IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i10p2829-d176974.html
   My bibliography  Save this article

Wind Power Consumption Research Based on Green Economic Indicators

Author

Listed:
  • Xiuyun Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Yibing Zhou

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Junyu Tian

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Jian Wang

    (State Grid Sanmenxia Power Supply Company, Sanmenxia 472000, China)

  • Yang Cui

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

Abstract

As a representative form of new energy generation, wind power has effectively alleviated environmental pollution and energy shortages. This paper constructs a green economic indicator to measure the degree of coordinated development of environmental and social benefits. To increase the amount of wind power consumption, an economic dispatch model based on the coordinated operation of cogeneration units and electric boilers was established; we also introduced the green certificate transaction cost, which effectively meets the strategic needs of China’s energy low-carbon transformation top-level system design. Wind power output has instability and volatility, so it puts higher requirements on the stable operation of thermal power units. To solve the stability problem, this paper introduces the output index of the thermal power unit and rationally plans the unit combination strategy, as well as introducing the concept of chance-constrained programming due to the uncertainty of load and wind power in the model. Uncertainty factors are transformed into load forecasting errors and wind power prediction errors for processing. Based on the normal distribution theory, the uncertainty model is transformed into a certain equivalence class model, and the improved disturbance mutated particle swarm optimization algorithm is used to solve the problem. Finally, the validity and feasibility of the proposed model are verified based on the IEEE30 node system.

Suggested Citation

  • Xiuyun Wang & Yibing Zhou & Junyu Tian & Jian Wang & Yang Cui, 2018. "Wind Power Consumption Research Based on Green Economic Indicators," Energies, MDPI, vol. 11(10), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2829-:d:176974
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/10/2829/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/10/2829/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luickx, Patrick J. & Delarue, Erik D. & D'haeseleer, William D., 2010. "Impact of large amounts of wind power on the operation of an electricity generation system: Belgian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 2019-2028, September.
    2. Meng Xiong & Feng Gao & Kun Liu & Siyun Chen & Jiaojiao Dong, 2015. "Optimal Real-Time Scheduling for Hybrid Energy Storage Systems and Wind Farms Based on Model Predictive Control," Energies, MDPI, vol. 8(8), pages 1-32, August.
    3. Sousa, Jorge A.M. & Teixeira, Fábio & Faias, Sérgio, 2014. "Impact of a price-maker pumped storage hydro unit on the integration of wind energy in power systems," Energy, Elsevier, vol. 69(C), pages 3-11.
    4. Xiuyun Wang & Jian Wang & Biyuan Tian & Yang Cui & Yu Zhao, 2018. "Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints," Energies, MDPI, vol. 11(4), pages 1-19, April.
    5. Aboelsood Zidan & Hossam A. Gabbar, 2016. "DG Mix and Energy Storage Units for Optimal Planning of Self-Sufficient Micro Energy Grids," Energies, MDPI, vol. 9(8), pages 1-18, August.
    6. Erren Yao & Huanran Wang & Long Liu & Guang Xi, 2014. "A Novel Constant-Pressure Pumped Hydro Combined with Compressed Air Energy Storage System," Energies, MDPI, vol. 8(1), pages 1-18, December.
    7. Rongxiang Yuan & Jun Ye & Jiazhi Lei & Timing Li, 2016. "Integrated Combined Heat and Power System Dispatch Considering Electrical and Thermal Energy Storage," Energies, MDPI, vol. 9(6), pages 1-17, June.
    8. Xinshuo Zhang & Guangwen Ma & Weibin Huang & Shijun Chen & Shuai Zhang, 2018. "Short-Term Optimal Operation of a Wind-PV-Hydro Complementary Installation: Yalong River, Sichuan Province, China," Energies, MDPI, vol. 11(4), pages 1-19, April.
    9. Pengwei Cong & Wei Tang & Lu Zhang & Bo Zhang & Yongxiang Cai, 2017. "Day-Ahead Active Power Scheduling in Active Distribution Network Considering Renewable Energy Generation Forecast Errors," Energies, MDPI, vol. 10(9), pages 1-20, August.
    10. Hongyu Long & Ruilin Xu & Jianjun He, 2011. "Incorporating the Variability of Wind Power with Electric Heat Pumps," Energies, MDPI, vol. 4(10), pages 1-15, October.
    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. Can Ding & Yiyuan Zhou & Qingchang Ding & Kaiming Li, 2022. "Integrated Carbon-Capture-Based Low-Carbon Economic Dispatch of Power Systems Based on EEMD-LSTM-SVR Wind Power Forecasting," Energies, MDPI, vol. 15(5), pages 1-27, February.

    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. Yanjuan Yu & Hongkun Chen & Lei Chen, 2018. "Comparative Study of Electric Energy Storages and Thermal Energy Auxiliaries for Improving Wind Power Integration in the Cogeneration System," Energies, MDPI, vol. 11(2), pages 1-16, January.
    2. Ping Li & Haixia Wang & Quan Lv & Weidong Li, 2017. "Combined Heat and Power Dispatch Considering Heat Storage of Both Buildings and Pipelines in District Heating System for Wind Power Integration," Energies, MDPI, vol. 10(7), pages 1-19, June.
    3. Yi, Ji Hyun & Ko, Woong & Park, Jong-Keun & Park, Hyeongon, 2018. "Impact of carbon emission constraint on design of small scale multi-energy system," Energy, Elsevier, vol. 161(C), pages 792-808.
    4. Wang, Jiangjiang & Deng, Hongda & Qi, Xiaoling, 2022. "Cost-based site and capacity optimization of multi-energy storage system in the regional integrated energy networks," Energy, Elsevier, vol. 261(PA).
    5. Huang, Shih-Chieh & Lo, Shang-Lien & Lin, Yen-Ching, 2013. "Application of a fuzzy cognitive map based on a structural equation model for the identification of limitations to the development of wind power," Energy Policy, Elsevier, vol. 63(C), pages 851-861.
    6. Chen, Hao & Wang, Huanran & Li, Ruixiong & Sun, Hao & Ge, Gangqiang & Ling, Lanning, 2022. "Experimental and analytical investigation of near-isothermal pumped hydro-compressed air energy storage system," Energy, Elsevier, vol. 249(C).
    7. Vassilis M. Charitopoulos & Mathilde Fajardy & Chi Kong Chyong & David M. Reiner, 2022. "The case of 100% electrification of domestic heat in Great Britain," Working Papers EPRG2206, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    8. Hai Lu & Jiaquan Yang & Kari Alanne, 2018. "Energy Quality Management for a Micro Energy Network Integrated with Renewables in a Tourist Area: A Chinese Case Study," Energies, MDPI, vol. 11(4), pages 1-24, April.
    9. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    10. Dong, Lijun & Kang, Xiaojun & Pan, Mengqi & Zhao, Man & Zhang, Feng & Yao, Hong, 2020. "B-matching-based optimization model for energy allocation in sea surface monitoring," Energy, Elsevier, vol. 192(C).
    11. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Pan, Bo & Qi, Shiqiang, 2020. "Two-stage stochastic optimal operation of integrated electricity and heat system considering reserve of flexible devices and spatial-temporal correlation of wind power," Applied Energy, Elsevier, vol. 275(C).
    12. Huang, Jinbo & Li, Zhigang & Wu, Q.H., 2017. "Coordinated dispatch of electric power and district heating networks: A decentralized solution using optimality condition decomposition," Applied Energy, Elsevier, vol. 206(C), pages 1508-1522.
    13. Guelpa, Elisa & Verda, Vittorio, 2019. "Compact physical model for simulation of thermal networks," Energy, Elsevier, vol. 175(C), pages 998-1008.
    14. Kalyani Makarand Kurundkar & Geetanjali Abhijit Vaidya, 2023. "Stochastic Security-Constrained Economic Dispatch of Load-Following and Contingency Reserves Ancillary Service Using a Grid-Connected Microgrid during Uncertainty," Energies, MDPI, vol. 16(6), pages 1-25, March.
    15. Calvillo, C.F. & Sánchez-Miralles, A. & Villar, J. & Martín, F., 2016. "Optimal planning and operation of aggregated distributed energy resources with market participation," Applied Energy, Elsevier, vol. 182(C), pages 340-357.
    16. Vandermeulen, Annelies & Van Oevelen, Tijs & van der Heijde, Bram & Helsen, Lieve, 2020. "A simulation-based evaluation of substation models for network flexibility characterisation in district heating networks," Energy, Elsevier, vol. 201(C).
    17. Jingjing Zhai & Xiaobei Wu & Zihao Li & Shaojie Zhu & Bo Yang & Haoming Liu, 2021. "Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations," Energies, MDPI, vol. 14(4), pages 1-33, February.
    18. Ji, Huichao & Wang, Haixin & Yang, Junyou & Feng, Jiawei & Yang, Yongyue & Okoye, Martin Onyeka, 2021. "Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks," Energy, Elsevier, vol. 234(C).
    19. Qin, Xin & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "A generalized quasi-dynamic model for electric-heat coupling integrated energy system with distributed energy resources," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    20. Guangyi Wu & Xiangxin Shao & Hong Jiang & Shaoxin Chen & Yibing Zhou & Hongyang Xu, 2020. "Control Strategy of the Pumped Storage Unit to Deal with the Fluctuation of Wind and Photovoltaic Power in Microgrid," Energies, MDPI, vol. 13(2), pages 1-23, January.

    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:gam:jeners:v:11:y:2018:i:10:p:2829-:d:176974. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.