基于多学科协同理论的可再生能源消纳责任权重测算方法

doi:10.12204/j.issn.1000-7229.2021.01.006

电力建设 ›› 2021, Vol. 42 ›› Issue (1): 49-58.doi: 10.12204/j.issn.1000-7229.2021.01.006

• 消纳可再生能源的电力市场政策与机制·栏目主持别朝红教授、丁涛副教授· • 上一篇下一篇

基于多学科协同理论的可再生能源消纳责任权重测算方法

陈荃¹, 吴科成¹, 曲毅¹, 何春庚¹, 黄彬彬², 谢敏²

1.广东电网有限责任公司,广州市 510699
2.华南理工大学电力学院,广州市 510640

收稿日期:2020-09-28 出版日期:2021-01-01 发布日期:2021-01-07
通讯作者: 黄彬彬
作者简介:陈荃(1981),女,硕士,高级工程师,长期从事电网规划、清洁能源消纳等工作;|吴科成(1982),男,硕士,高级工程师,长期从事电网规划、节能环保等工作;|曲毅(1971),男,硕士,高级工程师,长期从事节能环保、线损管理等工作;|何春庚(1967),男,学士,高级工程师,长期从事节能环保、投资管理、工程造价等工作;|谢敏(1978),女,博士,副教授,主要研究方向为电力系统优化、运行和控制。
基金资助:
广东电网公司电力规划专题研究项目(030000QQ00190007);广东省自然科学基金项目(2018A0303130134)

Research on Optimal Weight Index Calculation for Renewable Energy Applying Multidisciplinary Collaborative Optimization Theory

CHEN Quan¹, WU Kecheng¹, QU Yi¹, HE Chungeng¹, HUANG Binbin², XIE Min²

1. Guangdong Power Grid Co., Ltd., Guangzhou 510699, China
2. School of Electric Power, South China University of Technology, Guangzhou 510640, China

Received:2020-09-28 Online:2021-01-01 Published:2021-01-07
Contact: HUANG Binbin
Supported by:
Special Research Project on Power Planning of Guangdong Power Grid Corporation(030000QQ00190007);Natural Science Foundation of Guangdong Province(2018A0303130134)

摘要/Abstract

摘要：

随着可再生能源电力消纳保障机制的不断完善,科学合理地设定各省各地区的可再生能源消纳权重是引领可再生能源电力消费长效发展的重要环节。基于多学科协同优化理论,综合考虑地区的实际网架与电网运行情况,分析具有波动性的大量可再生能源并网后对传统可控机组带来的影响,在碳排放配额实施的背景下,文章提出了可再生能源消纳责任最优权重指标测算方法,通过设立最优综合成本的系统目标函数以及不同考虑角度下的并行子学科,以系统学科与子学科循环迭代的方式寻找全局最优解,并以IEEE 118节点系统以及某省实际电网算例对所提模型和算法进行验证。

关键词: 可再生能源, 消纳权重, 多学科协同优化, 碳排放配额, 综合成本

Abstract:

With the continuous improvement of the guarantee mechanism of renewable energy power accommodation, it is an important link to lead the long-term development of renewable energy power accommodation to scientifically and rationally set the weight of renewable energy accommodation for each province and region. Applying multidisciplinary collaborative optimization theory, the actual grid structure and grid operation in the region is comprehensively considered in this paper. The influence of large amount of volatile renewable energy on the traditional controllable generating units is analyzed. Under the background of the implementation of carbon emission quotas, a method for calculating the optimal weight index of renewable energy is proposed. By setting up the system objective function of the optimal comprehensive cost and the parallel sub-disciplines under different consideration aspects, the global optimal solution is found by means of cyclic iteration of the system discipline and sub-discipline. The proposed model and algorithm are verified by an example of IEEE 118-node system and an actual power grid in a province.

Key words: renewable energy, power consumption weight, multidisciplinary collaborative optimization, carbon emission quota, comprehensive cost

中图分类号:

TM61

陈荃, 吴科成, 曲毅, 何春庚, 黄彬彬, 谢敏. 基于多学科协同理论的可再生能源消纳责任权重测算方法[J]. 电力建设, 2021, 42(1): 49-58.

CHEN Quan, WU Kecheng, QU Yi, HE Chungeng, HUANG Binbin, XIE Min. Research on Optimal Weight Index Calculation for Renewable Energy Applying Multidisciplinary Collaborative Optimization Theory[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 49-58.

图/表 13

图1 多学科协同优化算法概念

Fig.1 The concept map of multidisciplinary collaborative optimization

图2 系统爬坡和滑坡能力要求

Fig.2 Requirements on system capability for climbing and landslide

图3 可再生能源消纳责任权重计算流程

Fig.3 Flow chart of model calculation for optimal index of renewable energy

图4 IEEE 118节点测试系统负荷曲线

Fig.4 Load curve of IEEE 118-node test system

图5 典型日部分风电场及光伏场预测出力

Fig.5 Forecast output of typical daily part of wind farm and photovoltaic field

图6 典型机组出力结果

Fig.6 The results of typical unit outputs

表1 各机组总电量的比重表

Table 1 Percentage of total electricity of each unit

表2 多学科优化计算结果

Table 2 Results of multidisciplinary optimization

表3 不同渗透率下权重指标结果

Table 3 Results of weight index under different permeability

表4 2种方法的优化结果对比

Table 4 Comparison of optimization results between the two methods

表5 不同计算方法的耗时对比

Table 5 Comparison of time consuming of different calculation methods

图7 4个典型日下的总负荷曲线

Fig.7 Total load curves for four typical days

Table 6 Electricity, percentage and cost of each type of unit in four seasons

参考文献 22

[1]	曾鸣, 王雨晴 . 我国新一代电力系统建设政策建议[J]. 中国电力企业管理, 2019(10):54-57.
[2]	谢国辉, 李琼慧, 王乾坤 , 等. 可再生能源配额制的国外实践及相关启示[J]. 能源技术经济, 2012,24(7):19-22.
	XIE Guohui, LI Qionghui, WANG Qiankun , et al. Practice and lessons of renewable portfolio standard in foreign countries[J]. Energy Technology and Economics, 2012,24(7):19-22.
[3]	任东明, 张宝秀, 张锦秋 . 可再生能源发电配额制政策(RPS)研究[J]. 中国人口资源与环境, 2002(2):117-120.
	REN Dongming, ZHANG Baoxiu, ZHANG Jinqiu . Study on the implementation of RPS in China[J]. China Population, Resources and Environment, 2002(2):117-120.
[4]	NAKAKUKI S, KUDO H . Discussion points in Japan’s renewable energy promotion policy: Effect, impact and issues of Japanese RPS[R]. IEEJ, 2003.
[5]	European Renewable Energy Council. Renewable energy policy review[R]. EREC, 2004.
[6]	JUNGINGER M, AGTERBOSCH S, FAAIJ A , et al. Renewable electricity in the Netherlands[J]. Energy Policy, 2004,32(9):1053-1073. doi: 10.1016/S0301-4215(03)00063-6 URL
[7]	MUIS Z A, HASHIM H, MANAN Z A , et al. Optimal planning of renewable energy-integrated electricity generation schemes with CO2 reduction target[J]. Renewable Energy, 2010,35(11):2562-2570. doi: 10.1016/j.renene.2010.03.032 URL
[8]	PALMER K, BURTRAW D . Cost-effectiveness of renewable electricity policies[J]. Energy Economics, 2005,27(6):873-894. doi: 10.1016/j.eneco.2005.09.007 URL
[9]	衣博文, 许金华, 范英 . 我国可再生能源配额制中长期目标的最优实现路径及对电力行业的影响分析[J]. 系统工程学报, 2017,32(3):313-324.
	YI Bowen, XU Jinhua, FAN Ying . Optimal pathway and impact of achieving the mid-long term renewable portfolio standard in China[J]. Journal of Systems Engineering, 2017,32(3):313-324.
[10]	孙慧娟 . 我国可再生能源配额目标构建研究[D]. 北京:北京化工大学, 2012.
	SUN Huijuan . The construction of renewable electricity target[D]. Beijing: Beijing University of Chemical Technology, 2012.
[11]	吴锐, 何永秀 . 可再生能源配额的优化配置模型[J]. 电力建设, 2014,35(6):147-152. doi: 10.3969/j.issn.1000-7229.2014.06.028 URL
	WU Rui, HE Yongxiu . Optimal allocation model of renewable energy quota[J]. Electric Power Construction, 2014,35(6):147-152. doi: 10.3969/j.issn.1000-7229.2014.06.028 URL
[12]	马昕, 李旭垚, 钟维琼 . 基于双目标节能调度的可再生能源配额制福利效果评估方法研究[J]. 可再生能源, 2015,33(1):134-142.
	MA Xin, LI Xuyao, ZHONG Weiqiong . Research on the evaluation method of the RPS’ welfare effect based on a dual-goal energy-saving dispatch model[J]. Renewable Energy Resources, 2015,33(1):134-142.
[13]	赵航 . 基于学习曲线的可再生能源配额目标研究[D]. 北京: 北京化工大学, 2014.
	ZHAO Hang . Based on the learning curve of renewable electricity target[D]. Beijing: Beijing University of Chemical Technology, 2014.
[14]	SOBIESZCZANSKI-SOBIESKI J, CHOPRA I . Multidisciplinary optimization of aeronautical systems[J]. Journal of Aircraft, 1990,27(12):977-978. doi: 10.2514/3.56840 URL
[15]	李海燕, 马明旭, 井元伟 . 基于非支配排序遗传算法的的多学科鲁棒协同优化方法[J]. 控制理论与应用, 2011,28(4):561-566.
	LI Haiyan, MA Mingxu, JING Yuanwei . Multidisciplinary robust collaborative optimization based on non-dominated sorting genetic algorithm[J]. Control Theory & Applications, 2011,28(4):561-566.
[16]	JAFARSALEHI A, FAZELEY H R, MIRSHAMS M . Conceptual remote sensing satellite design optimization under uncertainty[J]. Aerospace Science and Technology, 2016,55:377-391. doi: 10.1016/j.ast.2016.06.014 URL
[17]	LUO W L, LYU W J . An application of multidisciplinary design optimization to the hydrodynamic performances of underwater robots[J]. Ocean Engineering, 2015,104:686-697. doi: 10.1016/j.oceaneng.2015.06.011 URL
[18]	凌昊, 程远胜, 刘均 , 等. 一种新的多学科协同优化方法及其工程应用[J]. 中国造船, 2011,52(2):87-99.
	LING Hao, CHENG Yuansheng, LIU Jun , et al. An improved multidiscipline collaborative optimization algorithm and its application in submarine subdivision[J]. Shipbuilding of China, 2011,52(2):87-99.
[19]	ALBUQUERQUE P F, GAMBOA P V, SILVESTRE M A . Mission-based multidisciplinary aircraft design optimization methodology tailored for adaptive technologies[J]. Journal of Aircraft, 2018,55(2):755-770. doi: 10.2514/1.C034403 URL
[20]	WANG D P, WANG G G, NATERER G F . Extended collaboration pursuing method for solver larger multidisciplinary design optimization problems[J]. AIAA Journal, 2007,45(6):1208-1221. doi: 10.2514/1.21167 URL
[21]	胡嘉骅, 文福拴, 马莉 , 等. 电力系统运行灵活性与灵活调节产品[J]. 电力建设, 2019,40(4):70-80.
	HU Jiahua, WEN Fushuan, MA Li , et al. Power system operation flexibility and flexible ramping products[J]. Electric Power Construction, 2019,40(4):70-80.
[22]	刘英琪, 谢敏, 韦薇 , 等. 高比例风电接入的电力系统灵活性评估与优化[J]. 电力建设, 2019,40(9):1-10.
	LIU Yingqi, XIE Min, WEI Wei , et al. Assessment and optimization for power system flexibility with high proportion of wind power[J]. Electric Power Construction, 2019,40(9):1-10.

基于多学科协同理论的可再生能源消纳责任权重测算方法

Research on Optimal Weight Index Calculation for Renewable Energy Applying Multidisciplinary Collaborative Optimization Theory

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 22

相关文章 15

编辑推荐

Metrics

本文评价

[1]	董凌1， 2，年珩3，范越2，赵建勇3，张智3，林振智3. 能源互联网背景下共享储能的商业模式探索与实践[J]. 电力建设, 2020, 41(4): 38-44.
[2]	郭莉，吴晨，陈立，丛小涵，苗曦云，王蓓蓓. 可再生能源跨区消纳的受端电网阻塞机理分析[J]. 电力建设, 2020, 41(2): 21-29.
[3]	潘尔生, 宋毅, 原凯, 郭玥, 程浩忠, 张沈习. 考虑可再生能源接入的综合能源系统规划评述与展望[J]. 电力建设, 2020, 41(12): 3-13.
[4]	吕振华, 李强, 韩华春, 王大朔, 马瑞. 区域综合能源系统双层多目标模糊优化模型预测控制方法[J]. 电力建设, 2020, 41(12): 123-132.
[5]	杜丽佳，靳小龙，何伟，穆云飞，贾宏杰，魏炜. 考虑电动汽车和虚拟储能系统优化调度的楼宇微网联络线功率平滑控制方法[J]. 电力建设, 2019, 40(8): 26-33.
[6]	陈启超，李晖，王帅，史锐，张宁，戚庆茹，王菲. 促进可再生能源消纳的多能互补时序运行模拟技术[J]. 电力建设, 2019, 40(7): 18-25.
[7]	胡嘉骅，文福拴，马莉，彭生江，范孟华，徐昊亮. 电力系统运行灵活性与灵活调节产品[J]. 电力建设, 2019, 40(4): 70-80.
[8]	王晞，苟竞，苏韵掣，杨新婷，欧阳雪彤，李奥，樊金柱，李华强. 基于联合加权熵的高比例可再生能源电网适应性扩展规划[J]. 电力建设, 2019, 40(12): 45-54.
[9]	秦玉杰，胡健，焦提操. 基于泛在电力物联网的分布式可再生能源（DRE）理性消纳调峰模型[J]. 电力建设, 2019, 40(12): 120-128.
[10]	林鸿基，闫园，文福拴，胡嘉骅，曲昊源，赵铮. 高比例可再生能源电力系统中计及灵活调节产品的实时调度[J]. 电力建设, 2019, 40(10): 18-27.
[11]	葛睿，陈龙翔，汤俊，黎灿兵，胡苗. 跨区域省间可再生能源增量现货市场设计与实践[J]. 电力建设, 2019, 40(1): 11-18.
[12]	王腾飞，王辉，冷亚军，赵文会. RPS与偏差电量考核下考虑微电网群的售电公司盈利模型[J]. 电力建设, 2019, 40(1): 96-103.
[13]	牛萌，肖宇，刘锋，赵鹏程，赵波. 可再生能源接入对氢储能系统的影响及控制策略[J]. 电力建设, 2018, 39(4): 28-.
[14]	徐少华1，李建林1，宋新甫2，李绚绚3，王定美4. 大规模储能系统提升西北地区可再生能源消纳能力分析[J]. 电力建设, 2018, 39(4): 67-.
[15]	谢珍建1，胡卫利2，谈健1，肖晶3，武赓4，刘洋4，曾鸣4. 面向区域能源服务商的智能楼宇需求侧响应优化策略[J]. 电力建设, 2018, 39(3): 116-.