IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v258y2026ics0960148125026436.html

Lightweight probability forecasting and local control of photovoltaic integrated with energy storage system in active distribution networks

Author

Listed:
  • Zhao, Jinli
  • Liu, Zhiwei
  • Ji, Haoran
  • Yu, Lei
  • Yuanlv, Zerui
  • Duan, Shuyin
  • Song, Guanyu
  • Yu, Hao
  • Li, Peng

Abstract

The increase in the use of photovoltaics (PVs) has exacerbated reverse power flow and voltage violations in active distribution networks (ADNs). Photovoltaic integrated with energy storage system (PV-ESS) can effectively alleviate the aforementioned issues by controlling the ESS. To achieve efficient control of ESS, accurate forecasting of PV power is required. Edge computing provides a localized solution for predictive control of PV-ESS. However, there are several challenges with edge computing, including limited computational resources at the edge and high uncertainty of the PV power. Thus, this paper proposes a lightweight probabilistic forecasting and local control method for PV-ESS. First, the complex teacher forecasting model in the cloud is compressed into a compact student model at the edge based on knowledge distillation. Quantile regression and kernel density estimation methods are used to obtain the conditional quantile and probability density function of the PV power. Following this, based on probabilistic forecasting, a local control method of ESS is proposed by constructing linearized chance constraints of the PV power fluctuations at the grid-connected point. Finally, the effectiveness of the proposed method is verified based on a typical PV dataset and the IEEE 33-node system. The results show that the proposed method can realize accurate forecasting of PV power under limited computational resources Furthermore, by smoothing PV power fluctuations through local control of the ESS, it reduces the average voltage deviation by 26.23 % and 17.88 % on different test days.

Suggested Citation

  • Zhao, Jinli & Liu, Zhiwei & Ji, Haoran & Yu, Lei & Yuanlv, Zerui & Duan, Shuyin & Song, Guanyu & Yu, Hao & Li, Peng, 2026. "Lightweight probability forecasting and local control of photovoltaic integrated with energy storage system in active distribution networks," Renewable Energy, Elsevier, vol. 258(C).
    • Handle: RePEc:eee:renene:v:258:y:2026:i:c:s0960148125026436
    DOI: 10.1016/j.renene.2025.124979
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125026436
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.124979?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Yunfei & Liu, Yan & Cai, Hanhu & Zhang, Junran, 2023. "An innovative short-term multihorizon photovoltaic power output forecasting method based on variational mode decomposition and a capsule convolutional neural network," Applied Energy, Elsevier, vol. 343(C).
    2. Xu, Zirong & Tang, Zhiyuan & Chen, Yongdong & Liu, Youbo & Gao, Hongjun & Xu, Xiao, 2025. "Optimal robust allocation of distributed modular energy storage system in distribution networks for voltage regulation," Applied Energy, Elsevier, vol. 388(C).
    3. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Su, Huaying, 2022. "Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems," Applied Energy, Elsevier, vol. 324(C).
    4. Wu, Yingzi & Chen, Yuwei & Li, Zhiyi & Golshannavaz, Sajjad, 2025. "Robust Co-planning of distributed photovoltaics and energy storage for enhancing the hosting capacity of active distribution networks," Renewable Energy, Elsevier, vol. 253(C).
    5. Bashir, Tasarruf & Wang, Huifang & Tahir, Mustafa & Zhang, Yixiang, 2025. "Wind and solar power forecasting based on hybrid CNN-ABiLSTM, CNN-transformer-MLP models," Renewable Energy, Elsevier, vol. 239(C).
    6. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    7. Yang, Dazhi & Yang, Guoming & Liu, Bai, 2023. "Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction," Renewable Energy, Elsevier, vol. 215(C).
    8. Liu, Benxi & Liu, Tengyuan & Hu, Zhenghe & Liao, Shengli & Cheng, Chuntian, 2025. "Short-term scheduling model for cascade diversion hydropower-wind-solar hybrid systems with Wasserstein-based distributionally robust chance constraints," Renewable Energy, Elsevier, vol. 254(C).
    9. Thaker, Jayesh & Höller, Robert, 2024. "Hybrid model for intra-day probabilistic PV power forecast," Renewable Energy, Elsevier, vol. 232(C).
    10. Tang, Wenhu & Huang, Yunlin & Qian, Tong & Wei, Cihang & Wu, Jianzhong, 2025. "Coordinated central-local control strategy for voltage management in PV-integrated distribution networks considering energy storage degradation," Applied Energy, Elsevier, vol. 389(C).
    11. Kolhe, Mohan Lal & Rasul, M.J.M.A., 2020. "3-Phase grid-connected building integrated photovoltaic system with reactive power control capability," Renewable Energy, Elsevier, vol. 154(C), pages 1065-1075.
    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. Liu, Weican & Gai, Mei, 2025. "PV-MLP: A lightweight patch-based multi-layer perceptron network with time–frequency domain fusion for accurate long-sequence photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 251(C).
    2. J. N. Otshwe & Bin Li & Ngouokoua J. Chabrol & Bing Qi & Loris M. Tshish, 2025. "Graph Convolutional-Optimization Framework for Carbon-Conscious Grid Management," Sustainability, MDPI, vol. 17(17), pages 1-25, September.
    3. Seman, Laio Oriel & Stefenon, Stefano Frizzo & Yow, Kin-Choong & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2026. "Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive models," Renewable Energy, Elsevier, vol. 257(C).
    4. Zhao, Lingyu & Qu, Fuming & Ji, Yaming & Liu, Jinhai & Zuo, Fengyuan, 2025. "A short-term wind power forecasting method based on evolution-framed fuzzy GANs," Renewable Energy, Elsevier, vol. 254(C).
    5. Lu, Peng & Li, Junhao & Liu, Shuting & Wu, Yuanbao & Zhang, Ning & Lan, Hanqing & Li, Kangping & Siano, Pierluigi, 2026. "Hierarchical reserve-based distributionally robust chance-constrained optimization for integrated electricity-heat systems during cold waves," Applied Energy, Elsevier, vol. 402(PB).
    6. Wang, Jin & Zhao, Zhipeng & Zhou, Jinglin & Cheng, Chuntian & Su, Huaying, 2024. "Co-optimization for day-ahead scheduling and flexibility response mode of a hydro–wind–solar hybrid system considering forecast uncertainty of variable renewable energy," Energy, Elsevier, vol. 311(C).
    7. Zhang, Zongbin & Huang, Xiaoqiao & Li, Chengli & Cheng, Feiyan & Tai, Yonghang, 2025. "CRAformer: A cross-residual attention transformer for solar irradiation multistep forecasting," Energy, Elsevier, vol. 320(C).
    8. Wang, Tao & Xu, Ye & Qin, Yu & Wang, Xu & Zheng, Feifan & Li, Wei, 2025. "Short-term PV forecasting of multiple scenarios based on multi-dimensional clustering and hybrid transformer-BiLSTM with ECPO," Energy, Elsevier, vol. 334(C).
    9. Shi, Yunhong & Li, Chengjiang & Wang, Honglei & Wang, Xiaolin & Negnevitsky, Michael, 2025. "A novel scheduling strategy of a hybrid wind-solar-hydro system for smoothing energy and power fluctuations," Energy, Elsevier, vol. 320(C).
    10. Bayode, Israel A. & Ba-Alawi, Abdulrahman H. & Nguyen, Hai-Tra & Woo, Taeyong & Yoo, ChangKyoo, 2025. "Long-term policy guidance for sustainable energy transition in Nigeria: A deep learning-based peak load forecasting with econo-environmental scenario analysis," Energy, Elsevier, vol. 322(C).
    11. Antonesi, Gabriel & Cioara, Tudor & Anghel, Ionut & Michalakopoulos, Vasilis & Sarmas, Elissaios & Toderean, Liana, 2025. "A systematic review of transformers and large language models in the energy sector: towards agentic digital twins," Applied Energy, Elsevier, vol. 401(PA).
    12. Peng, Zhiwen & Guo, Aijun & Chang, Jianxia & Wang, Yimin & Wang, Xuebin & Zhang, Junhao, 2025. "Cluster-type open-loop pumped storage power stations with hydraulic connections exacerbate water level fluctuations of conventional hydropower stations and alter hydropower flexibility," Energy, Elsevier, vol. 334(C).
    13. Xing, Qianyi & Huang, Xiaojia & Wang, Kang & Wang, Jianzhou & Wang, Shuai, 2025. "MIG-EWPFS: An ensemble probabilistic wind speed forecasting system integrating multi-dimensional feature extraction, hybrid quantile regression, and Knee improved multi-objective optimization," Energy, Elsevier, vol. 324(C).
    14. Tian, Zhirui & Liang, Bingjie, 2025. "PVMTF: End-to-end long-sequence time-series forecasting frameworks based on patch technique and information fusion coding for mid-term photovoltaic power forecasting," Applied Energy, Elsevier, vol. 396(C).
    15. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik, 2025. "PV Generation Prediction Using Multilayer Perceptron and Data Clustering for Energy Management Support," Energies, MDPI, vol. 18(6), pages 1-16, March.
    16. Ahmadi, Mehrnaz & Aly, Hamed & Khashei, Mehdi, 2025. "Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM," Energy, Elsevier, vol. 334(C).
    17. Ridha, Hussein Mohammed & Ahmadipour, Masoud & Alghrairi, Mokhalad & Hizam, Hashim & Mirjalili, Seyedali & Zubaidi, Salah L. & Mohammed S, Marwa Y., 2026. "A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine," Renewable Energy, Elsevier, vol. 256(PA).
    18. Mohammad Mehdi Sharifi Nevisi & Mehrdad Shoeibi & Francisco Hernando-Gallego & Diego Martín & Sarvenaz Sadat Khatami, 2025. "An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids," Energies, MDPI, vol. 18(10), pages 1-35, May.
    19. Hategan, Sergiu-Mihai & Stefu, Nicoleta & Petreus, Dorin & Szilagyi, Eniko & Patarau, Toma & Paulescu, Marius, 2025. "Short-term forecasting of PV power based on aggregated machine learning and sky imagery approaches," Energy, Elsevier, vol. 316(C).
    20. Shi, Zhihan & Zhang, Guangming & Lu, Chao & Zhou, Xiaoxiong & Lv, Xiaodong, 2025. "Dynamic Spatio-Temporal Graph-Enhanced KANformer for high-fidelity ultra-short-term wind power forecasting," Energy, Elsevier, vol. 337(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:258:y:2026:i:c:s0960148125026436. 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.