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2026, 02, v.25 53-62
Modeling and simulation of wind power-photovoltaic-thermal power cooperative optimal scheduling based on stepped carbon price
Email: chinajjl@ntu.edu.cn;
DOI: 10.12194/j.ntu.20250104001
Published:   2025-04-09
Publication Date:   2025-04-09
Online:   2025-04-09
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Abstract:

To enhance the absorption rate of new energy within the power dispatching of new energy systems, the uncertainty of new energy output must be addressed, and an appropriate low-carbon management mechanism should be designed properly. In this paper, the adaptive kernel density estimation method is used to model the output characteristics of wind power and photovoltaic systems. The correlation between wind power and photovoltaic systems is described using the Copula function, and Monte Carlo simulations are integrated to illustrate wind-solar uncertainties. By obtaining the joint output scenario of wind and solar power and their probability characteristics, a collaborative optimization power dispatching model for wind-solar-thermal power systems is established. Subsequently, the ladder-type carbon price mechanism is introduced into the dispatching model to further reduce carbon emissions, transforming the dual-objective optimization problem into a single-objective optimization problem. A case study confirms that carbon emissions are reduced by 4.02% after the stepped carbon price mechanism is implemented, ultimately increasing the new energy absorption rate from 14.04% to 71.06%, validating the rationality and validity of the model and methods developed in this work.

References

[1]JIN J L, WEN Q L, QIU Y R, et al. Distributed robust optimization for low-carbon dispatch of wind-thermal power under uncertainties[J]. Environmental Science and Pollution Research, 2023, 30(8):20980-20994.

[2]董昱,孙荣富,丁然,等.考虑风速不确定性的风电场暂态有功支撑能力在线评估方法[J].电力系统自动化,2023, 47(18):101-110.DONG Y, SUN R F, DING R, et al. Online evaluation method for transient active power support capability of wind farm considering wind speed uncertainty[J]. Automation of Electric Power Systems, 2023, 47(18):101-110.(in Chinese)

[3]杨捷凯,马平,李立伟.大规模风电接入微电网的两阶段分布式鲁棒储能容量优化方法[J].电气工程学报,2025, 20(2):336-344.YANG J K, MA P, LI L W. Two-stage distributed robust energy storage capacity optimization method for large-scale wind power access to micro grid[J]. Journal of Electrical Engineering, 2025, 20(2):336-344.(in Chinese)

[4]马喜平,何世恩,姚寅,等.计及风速不确定性及相关性的风电场分区虚拟惯量估计[J].电力系统保护与控制,2022, 50(10):123-131.MA X P, HE S E, YAO Y, et al. Virtual inertia estimation of wind farm zones with wind speed uncertainty and correlation[J]. Power System Protection and Control, 2022,50(10):123-131.(in Chinese)

[5]温晴岚,程思齐,邱雅茹,等.导入碳交易的新能源发电系统调度管理研究:以风电为例[J].环境经济研究,2022, 7(2):31-51.WEN Q L, CHENG S Q, QIU Y R, et al. Research on scheduling management of new energy power generation system with carbon trading:taking wind power as an example[J]. Journal of Environmental Economics, 2022, 7(2):31-51.(in Chinese)

[6]孙天贺,靳双源,刘国斌,等.考虑源荷不确定性的光伏小镇鲁棒优化配置[J].电力工程技术,2023, 42(6):42-51.SUN T H, JIN S Y, LIU G B, et al. A robust optimization approach for capacity configuration of solar towns considering supply-demand uncertainties[J]. Electric Power Engineering Technology, 2023, 42(6):42-51.(in Chinese)

[7]REN Y S, HUYNH T L D, LIU P Z, et al. Is the carbon emission trading scheme conducive to promoting energy transition? Some empirical evidence from China[J]. Energy Economics, 2024, 134:107629.

[8]WANG M L, WANG X Y, LIU Z C, et al. How can carbon trading promote the green innovation efficiency of manufacturing enterprises?[J]. Energy Strategy Reviews,2024, 53:101420.

[9]张树森,郭源善,权隆,等.考虑环保与经济效益的电力系统优化调度模型研究[J].自动化技术与应用,2022,41(6):99-102.ZHANG S S, GUO Y S, QUAN L, et al. Research on optimal dispatching model of power system considering environmental protection and economic benefits[J]. Techniques of Automation and Applications , 2022, 41(6):99-102.(in Chinese)

[10]沈美娜.计及碳交易和绿证交易的风火发电权交易竞价博弈[D].北京:华北电力大学,2021.SHEN M N. Bid game of wind-thermal power generating rights trading considering carbon trading and green certificate trading[D]. Beijing:North China Electric Power University, 2021.(in Chinese)

[11]张菁,林毓军,齐晓光,等.考虑碳税与碳交易替代效应的电力系统低碳经济调度方法[J].电力建设,2022,43(6):1-11.ZHANG J, LIN Y J, QI X G, et al. Low-carbon economic dispatching method for power system considering the substitution effect of carbon tax and carbon trading[J]. Electric Power Construction, 2022, 43(6):1-11.(in Chinese)

[12]宋中炜.基于动态CGE的中国碳中和路径研究[D].秦皇岛:燕山大学,2022.SONG Z W. Study on carbon neutrality pathway in China based on adynamic CGE model[D]. Qinhuangdao:Yanshan University, 2022.(in Chinese)

[13]白彦锋,岳童,童健.碳税征管的理论实践与策略选择[J].经济理论与经济管理,2023, 43(11):17-29.BAI Y F, YUE T, TONG J. Theory, practice and strategy selection of carbon tax collection and management[J]. Economic Theory and Business Management, 2023, 43(11):17-29.(in Chinese)

[14]WANG M, YING F X, NAN Q R. Refined offshore wind speed prediction:leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts[J]. Engineering Applications of Artificial Intelligence, 2024, 133:108435.

[15]ZENG L J, XU J Z, WANG Y B, et al. Day-ahead interval scheduling strategy of power systems based on improved adaptive diffusion kernel density estimation[J]. International Journal of Electrical Power&Energy Systems,2023, 147:108850.

[16]ZHANG Q, LI Y P, HUANG G H, et al. Copula function with Variational Bayesian Monte Carlo for unveiling uncertainty impacts on meteorological and agricultural drought propagation[J]. Journal of Hydrology, 2023, 622:129669.

[17]HUANG Y, ZHANG B Z, PANG H Z, et al. Spatio-temporal wind speed prediction based on Clayton Copula function with deep learning fusion[J]. Renewable Energy,2022, 192:526-536.

[18]PORTILLO JUAN N, NEGRO VALDECANTOS V. A novel model for the study of future maritime climate using artificial neural networks and Monte Carlo simulations under the context of climate change[J]. Ocean Modelling ,2024, 190:102384.

[19]SILBER F, SCHEUBNER S, MÄRTZ A. Analysis of the simultaneity factor of fast-charging sites using Monte-Carlo simulation[J]. International Journal of Electrical Power&Energy Systems, 2024, 155:109540.

Basic Information:

DOI:10.12194/j.ntu.20250104001

China Classification Code:TM73;TM743

Citation Information:

[1]ZHOU Chaoyang,ZHAO Liya,LI Linlin ,et al.Modeling and simulation of wind power-photovoltaic-thermal power cooperative optimal scheduling based on stepped carbon price[J].Journal of Nantong University (Natural Science Edition),2026,25(02):53-62.DOI:10.12194/j.ntu.20250104001.

Fund Information:

江苏省研究生科研与实践创新计划项目(KYCX24_3624); 南通市科技计划项目(MS2023001)

Published:  

2025-04-09

Publication Date:  

2025-04-09

Online:  

2025-04-09

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