IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v329y2023ics0306261922015550.html
   My bibliography  Save this article

Probabilistic harmonic forecasting of the distribution system considering time-varying uncertainties of the distributed energy resources and electrical loads

Author

Listed:
  • Li, Yahui
  • Sun, Yuanyuan
  • Wang, Qingyan
  • Sun, Kaiqi
  • Li, Ke-Jun
  • Zhang, Yan

Abstract

Distributed energy resources (DER) and electrical loads have grown rapidly in response to concerns about energy sustainability and rising energy demand. However, to realize energy conversion, a large number of power electronic converters are used, resulting in serious harmonic issues in the distribution system. Meanwhile, the random and intermittent characteristics of DER and electrical load bring strong uncertainties to the distribution system. It not only affects the safe and stable operation but also leads to the new stochastic characteristics of harmonics. The study proposes a novel probabilistic harmonic power flow method that takes into account DER and electrical load uncertainties in order to effectively forecast and analyze the uncertain harmonic distortion. Firstly, the time-varying states with stochastic characteristics are determined to represent the uncertainties of electrical load, distributed photovoltaic power, and distributed wind power. The proposed method adapts to time-varying uncertain variable analysis while requiring less computation. Then, a novel constant-weight point estimate method based on the Nataf transformation is proposed to obtain the statistical features of the uncertainties. By simplifying the approximation process, the uncertain variable can be estimated more rapidly and effectively. Moreover, the interaction between multiple uncertain variables is also considered with the correlation coefficient matrix, which can analyze the harmonic coupling interaction in the system. Finally, the probabilistic harmonic power flow is developed considering the time-varying stochastic characteristic of uncertainties. On this basis, the proposed method can be used to forecast the harmonic distortion and also analyze the daily or seasonal statistical features. The proposed probabilistic harmonic power flow method's effectiveness is validated using real-field measured data.

Suggested Citation

  • Li, Yahui & Sun, Yuanyuan & Wang, Qingyan & Sun, Kaiqi & Li, Ke-Jun & Zhang, Yan, 2023. "Probabilistic harmonic forecasting of the distribution system considering time-varying uncertainties of the distributed energy resources and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s0306261922015550
    DOI: 10.1016/j.apenergy.2022.120298
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922015550
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120298?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. Chen, Houhe & Wang, Di & Zhang, Rufeng & Jiang, Tao & Li, Xue, 2022. "Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties," Applied Energy, Elsevier, vol. 308(C).
    2. Madurai Elavarasan, Rajvikram & Pugazhendhi, Rishi & Jamal, Taskin & Dyduch, Joanna & Arif, M.T. & Manoj Kumar, Nallapaneni & Shafiullah, GM & Chopra, Shauhrat S. & Nadarajah, Mithulananthan, 2021. "Envisioning the UN Sustainable Development Goals (SDGs) through the lens of energy sustainability (SDG 7) in the post-COVID-19 world," Applied Energy, Elsevier, vol. 292(C).
    3. Yan, Chao & Geng, Xinbo & Bie, Zhaohong & Xie, Le, 2022. "Two-stage robust energy storage planning with probabilistic guarantees: A data-driven approach," Applied Energy, Elsevier, vol. 313(C).
    4. Sun, Yuanyuan & Xie, Xiangmin & Wang, Qingyan & Zhang, Linghan & Li, Yahui & Jin, Zongshuai, 2020. "A bottom-up approach to evaluate the harmonics and power of home appliances in residential areas," Applied Energy, Elsevier, vol. 259(C).
    5. Liu, Zhengxuan & Sun, Pengchen & Xie, Mingjing & Zhou, Yuekuan & He, Yingdong & Zhang, Guoqiang & Chen, Dachuan & Li, Shuisheng & Yan, Zhongjun & Qin, Di, 2021. "Multivariant optimization and sensitivity analysis of an experimental vertical earth-to-air heat exchanger system integrating phase change material with Taguchi method," Renewable Energy, Elsevier, vol. 173(C), pages 401-414.
    6. Beaudry, Gabrielle & Pasquier, Philippe & Marcotte, Denis, 2021. "A fast convolution-based method to simulate time-varying flow rates in closed-loop and standing column well ground heat exchangers," Renewable Energy, Elsevier, vol. 174(C), pages 55-72.
    7. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2013. "Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation," Renewable Energy, Elsevier, vol. 55(C), pages 532-543.
    8. Allen C. Miller, III & Thomas R. Rice, 1983. "Discrete Approximations of Probability Distributions," Management Science, INFORMS, vol. 29(3), pages 352-362, March.
    9. Fairley, Iain & Lewis, Matthew & Robertson, Bryson & Hemer, Mark & Masters, Ian & Horrillo-Caraballo, Jose & Karunarathna, Harshinie & Reeve, Dominic E., 2020. "A classification system for global wave energy resources based on multivariate clustering," Applied Energy, Elsevier, vol. 262(C).
    10. Jithendranath, J. & Das, Debapriya & Guerrero, Josep M., 2021. "Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation," Energy, Elsevier, vol. 222(C).
    11. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    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. Maciej Kuboń & Zbigniew Skibko & Sylwester Tabor & Urszula Malaga-Toboła & Andrzej Borusiewicz & Wacław Romaniuk & Janusz Zarajczyk & Pavel Neuberger, 2023. "Analysis of Voltage Distortions in the Power Grid Arising from Agricultural Biogas Plant Operation," Energies, MDPI, vol. 16(17), pages 1-21, August.
    2. Shen, Haotian & Zhang, Hualiang & Xu, Yujie & Chen, Haisheng & Zhang, Zhilai & Li, Wenkai & Su, Xu & Xu, Yalin & Zhu, Yilin, 2024. "Two stage robust economic dispatching of microgrid considering uncertainty of wind, solar and electricity load along with carbon emission predicted by neural network model," Energy, Elsevier, vol. 300(C).

    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. O., Yugeswar Reddy & J., Jithendranath & Chakraborty, Ajoy Kumar & Guerrero, Josep M., 2022. "Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties," Applied Energy, Elsevier, vol. 307(C).
    2. Mehdi Neshat & Nataliia Y. Sergiienko & Erfan Amini & Meysam Majidi Nezhad & Davide Astiaso Garcia & Bradley Alexander & Markus Wagner, 2020. "A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea," Energies, MDPI, vol. 13(20), pages 1-23, October.
    3. Karl Friedrich Mina & Gerald H. L. Cheang & Carl Chiarella, 2015. "Approximate Hedging Of Options Under Jump-Diffusion Processes," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1-26.
    4. Jiang, Tao & Wu, Chenghao & Huang, Tao & Zhang, Rufeng & Li, Xue, 2024. "Optimal market participation of VPPs in TSO-DSO coordinated energy and flexibility markets," Applied Energy, Elsevier, vol. 360(C).
    5. Seong-Hyeon Cha & Sun-Hyeok Kwak & Woong Ko, 2023. "A Robust Optimization Model of Aggregated Resources Considering Serving Ratio for Providing Reserve Power in the Joint Electricity Market," Energies, MDPI, vol. 16(20), pages 1-27, October.
    6. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    7. Thomas W. Keelin & Bradford W. Powley, 2011. "Quantile-Parameterized Distributions," Decision Analysis, INFORMS, vol. 8(3), pages 206-219, September.
    8. Tanaka, Ken'ichiro & Toda, Alexis Akira, 2015. "Discretizing Distributions with Exact Moments: Error Estimate and Convergence Analysis," University of California at San Diego, Economics Working Paper Series qt7g23r5kh, Department of Economics, UC San Diego.
    9. Robert K. Hammond & J. Eric Bickel, 2013. "Reexamining Discrete Approximations to Continuous Distributions," Decision Analysis, INFORMS, vol. 10(1), pages 6-25, March.
    10. Andrés Fernández-Miguel & Maria Pia Riccardi & Valerio Veglio & Fernando E. García-Muiña & Alfonso P. Fernández del Hoyo & Davide Settembre-Blundo, 2022. "Disruption in Resource-Intensive Supply Chains: Reshoring and Nearshoring as Strategies to Enable Them to Become More Resilient and Sustainable," Sustainability, MDPI, vol. 14(17), pages 1-13, August.
    11. Wang, Richard & Ye, Zhongnan & Lu, Miaojia & Hsu, Shu-Chien, 2022. "Understanding post-pandemic work-from-home behaviours and community level energy reduction via agent-based modelling," Applied Energy, Elsevier, vol. 322(C).
    12. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    13. Ahmed Ginidi & Sherif M. Ghoneim & Abdallah Elsayed & Ragab El-Sehiemy & Abdullah Shaheen & Attia El-Fergany, 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    14. Tong, Ziqiang & Mansouri, Seyed Amir & Huang, Shoujun & Rezaee Jordehi, Ahmad & Tostado-Véliz, Marcos, 2023. "The role of smart communities integrated with renewable energy resources, smart homes and electric vehicles in providing ancillary services: A tri-stage optimization mechanism," Applied Energy, Elsevier, vol. 351(C).
    15. Hagreaves Kumba & Oludolapo A. Olanrewaju & Ratidzo Pasipamire, 2024. "Integration of Renewable Energy Technologies for Sustainable Development in South Africa: A Focus on Grid-Connected PV Systems," Energies, MDPI, vol. 17(12), pages 1-22, June.
    16. Simon Krüner & Christoph M. Hackl, 2022. "Nonlinear Modelling and Control of a Power Smoothing System for a Novel Wave Energy Converter Prototype," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    17. Yijing Li & Prakash P. Shenoy, 2012. "A Framework for Solving Hybrid Influence Diagrams Containing Deterministic Conditional Distributions," Decision Analysis, INFORMS, vol. 9(1), pages 55-75, March.
    18. Neary, Vincent S. & Ahn, Seongho, 2023. "Global atlas of extreme significant wave heights and relative risk ratios," Renewable Energy, Elsevier, vol. 208(C), pages 130-140.
    19. Quan, Hao & Lv, Junjie & Guo, Jian & Zhang, Wenjie, 2022. "Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study," Applied Energy, Elsevier, vol. 316(C).
    20. Liu, Songquan & Moskowitz, Herbert & Plante, Robert & Preckel, Paul V., 2002. "Product and process yield estimation with Gaussian quadrature (GQ) reduction: Improvements over the GQ full factorial approach," European Journal of Operational Research, Elsevier, vol. 140(3), pages 655-669, August.

    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:appene:v:329:y:2023:i:c:s0306261922015550. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.