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Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants

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  • Wei, Wei
  • Liu, Feng
  • Wang, Jianhui
  • Chen, Laijun
  • Mei, Shengwei
  • Yuan, Tiejiang

Abstract

Utilizing clean renewable generation and carbon capture plants (CCPs) can remarkably reduce the carbon emission from electricity production. Because operating carbon capture facility consumers additional energy, minimizing the production cost and reducing the carbon emission may conflict with each other. To compromise these two objectives and cope with uncertain wind generation, this paper proposes a robust environmental-economic dispatch (EED) method that jointly optimizes energy and reserve schedules in the upcoming dispatch period. The operating characteristic of CCP and the volatility of wind energy are considered in the proposed model. Because both objectives are convex functions, the Pareto front can be readily computed by using the ε-constraint method. The Nash bargaining criterion is adopted to determine a fair trade-off between the generation cost and the carbon emission in the absence of a clear carbon tax or emission cap. A second-order cone program (SOCP) is proposed to locate the bargaining solution on the Pareto front. An adaptive scenario generation algorithm is derived to solve the robust EED problem in a tractable manner. The PJM 5-bus system is used to illustrate the obtained dispatch strategy, and demonstrate the contribution of CCPs on reducing the carbon emissions and enhancing the operational flexibility. Case studies on the IEEE 118-bus system corroborate the applicability of the proposed method.

Suggested Citation

  • Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
  • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:674-684
    DOI: 10.1016/j.apenergy.2016.09.013
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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Aneke, Mathew & Wang, Meihong, 2015. "Process analysis of pressurized oxy-coal power cycle for carbon capture application integrated with liquid air power generation and binary cycle engines," Applied Energy, Elsevier, vol. 154(C), pages 556-566.
    3. Wang, Jiadong & Wang, Jianhui & Liu, Cong & Ruiz, Juan P., 2013. "Stochastic unit commitment with sub-hourly dispatch constraints," Applied Energy, Elsevier, vol. 105(C), pages 418-422.
    4. Kumar, Pawan & Kim, Ki-Hyun, 2016. "Recent progress and innovation in carbon capture and storage using bioinspired materials," Applied Energy, Elsevier, vol. 172(C), pages 383-397.
    5. Ben-Mansour, R. & Habib, M.A. & Bamidele, O.E. & Basha, M. & Qasem, N.A.A. & Peedikakkal, A. & Laoui, T. & Ali, M., 2016. "Carbon capture by physical adsorption: Materials, experimental investigations and numerical modeling and simulations – A review," Applied Energy, Elsevier, vol. 161(C), pages 225-255.
    6. Jubril, A.M. & Olaniyan, O.A. & Komolafe, O.A. & Ogunbona, P.O., 2014. "Economic-emission dispatch problem: A semi-definite programming approach," Applied Energy, Elsevier, vol. 134(C), pages 446-455.
    7. Vahidinasab, V. & Jadid, S., 2009. "Multiobjective environmental/techno-economic approach for strategic bidding in energy markets," Applied Energy, Elsevier, vol. 86(4), pages 496-504, April.
    8. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    9. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    10. Tsai, Ming-Tang & Yen, Chih-Wei, 2011. "The influence of carbon dioxide trading scheme on economic dispatch of generators," Applied Energy, Elsevier, vol. 88(12), pages 4811-4816.
    11. Nash, John, 1950. "The Bargaining Problem," Econometrica, Econometric Society, vol. 18(2), pages 155-162, April.
    12. Ren, Hongbo & Zhou, Weisheng & Nakagami, Ken'ichi & Gao, Weijun & Wu, Qiong, 2010. "Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 87(12), pages 3642-3651, December.
    13. Solomon, A.A. & Kammen, Daniel M. & Callaway, D., 2014. "The role of large-scale energy storage design and dispatch in the power grid: A study of very high grid penetration of variable renewable resources," Applied Energy, Elsevier, vol. 134(C), pages 75-89.
    14. Jin, Jingliang & Zhou, Dequn & Zhou, Peng & Miao, Zhuang, 2014. "Environmental/economic power dispatch with wind power," Renewable Energy, Elsevier, vol. 71(C), pages 234-242.
    Full references (including those not matched with items on IDEAS)

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    7. Chen, F. & Huang, G.H. & Fan, Y.R. & Chen, J.P., 2017. "A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning," Applied Energy, Elsevier, vol. 187(C), pages 291-309.
    8. Jin, Jingliang & Wen, Qinglan & Cheng, Siqi & Qiu, Yaru & Zhang, Xianyue & Guo, Xiaojun, 2022. "Optimization of carbon emission reduction paths in the low-carbon power dispatching process," Renewable Energy, Elsevier, vol. 188(C), pages 425-436.
    9. Moret, Stefano & Babonneau, Frédéric & Bierlaire, Michel & Maréchal, François, 2020. "Overcapacity in European power systems: Analysis and robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    10. Zhang, Menglin & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2018. "A systematic approach for the joint dispatch of energy and reserve incorporating demand response," Applied Energy, Elsevier, vol. 230(C), pages 1279-1291.
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    12. Shin, Hansol & Kim, Tae Hyun & Kim, Hyoungtae & Lee, Sungwoo & Kim, Wook, 2019. "Environmental shutdown of coal-fired generators for greenhouse gas reduction: A case study of South Korea," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
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    15. Qingshan Xu & Yifan Ding & Aixia Zheng, 2017. "An Optimal Dispatch Model of Wind-Integrated Power System Considering Demand Response and Reliability," Sustainability, MDPI, vol. 9(5), pages 1-20, May.

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