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Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study

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  • Quan, Hao
  • Srinivasan, Dipti
  • Khosravi, Abbas

Abstract

The uncertainties of renewable energy have brought great challenges to power system commitment, dispatches and reserve requirement. This paper presents a comparative study on integration of renewable generation uncertainties into SCUC (stochastic security-constrained unit commitment) considering reserve and risk. Renewable forecast uncertainties are captured by a list of PIs (prediction intervals). A new scenario generation method is proposed to generate scenarios from these PIs. Different system uncertainties are considered as scenarios in the stochastic SCUC problem formulation. Two comparative simulations with single (E1: wind only) and multiple sources of uncertainty (E2: load, wind, solar and generation outages) are investigated. Five deterministic and four stochastic case studies are performed. Different generation costs, reserve strategies and associated risks are compared under various scenarios. Demonstrated results indicate the overall costs of E2 is lower than E1 due to penetration of solar power and the associated risk in deterministic cases of E2 is higher than E1. It implies the superimposed effect of uncertainties during uncertainty integration. The results also demonstrate that power systems run a higher level of risk during peak load hours, and that stochastic models are more robust than deterministic ones.

Suggested Citation

  • Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2016. "Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study," Energy, Elsevier, vol. 103(C), pages 735-745.
    • Handle: RePEc:eee:energy:v:103:y:2016:i:c:p:735-745
    DOI: 10.1016/j.energy.2016.03.007
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    Cited by:

    1. Zhang, Heng & Hu, Xiao & Cheng, Haozhong & Zhang, Shenxi & Hong, Shaoyun & Gu, Qingfa, 2021. "Coordinated scheduling of generators and tie lines in multi-area power systems under wind energy uncertainty," Energy, Elsevier, vol. 222(C).
    2. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    3. Goudarzi, Arman & Viray, Z.N.C. & Siano, Pierluigi & Swanson, Andrew G. & Coller, John V. & Kazemi, Mehdi, 2017. "A probabilistic determination of required reserve levels in an energy and reserve co-optimized electricity market with variable generation," Energy, Elsevier, vol. 130(C), pages 258-275.
    4. Li, Chaoshun & Wang, Wenxiao & Wang, Jinwen & Chen, Deshu, 2019. "Network-constrained unit commitment with RE uncertainty and PHES by using a binary artificial sheep algorithm," Energy, Elsevier, vol. 189(C).
    5. Dong, Jizhe & Li, Yuanhan & Zuo, Shi & Wu, Xiaomei & Zhang, Zuyao & Du, Jiang, 2023. "An intraperiod arbitrary ramping-rate changing model in unit commitment," Energy, Elsevier, vol. 284(C).
    6. Xing Chen & Suhua Lou & Yanjie Liang & Yaowu Wu & Xianglu He, 2021. "Optimal Scheduling of a Regional Power System Aiming at Accommodating Clean Energy," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    7. Emmanuel Hernández-Mayoral & Manuel Madrigal-Martínez & Jesús D. Mina-Antonio & Reynaldo Iracheta-Cortez & Jesús A. Enríquez-Santiago & Omar Rodríguez-Rivera & Gregorio Martínez-Reyes & Edwin Mendoza-, 2023. "A Comprehensive Review on Power-Quality Issues, Optimization Techniques, and Control Strategies of Microgrid Based on Renewable Energy Sources," Sustainability, MDPI, vol. 15(12), pages 1-53, June.
    8. Liu, Guangbiao & Zhou, Jianzhong & Jia, Benjun & He, Feifei & Yang, Yuqi & Sun, Na, 2019. "Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method," Applied Energy, Elsevier, vol. 238(C), pages 643-667.
    9. Gandhi, Oktoviano & Rodríguez-Gallegos, Carlos D. & Zhang, Wenjie & Srinivasan, Dipti & Reindl, Thomas, 2018. "Economic and technical analysis of reactive power provision from distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 827-841.
    10. Zhou, Min & Wang, Bo & Li, Tiantian & Watada, Junzo, 2018. "A data-driven approach for multi-objective unit commitment under hybrid uncertainties," Energy, Elsevier, vol. 164(C), pages 722-733.
    11. Gerardo J. Osório & Miadreza Shafie-khah & Juan M. Lujano-Rojas & João P. S. Catalão, 2018. "Scheduling Model for Renewable Energy Sources Integration in an Insular Power System," Energies, MDPI, vol. 11(1), pages 1-16, January.
    12. Whei-Min Lin & Chung-Yuen Yang & Chia-Sheng Tu & Ming-Tang Tsai, 2018. "An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment," Energies, MDPI, vol. 11(6), pages 1-17, June.
    13. Sharifzadeh, Mahdi & Lubiano-Walochik, Helena & Shah, Nilay, 2017. "Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 385-398.
    14. Erica Ocampo & Yen-Chih Huang & Cheng-Chien Kuo, 2020. "Feasible Reserve in Day-Ahead Unit Commitment Using Scenario-Based Optimization," Energies, MDPI, vol. 13(20), pages 1-17, October.
    15. Doubleday, Kate & Lara, José Daniel & Hodge, Bri-Mathias, 2022. "Investigation of stochastic unit commitment to enable advanced flexibility measures for high shares of solar PV," Applied Energy, Elsevier, vol. 321(C).
    16. Wang, Wenxiao & Li, Chaoshun & Liao, Xiang & Qin, Hui, 2017. "Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm," Applied Energy, Elsevier, vol. 187(C), pages 612-626.
    17. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    18. Yang, Zhile & Li, Kang & Guo, Yuanjun & Feng, Shengzhong & Niu, Qun & Xue, Yusheng & Foley, Aoife, 2019. "A binary symmetric based hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles," Energy, Elsevier, vol. 170(C), pages 889-905.
    19. Li, Peixian & Ng, Jeremy & Lu, Yujie, 2022. "Accelerating the adoption of renewable energy certificate: Insights from a survey of corporate renewable procurement in Singapore," Renewable Energy, Elsevier, vol. 199(C), pages 1272-1282.
    20. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2016. "Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory," Energy, Elsevier, vol. 114(C), pages 846-861.

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