is not practical enough in engineering practice. To address the problems, this paper proposes a cluster voltage control method for distributed photovoltaic grid-connected distribution network. First, it partitions the distribution network into clusters, and different clusters exchange terminal voltage information through a “virtual slack bus.” Then, in each cluster, based on the control strategy of “reactive power compensation first, active power curtailment later,” it employs an improved differential evolution (IDE) algorithm based on Cauchy disturbance to control the voltage. Simulation results in two different distribution systems show that the proposed method not only greatly improves the operational efficiency of the algorithm but also effectively controls the voltage of the distribution network, and maximizes the consumption capacity of DPVs based on qualified voltage.

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Keywords

distributed photovoltaics (DPVs) / cluster partitioning / improved differential evolution algorithm / voltage control / consumption capacity of distributed photovoltaics

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Jing ZHANG, Tonghe WANG, Jiongcong CHEN, Zhuoying LIAO, Jie SHU. Cluster voltage control method for “Whole County” distributed photovoltaics based on improved differential evolution algorithm. Front. Energy, 2023, 17(6): 782‒795 https://doi.org/10.1007/s11708-023-0905-8

References

[1]
Bonthagorla P K, Mikkili S, Optimal P V. Array configuration for extracting maximum power under partial shading conditions by mitigating mismatching power losses. CSEE Journal of Power and Energy Systems, 2022, 8(2): 499–510
[2]
Swenson R. The solar evolution: Much more with way less, right now—The disruptive shift to renewables. Energies, 2016, 9(9): 676
CrossRef Google scholar
[3]
Martins F, Felgueiras C, Smitkova M. . Analysis of fossil fuel energy consumption and environmental impacts in European countries. Energies, 2019, 12(6): 964
CrossRef Google scholar
[4]
Wang S C. Current status of PV in China and its future forecast. CSEE Journal of Power and Energy Systems, 2020, 6(1): 72–82
[5]
Ahadi A, Miryousefi Aval S M, Hayati H. Generating capacity adequacy evaluation of large-scale, grid-connected photovoltaic systems. Frontiers in Energy, 2016, 10(3): 308–318
CrossRef Google scholar
[6]
Shahsavari A, Akbari M. Potential of solar energy in developing countries for reducing energy-related emissions. Renewable & Sustainable Energy Reviews, 2018, 90: 275–291
CrossRef Google scholar
[7]
Singla P, Duhan M, Saroha S. A comprehensive review and analysis of solar forecasting techniques. Frontiers in Energy, 2022, 16(2): 187–223
CrossRef Google scholar
[8]
Lu Q, Yu H, Zhao K L. . Residential demand response considering distributed PV consumption: A model based on China’s PV policy. Energy, 2019, 172: 443–456
CrossRef Google scholar
[9]
Liu S Y, Bie Z H, Liu F. . Policy implication on distributed generation PV trading in China. Energy Procedia, 2019, 159: 436–441
CrossRef Google scholar
[10]
Wu Y N, Xu M J, Tao Y. . A critical barrier analysis framework to the development of rural distributed PV in China. Energy, 2022, 245: 123277
CrossRef Google scholar
[11]
Gandhi O, Kumar D S, Rodríguez-Gallegos C D. . Review of power system impacts at high PV penetration Part I: Factors limiting PV penetration. Solar Energy, 2020, 210: 181–201
CrossRef Google scholar
[12]
Chen J L, Xu X Y, Yan Z. . Data-driven distribution network topology identification considering correlated generation power of distributed energy resource. Frontiers in Energy, 2022, 16(1): 121–129
CrossRef Google scholar
[13]
Zhang Y J, Qiao Y, Lu Z X. . Voltage control for partially visible distribution networks with high DG penetration. Power System Technology, 2019, 43(5): 1528–1535
[14]
Camilo F M, Almeida M E, Castro R. . Multi-conductor line models for harmonic load-flow calculations in LV networks with high penetration of PV generation. Journal of Modern Power Systems and Clean Energy, 2022, 10(5): 1288–1301
CrossRef Google scholar
[15]
Hashemi S, Ostergaard J. Methods and strategies for overvoltage prevention in low voltage distribution systems with PV. IET Renewable Power Generation, 2017, 11(2): 205–214
CrossRef Google scholar
[16]
Nour A M M, Hatata A Y, Helal A A. . Review on voltage-violation mitigation techniques of distribution networks with distributed rooftop PV systems. IET Generation, Transmission & Distribution, 2020, 14(3): 349–361
CrossRef Google scholar
[17]
Xu J, Fu H B, Liao S Y. . Demand-side management based on model predictive control in distribution network for smoothing distributed photovoltaic power fluctuations. Journal of Modern Power Systems and Clean Energy, 2022, 10(5): 1326–1336
CrossRef Google scholar
[18]
Jamal T, Urmee T, Calais M. . Technical challenges of PV deployment into remote Australian electricity networks: A review. Renewable & Sustainable Energy Reviews, 2017, 77: 1309–1325
CrossRef Google scholar
[19]
Yang A Q, Cai Y X, Chen X P. . An adaptive control for supporting village power grid integrating residential PV power generation. Energy Reports, 2022, 8: 3350–3359
CrossRef Google scholar
[20]
LiQ RZhang J C. Solutions of voltage beyond limits in distribution network with distributed photovoltaic generators. Automation of Electric Power Systems, 2015, 39(22): 117–123 (in Chinese)
[21]
WangYWen F SZhaoB, . Analysis and countermeasures of voltage violation problems caused by high-density distributed photovoltaics. Proceedings of CSEE, 2016, 36(5): 1200–1206 (in Chinese)
[22]
Wang C L, Tao Y G. Locally and globally optimal solutions of global optimisation for max-plus linear systems. IET Control Theory & Applications, 2022, 16(2): 219–228
CrossRef Google scholar
[23]
Zhao B, Xu Z C, Xu C. . Network partition based zonal voltage control for distribution networks with distributed PV systems. IEEE Transactions on Smart Grid, 2018, 9(5): 4087–4098
CrossRef Google scholar
[24]
Chai Y Y, Guo L, Wang C S. . Network partition and voltage coordination control for distribution networks with high penetration of distributed PV units. IEEE Transactions on Power Systems, 2018, 33(3): 3396–3407
CrossRef Google scholar
[25]
Ding J J, Zhang Q, Hu S J. . Clusters partition and zonal voltage regulation for distribution networks with high penetration of PVs. IET Generation, Transmission & Distribution, 2018, 12(22): 6041–6051
CrossRef Google scholar
[26]
Cao D, Zhao J B, Hu W H. . Attention enabled multi-agent DRL for decentralized Volt-VAR control of active distribution system using PV inverters and SVCs. IEEE Transactions on Sustainable Energy, 2021, 12(3): 1582–1592
CrossRef Google scholar
[27]
Liu L J, Zhang Y, Da C. . Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi-level programming. International Transactions on Electrical Energy Systems, 2020, 30(6): e12366
CrossRef Google scholar
[28]
Sun J, Xu J, Ke D P. . Cluster partition for distributed energy resources in Regional Integrated Energy System. Energy Reports, 2023, 9: 613–619
CrossRef Google scholar
[29]
Newman M E J. Fast algorithm for detecting community structure in networks. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 2004, 69(6): 066133
CrossRef Google scholar
[30]
Newman M E J. Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(23): 8577–8582
CrossRef Google scholar
[31]
Deng L B, Sun H L, Zhang L L. . η code: A differential evolution with η Cauchy operator for global numerical optimization. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 88517–88533
CrossRef Google scholar
[32]
Qian J, Wang P, Chen G G. Improved gravitational search algorithm and novel power flow prediction network for multi-objective optimal active dispatching problems. Expert Systems with Applications, 2023, 223: 119863
CrossRef Google scholar
[33]
WagleRSharma PSharmaC, . Optimal power flow based coordinated reactive and active power control to mitigate voltage violations in smart inverter enriched distribution network. International Journal of Green Energy, 2023: 1–17

Acknowledgements

This work was supported by the National Key R&D Plan Program of China (Grant No. 2022YFE0120700), the Special Fund for Science and Technology Innovation of Jiangsu Province (Grant No. BE2022610), and Zhuhai Industry Core Technology and Key Project (Grant No. 2220004002344).

Competing interests

The authors declare that they have no competing interests.

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2023 Higher Education Press 2023
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