IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v225y2018icp27-41.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918307244
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.05.031?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    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. Yuanyuan, Zhang & Huiru, Zhao & Bingkang, Li, 2023. "Distributionally robust comprehensive declaration strategy of virtual power plant participating in the power market considering flexible ramping product and uncertainties," Applied Energy, Elsevier, vol. 343(C).
    2. Keeratimahat, Kanyawee & Bruce, Anna & MacGill, Iain, 2021. "Analysis of short-term operational forecast deviations and controllability of utility-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 167(C), pages 343-358.
    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).
    4. Zhang, Jian & Cui, Mingjian & He, Yigang, 2020. "Robustness and adaptability analysis for equivalent model of doubly fed induction generator wind farm using measured data," Applied Energy, Elsevier, vol. 261(C).
    5. Abdollahi, Elnaz & Wang, Haichao & Lahdelma, Risto, 2019. "Parametric optimization of long-term multi-area heat and power production with power storage," Applied Energy, Elsevier, vol. 235(C), pages 802-812.
    6. Liu, Yuanzhi & Zhang, Jie, 2019. "Design a J-type air-based battery thermal management system through surrogate-based optimization," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Mirzaei, Mohammad Amin & Sadeghi-Yazdankhah, Ahmad & Mohammadi-Ivatloo, Behnam & Marzband, Mousa & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Integration of emerging resources in IGDT-based robust scheduling of combined power and natural gas systems considering flexible ramping products," Energy, Elsevier, vol. 189(C).
    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).

    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. de Jong, Pieter & Dargaville, Roger & Silver, Jeremy & Utembe, Steven & Kiperstok, Asher & Torres, Ednildo Andrade, 2017. "Forecasting high proportions of wind energy supplying the Brazilian Northeast electricity grid," Applied Energy, Elsevier, vol. 195(C), pages 538-555.
    2. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    3. Nelson, James R. & Johnson, Nathan G., 2020. "Model predictive control of microgrids for real-time ancillary service market participation," Applied Energy, Elsevier, vol. 269(C).
    4. Feng, Cong & Sun, Mucun & Cui, Mingjian & Chartan, Erol Kevin & Hodge, Bri-Mathias & Zhang, Jie, 2019. "Characterizing forecastability of wind sites in the United States," Renewable Energy, Elsevier, vol. 133(C), pages 1352-1365.
    5. Soria, Rafael & Lucena, André F.P. & Tomaschek, Jan & Fichter, Tobias & Haasz, Thomas & Szklo, Alexandre & Schaeffer, Roberto & Rochedo, Pedro & Fahl, Ulrich & Kern, Jürgen, 2016. "Modelling concentrated solar power (CSP) in the Brazilian energy system: A soft-linked model coupling approach," Energy, Elsevier, vol. 116(P1), pages 265-280.
    6. Silveira, Jose Ronaldo & Brandao, Danilo Iglesias & Fernandes, Nicolas T.D. & Uturbey, Wadaed & Cardoso, Braz, 2021. "Multifunctional dispatchable microgrids," Applied Energy, Elsevier, vol. 282(PA).
    7. Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    8. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    9. Cheng, Meng & Sami, Saif Sabah & Wu, Jianzhong, 2017. "Benefits of using virtual energy storage system for power system frequency response," Applied Energy, Elsevier, vol. 194(C), pages 376-385.
    10. Cerovac, Tin & Ćosić, Boris & Pukšec, Tomislav & Duić, Neven, 2014. "Wind energy integration into future energy systems based on conventional plants – The case study of Croatia," Applied Energy, Elsevier, vol. 135(C), pages 643-655.
    11. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    12. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    13. 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.
    14. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.
    15. Dai, Jiangyu & Wu, Shiqiang & Han, Guoyi & Weinberg, Josh & Xie, Xinghua & Wu, Xiufeng & Song, Xingqiang & Jia, Benyou & Xue, Wanyun & Yang, Qianqian, 2018. "Water-energy nexus: A review of methods and tools for macro-assessment," Applied Energy, Elsevier, vol. 210(C), pages 393-408.
    16. Vorushylo, Inna & Keatley, Patrick & Shah, Nikhilkumar & Green, Richard & Hewitt, Neil, 2018. "How heat pumps and thermal energy storage can be used to manage wind power: A study of Ireland," Energy, Elsevier, vol. 157(C), pages 539-549.
    17. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
    18. Shi, Jie & Wang, Luhao & Lee, Wei-Jen & Cheng, Xingong & Zong, Xiju, 2019. "Hybrid Energy Storage System (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction," Applied Energy, Elsevier, vol. 256(C).
    19. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    20. Cleary, Kathryne & Palmer, Karen, 2020. "Encouraging Electrification through Energy Service Subscriptions," RFF Working Paper Series 20-09, Resources for the Future.

    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:eee:appene:v:225:y:2018:i:c:p:27-41. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.