Day ahead scheduling model of wind power system based on fuzzy stochastic chance constraints—considering source-load dual-side uncertainty case

Bitian Wu; Bitian Wu

doi:10.3934/energy.2025018

AIMS Energy

2025, Volume 13, Issue 3: 471-492. doi: 10.3934/energy.2025018

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Day ahead scheduling model of wind power system based on fuzzy stochastic chance constraints—considering source-load dual-side uncertainty case

Bitian Wu ^,

School of Advanced Manufacturing, Nanchang University, Nanchang, 330031, China

Received: 06 December 2024 Revised: 02 April 2025 Accepted: 07 May 2025 Published: 22 May 2025

As global energy tensions increase, the demand for clean energy is growing exponentially. Although wind power is growing rapidly, it introduces significant stability challenges to power system dispatch due to its intermittency and variability. To address this challenge, a nonparametric kernel density was employed to model wind power output, and a multi-objective optimization model was proposed for day-ahead scheduling of wind power generation systems. First, by comparing the fitting effects of parameter distribution and kernel density function on wind power prediction errors, a kernel density function-based wind power output model was established. At the same time, the fuzzy stochastic constraint rule was introduced to constrain the uncertainty of the source and load sides of the wind power system, with the aim of minimizing the system operation cost and carbon emissions. The experimental results show that in the multi-objective optimization experiment, the system cost of multi-objective optimization increased by 17.19%, and the carbon emissions decreased by 51.99% compared with the single cost optimization goal. Compared with the single environmental optimization objective, the system cost of the multi-objective optimization decreased by 16.11%, and the carbon emissions increased by 15.15%. The above data indicate that the optimized scheduling scheme adopted in the study can not only save economic costs but also consider certain environmental protection measures. This research result can provide a new direction for the scheduling research of power systems, including wind power, and has an important reference value for the scheduling of actual power systems.
- fuzzy stochastic chance constraints,
- source-load bilateral uncertainty,
- multi-objective optimization,
- day-ahead scheduling,
- wind power systems
Citation: Bitian Wu. Day ahead scheduling model of wind power system based on fuzzy stochastic chance constraints—considering source-load dual-side uncertainty case[J]. AIMS Energy, 2025, 13(3): 471-492. doi: 10.3934/energy.2025018

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Abstract

As global energy tensions increase, the demand for clean energy is growing exponentially. Although wind power is growing rapidly, it introduces significant stability challenges to power system dispatch due to its intermittency and variability. To address this challenge, a nonparametric kernel density was employed to model wind power output, and a multi-objective optimization model was proposed for day-ahead scheduling of wind power generation systems. First, by comparing the fitting effects of parameter distribution and kernel density function on wind power prediction errors, a kernel density function-based wind power output model was established. At the same time, the fuzzy stochastic constraint rule was introduced to constrain the uncertainty of the source and load sides of the wind power system, with the aim of minimizing the system operation cost and carbon emissions. The experimental results show that in the multi-objective optimization experiment, the system cost of multi-objective optimization increased by 17.19%, and the carbon emissions decreased by 51.99% compared with the single cost optimization goal. Compared with the single environmental optimization objective, the system cost of the multi-objective optimization decreased by 16.11%, and the carbon emissions increased by 15.15%. The above data indicate that the optimized scheduling scheme adopted in the study can not only save economic costs but also consider certain environmental protection measures. This research result can provide a new direction for the scheduling research of power systems, including wind power, and has an important reference value for the scheduling of actual power systems.

References

[1]	Padhi S, Panigrahi BP, Dash D (2020) Solving dynamic economic emission dispatch problem with uncertainty of wind and load using whale optimization algorithm. J Inst Eng India Ser B 101: 65–78. https://doi.org/10.1007/s40031-020-00435-y doi: 10.1007/s40031-020-00435-y
[2]	Bansal S (2021) Nature-inspired hybrid multi-objective optimization algorithms in search of near-ogrs to eliminate fwm noise signals in optical wdm systems and their performance comparison. J Inst Eng India Ser B 102: 743–769. https://doi.org/10.1007/s40031-021-00587-5 doi: 10.1007/s40031-021-00587-5
[3]	Wang X, Wang Y, Peng J, et al. (2023) Multivariate long sequence time-series forecasting using dynamic graph learning. J Amb Intel Hum Comp 14: 7679–7693. https://doi.org/10.1007/s12652-023-04579-9 doi: 10.1007/s12652-023-04579-9
[4]	Bansal S, Singh AK, Gupta N (2020) Optimal golomb ruler sequences generation for optical WDM systems: A novel parallel hybrid multi-objective bat algorithm. J Inst Eng (India): Series B 98: 43–64. https://doi.org/10.1007/s40031-016-0249-1 doi: 10.1007/s40031-016-0249-1
[5]	Ram SDK, Srivastava S, Mishra KK (2022) A multi-objective generalized teacher-learning-based-optimization algorithm. J Inst Eng India Ser B 103: 1415–1430. https://doi.org/10.1007/s40031-022-00731-9 doi: 10.1007/s40031-022-00731-9
[6]	Wang C, Liu Z, Wei H, et al. (2021) Hybrid deep learning model for short-term wind speed forecasting based on time series decomposition and gated recurrent unit. Complex Syst Model Simul 1: 308–321. https://doi.org/10.23919/CSMS.2021.0026 doi: 10.23919/CSMS.2021.0026
[7]	Huang C, Gu J, Liu H, et al. (2019) Economical optimization of grid power factor using predictive data. IEEE/CAA J Autom Sin 6: 258–267. https://doi.org/10.1109/JAS.2017.7510691 doi: 10.1109/JAS.2017.7510691
[8]	Fernández-Guillamón A, Sarasúa JI, Chazarra M, et al. (2020) Frequency control analysis based on unit commitment schemes with high wind power integration: A Spanish isolated power system case study. Int J Electr Power Energy Syst 121: 106044. https://doi.org/10.1016/j.ijepes.2020.106044 doi: 10.1016/j.ijepes.2020.106044
[9]	Griche I, Messalti S, Saoudi K (2019) Parallel fuzzy logic and PI controller for transient stability and voltage regulation of power system including wind turbine. Przegl Elektrotech 95: 51–56. https://doi.org/10.15199/48.2019.09.10 doi: 10.15199/48.2019.09.10
[10]	Liu Y, Wang H, Han S, et al. (2019) Quantitative method for evaluating detailed volatility of wind power at multiple temporal-spatial scales. Glob Energy Interconnect 2: 318–327. https://doi.org/10.1016/j.gloei.2019.11.004 doi: 10.1016/j.gloei.2019.11.004
[11]	Mummey J, Sauer IL, Ramos DS, et al. (2019) Important issues and results when considering the stochastic representation of wind power plants in a generation optimization model: An application to the large Brazilian interconnected power system. J Energy Power Eng 11: 320–332. https://doi.org/10.4236/epe.2019.118020 doi: 10.4236/epe.2019.118020
[12]	Yuan C, Yan X (2019) Multi-stage coordinated dynamic VAR source placement for voltage stability enhancement of wind-energy power system. IET Gener Transm Distrib 14: 1104–1113. https://doi.org/10.1049/iet-gtd.2019.0126 doi: 10.1049/iet-gtd.2019.0126
[13]	Touqeer M, Umer R, Ali MI (2021) A chance-constraint programming model with interval-valued pythagorean fuzzy constraints. J Intell Fuzzy Syst 40: 11183–11199. https://doi.org/10.3233/JIFS-202383 doi: 10.3233/JIFS-202383
[14]	Zolfaghari S, Mousavi SM (2021) A novel mathematical programming model for multi-mode project portfolio selection and scheduling with flexible resources and due dates under interval-valued fuzzy random uncertainty. Expert Syst Appl 182: 115207. https://doi.org/10.1016/j.eswa.2021.115207 doi: 10.1016/j.eswa.2021.115207
[15]	Zhang X, Liu Y (2024) Two-stage stochastic robust optimal scheduling of virtual power plant considering source load uncertainty. Eng Rep 6: e13005. https://doi.org/10.1002/eng2.13005 doi: 10.1002/eng2.13005
[16]	Li J, Xu T, Gu Y, et al. (2024) Source-load coordinated optimal scheduling considering the high energy load of electrofused magnesium and wind power uncertainty. Energy Eng 121: 2777–2783. https://doi.org/10.32604/ee.2024.052331 doi: 10.32604/ee.2024.052331
[17]	Chakrabarti A, Chakrabarty K (2019) A proposal to adjust the time-keeping systems for savings in cycling operation and carbon emission. J Inst Eng India Ser B 100: 541–550. https://doi.org/10.1007/s40031-019-00419-7 doi: 10.1007/s40031-019-00419-7
[18]	Singh U, Rizwan M (2023) Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system. J Ambient Intell Humaniz Comput 14: 8035–8044. https://doi.org/10.1007/s12652-022-03878-x doi: 10.1007/s12652-022-03878-x
[19]	Avvari RK, Vinod Kumar DM (2022) Multi-objective optimal power flow with efficient constraint handling using hybrid decomposition and local dominance method. J Inst Eng India Ser B 103: 1643–1658. https://doi.org/10.1007/s40031-022-00748-0 doi: 10.1007/s40031-022-00748-0
[20]	Zhang W, Guo W, Huang J, et al. (2024) Study on optimal scheduling of energy storage participation in power market considering source-load uncertainty. J Phys Conf Ser 2771: 012015. https://doi.org/10.1088/1742-6596/2771/1/012015 doi: 10.1088/1742-6596/2771/1/012015
[21]	Li N, Zheng B, Wang G, et al. (2024) Two-stage robust optimization of integrated energy systems considering uncertainty in carbon source load. Processes 12: 1921. https://doi.org/10.3390/pr12091921 doi: 10.3390/pr12091921
[22]	Jia D, Cao M, Sun J, et al. (2024) Interval constrained multi-objective optimization scheduling method for island-integrated energy systems based on meta-learning and enhanced proximal policy optimization. Electronics 13: 3579. https://doi.org/10.3390/electronics13173579 doi: 10.3390/electronics13173579
[23]	Son Y, Zhang X, Yoon Y, et al. (2023) LSTM-GAN based cloud movement prediction in satellite images for PV forecast. J Ambient Intell Humaniz Comput 14: 12373–12386. https://doi.org/10.1007/s12652-022-04333-7 doi: 10.1007/s12652-022-04333-7
[24]	Suganya R, Kanagavalli R (2021) Gradient flow-based deep residual networks for enhancing visibility of scenery images degraded by foggy weather conditions. J Ambient Intell Humaniz Comput 12: 1503–1516. https://doi.org/10.1007/s12652-020-02225-2 doi: 10.1007/s12652-020-02225-2
[25]	Gupta V, Mittal M, Mittal V, et al. (2023) ECG signal analysis based on the spectrogram and spider monkey optimisation technique. J Inst Eng India Ser B 104: 153–164. https://doi.org/10.1007/s40031-022-00831-6 doi: 10.1007/s40031-022-00831-6
[26]	Sénéchal P, Perroud H, Kedziorek MAM, et al. (2005) Non destructive geophysical monitoring of water content and fluid conductivity anomalies in the near surface at the border of an agricultural. Subsurf Sens Technol Appl 6: 167–192. https://doi.org/10.1007/s11220-005-0005-0 doi: 10.1007/s11220-005-0005-0
[27]	Fasil OK, Rajesh R (2023) Epileptic seizure classification using shifting sample difference of EEG signals. J Ambient Intell Humaniz Comput 14: 11809–11822. https://doi.org/10.1007/s12652-022-03737-9 doi: 10.1007/s12652-022-03737-9
[28]	Sun J, Zhu W, Jiang Y (2024) A multi-stage scheduling optimization method for distribution networks based on extreme learning algorithms. J Phys Conf Ser 2896: 012038. https://doi.org/10.1088/1742-6596/2896/1/012038 doi: 10.1088/1742-6596/2896/1/012038
[29]	Gupta V (2023) Application of chaos theory for arrhythmia detection in pathological databases. Int J Med Eng Inform 15: 191–202. https://doi.org/10.1504/IJMEI.2023.129353 doi: 10.1504/IJMEI.2023.129353
[30]	Dong X, Deng S, Wang D (2022) A short-term power load forecasting method based on k-means and SVM. J Ambient Intell Humaniz Comput 13: 5253–5267. https://doi.org/10.1007/s12652-021-03444-x doi: 10.1007/s12652-021-03444-x
[31]	Rao AN, Naik R, Devi N (2021) On maximizing the coverage and network lifetime in wireless sensor networks through multi-objective metaheuristics. J Inst Eng India Ser B 102: 111–122. https://doi.org/10.1007/s40031-020-00516-y doi: 10.1007/s40031-020-00516-y
[32]	Fei X, Ma J, Zhang J, et al. (2025) A novel multi-task algorithm for operational optimization of coal mine integrated energy system under multiple uncertainties. J Comput Des Eng 12: 1–13. https://doi.org/10.1093/jcde/qwaf004 doi: 10.1093/jcde/qwaf004
[33]	Dou Z, Zhang C, Duan C, et al. (2024) Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag. J Comput Methods Sci Eng 24: 935-953. https://doi.org/10.3233/JCM-247299 doi: 10.3233/JCM-247299
[34]	Wang Y, Zhou L, Guo Z (2025) Research on the power and energy balance scheduling problem in new-type power systems. J Phys Conf Ser 2936: 012038. https://doi.org/10.1088/1742-6596/2936/1/012038 doi: 10.1088/1742-6596/2936/1/012038
[35]	Nayak JR, Shaw B, Sahu BK (2023) A fuzzy adaptive symbiotic organism search based hybrid wavelet transform-extreme learning machine model for load forecasting of power system: A case study. J Ambient Intell Humaniz Comput 14: 10833–10847. https://doi.org/10.1007/s12652-022-04355-1 doi: 10.1007/s12652-022-04355-1
[36]	Danandeh Mehr A, Rikhtehgar Ghiasi A, Yaseen ZM (2023) A novel intelligent deep learning predictive model for meteorological drought forecasting. J Ambient Intell Humaniz Comput 14: 10441–10455. https://doi.org/10.1007/s12652-022-03701-7 doi: 10.1007/s12652-022-03701-7
[37]	Hao LX, Zhao MJ, Pei LL, et al. (2024) Dispatching decision optimization of wind/solar/hydrogen storage highway microgrid based on improved Pareto algorithm. J Transp Eng 24: 71–82.
[38]	Lee CY, Tuegeh M (2020) Optimal optimisation-based microgrid scheduling considering impacts of unexpected forecast errors due to the uncertainty of renewable generation and loads fluctuation. IET Renew Power Gen 14: 321–331. https://doi.org/10.1049/iet-rpg.2019.0635 doi: 10.1049/iet-rpg.2019.0635
[39]	Li Y, Wang K, Gao B, et al. (2021) Interval optimization based operational strategy of integrated energy system under renewable energy resources and loads uncertainties. Int J Energy Res 45: 3142–3156. https://doi.org/10.1002/er.6009 doi: 10.1002/er.6009

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