OPTIMAL SCHEDULING OF INTEGRATED ENERGY SYSTEMS CONSIDERING DYNAMIC CARBON EMISSION FACTORS AND WIND POWER FORECASTS

Zhang Zhenhua; Zeng Linjun; Huang Weiying; Xu Jiani; Qing Wei; Li Huanting

doi:10.19912/j.0254-0096.tynxb.2025-0242

PDF(1772 KB)

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 18-29. DOI: 10.19912/j.0254-0096.tynxb.2025-0242

OPTIMAL SCHEDULING OF INTEGRATED ENERGY SYSTEMS CONSIDERING DYNAMIC CARBON EMISSION FACTORS AND WIND POWER FORECASTS

Zhang Zhenhua, Zeng Linjun, Huang Weiying, Xu Jiani, Qing Wei, Li Huanting

Author information +

History +

Abstract

To improve the utilization rate of renewable energy sources and reduce the carbon emission level of integrated energy system （IES）, an optimal scheduling strategy of IES is proposed based on dynamic carbon emission factor and wind power prediction. Firstly, an IES architecture is established, along with a carbon emission measurement method that considers dynamic emission factors. Secondly, a combined wind power interval prediction model based on CNN-BiGRU-Attention is proposed in view of the safe and stable operation problems caused by the volatility and intermittency of wind power generation. The effectiveness of this model is verified by comparing the proposed model with a variety of traditional prediction models for wind power prediction. Finally, taking the minimization of the sum of energy purchase cost, carbon trading cost and wind curtailment cost as the low-carbon economic operation goal, the proposed scheduling strategy is verified to be able to effectively reduce carbon emissions while lowering the system operation cost by setting up several operation scenarios.

Key words

carbon emission factor / wind power / integrated energy system / carbon emission trading

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Zhang Zhenhua, Zeng Linjun, Huang Weiying, Xu Jiani, Qing Wei, Li Huanting. OPTIMAL SCHEDULING OF INTEGRATED ENERGY SYSTEMS CONSIDERING DYNAMIC CARBON EMISSION FACTORS AND WIND POWER FORECASTS[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 18-29 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0242

References

[1] 王东升, 杜鹏, 张国彦, 等. 促进风电消纳的电热综合能源系统优化调度研究综述[J]. 煤炭经济研究, 2024, 44(11): 113-121.
Wang D S, Du P, Zhang G Y, et al.Areview of optimal dispatch of combined power and heat integrated energy system to promote wind power consumption[J]. Coal Economic Research, 2024, 44(11): 113-121.
[2] 闫长城. 基于深度学习的风电功率预测算法研究[D]. 天津: 天津理工大学, 2024.
Yan C C.Research on wind power prediction algorithm based on deep learning[D]. Tianjin: Tianjin University of Technology, 2024.
[3] 张振威. 计及风电不确定性的区域间综合能源系统分布式优化调度研究[D]. 济南: 山东大学, 2022.
Zhang Z W.Research on distributed optimal dispatch of inter-regional integrated energy system considering wind power uncertainty[D]. Jinan: Shandong University, 2022.
[4] 罗林聪, 丁跃清, 邢鑫, 等. 碳达峰碳中和目标下碳计量发展现状、挑战及对策建议[J]. 计量与测试技术, 2024, 50(12): 11-15.
Luo L C, Ding Y Q, Xing X, et al.Development status, challenges and recommendations for carbon measurement in the context of emission peak and carbon neutrality goals[J]. Metrology & Measurement Technique, 2024, 50(12): 11-15.
[5] Kotagodahetti R, Hewage K, Karunathilake H, et al.Evaluating carbon capturing strategies for emissions reduction in community energy systems: a life cycle thinking approach[J]. Energy, 2021, 232: 121012.
[6] Lou Y L, Ye Y Y, Yang Y Z, et al.Long-term carbon emission reduction potential of building retrofits with dynamically changing electricity emission factors[J]. Building and Environment, 2022, 210: 108683.
[7] 葛津铭, 刘志文, 王朝斌, 等. 考虑需求响应的高比例光伏配电网低碳调度[J]. 电网技术, 2024, 48(5): 1929-1937.
Ge J M, Liu Z W, Wang C B, et al.Low-carbon dispatching of high proportion photovoltaic distribution network considering demand response[J]. Power System Technology, 2024, 48(5): 1929-1937.
[8] Lu Q, Guo Q S, Zeng W.Optimization scheduling of integrated energy service system in community: a bi-layer optimization model considering multi-energy demand response and user satisfaction[J]. Energy, 2022, 252: 124063.
[9] Li L X, Zhang S, Cao X L, et al.Assessing economic and environmental performance of multi-energy sharing communities considering different carbon emission responsibilities under carbon tax policy[J]. Journal of Cleaner Production, 2021, 328: 129466.
[10] 程昱舒, 郭晓霞, 刘青. 基于动态碳排放因子的台区碳减排收益量化模型[J]. 现代电力, 2025, 42(1): 168-175.
Cheng Y S, Guo X X, Liu Q.Quantification model of carbon emission reduction benefits based on dynamic carbon emission factors in a substation area[J]. Modern Electric Power, 2025, 42(1): 168-175.
[11] 杨宏伟. 面向风电功率预测的微尺度天气预报卷积神经网络方法研究[D]. 北京: 华北电力大学, 2022.
Yang H W.Research on microscale weather prediction method of wind power prediction based on convolutional neural network[D]. Beijing: North China Electric Power University, 2022.
[12] 张胜杰, 张雪锋, 谢忠洲, 等. 基于LSTM的风电功率预测方法研究[J]. 能源与节能, 2024(8): 11-13.
Zhang S J, Zhang X F, Xie Z Z, et al.Power prediction method of wind power based on LSTM[J]. Energy and Conservation, 2024(8): 11-13.
[13] Kisvari A, Lin Z, Liu X L.Wind power forecasting-A data-driven method along with gated recurrent neural network[J]. Renewable Energy, 2021, 163: 1895-1909.
[14] 巩冠华, 娄柯, 尹杰, 等. 基于注意力机制的CNN-BiGRU模型预测风力发电功率[J]. 安徽工程大学学报, 2024, 39(2): 47-54.
Gong G H, Lou K, Yin J, et al.Forecast of wind power generation based on AM of CNN-BiGRU[J]. Journal of Anhui Polytechnic University, 2024, 39(2): 47-54.
[15] 张浩田, 温蜜, 李晋国, 等. 数据驱动的时间注意力卷积风电功率预测模型[J]. 太阳能学报, 2022, 43(10): 167-176.
Zhang H T, Wen M, Li J G, et al.Data driven time attention convolution wind power prediction model[J]. Acta Energiae Solaris Sinica, 2022, 43(10): 167-176.
[16] 黄棋悦, 严楠, 钟旭佳. 基于生成对抗网络的风电爬坡功率预测[J]. 太阳能学报, 2023, 44(1): 226-231.
Huang Q Y, Yan N, Zhong X J.Wind power ramping events prediction based on generative adversarial network[J]. Acta Energiae Solaris Sinica, 2023, 44(1): 226-231.
[17] 张渊涛. 风电功率预测与含风电的电力系统优化调度研究[D]. 大庆: 东北石油大学, 2018.
Zhang Y T.Research on wind power forecasting and optimal scheduling of power system with wind power[D]. Daqing: Northeast Petroleum University, 2018.
[18] 赵敏, 王孟军, 刁海岸, 等. 基于CEEMD-CNN-GRU的风电功率预测[J]. 四川轻化工大学学报(自然科学版), 2024, 37(4): 68-74.
Zhao M, Wang M J, Diao H A, et al.Wind power prediction based on CEEMD-CNN-GRU[J]. Journal of Sichuan University of Science & Engineering (Natural Science Edition), 2024, 37(4): 68-74.
[19] 乔宽龙, 董存, 车建峰, 等. 时空特性下基于图卷积神经网络的风电集群功率短期预测方法[J]. 太阳能学报, 2024, 45(5): 95-103.
Qiao K L, Dong C, Che J F, et al.Short-term prediction method of wind power clusters based on graph convolution neural network under spatio-temporal characteristics[J]. Acta Energiae Solaris Sinica, 2024, 45(5): 95-103.
[20] 唐清苇, 向月, 代佳琨, 等. 基于CNN-LSTM的风电场发电功率迁移预测方法[J]. 工程科学与技术, 2024, 56(2): 91-99.
Tang Q W, Xiang Y, Dai J K, et al.Wind farm power transfer forecasting method based on CNN-LSTM[J]. Advanced Engineering Sciences, 2024, 56(2): 91-99.
[21] 李中豪. 基于改进风电功率预测的综合能源系统低碳优化调度[D]. 青岛: 青岛大学, 2023.
Li Z H.Low-carbon optimal dispatch of integrated energy systems based on improved wind power prediction. Qingdao: Qingdao University, 2023.
[22] 刘芳. 基于改进BP神经网络的风电功率预测方法研究[D]. 杭州: 浙江大学, 2020.
Liu F.Wind power forecasting based on improved BP neural network[D]. Hangzhou: Zhejiang University, 2020.
[23] 马燕峰, 范振亚, 刘伟东, 等. 考虑碳权交易和风荷预测误差随机性的环境经济调度[J]. 电网技术, 2016, 40(2): 412-418.
Ma Y F, Fan Z Y, Liu W D, et al.Environmental and economic dispatch considering carbon trading credit and randomicity of wind power and load forecast error[J]. Power System Technology, 2016, 40(2): 412-418.
[24] 柳天虹, 齐胜利, 裔扬, 等. 基于分位数回归的改进权重GRU风电功率区间预测[J]. 太阳能学报, 2024, 45(12): 291-298.
Liu T H, Qi S L, Yi Y, et al.Improved weighted gru wind power interval prediction based on quantile regression[J]. Acta Energiae Solaris Sinica, 2024, 45(12): 291-298.
[25] 崔曦文, 张潇丹, 牛东晓. 基于双层分解与MOISMA-SVM的超短期风电功率点-区间预测[J]. 计算机工程, 2026, 52(3): 376-391.
Cui X W, Zhang X D, Niu D X.Ultra-short-term wind power point-interval prediction based on two-layer decomposition and MOISMA-SVM[J]. Computer Engineering, 2026, 52(3): 376-391.
[26] 中华人民共和国生态环境部. 2019年度减排项目中国区域电网基准线排放因子[EB/OL]. [2022.11.21]. https://www.mee.gov.cn/ywgz/ydqhbh/wsqtkz/202012/t20201229_815386.shtml.
Ministry of Ecological Environment of the People's Republic of China. China regional grid baseline emission factors for FY2019 emission reduction programs [EB/OL]. [2022.11.21]. https://www.mee.gov.cn/ywgz/ydqhbh/wsqtkz/202012/t20201229_815386.shtml