IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v209y2023icp325-339.html
   My bibliography  Save this article

Measuring environmental performance of power dispatch influenced by low-carbon approaches

Author

Listed:
  • Jin, Jingliang
  • Wen, Qinglan
  • Zhao, Liya
  • Zhou, Chaoyang
  • Guo, Xiaojun

Abstract

Wind power integration and carbon reduction cooperation, as low-carbon approaches, are introduced into the current power dispatching process under carbon reduction requirements. In order to measure the environmental performance of power dispatch influenced by the above approaches, this paper firstly presents a low-carbon power dispatch model for wind power integrated system imported with carbon reduction cooperation. Next, the methods related to wind speed prediction, optimization algorithm and efficiency evaluation are fully discussed. Finally, empirical analysis shows that: (1) the overall effect of ARIMA-NARX model is superior to that of ARIMA model for wind speed prediction, whose MSE is reduced by 79.73%, (2) compared with GA and PSO, GA-PSO has better effect of optimization, which can effectively jump out of the local optimal value, and its convergence speed keeps improving, (3) the two low-carbon approaches can facilitate the achievement of system carbon reduction targets with good environmental performance of power dispatch, and (4) low-carbon power dispatching strategies taking full account of these two approaches are developed, which proves to be of reliability, economy and environment.

Suggested Citation

  • Jin, Jingliang & Wen, Qinglan & Zhao, Liya & Zhou, Chaoyang & Guo, Xiaojun, 2023. "Measuring environmental performance of power dispatch influenced by low-carbon approaches," Renewable Energy, Elsevier, vol. 209(C), pages 325-339.
  • Handle: RePEc:eee:renene:v:209:y:2023:i:c:p:325-339
    DOI: 10.1016/j.renene.2023.04.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123004743
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.04.024?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. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
    3. Basu, M., 2019. "Squirrel search algorithm for multi-region combined heat and power economic dispatch incorporating renewable energy sources," Energy, Elsevier, vol. 182(C), pages 296-305.
    4. Zhou, P. & Delmas, M.A. & Kohli, A., 2017. "Constructing meaningful environmental indices: A nonparametric frontier approach," Journal of Environmental Economics and Management, Elsevier, vol. 85(C), pages 21-34.
    5. Ciwei, Gao & Yang, Li, 2010. "Evolution of China's power dispatch principle and the new energy saving power dispatch policy," Energy Policy, Elsevier, vol. 38(11), pages 7346-7357, November.
    6. Veselov, Fedor & Pankrushina, Tatiana & Khorshev, Andrey, 2021. "Comparative economic analysis of technological priorities for low-carbon transformation of electric power industry in Russia and the EU," Energy Policy, Elsevier, vol. 156(C).
    7. Qin, Quande & Liu, Yuan & Huang, Jia-Ping, 2020. "A cooperative game analysis for the allocation of carbon emissions reduction responsibility in China's power industry," Energy Economics, Elsevier, vol. 92(C).
    8. Zhao, Shihao & Li, Kang & Yang, Zhile & Xu, Xinzhi & Zhang, Ning, 2022. "A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies," Applied Energy, Elsevier, vol. 314(C).
    9. 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.
    10. Klaassen, Ger & Nentjes, Andries & Smith, Mark, 2005. "Testing the theory of emissions trading: Experimental evidence on alternative mechanisms for global carbon trading," Ecological Economics, Elsevier, vol. 53(1), pages 47-58, April.
    11. Xiang, Yue & Wu, Gang & Shen, Xiaodong & Ma, Yuhang & Gou, Jing & Xu, Weiting & Liu, Junyong, 2021. "Low-carbon economic dispatch of electricity-gas systems," Energy, Elsevier, vol. 226(C).
    12. Hu, Zhaoguang & Yuan, Jiahai & Hu, Zheng, 2011. "Study on China's low carbon development in an Economy-Energy-Electricity-Environment framework," Energy Policy, Elsevier, vol. 39(5), pages 2596-2605, May.
    13. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    14. Peng, Xiaokang & Liu, Zicheng & Jiang, Dong, 2021. "A review of multiphase energy conversion in wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    15. Wei, Yi-Ming & Chen, Hao & Chyong, Chi Kong & Kang, Jia-Ning & Liao, Hua & Tang, Bao-Jun, 2018. "Economic dispatch savings in the coal-fired power sector: An empirical study of China," Energy Economics, Elsevier, vol. 74(C), pages 330-342.
    16. López, Germánico & Arboleya, Pablo, 2022. "Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador," Renewable Energy, Elsevier, vol. 183(C), pages 351-368.
    17. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    18. Wang, Wei & Sun, Bo & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2020. "An improved min-max power dispatching method for integration of variable renewable energy," Applied Energy, Elsevier, vol. 276(C).
    19. Zhao, Xiaoli & Liu, Suwei & Yan, Fengguang & Yuan, Ziqian & Liu, Zhiwen, 2017. "Energy conservation, environmental and economic value of the wind power priority dispatch in China," Renewable Energy, Elsevier, vol. 111(C), pages 666-675.
    20. Wang, Qunwei & Su, Bin & Zhou, Peng & Chiu, Ching-Ren, 2016. "Measuring total-factor CO2 emission performance and technology gaps using a non-radial directional distance function: A modified approach," Energy Economics, Elsevier, vol. 56(C), pages 475-482.
    21. Zhong, Haiwang & Xia, Qing & Chen, Yuguo & Kang, Chongqing, 2015. "Energy-saving generation dispatch toward a sustainable electric power industry in China," Energy Policy, Elsevier, vol. 83(C), pages 14-25.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Wu, Xiuqin & Zhao, Jinsong & Zhang, Dayong & Lee, Wen-Chieh & Yu, Chin-Hsien, 2022. "Resource misallocation and the development of hydropower industry," Applied Energy, Elsevier, vol. 306(PA).
    3. Chen, Hao & Cui, Jian & Song, Feng & Jiang, Zhigao, 2022. "Evaluating the impacts of reforming and integrating China's electricity sector," Energy Economics, Elsevier, vol. 108(C).
    4. Bashir, Hassan & Sibtain, Muhammad & Hanay, Özge & Azam, Muhammad Imran & Qurat-ul-Ain, & Saleem, Snoober, 2023. "Decomposition and Harris hawks optimized multivariate wind speed forecasting utilizing sequence2sequence-based spatiotemporal attention," Energy, Elsevier, vol. 278(PB).
    5. Hao Chen & Chi Kong Chyong & Jia-Ning Kang & Yi-Ming Wei, 2018. "Economic dispatch in the electricity sector in China: potential benefits and challenges ahead," Working Papers EPRG 1819, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    6. Wang, Huaizhi & Xue, Wenli & Liu, Yitao & Peng, Jianchun & Jiang, Hui, 2020. "Probabilistic wind power forecasting based on spiking neural network," Energy, Elsevier, vol. 196(C).
    7. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    8. Lu, Peng & Ye, Lin & Tang, Yong & Zhao, Yongning & Zhong, Wuzhi & Qu, Ying & Zhai, Bingxu, 2021. "Ultra-short-term combined prediction approach based on kernel function switch mechanism," Renewable Energy, Elsevier, vol. 164(C), pages 842-866.
    9. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
    10. Xia, Fan & Xu, Jintao, 2020. "Green total factor productivity: A re-examination of quality of growth for provinces in China," China Economic Review, Elsevier, vol. 62(C).
    11. Urazel, Burak & Keskin, Kemal, 2023. "A new solution approach for non-convex combined heat and power economic dispatch problem considering power loss," Energy, Elsevier, vol. 278(PB).
    12. Wei, Yi-Ming & Chen, Hao & Chyong, Chi Kong & Kang, Jia-Ning & Liao, Hua & Tang, Bao-Jun, 2018. "Economic dispatch savings in the coal-fired power sector: An empirical study of China," Energy Economics, Elsevier, vol. 74(C), pages 330-342.
    13. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    14. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    15. Song, Feng & Bi, De & Wei, Chu, 2019. "Market segmentation and wind curtailment: An empirical analysis," Energy Policy, Elsevier, vol. 132(C), pages 831-838.
    16. Saeed Salah & Husain R. Alsamamra & Jawad H. Shoqeir, 2022. "Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms," Energies, MDPI, vol. 15(7), pages 1-16, April.
    17. Li, Mingquan & Gao, Huiwen & Abdulla, Ahmed & Shan, Rui & Gao, Shuo, 2022. "Combined effects of carbon pricing and power market reform on CO2 emissions reduction in China's electricity sector," Energy, Elsevier, vol. 257(C).
    18. Li, Mingquan & Patiño-Echeverri, Dalia & Zhang, Junfeng (Jim), 2019. "Policies to promote energy efficiency and air emissions reductions in China's electric power generation sector during the 11th and 12th five-year plan periods: Achievements, remaining challenges, and ," Energy Policy, Elsevier, vol. 125(C), pages 429-444.
    19. Lu, Peng & Ye, Lin & Pei, Ming & Zhao, Yongning & Dai, Binhua & Li, Zhuo, 2022. "Short-term wind power forecasting based on meteorological feature extraction and optimization strategy," Renewable Energy, Elsevier, vol. 184(C), pages 642-661.
    20. Chenxi Xiang & Xinye Zheng & Feng Song & Jiang Lin & Zhigao Jiang, 2023. "Assessing the roles of efficient market versus regulatory capture in China’s power market reform," Nature Energy, Nature, vol. 8(7), pages 747-757, July.

    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:renene:v:209:y:2023:i:c:p:325-339. 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.journals.elsevier.com/renewable-energy .

    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.