IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1301-d1353541.html
   My bibliography  Save this article

Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity

Author

Listed:
  • Xiaoqing Wang

    (Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Connection, Xinjiang University, Urumqi 830049, China)

  • Xin Du

    (Goldwind Science & Technology Co., Ltd., Urumqi 830026, China)

  • Haiyun Wang

    (Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Connection, Xinjiang University, Urumqi 830049, China)

  • Sizhe Yan

    (Engineering Research Center of Education Ministry for Renewable Energy Power Generation and Grid Connection, Xinjiang University, Urumqi 830049, China)

  • Tianyuan Fan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.

Suggested Citation

  • Xiaoqing Wang & Xin Du & Haiyun Wang & Sizhe Yan & Tianyuan Fan, 2024. "Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity," Energies, MDPI, vol. 17(6), pages 1-16, March.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1301-:d:1353541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1301/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/6/1301/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xu, Jian & Chen, Yuanfeng & Liao, Siyang & Sun, Yuanzhang & Yao, Liangzhong & Fu, Haobo & Jiang, Xueyi & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao, 2019. "Demand side industrial load control for local utilization of wind power in isolated grids," Applied Energy, Elsevier, vol. 243(C), pages 47-56.
    2. Chunyang Hao & Yibo Wang & Chuang Liu & Guanglie Zhang & Hao Yu & Dongzhe Wang & Jingru Shang, 2023. "Research on Two-Stage Regulation Method for Source–Load Flexibility Transformation in Power Systems," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    3. Solomon Feleke & Balamurali Pydi & Raavi Satish & Hossam Kotb & Mohammed Alenezi & Mokhtar Shouran, 2023. "Frequency Stability Enhancement Using Differential-Evolution- and Genetic-Algorithm-Optimized Intelligent Controllers in Multiple Virtual Synchronous Machine Systems," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    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. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    2. Pang, Simian & Zheng, Zixuan & Xiao, Xianyong & Huang, Chunjun & Zhang, Shu & Li, Jie & Zong, Yi & You, Shi, 2022. "Collaborative power tracking method of diversified thermal loads for optimal demand response: A MILP-Based decomposition algorithm," Applied Energy, Elsevier, vol. 327(C).
    3. Gang Zhang & Yaning Zhu & Tuo Xie & Kaoshe Zhang & Xin He, 2022. "Wind Power Consumption Model Based on the Connection between Mid- and Long-Term Monthly Bidding Power Decomposition and Short-Term Wind-Thermal Power Joint Dispatch," Energies, MDPI, vol. 15(19), pages 1-25, September.

    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:17:y:2024:i:6:p:1301-:d:1353541. 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.