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Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations

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  • Cui, Mingjian
  • Zhang, Jie

Abstract

The increasing solar power penetration causes the need of additional flexibility for power system operations. Market-based flexible ramping services have been proposed in several balance authorities to address this issue. However, the ramping requirements in multi-timescale power system operations are not well defined and still challenging to be accurately estimated. To this end, this paper develops a multi-timescale unit commitment and economic dispatch model to estimate the ramping requirements. Furthermore, a solar power ramping product (SPRP) is developed and integrated into the multi-timescale dispatch model that considers new objective functions, ramping capacity limits, active power limits, and flexible ramping requirements. To find the optimal ramping requirement based on the level of uncertainty in netload, a surrogate-based optimization model is developed to approximate the objective function of the multi-timescale dispatch model that considers both economic and reliability benefits of the balancing authorities. Numerical simulations on a modified IEEE 118-bus system show that a better estimation of ramping requirements could enhance both the reliability and economic benefits of the system. The use of SPRP can reduce the flexible ramping reserves provided by conventional generators.

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  • Cui, Mingjian & Zhang, Jie, 2018. "Estimating ramping requirements with solar-friendly flexible ramping product in multi-timescale power system operations," Applied Energy, Elsevier, vol. 225(C), pages 27-41.
    • Handle: RePEc:eee:appene:v:225:y:2018:i:c:p:27-41
    DOI: 10.1016/j.apenergy.2018.05.031
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    1. Chaudhry, Nikhil & Hughes, Larry, 2012. "Forecasting the reliability of wind-energy systems: A new approach using the RL technique," Applied Energy, Elsevier, vol. 96(C), pages 422-430.
    2. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    3. Janko, Samantha A. & Arnold, Michael R. & Johnson, Nathan G., 2016. "Implications of high-penetration renewables for ratepayers and utilities in the residential solar photovoltaic (PV) market," Applied Energy, Elsevier, vol. 180(C), pages 37-51.
    4. Li, Gengfeng & Bie, Zhaohong & Kou, Yu & Jiang, Jiangfeng & Bettinelli, Mattia, 2016. "Reliability evaluation of integrated energy systems based on smart agent communication," Applied Energy, Elsevier, vol. 167(C), pages 397-406.
    5. Wang, Jianxiao & Zhong, Haiwang & Tang, Wenyuan & Rajagopal, Ram & Xia, Qing & Kang, Chongqing & Wang, Yi, 2017. "Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products," Applied Energy, Elsevier, vol. 205(C), pages 294-303.
    6. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    7. Cui, Mingjian & Zhang, Jie & Feng, Cong & Florita, Anthony R. & Sun, Yuanzhang & Hodge, Bri-Mathias, 2017. "Characterizing and analyzing ramping events in wind power, solar power, load, and netload," Renewable Energy, Elsevier, vol. 111(C), pages 227-244.
    8. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2015. "Value of flexible electric vehicles in providing spinning reserve services," Applied Energy, Elsevier, vol. 157(C), pages 60-74.
    9. Welsch, Manuel & Deane, Paul & Howells, Mark & Ó Gallachóir, Brian & Rogan, Fionn & Bazilian, Morgan & Rogner, Hans-Holger, 2014. "Incorporating flexibility requirements into long-term energy system models – A case study on high levels of renewable electricity penetration in Ireland," Applied Energy, Elsevier, vol. 135(C), pages 600-615.
    10. Wang, Qin & Wu, Hongyu & Florita, Anthony R. & Brancucci Martinez-Anido, Carlo & Hodge, Bri-Mathias, 2016. "The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales," Applied Energy, Elsevier, vol. 184(C), pages 696-713.
    11. Yang, Yuanchao & Wang, Jianhui & Guan, Xiaohong & Zhai, Qiaozhu, 2012. "Subhourly unit commitment with feasible energy delivery constraints," Applied Energy, Elsevier, vol. 96(C), pages 245-252.
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    3. Li, Binghui & Feng, Cong & Siebenschuh, Carlo & Zhang, Rui & Spyrou, Evangelia & Krishnan, Venkat & Hobbs, Benjamin F. & Zhang, Jie, 2022. "Sizing ramping reserve using probabilistic solar forecasts: A data-driven method," Applied Energy, Elsevier, vol. 313(C).
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    8. Sreekumar, Sreenu & Yamujala, Sumanth & Sharma, Kailash Chand & Bhakar, Rohit & Simon, Sishaj P. & Rana, Ankur Singh, 2022. "Flexible Ramp Products: A solution to enhance power system flexibility," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    9. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Fang, Lurui & Yan, Ke, 2022. "Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control," Renewable Energy, Elsevier, vol. 195(C), pages 147-166.
    10. Fan, Shuai & Liu, Jiang & Wu, Qing & Cui, Mingjian & Zhou, Huan & He, Guangyu, 2020. "Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm," Applied Energy, Elsevier, vol. 277(C).

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