IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v352y2023ics0306261923013120.html
   My bibliography  Save this article

Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation

Author

Listed:
  • Wang, Zhongliang
  • Zhu, Hongyu
  • Zhang, Dongdong
  • Goh, Hui Hwang
  • Dong, Yunxuan
  • Wu, Thomas

Abstract

The simulation technology of wind and solar power output can provide data support for the planning of new energy stations and the optimization and scheduling of power systems. In order to solve the problem that the existing output models can not accurately describe the dynamic spatio-temporal dependence between wind and solar output, a dynamic spatiotemporal correlation model of wind and solar output based on Markov stochastic process and Copula function theory is proposed. Firstly, wind power and photovoltaic output are regarded as a stochastic process, and the time autocorrelation models of wind power and photovoltaic output are constructed based on a one-dimensional Markov chain and hybrid Copula function. Next, two one-dimensional Markov chains were coupled to form a two-dimensional Markov chain, and a spatial correlation model between wind and solar output was constructed using the dynamic SJC Copula function. Take the measured data of adjacent wind farms and photovoltaic power stations in Hami, Xinjiang as an example for simulation. The simulation results show that the proposed model can effectively reflect the spatio-temporal correlation of the original data and reflect the dynamic changes in the correlation between wind and solar energy.

Suggested Citation

  • Wang, Zhongliang & Zhu, Hongyu & Zhang, Dongdong & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013120
    DOI: 10.1016/j.apenergy.2023.121948
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923013120
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121948?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. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2013. "First and second order semi-Markov chains for wind speed modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1194-1201.
    2. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    3. Mayer, Martin János & Yang, Dazhi, 2023. "Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    4. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    5. Blanco, J.M. & Vazquez, L. & Peña, F. & Diaz, D., 2013. "New investigation on diagnosing steam production systems from multivariate time series applied to thermal power plants," Applied Energy, Elsevier, vol. 101(C), pages 589-599.
    6. Li, Wenzhe & Jia, Xiaodong & Li, Xiang & Wang, Yinglu & Lee, Jay, 2021. "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, Elsevier, vol. 164(C), pages 242-253.
    7. Pearre, Nathaniel & Swan, Lukas, 2020. "Combining wind, solar, and in-stream tidal electricity generation with energy storage using a load-perturbation control strategy," Energy, Elsevier, vol. 203(C).
    8. Costoya, X. & deCastro, M. & Carvalho, D. & Gómez-Gesteira, M., 2023. "Assessing the complementarity of future hybrid wind and solar photovoltaic energy resources for North America," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Qin, Zhilong & Li, Wenyuan & Xiong, Xiaofu, 2013. "Incorporating multiple correlations among wind speeds, photovoltaic powers and bus loads in composite system reliability evaluation," Applied Energy, Elsevier, vol. 110(C), pages 285-294.
    10. Cervone, A. & Carbone, G. & Santini, E. & Teodori, S., 2016. "Optimization of the battery size for PV systems under regulatory rules using a Markov-Chains approach," Renewable Energy, Elsevier, vol. 85(C), pages 657-665.
    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. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
    2. Zhang, Zhi & Zhang, Yuquan & Zheng, Yuan & Zhang, Jisheng & Fernandez-Rodriguez, Emmanuel & Zang, Wei & Ji, Renwei, 2023. "Power fluctuation and wake characteristics of tidal stream turbine subjected to wave and current interaction," Energy, Elsevier, vol. 264(C).
    3. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    4. Zhang, Jiao & Li, Youping & Liu, Chunqiong & Wu, Bo & Shi, Kai, 2022. "A study of cross-correlations between PM2.5 and O3 based on Copula and Multifractal methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    5. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    6. Xiang Ying & Keke Zhao & Zhiqiang Liu & Jie Gao & Dongxiao He & Xuewei Li & Wei Xiong, 2022. "Wind Speed Prediction via Collaborative Filtering on Virtual Edge Expanding Graphs," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
    7. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
    8. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    9. Anderson Mitterhofer Iung & Fernando Luiz Cyrino Oliveira & André Luís Marques Marcato, 2023. "A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence," Energies, MDPI, vol. 16(3), pages 1-24, January.
    10. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
    11. Junqiang Wang & Xiaolong Qiang & Zhengcheng Ren & Hongbo Wang & Yongbo Wang & Shuoliang Wang, 2023. "Time-Series Well Performance Prediction Based on Convolutional and Long Short-Term Memory Neural Network Model," Energies, MDPI, vol. 16(1), pages 1-16, January.
    12. Ma, Jinrui & Fouladirad, Mitra & Grall, Antoine, 2018. "Flexible wind speed generation model: Markov chain with an embedded diffusion process," Energy, Elsevier, vol. 164(C), pages 316-328.
    13. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    14. Cai, Baoping & Liu, Yonghong & Ma, Yunpeng & Huang, Lei & Liu, Zengkai, 2015. "A framework for the reliability evaluation of grid-connected photovoltaic systems in the presence of intermittent faults," Energy, Elsevier, vol. 93(P2), pages 1308-1320.
    15. Maegebier, Alexander, 2013. "Valuation and risk assessment of disability insurance using a discrete time trivariate Markov renewal reward process," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 802-811.
    16. Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
    17. Jianwei Gao & Yu Yang & Fangjie Gao & Pengcheng Liang, 2021. "Optimization of Electric Vehicles Based on Frank-Copula- GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research," Energies, MDPI, vol. 14(19), pages 1-15, September.
    18. Lahmiri, Salim, 2016. "Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 388-396.
    19. Francesco Nicoletti & Piero Bevilacqua, 2024. "Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support," Energies, MDPI, vol. 17(2), pages 1-22, January.
    20. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.

    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:352:y:2023:i:c:s0306261923013120. 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.