Multi-Energy Coordinated Carbon Emission Optimization Model for Smart Integrated Energy Park Based on Cloud-Edge Collaborative Solution
Cheng Songqing1, Teng Yun1, Lu Guoqiang2, Chen Zhe3
1. School of Electrical Engineering Shenyang University of Technology Shenyang 110870 China; 2. State Grid Qinghai Electric Power Co. Ltd Xining 810001 China; 3. Department of Energy Technology Aalborg University Aalborg DK-9220 Denmark
Abstract：This paper proposed a multi-energy coordinated carbon emission optimization method for smart integrated energy parks based on cloud-edge collaborative solution to address the issue of difficulty in achieving low-carbon operation within the park due to the distribution of information technology processes. Firstly, a dynamic carbon reduction model for cloud-edge collaboration in smart parks is established based on carbon emission flow theory, taking the uncertainty of equipment response in the system into account. By establishing affine functions and setting noise elements, the uncertainty of renewable energy output and load response can be quantified, and the uncertainty can be embedded into the objective function to improve the stability of cloud edge collaborative solution. Then, to dynamically solve the carbon reduction plan for smart parks, this paper establishes a cloud-edge collaborative solution network based on federated transfer learning theory, realizes parallel training of the cloud edge network, and accelerates the model training process. By combining the convolutional block attention module (CBAM) mechanism, particle swarm optimization (PSO) algorithm, and bidirectional long short-term memory (BiLSTM) model, and utilizing the key feature perception ability of CBAM, the training efficiency of BiLSTM model can be improved. The PSO algorithm is used to assist the neural network in optimization, and the training process of the model is guided by heuristic algorithms, avoiding the problem of the neural network getting stuck in local optima and convergence difficulties. Enable the established CBAM-BiLSTM-PSO cloud-edge collaborative solution network to simultaneously achieve new energy power output prediction, multi-energy load prediction, and the solution and optimization of low-carbon scheduling plans. During the operation of the CBAM-BiLSTM-PSO cloud-edge collaborative solution network, the operation data of the smart park is input through a convolutional network, and feature extraction is achieved through the CBAM module. The extracted feature data is input into the BiLSTM-PSO network of the cloud and edge nodes. Only one BiLSTM-PSO network is set up at the edge nodes of renewable energy to achieve renewable energy power output prediction. Two BiLSTM-PSO networks are set up at the edge nodes of multi-energy loads to achieve load energy consumption plan prediction and optimization, respectively. Set up one BiLSTM-PSO network in the cloud node to achieve iterative solution of scheduling plans. The model mainly includes five parts: feature extraction, source load power prediction, initial scheduling plan solving, load energy consumption plan optimization, and scheduling plan optimization. Finally, the effectiveness of the method proposed in this paper is verified by taking the actual integrated energy system as an example. The results show that the CBAM-BiLSTM-PSO network established in this paper effectively improves the training efficiency of the prediction model and the accuracy of model prediction, and significantly improves the prediction performance of the traditional single model and combination methods. Moreover, after optimization, the carbon emission reduction demand of the park is jointly borne by the source side and the load side, reducing the system regulation pressure. Compared with single cloud computing and distributed computing, it has better optimization effect and shorter training time. Moreover, the carbon emissions of the park are reduced by 8% after optimization, which verifies the effectiveness and superiority of the proposed method in the low-carbon operation of IES.
程嵩晴, 滕云, 卢国强, 陈哲. 基于云-边协同求解的智慧综合能源园区多能协调碳排放优化模型[J]. 电工技术学报, 2026, 41(9): 3051-3069.
Cheng Songqing, Teng Yun, Lu Guoqiang, Chen Zhe. Multi-Energy Coordinated Carbon Emission Optimization Model for Smart Integrated Energy Park Based on Cloud-Edge Collaborative Solution. Transactions of China Electrotechnical Society, 2026, 41(9): 3051-3069.
[1] 张献, 丁可浩, 赵黎媛, 等. 计及电动汽车混合充电系统接入的综合能源系统鲁棒优化调度[J]. 电工技术学报, 2025, 40(14): 4446-4459. Zhang Xian, Ding Kehao, Zhao Liyuan, et al.Robust optimal scheduling of integrated energy system considering electric vehicle hybrid charging system[J]. Transactions of China Electrotechnical Society, 2025, 40(14): 4446-4459. [2] 李鹏, 刘浩, 李雨薇. 考虑两阶段碳交易的多园区综合能源微网混合博弈优化运行方法[J]. 电工技术学报, 2025, 40(15): 4788-4803. Li Peng, Liu Hao, Li Yuwei.Multi-park integrated energy microgrids hybrid game optimization strategy considering two-stage carbon trading[J]. Transactions of China Electrotechnical Society, 2025, 40(15): 4788-4803. [3] 张薇, 王浚宇, 杨茂, 等. 基于分布式双层强化学习的区域综合能源系统多时间尺度优化调度[J]. 电工技术学报, 2025, 40(11): 3529-3544. Zhang Wei, Wang Junyu, Yang Mao, et al.The multi-time-scale optimal scheduling for regional integrated energy system based on the distributed bi-layer reinforcement learning[J]. Transactions of China Electrotechnical Society, 2025, 40(11): 3529-3544. [4] 吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485. Wu Mengxue, Fang Fang.Distributionally robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485. [5] 贠韫韵, 张大海, 王小君, 等. 考虑光热电站及富氧燃烧捕集技术的电热气综合能源系统低碳运行优化[J]. 电工技术学报, 2023, 38(24): 6709-6726. Yun Yunyun, Zhang Dahai, Wang Xiaojun, et al.Low-carbon operational optimization of integrated electricity-heat-gas energy system considering concentrating solar power plant and oxygen-enriched combustion capture technology[J]. Transactions of China Electro-technical Society, 2023, 38(24): 6709-6726. [6] 潘超, 范宫博, 王锦鹏, 等. 灵活性资源参与的电热综合能源系统低碳优化[J]. 电工技术学报, 2023, 38(6): 1633-1647. Pan Chao, Fan Gongbo, Wang Jinpeng, et al.Low-carbon optimization of electric and heating integrated energy system with flexible resource participation[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1633-1647. [7] 刘晓军, 熊健, 王艺博, 等. 考虑不确定变量变分模态分解及绿证-碳联合交易的综合能源系统经济优化调度[J]. 电工技术学报, 2025, 40(13): 4276-4291. Liu Xiaojun, Xiong Jian, Wang Yibo, et al.Economic optimization of integrated energy system scheduling considering uncertainty variables variational mode decomposition and green certificate-carbon joint trading[J]. Transactions of China Electrotechnical Society, 2025, 40(13): 4276-4291. [8] 王杰, 贾宏杰, 靳小龙, 等. 基于电-氢-热P2P交易的分布式智能电网低碳优化运行方法[J]. 电力系统自动化, 2025, 49(9): 40-51. Wang Jie, Jia Hongjie, Jin Xiaolong, et al.Low-carbon operation optimization method for distributed smart grid based on electricity-hydrogen-heat peer-to-peer trading[J]. Automation of Electric Power Systems, 2025, 49(9): 40-51. [9] 万屹, 侯慧, 戈翔迪, 等. 考虑碳排放多级利用的综合能源系统两阶段混合博弈共赢策略[J]. 电力系统自动化, 2025, 49(5): 69-79. Wan Yi, Hou Hui, Ge Xiangdi, et al.Two-stage mixed-game win-win strategy for integrated energy system considering multi-stage utilization of carbon emission[J]. Automation of Electric Power Systems, 2025, 49(5): 69-79. [10] 孙毅, 鲍荟谕, 郑顺林, 等. 基于数据分解与知识蒸馏的多能负荷与碳排放联合预测模型[J]. 中国电机工程学报, 2025, 45(20): 7997-8011. Sun Yi, Bao Huiyu, Zheng Shunlin, et al.Multi-energy load and carbon emission prediction model based on data decomposition and knowledge distillation[J]. Proceedings of the CSEE, 2025, 45(20): 7997-8011. [11] 杨畅, 李正烁, 薛屹洵, 等. 基于双步投影算法的多区域互联电热综合能源系统的三层分布式可信分布鲁棒优化调度[J]. 中国电机工程学报, 2025, 45(6): 2097-2110. Yang Chang, Li Zhengshuo, Xue Yixun, et al.Trilevel distributed credible distributionally robust optimization of multi-area integrated electricity and heat systems based on two-step projection algorithm[J]. Proceedings of the CSEE, 2025, 45(6): 2097-2110. [12] 张雨曼, 刘学智, 严正, 等. 光伏-储能-热电联产综合能源系统分解协调优化运行研究撤回[J]. 电工技术学报, 2020, 35(11): 2372-2386. Zhang Yuman, Liu Xuezhi, Yan Zheng, et al.Decomposition-coordination based optimization for PV-BESS-CHP integrated energy systems[J]. Transactions of China Electrotechnical Society, 2020, 35(11): 2372-2386. [13] 罗清局, 朱继忠. 基于改进交替方向乘子法的电-气综合能源系统优化调度[J]. 电工技术学报, 2024, 39(9): 2797-2809. Luo Qingju, Zhu Jizhong.Optimal dispatch of integrated electricity and gas system based on modified alternating direction method of multipliers[J]. Transactions of China Electrotechnical Society, 2024, 39(9): 2797-2809. [14] Wang Xiaokang, Yang L T, Xie Xia, et al.A cloud-edge computing framework for cyber-physical-social services[J]. IEEE Communications Magazine, 2017, 55(11): 80-85. [15] Yang Jun, Sun Fengyuan, Wang Haitao.Distributed collaborative optimal economic dispatch of integrated energy system based on edge computing[J]. Energy, 2023, 284: 129194. [16] Zhu Xu, Yang Jun, Zhan Xiangpeng, et al.Cloud-edge collaborative distributed optimal dispatching strategy for an electric-gas integrated energy system considering carbon emission reductions[J]. International Journal of Electrical Power & Energy Systems, 2022, 143: 108458. [17] 胡安妮, 张天策, 李庚银, 等. 考虑电动汽车参数一致性的虚拟电厂云-边协同调度方法[J]. 电力系统自动化, 2025, 49(20): 113-124. Hu Anni, Zhang Tiance, Li Gengyin, et al.Cloud-edge collaborative scheduling method for virtual power plants considering consistency of electric vehicle parameters[J]. Automation of Electric Power Systems, 2025, 49(20): 113-124. [18] 康伦, 向海燕, 余平, 等. 基于云-边协同技术的综合能源系统优化调度方法研究[J]. 电力设备管理, 2025(1): 232-234. Kang Lun, Xiang Haiyan, Yu Ping, et al.Research on optimal scheduling method of integrated energy system based on cloud-edge collaboration technology[J]. Electric Power Equipment Management, 2025(1): 232-234. [19] 苏建平, 王培尧, 冯坤, 等. 基于云-边协同的分布式光伏超短期功率预测方法[J]. 中国新技术新产品, 2025(1): 7-9. Su Jianping, Wang Peiyao, Feng Kun, et al.Ultra-short-term power prediction method of distributed photovoltaic based on cloud edge collaboration[J]. New Technology & New Products of China, 2025(1): 7-9. [20] 郑悦. 基于云-边资源协同的智能配电网优化运营策略研究[J]. 自动化与仪器仪表, 2024(12): 276-280. Zheng Yue.Optimization and operation strategy of intelligent distribution network based on cloud edge resource collaboration[J]. Automation & Instrumentation, 2024(12): 276-280. [21] 李志颖, 王鸿, 王致杰. 考虑功率可调节裕度的综合能源系统云-边协同优化[J]. 上海电机学院学报, 2024, 27(3): 125-130. Li Zhiying, Wang Hong, Wang Zhijie.Cloud-edge collaborative optimization of integrated energy system considering power adjustable margin[J]. Journal of Shanghai Dianji University, 2024, 27(3): 125-130. [22] 陈厚合, 茅文玲, 张儒峰, 等. 基于碳排放流理论的电力系统源-荷协调低碳优化调度[J]. 电力系统保护与控制, 2021, 49(10): 1-11. Chen Houhe, Mao Wenling, Zhang Rufeng, et al.Low-carbon optimal scheduling of a power system source-load considering coordination based on carbon emission flow theory[J]. Power System Protection and Control, 2021, 49(10): 1-11. [23] 邱彬, 宋绍鑫, 王凯, 等. 计及需求响应和阶梯型碳交易机制的区域综合能源系统优化运行[J]. 电力系统及其自动化学报, 2022, 34(5): 87-95, 101. Qiu Bin, Song Shaoxin, Wang Kai, et al.Optimal operation of regional integrated energy system considering demand response and ladder-type carbon trading mechanism[J]. Proceedings of the CSU-EPSA, 2022, 34(5): 87-95, 101. [24] Asghar R, Quercio M, Sabino L, et al.A novel dual-stream attention-based hybrid network for solar power forecasting[J]. IEEE Access, 2025, 13: 59596-59609. [25] Xu Peilong, Lan Dan, Yang Haolin, et al.Ship formation and route optimization design based on improved PSO and D-P algorithm[J]. IEEE Access, 2025, 13: 15529-15546. [26] 刘可真, 苟家萁, 骆钊, 等. 基于粒子群优化-长短期记忆网络模型的变压器油中溶解气体浓度预测方法[J]. 电网技术, 2020, 44(7): 2778-2785. Liu Kezhen, Gou Jiaqi, Luo Zhao, et al.Prediction of dissolved gas concentration in transformer oil based on PSO-LSTM model[J]. Power System Technology, 2020, 44(7): 2778-2785. [27] Bao Huiyu, Sun Yi, Peng Jie, et al.Collaborative forecasting management model for multi-energy microgrid considering load response characterization[J]. IET Renewable Power Generation, 2024, 18(14): 2360-2380. [28] 张儒峰, 姜涛, 李国庆, 等. 考虑电转气消纳风电的电-气综合能源系统双层优化调度[J]. 中国电机工程学报, 2018, 38(19): 5668-5678, 5924. Zhang Rufeng, Jiang Tao, Li Guoqing, et al.Bi-level optimization dispatch of integrated electricity-natural gas systems considering P2G for wind power accommodation[J]. Proceedings of the CSEE, 2018, 38(19): 5668-5678, 5924. [29] 罗文健, 张靖, 何宇, 等. 基于优势柔性策略-评价算法和迁移学习的区域综合能源系统优化调度[J]. 电网技术, 2023, 47(4): 1601-1615. Luo Wenjian, Zhang Jing, He Yu, et al.Optimal scheduling of regional integrated energy system based on advantage learning soft actor-critic algorithm and transfer learning[J]. Power System Technology, 2023, 47(4): 1601-1615. [30] 瞿小斌, 文云峰, 叶希, 等. 基于串行和并行ADMM算法的电-气能量流分布式协同优化[J]. 电力系统自动化, 2017, 41(4): 12-19. Qu Xiaobin, Wen Yunfeng, Ye Xi, et al.Distributed optimization of electric-gas integrated energy flow using serial and parallel iterative modes for alternating direction method of multipliers[J]. Automation of Electric Power Systems, 2017, 41(4): 12-19.
2026, Vol. 41 
