计及风光不确定性含碳排放和碳惩罚的虚拟电厂优化调度策略

doi:10.16182/j.issn1004731x.joss.23-0840

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (2): 305-319.doi: 10.16182/j.issn1004731x.joss.23-0840

• 专栏：大模型赋能能源互联网规划运行 • 上一篇下一篇

计及风光不确定性含碳排放和碳惩罚的虚拟电厂优化调度策略

税纪钧¹(), 彭道刚¹^,²(), 宋炎侃³, 周强³

^1.上海电力大学自动化工程学院, 上海 200090
^2.上海发电过程智能管控工程技术研究中心, 上海 200090
^3.清华四川能源互联网研究院, 四川成都 610042

收稿日期:2023-07-05 修回日期:2023-08-09 出版日期:2024-02-15 发布日期:2024-02-04
通讯作者: 彭道刚 E-mail:shuijijun@mail.shiep.edu.cn;pengdaogang@shiep.edu.cn
第一作者简介:税纪钧(1999-)，男，硕士生，研究方向为虚拟电厂优化运行调度。E-mail： shuijijun@mail.shiep.edu.cn
基金资助:
国家自然科学基金(92067105);上海市“科技创新行动计划”地方院校能力建设专项(20020500500)

Optimal Scheduling Strategy of Virtual Power Plant with Carbon Emission and Carbon Penalty Considering Uncertainty of Wind Power and Photovoltaic Power

Shui Jijun¹(), Peng Daogang¹^,²(), Song Yankan³, Zhou Qiang³

^1.College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
^2.Shanghai Engineering Research Center of Intelligent Management and Control for Power Process, Shanghai 200090, China
^3.Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China

Received:2023-07-05 Revised:2023-08-09 Online:2024-02-15 Published:2024-02-04
Contact: Peng Daogang E-mail:shuijijun@mail.shiep.edu.cn;pengdaogang@shiep.edu.cn

摘要/Abstract

摘要：

为使虚拟电厂更好地满足我国新型电力系统的发展需要，提出一种 考虑风光不确定性的含碳排放和碳惩罚的虚拟电厂优化调度策略。对光伏、风力、热电联产机组、储能系统进行数学描述，建立了考虑风光不确定性的风光出力模型；采用场景生成与削减法生成风光典型场景；以虚拟电厂整体运行效益最大为目标，引入碳排放成本和碳惩罚因子，并建立典型场景下虚拟电厂优化调度模型。结果表明：所提策略有利于挖掘储能系统潜力，提高系统经济性，降低碳排放量；在CloudPSS云仿真平台上搭建算例，进一步验证优化调度模型的有效性，为后续开展虚拟电厂优化调度提供了可行的实现途径。

关键词: 虚拟电厂, 优化调度, 不确定性, 场景生成与削减, CloudPSS

Abstract:

To better meet the development needs of China's new power system, an optimal scheduling strategy of virtual power plant(VPP) with carbon emission and carbon penalty considering the uncertainty of wind power and photovoltaic power is proposed. The mathematical description of photovoltaic(PV), wind turbine(WT), combined heat and power(CHP) unit and energy storage system(ESS) is carried out, and a wind-solar output model considering the uncertainty is established. The scenario generation and reduction method is used to generate the typical scenario. To maximize the overall operation benefit of VPP, considering carbon emission cost and carbon penalty, an optimal scheduling model of VPP in typical scenario is established. Example results show that the proposed strategy is conducive to tapping the potential of ESS, improving the system economy and reducing the carbon emission. The example built on CloudPSS simulation platform further verifies the effectiveness of the strategy. It provides a feasible way for the subsequent optimization scheduling of virtual power plants.

Key words: virtual power plant, optimal scheduling, uncertainty, scenario generation and reduction, CloudPSS

中图分类号:

TP391.9

税纪钧,彭道刚,宋炎侃等 . 计及风光不确定性含碳排放和碳惩罚的虚拟电厂优化调度策略[J]. 系统仿真学报, 2024, 36(2): 305-319.

Shui Jijun,Peng Daogang,Song Yankan,et al . Optimal Scheduling Strategy of Virtual Power Plant with Carbon Emission and Carbon Penalty Considering Uncertainty of Wind Power and Photovoltaic Power[J]. Journal of System Simulation, 2024, 36(2): 305-319.

图/表 19

图1

图2

表1

图3

表2

光伏参数

$V M P P$ /V	$I M P P$ /A	$V o c$ /V	$I s c$ /A	$N P V$
37	8.38	45.9	8.96	2 000

表2

表3

风机参数

$P r W T$ /kW	$v r$ /(m/s)	$v i n$ /(m/s)	$v o u t$ /(m/s)	$N W T$
300	8	4	20	3

表3

表4

热电联产机组参数

P m a x C H P

/kW

P m i n C H P

/kW

P C C H P

/kW

$C p o t e n t i a l$ /

(g/( $k W ⋅ h$ ))

η e C H P

η h C H P

1 000

100

500

0.8

0.5

表4

表5

储能系统参数

$P m a x E S S$ /kW	$Q m a x E S S$ /( $k W ⋅ h$ )	$η C, η D$ /%	$λ C D e e$	$C E S S, m a x$	$C E S S, m i n$	$C E S S, 0$	$σ s$ /%
300	1 000	90	0.1	0.95	0.2	0.5	5

表5

图4

图5

表6

不同场景下VPP运行效益明细 (元)

场景	$F$	$F 1$	$k e q P V$	$k e q W T$	$k e q E S S$	$k e q C H P$	$k r C H P$	$k b G$	$k c o s C$	$k p u b C$	$π s G$	$π e$	$π h$
I	5 147.0	14 520.6	87.1	206.2	20.8	292.5	9 212.9	2 209.7	6 695.4	2 678.2	46.8	25 662.0	840.9
II	4 662.9	14 693.9	87.1	206.2	9.7	313.0	9 859.0	1 977.7	7 165.0	2 866.0	584.6	25 662.0	899.9
III	4 173.1	14 461.8	83.5	206.2	—	321.0	10 112.3	1 887.8	7 349.1	2 939.6	487.6	25 662.0	923.0
IV	4 795.2	14 614.3	87.1	206.2	10.5	306.4	9 650.7	2 057.9	7 013.6	2 805.4	390.2	25 662.0	880.9
V	5 319.0	14 551.6	87.1	206.2	25.6	288.1	9 074.3	2 304.3	6 594.7	2 637.9	46.8	25 662.0	828.3
VI	5 423.9	14 600.7	87.1	206.2	28.1	286.3	9 019.4	2 304.3	6 554.8	2 621.9	46.8	25 662.0	823.3

表6

图6

图7

图8

图9

图10

图11

图12

图13

参考文献 28

1	黄震, 谢晓敏, 张庭婷. " 双碳"背景下我国中长期能源需求预测与转型路径研究[J]. 中国工程科学, 2022, 24( 6): 8- 18.
	Huang Zhen, Xie Xiaomin, Zhang Tingting. Medium-and Long-term Energy Demand of China and Energy Transition Pathway Toward Carbon Neutrality[J]. Strategic Study of CAE, 2022, 24( 6): 8- 18.
2	习近平. 高举中国特色社会主义伟大旗帜为全面建设社会主义现代化国家而团结奋斗-在中国共产党第二十次全国代表大会上的报告[J]. 中华人民共和国国务院公报, 2022( 30): 4- 27.
3	国家能源局. 国家能源局组织发布«新型电力系统发展蓝皮书»[EB/OL]. ( 2023-06-02) [ 2023-07-01]. .
4	赵昊天, 王彬, 潘昭光, 等. 支撑云-群-端协同调度的多能园区虚拟电厂: 研发与应用[J]. 电力系统自动化, 2021, 45( 5): 111- 121.
	Zhao Haotian, Wang Bin, Pan Zhaoguang, et al. Research and Application of Park-level Multi-energy Virtual Power Plants Supporting Cloud-cluster-end Multi-level Synergetic Dispatch[J]. Automation of Electric Power Systems, 2021, 45( 5): 111- 121.
5	赵冬梅, 殷加玞. 考虑源荷双侧不确定性的模糊随机机会约束优先目标规划调度模型[J]. 电工技术学报, 2018, 33( 5): 1076- 1085.
	Zhao Dongmei, Yin Jiafu. Fuzzy Random Chance Constrained Preemptive Goal Programming Scheduling Model Considering Source-side and Load-side Uncertainty[J]. Transactions of China Electrotechnical Society, 2018, 33( 5): 1076- 1085.
6	Lu Xinhui, Liu Zhaoxi, Ma Li, et al. A Robust Optimization Approach for Optimal Load Dispatch of Community Energy Hub[J]. Applied Energy, 2020, 259: 114195.
7	张大海, 贠韫韵, 王小君, 等. 计及风光不确定性的新能源虚拟电厂多时间尺度优化调度[J]. 太阳能学报, 2022, 43( 11): 529- 537.
	Zhang Dahai, Yun Yunyun, Wang Xiaojun, et al. Multi-time Scale of New Energy Scheduling Optimization for Virtual Power Plant Considering Uncertainty of Wind Power and Photovoltaic Power[J]. Acta Energiae Solaris Sinica, 2022, 43( 11): 529- 537.
8	黄勤坤, 邱瑜, 王飞, 等. 考虑多重不确定性的虚拟电厂随机优化调度[J]. 电网与清洁能源, 2022, 38( 11): 8- 16, 26.
	Huang Qinkun, Qiu Yu, Wang Fei, et al. Stochastic Optimal Scheduling of Virtual Power Plants Considering Multiple Uncertainties[J]. Power System and Clean Energy, 2022, 38( 11): 8- 16, 26.
9	杨洪朝, 杨迪, 孟科. 高比例可再生能源渗透下多虚拟电厂多时间尺度协调优化调度[J]. 智慧电力, 2021, 49( 2): 60- 68.
	Yang Hongzhao, Yang Di, Meng Ke. Multi-time Scale Coordination Optimal Scheduling of Multiple Virtual Power Plants with High-penetration Renewable Energy Integration[J]. Smart Power, 2021, 49( 2): 60- 68.
10	李翔宇, 赵冬梅. 基于模糊-概率策略实时反馈的虚拟电厂多时间尺度优化调度[J]. 电工技术学报, 2021, 36( 7): 1446- 1455.
	Li Xiangyu, Zhao Dongmei. Research on Multi-time Scale Optimal Scheduling of Virtual Power Plant Based on Real-time Feedback of Fuzzy-probability Strategy[J]. Transactions of China Electrotechnical Society, 2021, 36( 7): 1446- 1455.
11	葛晓琳, 曹旭丹, 李佾玲. 多虚拟电厂日前随机博弈与实时变时间尺度优化方法[J]. 电力自动化设备, 2023, 43( 11): 150- 157.
	Ge Xiaolin, Cao Xudan, Li Yiling. Day-ahead Stochastic Game and Real-time Adaptive Time Scale Optimization Method for Multiple Virtual Power Plants[J]. Electric Power Automation Equipment, 2023, 43( 11): 150- 157.
12	袁桂丽, 贾新潮, 房方, 等. 虚拟电厂源荷双侧热电联合随机优化调度[J]. 电网技术, 2020, 44( 8): 2932- 2940.
	Yuan Guili, Jia Xinchao, Fang Fang, et al. Joint Stochastic Optimal Scheduling of Heat and Power Considering Source and Load Sides of Virtual Power Plant[J]. Power System Technology, 2020, 44( 8): 2932- 2940.
13	申建建, 王月, 程春田, 等. 水风光多能互补发电调度问题研究现状及展望[J]. 中国电机工程学报, 2022, 42( 11): 3871- 3884, 中插1.
	Shen Jianjian, Wang Yue, Cheng Chuntian, et al. Research Status and Prospect of Generation Scheduling for Hydropower-wind-solar Energy Complementary System[J]. Proceedings of the CSEE, 2022, 42( 11): 3871- 3884, 中插1.
14	元志伟, 于松源, 房方, 等. 考虑高维不确定性的热电联产虚拟电厂优化调度[J]. 动力工程学报, 2023, 43( 2): 194- 204.
	Yuan Zhiwei, Yu Songyuan, Fang Fang, et al. Optimal Scheduling of Combined Heat and Power-virtual Power Plant Considering High-dimensional Uncertainty[J]. Journal of Chinese Society of Power Engineering, 2023, 43( 2): 194- 204.
15	刘鑫, 李扬, 史云鹏, 等. 计及用户参与不确定性的虚拟电厂分布鲁棒优化模型[J]. 电力自动化设备, 2022, 42( 7): 84- 93.
	Liu Xin, Li Yang, Shi Yunpeng, et al. Distributionally Robust Optimization Model of Virtual Power Plant Considering User Participation Uncertainty[J]. Electric Power Automation Equipment, 2022, 42( 7): 84- 93.
16	韩帅, 孙乐平, 卢健斌, 等. 含电动汽车的气电互联虚拟电厂区间多目标优化调度策略[J]. 储能科学与技术, 2022, 11( 5): 1428- 1436.
	Han Shuai, Sun Leping, Lu Jianbin, et al. Multi-objective Optimal Dispatch Strategy of Gas-electric Interconnected Virtual Power Plant Interval with Electric Vehicles[J]. Energy Storage Science and Technology, 2022, 11( 5): 1428- 1436.
17	Zhou Changyu, Huang Guohe, Chen Jiapei. Planning of Electric Power Systems Considering Virtual Power Plants with Dispatchable Loads Included: An Inexact Two-stage Stochastic Linear Programming Model[J]. Mathematical Problems in Engineering, 2018, 2018: 7049329.
18	刘思源, 艾芊, 郑建平, 等. 多时间尺度的多虚拟电厂双层协调机制与运行策略[J]. 中国电机工程学报, 2018, 38( 3): 753- 761, 后插10.
	Liu Siyuan, Ai Qian, Zheng Jianping, et al. Bi-level Coordination Mechanism and Operation Strategy of Multi-time Scale Multiple Virtual Power Plants[J]. Proceedings of the CSEE, 2018, 38( 3): 753- 761, 后插10.
19	李东东, 王啸林, 沈运帷, 等. 考虑多重不确定性的含需求响应及电碳交易的虚拟电厂优化调度策略[J]. 电力自动化设备, 2023, 43( 5): 210- 217, 251.
	Li Dongdong, Wang Xiaolin, Shen Yunwei, et al. Optimal Scheduling Strategy of Virtual Power Plant with Demand Response and Electricity-carbon Trading Considering Multiple Uncertainties[J]. Electric Power Automation Equipment, 2023, 43( 5): 210- 217, 251.
20	周任军, 孙洪, 唐夏菲, 等. 双碳量约束下风电-碳捕集虚拟电厂低碳经济调度[J]. 中国电机工程学报, 2018, 38( 6): 1675- 1683, 1904.
	Zhou Renjun, Sun Hong, Tang Xiafei, et al. Low-carbon Economic Dispatch Based on Virtual Power Plant Made Up of Carbon Capture Unit and Wind Power Under Double Carbon Constraint[J]. Proceedings of the CSEE, 2018, 38( 6): 1675- 1683, 1904.
21	袁桂丽, 刘骅骐, 禹建芳, 等. 含碳捕集热电机组的虚拟电厂热电联合优化调度[J]. 中国电机工程学报, 2022, 42( 12): 4440- 4448, 中插14.
	Yuan Guili, Liu Huaqi, Yu Jianfang, et al. Combined Heat and Power Optimal Dispatching in Virtual Power Plant with Carbon Capture Cogeneration Unit[J]. Proceedings of the CSEE, 2022, 42( 12): 4440- 4448, 中插14.
22	陈登勇, 刘方, 刘帅. 基于阶梯碳交易的含P2G-CCS耦合和燃气掺氢的虚拟电厂优化调度[J]. 电网技术, 2022, 46( 6): 2042- 2053.
	Chen Dengyong, Liu Fang, Liu Shuai. Optimization of Virtual Power Plant Scheduling Coupling with P2G-CCS and Doped with Gas Hydrogen Based on Stepped Carbon Trading[J]. Power System Technology, 2022, 46( 6): 2042- 2053.
23	Ullah Z, Arshad, Hassanin H, et al. Planning, Operation, and Design of Market-based Virtual Power Plant Considering Uncertainty[J]. Energies, 2022, 15( 19): 7290.
24	Prasad A A, Taylor R A, Kay M. Assessment of Direct Normal Irradiance and Cloud Connections Using Satellite Data Over Australia[J]. Applied Energy, 2015, 143: 301- 311.
25	Liu Zuoyu, Zheng Weimin, Qi Feng, et al. Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading[J]. Energies, 2018, 11( 6): 1488.
26	陈颖, 高仕林, 宋炎侃, 等. 面向新型电力系统的高性能电磁暂态云仿真技术[J]. 中国电机工程学报, 2022, 42( 8): 2854- 2863, 中插5.
	Chen Ying, Gao Shilin, Song Yankan, et al. High-performance Electromagnetic Transient Simulation for New-type Power System Based on Cloud Computing[J]. Proceedings of the CSEE, 2022, 42( 8): 2854- 2863, 中插5.
27	赖启平, 肖谭南, 李东晟, 等. 基于微分神经网络的风电机群低电压穿越特性建模[J]. 系统仿真学报, 2022, 34( 12): 2546- 2556.
	Lai Qiping, Xiao Tannan, Li Dongsheng, et al. Low Voltage Ride-through Modeling for Wind Turbines Based on Neural ODEs[J]. Journal of System Simulation, 2022, 34( 12): 2546- 2556.
28	沈沉, 陈颖, 黄少伟, 等. 新型电力系统仿真应用软件设计理念与发展路径[J]. 电力系统自动化, 2022, 46( 10): 75- 86.
	Shen Chen, Chen Ying, Huang Shaowei, et al. Design Idea and Development Path of Simulation Application Software for New Power System[J]. Automation of Electric Power Systems, 2022, 46( 10): 75- 86.

场景	本文	文献[ 7]	文献]12]	文献[ 19]
I	18.18	18.18	19.70	19.70
II	19.70	24.24	10.61	24.24
III	12.12	24.24	21.21	12.12
IV	24.24	12.12	24.24	13.64
V	13.64	9.09	9.09	18.18
VI	12.12	12.12	15.15	12.12

[1]	张立, 贺明玲, 尹秋霜, 李宁, 余乐安. 不确定条件下多周期应急物资配送优化研究[J]. 系统仿真学报, 2023, 35(8): 1669-1680.
[2]	刘金朋, 彭锦淳, 邓嘉明, 刘胡诗涵. 考虑电热联产的虚拟电厂两阶段鲁棒优化经济调度[J]. 系统仿真学报, 2023, 35(8): 1799-1813.
[3]	范雅倩, 于松源, 房方. 热电联产虚拟电厂两阶段分布鲁棒优化调度[J]. 系统仿真学报, 2023, 35(5): 1046-1058.
[4]	陈强, 王意, 李康顺. 双重需求响应的虚拟电厂建模与调度研究[J]. 系统仿真学报, 2023, 35(4): 822-832.
[5]	李东晟, 刘晔, 宋炎侃, 沈沉. 针对预想故障的风电集群电磁暂态等值建模方法[J]. 系统仿真学报, 2023, 35(10): 2101-2112.
[6]	刘兆鹏, 徐新海, 袁博文, 张金璐. 一种多分辨率仿真模型构建方法[J]. 系统仿真学报, 2023, 35(1): 202-211.
[7]	赖启平, 肖谭南, 李东晟, 沈沉. 基于微分神经网络的风电机群低电压穿越特性建模[J]. 系统仿真学报, 2022, 34(12): 2546-2556.
[8]	聂凯, 曾科军, 孟庆海. 基于深度贝叶斯网络学习的不确定性建模方法[J]. 系统仿真学报, 2022, 34(1): 79-85.
[9]	孟杨凯, 王正, 范加利. 基于禁忌算法对不确定性舰载机保障的调度优化研究[J]. 系统仿真学报, 2021, 33(10): 2363-2371.
[10]	邵璐璐, 韩继红, 牛侃, 刘小虎, 邵芳. 基于加权证据理论的故障模式分析方法研究[J]. 系统仿真学报, 2020, 32(5): 782-791.
[11]	张圣东, 杨秀英. 计入多因素作用的船舶轴承负荷研究[J]. 系统仿真学报, 2019, 31(6): 1158-1164.
[12]	冯晨微, 王艳. 云制造系统并行任务优化调度[J]. 系统仿真学报, 2019, 31(12): 2626-2635.
[13]	耿化品, 佟佳慧, 马洪涛. 飞行器半实物仿真系统不确定性度量方法研究[J]. 系统仿真学报, 2019, 31(12): 2671-2677.
[14]	张晶, 陈垚, 孙俊, 范洪博. 基于协同执行器的GCPS自适应调度模型[J]. 系统仿真学报, 2019, 31(10): 2112-2121.
[15]	陶九阳, 吴琳, 王驰, 褚君达, 廖鹰, 朱丰. 基于深度学习的战场态势变化速度预测模型[J]. 系统仿真学报, 2018, 30(3): 785-792.

计及风光不确定性含碳排放和碳惩罚的虚拟电厂优化调度策略

Optimal Scheduling Strategy of Virtual Power Plant with Carbon Emission and Carbon Penalty Considering Uncertainty of Wind Power and Photovoltaic Power

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 19

参考文献 28

相关文章 15

编辑推荐

Metrics

本文评价