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Renewable energy time series regulation strategy considering grid flexible load and N-1 faults

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  • Xiong, Yongkang
  • Zeng, Zhenfeng
  • Xin, Jianbo
  • Song, Guanhong
  • Xia, Yonghong
  • Xu, Zaide

Abstract

As the proportion of renewable energy in electricity generation continues to increase, the uncertainty of renewable energy has brought significant challenges to power system security. Traditional renewable energy dispatching methods have less research on power system failure risk analysis due to economic considerations. In this paper, a renewable energy time series regulation strategy considering N-1 faults is proposed to improve the power system economy and security. In this strategy, by researching flexible load aggregation models, the time-series regulation model of aggregation loads is determined. Then the network N-1 security analysis divided into generator detection and branch detection is conducted. Determining possible N-1 faults of the system, the scheme minimizes the operating cost and grid risk. Adopting the methods above, the most desired renewable energy time-series regulation results are obtained. The results have shown that after adopting the proposed strategy in the improved system, the solar and wind curtailment volume is reduced by 38.10%, and the operating cost is reduced by 24.60%. In a daily cycle, the maximum power of all branch power overruns at the most risky moment is reduced by 20.36%, and the sum of the total power of all branch power overruns is reduced by 40.30%.

Suggested Citation

  • Xiong, Yongkang & Zeng, Zhenfeng & Xin, Jianbo & Song, Guanhong & Xia, Yonghong & Xu, Zaide, 2023. "Renewable energy time series regulation strategy considering grid flexible load and N-1 faults," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223025343
    DOI: 10.1016/j.energy.2023.129140
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    References listed on IDEAS

    as
    1. Yin, WanJun & Qin, Xuan, 2022. "Cooperative optimization strategy for large-scale electric vehicle charging and discharging," Energy, Elsevier, vol. 258(C).
    2. Yang, Zhichun & Yang, Fan & Min, Huaidong & Tian, Hao & Hu, Wei & Liu, Jian & Eghbalian, Nasrin, 2023. "Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources," Energy, Elsevier, vol. 263(PA).
    3. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
    4. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    5. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    6. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    7. Hocaoglu, Fatih Onur & Karanfil, Fatih, 2013. "A time series-based approach for renewable energy modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 204-214.
    8. Aghaei, Jamshid & Muttaqi, Kashem M. & Azizivahed, Ali & Gitizadeh, Mohsen, 2014. "Distribution expansion planning considering reliability and security of energy using modified PSO (Particle Swarm Optimization) algorithm," Energy, Elsevier, vol. 65(C), pages 398-411.
    9. Sadeghi, Delnia & Hesami Naghshbandy, Ali & Bahramara, Salah, 2020. "Optimal sizing of hybrid renewable energy systems in presence of electric vehicles using multi-objective particle swarm optimization," Energy, Elsevier, vol. 209(C).
    10. Becker, Sarah & Frew, Bethany A. & Andresen, Gorm B. & Zeyer, Timo & Schramm, Stefan & Greiner, Martin & Jacobson, Mark Z., 2014. "Features of a fully renewable US electricity system: Optimized mixes of wind and solar PV and transmission grid extensions," Energy, Elsevier, vol. 72(C), pages 443-458.
    11. Xie, Rui & Wei, Wei & Li, Mingxuan & Dong, ZhaoYang & Mei, Shengwei, 2023. "Sizing capacities of renewable generation, transmission, and energy storage for low-carbon power systems: A distributionally robust optimization approach," Energy, Elsevier, vol. 263(PA).
    12. Xiong, Xiaoping & Qing, Guohua, 2023. "A hybrid day-ahead electricity price forecasting framework based on time series," Energy, Elsevier, vol. 264(C).
    13. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    14. Velasquez, Carlos E. & Zocatelli, Matheus & Estanislau, Fidellis B.G.L. & Castro, Victor F., 2022. "Analysis of time series models for Brazilian electricity demand forecasting," Energy, Elsevier, vol. 247(C).
    15. Wang, Jingjie & Qiu, Rujia & Xu, Bin & Wu, Hongbin & Tang, Longjiang & Zhang, Mingxing & Ding, Ming, 2023. "Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units," Energy, Elsevier, vol. 276(C).
    16. Lu, Xinhui & Li, Haobin & Zhou, Kaile & Yang, Shanlin, 2023. "Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response," Energy, Elsevier, vol. 262(PB).
    17. Gomes, A. & Antunes, C. Henggeler & Martinho, J., 2013. "A physically-based model for simulating inverter type air conditioners/heat pumps," Energy, Elsevier, vol. 50(C), pages 110-119.
    18. Jin, Baohong, 2023. "Impact of renewable energy penetration in power systems on the optimization and operation of regional distributed energy systems," Energy, Elsevier, vol. 273(C).
    19. Ren, Hongbo & Jiang, Zipei & Wu, Qiong & Li, Qifen & Lv, Hang, 2023. "Optimal planning of an economic and resilient district integrated energy system considering renewable energy uncertainty and demand response under natural disasters," Energy, Elsevier, vol. 277(C).
    20. Wang, Sen & Li, Fengting & Zhang, Gaohang & Yin, Chunya, 2023. "Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy," Energy, Elsevier, vol. 267(C).
    21. Zhang, Tairan & Sobhani, Behrouz, 2023. "Optimal economic programming of an energy hub in the power system while taking into account the uncertainty of renewable resources, risk-taking and electric vehicles using a developed routing method," Energy, Elsevier, vol. 271(C).
    Full references (including those not matched with items on IDEAS)

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