能源岛多能互补自适应变步长SMPC分层优化调度研究

doi:10.3969/j.issn.2097-0706.2026.01.003

综合智慧能源 ›› 2026, Vol. 48 ›› Issue (1): 23-33.doi: 10.3969/j.issn.2097-0706.2026.01.003

• 综合智慧能源系统优化与调度 • 上一篇下一篇

能源岛多能互补自适应变步长SMPC分层优化调度研究

李阔¹(), 唐洋¹(), 刘博涛²(), 黄浩城^3a^,^3b(), 邵贤杰^3a^,^3b(), 尹高俊^3a^,^3b(), 魏赏赏^3a^,^3b^,^*()

1.中电建新能源集团股份有限公司，北京 100101
2.润电能源科学技术有限公司，郑州 450018
3.河海大学 a.新能源学院； b.国家风力发电技术创新中心，江苏常州 213200

收稿日期:2025-07-22 修回日期:2025-08-05 出版日期:2026-01-25
通讯作者: *魏赏赏（1990），男，副教授，博士，从事风光新能源、储能及多能互补系统等方面的研究，weishsh@hhu.edu.cn。
作者简介:李阔（1996），男，工程师，硕士，从事海上风电与海洋能方面的研究，likuo0607@163.com；
唐洋（1988），男，高级工程师，从事风光储、综合能源等新能源开发方面的研究，ty676854@163.com；
刘博涛（1989），男，工程师，博士，从事风光火储技术方面的研究，looepr2011@163.com；
黄浩城（2000），男，博士生，从事可再生能源多能互补等方面的研究，huanghchhu0708@163.com；
邵贤杰（2000），男，硕士生，从事海上能源岛协同运行控制方面的研究，supoqiaji@163.com；
尹高俊（2001），男，硕士生，从事海上风电仿真平台开发方面的研究，yin1003895936@163.com。
基金资助:
国家自然科学基金项目(52406233);中国博士后科学基金面上项目(2024M750738);江苏省碳达峰碳中和科技创新专项资金项目(BT024004);常州市科技计划项目(525011412)

Research on hierarchical optimization scheduling of multi-energy complementary adaptive variable step-size SMPC for energy island

LI Kuo¹(), TANG Yang¹(), LIU Botao²(), HUANG Haocheng^3a^,^3b(), SHAO Xianjie^3a^,^3b(), YIN Gaojun^3a^,^3b(), WEI Shangshang^3a^,^3b^,^*()

1. Power China Renewable Energy Company Limited，Beijing 100101，China
2. Rundian Energy Science and Technology Company Limited，Zhengzhou 450018，China
3a. School of Renewable Energy；b. National Wind Power Technology Innovation Center，Hohai University，Changzhou 213200，China

Received:2025-07-22 Revised:2025-08-05 Published:2026-01-25
Supported by:
National Natural Science Foundation of China Project(52406233);China Postdoctoral Science Foundation General Funding Program(2024M750738);Technological Innovation Special Fund Project for Carbon Peaking and Carbon Neutrality in Jiangsu Province(BT024004);Changzhou Science and Technology Plan Project(525011412)

摘要/Abstract

摘要：

基于多目标分层优化求解方法，兼顾系统平衡与调度经济性，构建海上能源岛多能互补系统分层协同优化模型。为应对风电与光伏出力的波动性与随机性对系统调度的影响，采用随机模型预测控制（SMPC）方法对海上能源岛系统调度进行优化求解。提出一种自适应变步长SMPC的调度方法，该方法在SMPC滚动优化环节，通过偏差参考系数追踪实时的调度偏差程度并据此动态调整滚动优化步长，解决了传统SMPC调度方法在滚动优化环节存在的调度精度缺失与易陷入局部优化的问题，兼顾了调度的精确性与全局性。仿真结果表明，该方法可有效提升调度精度，缩短计算时间。

关键词: 分层优化, 海上能源岛, 多能互补系统, 自适应变步长, 随机模型预测控制

Abstract:

Based on a multi-objective hierarchical optimization method， a hierarchical collaborative optimization model for the multi-energy complementary system in an offshore energy island was constructed， considering both system balance and economic efficiency of scheduling. To address the impact of the volatility and randomness of wind and solar energy on the scheduling of the offshore energy island system， the stochastic model predictive control （SMPC） method was adopted to optimize the scheduling of the offshore energy island. A scheduling method of adaptive variable step-size SMPC was proposed. In the rolling optimization link of SMPC， the real-time scheduling deviation degree was tracked through a deviation reference coefficient using the proposed method， and the rolling optimization step-size was adjusted accordingly. This addressed the problems of scheduling accuracy loss and local optimization in the rolling optimization phase of the traditional SMPC scheduling method， thereby balancing scheduling accuracy and globality. The simulation results show that this method can effectively improve the scheduling accuracy and shorten calculation time.

Key words: hierarchical optimization, offshore energy island, multi-energy complementary system, adaptive variable step-size, stochastic model predictive control

中图分类号:

TK89

李阔, 唐洋, 刘博涛, 黄浩城, 邵贤杰, 尹高俊, 魏赏赏. 能源岛多能互补自适应变步长SMPC分层优化调度研究[J]. 综合智慧能源, 2026, 48(1): 23-33.

LI Kuo, TANG Yang, LIU Botao, HUANG Haocheng, SHAO Xianjie, YIN Gaojun, WEI Shangshang. Research on hierarchical optimization scheduling of multi-energy complementary adaptive variable step-size SMPC for energy island[J]. Integrated Intelligent Energy, 2026, 48(1): 23-33.

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参考文献 29

[1]	谭显东, 刘俊, 徐志成, 等. “双碳” 目标下“十四五” 电力供需形势[J]. 中国电力, 2021, 54(5): 1-6.
	TAN Xiandong, LIU Jun, XU Zhicheng, et al. Power supply and demand balance during the 14th Five-Year Plan period under the goal of carbon emission peak and carbon neutrality[J]. Electric Power, 2021, 54(5): 1-6.
[2]	GOSENS J, HEDENUS F, SANDÉN B A. Faster market growth of wind and PV in late adopters due to global experience build-up[J]. Energy, 2017, 131: 267-278. doi: 10.1016/j.energy.2017.05.046
[3]	张勋祥, 吴杰康, 孙烨桦, 等. 平抑海上风电波动的混合储能系统容量优化配置[J]. 综合智慧能源, 2024, 46(6): 54-65. doi: 10.3969/j.issn.2097-0706.2024.06.007
	ZHANG Xunxiang, WU Jiekang, SUN Yehua, et al. Capacity allocation optimization of hybrid energy storage systems considering fluctuation control on offshore wind power[J]. Integrated Intelligent Energy, 2024, 46(6): 54-65. doi: 10.3969/j.issn.2097-0706.2024.06.007
[4]	PRANDINI M, GARATTI S, LYGEROS J.A randomized approach to stochastic model predictive control[C]// Proceedings of 2012 IEEE 51st IEEE Conference on Decision and Control (CDC). IEEE, 2012: 7315-7320.
[5]	GONZÁLEZ E, SANCHIS J, GARCÍA-NIETO S, et al. A comparative study of stochastic model predictive controllers[J]. Electronics, 2020, 9(12): 2078. doi: 10.3390/electronics9122078
[6]	MESBAH A. Stochastic model predictive control with active uncertainty learning: A Survey on dual control[J]. Annual Reviews in Control, 2018, 45: 107-117. doi: 10.1016/j.arcontrol.2017.11.001
[7]	HEIRUNG T A N, PAULSON J A, O'LEARY J, et al. Stochastic model predictive control: How does it work?[J]. Computers & Chemical Engineering, 2018, 114: 158-170. doi: 10.1016/j.compchemeng.2017.10.026
[8]	MESBAH A. Stochastic model predictive control: An overview and perspectives for future research[J]. IEEE Control Systems Magazine, 2016, 36(6): 30-44.
[9]	张怡, 房方. 基于随机模型预测控制的智能楼宇能量管理方法[J]. 综合智慧能源, 2022, 44(10): 83-90. doi: 10.3969/j.issn.2097-0706.2022.10.011
	ZHANG Yi, FANG Fang. Smart building energy management strategy based on stochastic model predictive control[J]. Integrated Intelligent Energy, 2022, 44(10): 83-90. doi: 10.3969/j.issn.2097-0706.2022.10.011
[10]	MACIEJOWSKI J M, YANG X K. Fault tolerant control using Gaussian processes and model predictive control[C]// Proceedings of 2013 Conference on Control and Fault-Tolerant Systems (SysTol). IEEE, 2013: 1-12.
[11]	贾文杰. 基于鲁棒模型预测控制的风火储联合系统调频优化策略研究[D]. 杭州: 杭州电子科技大学, 2024.
	JIA Wenjie. Study on frequency regulation optimization strategy of integrated wind-thermal-storage system based on robust model predictive control[D]. Hangzhou: Hangzhou Dianzi University, 2024.
[12]	蒋李晋. 基于模型预测控制的风电集群有功分层协调控制策略研究[D]. 吉林: 东北电力大学, 2023.
	JIANG Lijin. Research on active power hierarchical coordinated control strategy of wind power cluster based on model predictive control[D]. Jilin:Northeast Electric Power University, 2023.
[13]	张磊光, 陈海涛, 吴赋章, 等. 基于可学习模型预测控制的含风电多微网频率控制方法[J]. 智慧电力, 2024, 52(10): 49-55, 87.
	ZHANG Leiguang, CHEN Haitao, WU Fuzhang, et al. Load frequency control method for multi microgrid with wind power based on learnable model predictive control[J]. Smart Power, 2024, 52(10): 49-55, 87.
[14]	WEI S S, LI Y G, GAO X H, et al. Multi-stage sensitivity analysis of distributed energy systems: A variance-based sobol method[J]. Journal of Modern Power Systems and Clean Energy, 2020, 8(5): 895-905. doi: 10.35833/MPCE.2020.000134
[15]	冯哲飞, 霍志红, 魏赏赏, 等. 基于自适应SMPC的梯级水-风-光互补系统多目标优化调度[J]. 可再生能源, 2023, 41(3): 352-360.
	FENG Zhefei, HUO Zhihong, WEI Shangshang, et al. Multi-objective optimal scheduling for hydro-wind-photovoltaic system based on adaptive stochastic model predictive control[J]. Renewable Energy Resources, 2023, 41(3): 352-360.
[16]	方朝雄, 吴晓升, 江岳文. 考虑暂态稳定性的网储多目标双层优化[J]. 电力建设, 2020, 41(7): 58-66. doi: 10.12204/j.issn.1000-7229.2020.07.008
	FANG Chaoxiong, WU Xiaosheng, JIANG Yuewen. Research on multi-objective bi-level optimization of network-storage considering transient stability[J]. Electric Power Construction, 2020, 41(7): 58-66. doi: 10.12204/j.issn.1000-7229.2020.07.008
[17]	WANG C H, MA Y, XIE J J, et al. Multi-objective energy dispatch with deep reinforcement learning for wind-solar-thermal-storage hybrid systems[J]. Journal of Energy Storage, 2025, 105: 114635. doi: 10.1016/j.est.2024.114635
[18]	LI Z H, YU T, CHEN Y X, et al. Multi-objective optimization dispatching strategy for wind-thermal-storage generation system incorporating temporal and spatial distribution control of air pollutant dispersion[J]. IEEE Access, 2020, 8: 44263-44275. doi: 10.1109/Access.6287639
[19]	程先龙, 马云, 韩军峰, 等. 基于改进非洲秃鹫优化算法的含风电场电力系统经济调度研究[J]. 综合智慧能源, 2025, 47(6): 37-46. doi: 10.3969/j.issn.2097-0706.2025.06.005
	CHENG Xianlong, MA Yun, HAN Junfeng, et al. Research on economic scheduling of power systems with wind farms based on improved African vulture optimization algorithm[J]. Integrated Intelligent Energy, 2025, 47(6): 37-46. doi: 10.3969/j.issn.2097-0706.2025.06.005
[20]	李雄威, 王昕, 徐家豪, 等. 考虑火电深度调峰的风光火储系统分层优化调度模型[J]. 油气与新能源, 2023, 35(6): 74-81.
	LI Xiongwei, WANG Xin, XU Jiahao, et al. A hierarchical optimal scheduling model of wind-photovoltaic-thermal-energy storage system considering deep peak shaving of thermal power[J]. Petroleum and New Energy, 2023, 35(6): 74-81.
[21]	叶泽, 李湘旗, 姜飞, 等. 考虑最优弃能率的风光火储联合系统分层优化经济调度[J]. 电网技术, 2021, 45(6): 2270-2280.
	YE Ze, LI Xiangqi, JIANG Fei, et al. Hierarchical optimization economic dispatching of combined wind-PV-thermal-energy storage system considering the optimal energy abandonment rate[J]. Power System Technology, 2021, 45(6): 2270-2280.
[22]	张彦, 张涛, 刘亚杰, 等. 基于随机模型预测控制的能源局域网优化调度研究[J]. 中国电机工程学报, 2016, 36(13): 3451-3462, 3364.
	ZHANG Yan, ZHANG Tao, LIU Yajie, et al. Stochastic model predictive control for energy management optimization of an energy local network[J]. Proceedings of the CSEE, 2016, 36(13): 3451-3462, 3364.
[23]	董雷, 刘梦夏, 陈乃仕, 等. 基于随机模型预测控制的分布式能源协调优化控制[J]. 电网技术, 2018, 42(10): 3219-3227.
	DONG Lei, LIU Mengxia, CHEN Naishi, et al. Coordinated optimal control of distributed energy based on stochastic model predictive control[J]. Power System Technology, 2018, 42(10): 3219-3227.
[24]	刘梦夏. 基于随机模型预测控制的配电网多源协调优化研究[D]. 北京: 华北电力大学, 2018.
	LIU Mengxia. Research on multi-source coordination optimization of distribution network based on stochasticmodel predictive control[D]. Beijing: North China Electric Power University, 2018.
[25]	王永杰, 吴文传, 张伯明. 考虑负荷量测和光伏不确定性的主动配电网鲁棒电压控制[J]. 电力系统自动化, 2015, 39(9): 138-144.
	WANG Yongjie, WU Wenchuan, ZHANG Boming. Robust voltage control model for active distribution network considering load and photovoltaic uncertainties[J]. Automation of Electric Power Systems, 2015, 39(9): 138-144.
[26]	HUANG H C, XU C, WEI S S, et al. Optimized scheduling of wind-solar energy storage system using adaptive variable step size SMPC[C]// Proceedings of Fifth International Conference on Green Energy, Environment, and Sustainable Development (GEESD 2024). SPIE, 2024: 70.
[27]	魏赏赏, 李益国, 张俊礼, 等. 冷热电综合能源系统变权重SMPC调度策略研究[J]. 工程热物理学报, 2021, 42(7): 1685-1691.
	WEI Shangshang, LI Yiguo, ZHANG Junli, et al. A variable-weight stochastic model predictive control operation strategy for integrated energy system[J]. Journal of Engineering Thermophysics, 2021, 42(7): 1685-1691.
[28]	江美慧, 许镇江, 牛统科, 等. 考虑综合需求响应的矿区综合能源系统日内多时间尺度滚动优化调度[J]. 综合智慧能源, 2025, 47(3): 73-83. doi: 10.3969/j.issn.2097-0706.2025.03.007
	JIANG Meihui, XU Zhenjiang, NIU Tongke, et al. Intra-day multi-time scale rolling optimization scheduling of mine integrated energy system considering integrated demand response[J]. Integrated Intelligent Energy, 2025, 47(3): 73-83. doi: 10.3969/j.issn.2097-0706.2025.03.007
[29]	聂雪颖, 程懋松, 左献迪, 等. 考虑风光消纳的风光核储混合系统容量优化[J]. 综合智慧能源, 2025, 47(1): 51-61. doi: 10.3969/j.issn.2097-0706.2025.01.007
	NIE Xueying, CHENG Maosong, ZUO Xiandi, et al. DAI Zhimin. Capacity optimization of wind-solar-nuclear-energy storage hybrid system considering wind and solar energy consumption[J]. Integrated Intelligent Energy, 2025, 47(1): 51-61. doi: 10.3969/j.issn.2097-0706.2025.01.007

典型日	MPC	SMPC	自适应变步长SMPC
4月15日（春）	0.92	0.28	0.25
7月15日（夏）	0.55	0.31	0.29
10月15日（秋）	0.89	0.24	0.21
1月15日（冬）	0.62	0.24	0.24

调度方法	4月15日（春）	7月15日（夏）	10月15日（秋）	1月15日（冬）	平均用时
MPC	156.42	142.92	127.26	156.16	145.69
SMPC	248.67	255.09	251.19	255.84	252.69
自适应变步长SMPC	227.23	237.17	242.08	239.11	236.39

能源岛多能互补自适应变步长SMPC分层优化调度研究

Research on hierarchical optimization scheduling of multi-energy complementary adaptive variable step-size SMPC for energy island

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