基于动态运行场景预测的多能微网实时能量优化调控方法

doi:10.19595/j.cnki.1000-6753.tces.250575

摘要
图/表
参考文献
相关文章 (7)

全文: PDF (1349 KB)
输出: BibTeX | EndNote (RIS)

摘要

多能微网（Multi-Energy Microgrid,MEMG）在推动能源效率提升与促进可再生分布式发电（Renewable Distributed Generation,RDG）消纳等方面展现出了巨大潜力,但其运行面临源于RDG和多能负荷的多维不确定性所引发的挑战。为此,本文提出了一种基于动态运行场景预测的MEMG实时能量优化调控方法。该方法针对MEMG运行场景构建了瓦瑟斯坦生成对抗网络,以无监督方式挖掘与表征其统计分布,并基于此创建了场景预测约束优化问题,实现了可捕捉多维不确定性的MEMG运行场景的高效、高质量以及逐时刻预测。基于预测场景形成了随机模型预测控制框架下的MEMG预调度模型,并构建了实时功率补偿模型以最为经济的方式对电热不平衡功率进行补偿。最后,通过数值仿真验证了所提MEMG运行场景预测和能量调控方法的有效性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王玉彬
	杨强
	夏明超
	陈奇芳
	孙谦浩

关键词 ：多能微网, 可再生分布式发电, 多维不确定性, 生成对抗网络, 场景预测, 随机模型预测控制

Abstract：

The multi-energy microgrid (MEMG) has a high degree of integration among various types of energy systems during the planning, construction and operation process, realizing synergistic planning and coordinated operation among multiple heterogeneous energy forms of production, consumption and storage units. Through the complementarity among various forms of energy, MEMG shows great potential in enhancing the comprehensive utilization efficiency of energy, reducing the cost of energy usage and promoting the accommodation of renewable distributed generation (RDG), which is of great significance in promoting energy transition and realizing sustainable development. However, the multi-dimensional uncertainties induced by the dynamic changes in multi-energy loads and the intermittent and stochastic nature of RDG pose great challenges to the optimal dispatch and reliable operation of MEMG, which need to be urgently addressed in the process of MEMG energy dispatch. To this end, a real-time energy optimal dispatch method for MEMG based on dynamic operation scenario forecasting was proposed in this paper. The method characterizes MEMG operation uncertainties by dynamically forecasting a set of operation scenarios at each time slot, thus effectively addressing the negative impact of multi-dimensional uncertainties on MEMG operation.
Firstly, a Wasserstein Generative Adversarial Network (WGAN) suitable for the characteristics of MEMG operation scenarios was constructed in the method to mine and characterize their intrinsic statistical distribution in an unsupervised manner. Secondly, a constrained optimization problem for scenario forecasting was formulated based on known information (observations and point predictions of uncertain variables) and combined with the well-trained WGAN. It achieves efficient, high-quality and time-to-time forecasting of MEMG operation scenarios by optimizing the input vectors of the generator to effectively capture the multi-dimensional uncertainties of MEMG for a coming period. Finally, a MEMG pre-scheduling model was developed in the stochastic model predictive control framework based on the forecasted scenarios to accurately obtain the MEMG pre-scheduling commands. A real-time power compensation model was also constructed to compensate for the unbalanced electric and thermal power in the most economical manner, thereby ensuring the real-time power balance of MEMG.
Comprehensive numerical simulations fully validate the effectiveness of the proposed MEMG operation scenario forecasting and real-time energy optimal dispatch method. The developed scenario forecasting method can effectively forecast the MEMG operation scenarios in different forecast time ranges and can effectively capture the edge distribution of the actual scenarios, realizing the accurate characterization of the uncertainties for MEMG operation in a coming period. Meanwhile, the real-time energy optimal dispatch method constructed based on the forecasted scenarios effectively mitigates the negative impact of multi-dimensional uncertainties on MEMG operation, and shows more significant economic benefits than the traditional model predictive control method.
In the future, the energy forms integrated into MEMG will be more diversified, the forms of MEMG will be more complex and diverse, and their control will be more intelligent. Therefore, future research will focus on how to use digital twins and other advanced tools to improve the operation state sensing level and fine dispatch performance of MEMG with close coupling among energy forms such as hydrogen, biomass, heat, gas, electricity, etc.

Key words： Multi-energy microgrid renewable distributed generation multi-dimensional uncertainties generative adversarial network scenario forecasting stochastic model predictive control

收稿日期: 2025-04-10

PACS:

TM73

基金资助:

国家自然科学基金（52337003,52177119）资助项目

通讯作者: 杨强男,1979年生,教授,博士生导师,研究方向为综合能源系统优化规划与运行调控,新能源电力基础设施智能化运维与能源电力基础设施综合安全。E-mail：qyang@zju.edu.cn

作者简介: 王玉彬男,1994年生,博士后,讲师,研究方向为综合能源系统优化运行、交通-能源融合。E-mail：ybwang@bjtu.edu.cn

引用本文:

王玉彬, 杨强, 夏明超, 陈奇芳, 孙谦浩. 基于动态运行场景预测的多能微网实时能量优化调控方法[J]. 电工技术学报, 0, (): 258107-258107. Wang Yubin, Yang Qiang, Xia Mingchao, Chen Qifang, Sun Qianhao. A Real-time Energy Optimal Dispatch Method for Multi-energy Microgrids Based on Dynamic Operation Scenario Forecasting. Transactions of China Electrotechnical Society, 0, (): 258107-258107.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.250575 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/258107

[1] 韩丽, 王冲, 于晓娇, 等. 考虑风电爬坡灵活调节的碳捕集电厂低碳经济调度[J]. 电工技术学报, 2024, 39(7): 2033-2045.
Han Li, Wang Chong, Yu Xiaojiao, et al.Low-carbon and economic dispatch considering the carbon capture power plants with flexible adjustment of wind power ramp[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 2033-2045.
[2] 羡一鸣, 安之, 刘斯伟, 等. 面向高比例新能源接入的电氢耦合系统设想及分析[J]. 电力自动化设备, 2024, 44(4): 1-8.
Xian Yiming, An Zhi, Liu Siwei, et al.Assumption and analysis of electricity-hydrogen coupling system for high proportion of new energy[J]. Electric Power Automation Equipment, 2024, 44(4): 1-8.
[3] 刘硕, 滕云, 陈哲. 融合减碳型多能源微网的城市能源系统环境-经济协调优化模型[J/OL]. 电工技术学报, 2025: 1-24. (2025-03-07). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.242075.
Liu Shuo, Teng Yun, Chen Zhe. Environmental-economic coordination optimization model of urban energy system integrating carbon-reducing multi-energy microgrid[J/OL]. Transactions of China Electrotechnical Society, 2025: 1-24. (2025-03-07). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.242075.
[4] 郭尊, 安之, 魏楠, 等. 计及氢能多环节利用和混合需求响应的综合能源系统多时间尺度低碳优化[J]. 供用电, 2024, 41(2): 52-63, 72.
Guo Zun, An Zhi, Wei Nan, et al.Multi-time scale low-carbon optimization of an integrated energy system considering the multi-link utilization of hydrogen energy and mixed demand response[J]. Distribution & Utilization, 2024, 41(2): 52-63, 72.
[5] Fu Xueqian, Niu Haosen.Key technologies and applications of agricultural energy Internet for agricultural planting and fisheries industry[J]. Information Processing in Agriculture, 2023, 10(3): 416-437.
[6] 陈明昊, 朱月瑶, 孙毅, 等. 计及高渗透率光伏消纳与深度强化学习的综合能源系统预测调控[J]. 电工技术学报, 2024, 39(19): 6054-6071, 6103.
Chen Minghao, Zhu Yueyao, Sun Yi, et al.The predictive-control optimization method for park integrated energy system considering the high penetration of photovoltaics and deep reinforcement learning[J]. Transactions of China Electrotechnical Society, 2024, 39(19): 6054-6071, 6103.
[7] 尚敬福, 马克睿, 花志浩, 等. 计及碳排放的综合能源系统优化调度及仿真实现[J]. 供用电, 2021, 38(11): 77-84.
Shang Jingfu, Ma Kerui, Hua Zhihao, et al.Optimal scheduling and simulation of integrated energy system considering carbon emission[J]. Distribution & Utilization, 2021, 38(11): 77-84.
[8] 郭怿, 明波, 黄强, 等. 考虑输电功率平稳性的水-风-光-储多能互补日前鲁棒优化调度[J]. 电工技术学报, 2023, 38(9): 2350-2363.
Guo Yi, Ming Bo, Huang Qiang, et al.Day-ahead robust optimal scheduling of hydro-wind-PV-storage complementary system considering the steadiness of power delivery[J]. Transactions of China Electrotechnical Society, 2023, 38(9): 2350-2363.
[9] Guo Shiliang, Li Pengpeng, Ma Kai, et al.Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles[J]. Applied Energy, 2022, 325: 119846.
[10] 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/OL]. 电工技术学报, 2024: 1-13. (2024-12-11). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.241696.
Zhang Xian, Ding Kehao, Zhao Liyuan, et al. Robust optimal scheduling of integrated energy system considering electric vehicle hybrid charging system[J/OL]. Transactions of China Electrotechnical Society, 2024: 1-13. (2024-12-11). https://link.cnki.net/doi/10.19595/j.cnki.1000-6753.tces.241696.
[11] Yang Zhao, Hu Junjie, Ai Xin, et al.Transactive energy supported economic operation for multi-energy complementary microgrids[J]. IEEE Transactions on Smart Grid, 2021, 12(1): 4-17.
[12] 左逢源, 张玉琼, 赵强, 等. 计及源荷不确定性的综合能源生产单元运行调度与容量配置两阶段随机优化[J]. 中国电机工程学报, 2022, 42(22): 8205-8215.
Zuo Fengyuan, Zhang Yuqiong, Zhao Qiang, et al.Two-stage stochastic optimization for operation scheduling and capacity allocation of integrated energy production unit considering supply and demand uncertainty[J]. Proceedings of the CSEE, 2022, 42(22): 8205-8215.
[13] 林雨眠, 熊厚博, 张笑演, 等. 计及新能源机会约束与虚拟储能的电-热系统分布式多目标优化调度[J]. 电工技术学报, 2024, 39(16): 5042-5059.
Lin Yumian, Xiong Houbo, Zhang Xiaoyan, et al.Distributed multi-objective optimal scheduling of integrated electric-heat system considering chance constraint of new energy and virtual storage[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5042-5059.
[14] 周承翰, 贾宏杰, 靳小龙, 等. 基于机会约束规划的智能楼宇与社区综合能源系统协调优化[J]. 电力系统自动化, 2023, 47(4): 42-50.
Zhou Chenghan, Jia Hongjie, Jin Xiaolong, et al.Coordinated optimization for intelligent building and integrated community energy system based on chance-constrained programming[J]. Automation of Electric Power Systems, 2023, 47(4): 42-50.
[15] Kouvaritakis B, Cannon M.Model predictive control[M]. Switzerland: Springer International Publishing, 2016.
[16] 叶林, 路朋, 赵永宁, 等. 含风电电力系统有功功率模型预测控制方法综述[J]. 中国电机工程学报, 2021, 41(18): 6181-6198.
Ye Lin, Lu Peng, Zhao Yongning, et al.Review of model predictive control for power system with large-scale wind power grid-connected[J]. Proceedings of the CSEE, 2021, 41(18): 6181-6198.
[17] Hu Jiefeng, Shan Yinghao, Guerrero J M, et al.Model predictive control of microgrids-An overview[J]. Renewable and Sustainable Energy Reviews, 2021, 136: 110422.
[18] liu Zhaoxi, Wu Qiuwei, Shahidehpour M, et al. Transactive real-time electric vehicle charging management for commercial buildings with PV on-site generation[J]. IEEE Transactions on Smart Grid, 2019, 10(5): 4939-4950.
[19] 贾文杰, 唐早, 曾平良, 等. 基于鲁棒模型预测控制的风火储联合系统调频优化策略[J]. 电测与仪表, 2023, 60(12): 27-35.
Jia Wenjie, Tang Zao, Zeng Pingliang, et al.Frequency regulation optimization strategy for wind-thermal-storage joint system based on robust model predictive control[J]. Electrical Measurement & Instrumentation, 2023, 60(12): 27-35.
[20] Zhao Zhuoli, Guo Juntao, Luo Xi, et al.Distributed robust model predictive control-based energy management strategy for islanded multi-microgrids considering uncertainty[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2107-2120.
[21] 谢云云, 杨正婷, 蔡胜, 等. 基于鲁棒模型预测控制的配电网供电恢复策略[J]. 电力系统自动化, 2021, 45(23): 123-131.
Xie Yunyun, Yang Zhengting, Cai Sheng, et al.Power supply restoration strategy for distribution network based on robust model prediction control[J]. Automation of Electric Power Systems, 2021, 45(23): 123-131.
[22] Li Zhengmao, Wu Lei, Xu Yan.Risk-averse coordinated operation of a multi-energy microgrid considering voltage/var control and thermal flow: an adaptive stochastic approach[J]. IEEE Transactions on Smart Grid, 2021, 12(5): 3914-3927.
[23] He Junqiang, Shi Changli, Wei Tongzhen, et al.Stochastic model predictive control of hybrid energy storage for improving AGC performance of thermal generators[J]. IEEE Transactions on Smart Grid, 2022, 13(1): 393-405.
[24] 唐早, 刘佳, 刘一奎, 等. 基于随机模型预测控制的火电-储能两阶段协同调频控制模型[J]. 电力系统自动化, 2023, 47(3): 86-95.
Tang Zao, Liu Jia, Liu Yikui, et al.Two-stage coordinated frequency regulation control model for thermal power and energy storage based on stochastic model predictive control[J]. Automation of Electric Power Systems, 2023, 47(3): 86-95.
[25] Zhao Baiyang, Zhao Zhigang, Huang Meng, et al.Model predictive control of solar PV-powered ice-storage air-conditioning system considering forecast uncertainties[J]. IEEE Transactions on Sustainable Energy, 2021, 12(3): 1672-1683.
[26] Morales J M, Mínguez R, Conejo A J.A methodology to generate statistically dependent wind speed scenarios[J]. Applied Energy, 2010, 87(3): 843-855.
[27] Arjovsky M, Chintala S, Bottou L.Wasserstein generative adversarial networks[C]//34th International Conference on Machine Learning (ICML), ACM, 2017.
[28] Chen Yize, Wang Xiyu, Zhang Baosen.An unsupervised deep learning approach for scenario forecasts[C]//2018 Power Systems Computation Conference (PSCC), Dublin, Ireland, 2018: 1-7.
[29] Wan Can, Xu Zhao, Pinson P, et al.Probabilistic forecasting of wind power generation using extreme learning machine[J]. IEEE Transactions on Power Systems, 2014, 29(3): 1033-1044.
[30] Gurobi[EB/OL].Available: https://www.gurobi.com/.
[31] Wang Yubin, Liu Yixian, Yang Qiang.Operational scenario generation and forecasting for integrated energy systems[J]. IEEE Transactions on Industrial Informatics, 2024, 20(2): 2920-2931.
[32] Fang Xiaolun, Wang Yubin, Dong Wei, et al.Optimal energy management of multiple electricity-hydrogen integrated charging stations[J]. Energy, 2023, 262: 125624.
[33] AEMO[EB/OL].https://www.aemo.com.au/.
[34] Yu Liang, Jiang Tao, Cao Yang.Energy cost minimization for distributed Internet data centers in smart microgrids considering power outages[J]. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(1): 120-130.
[35] Jiang Congmei, Mao Yongfang, Chai Yi, et al.Day-ahead renewable scenario forecasts based on generative adversarial networks[J]. International Journal of Energy Research, 2021, 45(5): 7572-7587.