多样化柔性负荷深度互动下的虚拟电厂分布鲁棒购售电策略

doi:10.12204/j.issn.1000-7229.2021.01.007

电力建设 ›› 2021, Vol. 42 ›› Issue (1): 59-66.doi: 10.12204/j.issn.1000-7229.2021.01.007

• 虚拟电厂·栏目主持胡泽春副教授、刘敦楠副教授、王宣元高级工程师· • 上一篇下一篇

多样化柔性负荷深度互动下的虚拟电厂分布鲁棒购售电策略

叶圣永¹, 魏俊¹, 阮贺彬², 刘洁颖¹, 刘旭娜¹, 高红均²

1.国网四川省电力公司经济技术研究院,成都市 610041
2.四川大学电气工程学院,成都市 610065

收稿日期:2020-05-12 出版日期:2021-01-01 发布日期:2021-01-07
作者简介:叶圣永(1974),男,博士,教授级高级工程师,主要研究方向为电力系统规划、稳定与控制等;|魏俊(1978),男,高级工程师,主要研究方向为电力系统规划、经济技术分析等;|阮贺彬(1997),女,博士研究生,主要研究方向为配电网运行管理、分布式控制等;|刘洁颖(1990),女,硕士,工程师,主要研究方向为电力市场及电能质量;|刘旭娜(1986),女,博士,高级工程师,主要研究方向为电力系统规划、电能质量评估;|高红均(1989),男,博士,副教授,主要研究方向为不确定优化在电力系统中的应用。
基金资助:
国家电网公司科技项目“适应复杂多类型市场主体接入的配电网分布式协调运行优化”(SGSCJY00GHJS1900011)

Distributional Robust Electricity Trading Strategy of Virtual Power Plant with Deep Interaction of Diversified Flexible Loads

YE Shengyong¹, WEI Jun¹, RUAN Hebin², LIU Jieying¹, LIU Xuna¹, GAO Hongjun²

1. Economic Research Institute of State Grid Sichuan Electric Power Company, Chengdu 610041, China
2. College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Received:2020-05-12 Online:2021-01-01 Published:2021-01-07
Supported by:
State Grid Corporation of China Research Program “Distributed Coordinated Operation of Distribution Network with Complex Multi-stakeholders Integrated”(SGSCJY00GHJS1900011)

摘要/Abstract

摘要：

随着当前电力改革以及电力市场的不断推进,虚拟电厂的发展已成为其重点组成部分,也是市场交易复杂因素的主要集中部分,其中柔性负荷和清洁能源给虚拟电厂带来了巨大的挑战。考虑不同类别用户柔性负荷的运行特性差异,分别对工业负荷、商业负荷和家用负荷进行了特性分析并建模,充分调动虚拟电厂内部的一些可控资源进行互动,并结合风电、光伏、燃气轮机(gas turbine,GT)运行特性,建立了考虑多样化柔性负荷深度互动的虚拟电厂购售电优化模型。此外,随着清洁能源的发展,针对风电、光伏所带来的不确定性挑战,提出了基于场景数据驱动的两阶段变量的分布鲁棒虚拟电厂购售电优化计算模型,再利用列与约束生成算法求解,算例分析验证了该优化模型的有效性。

关键词: 虚拟电厂, 多样化柔性负荷, 分布鲁棒, 协调优化

Abstract:

With the current electric power reform and the continuous promotion of the electric power market, the development of virtual power plant has become a key part of them. It is also the main concentration part of the complex factors of market transactions, and the flexible load and clean energy also bring great challenges to the virtual power plant. Considering the operation characteristics of different types of users’ flexible load, this paper analyzes and models the characteristics of industrial, commercial and household loads, fully mobilizes some controllable resources within the virtual power plant to interact, and combines the operation characteristics of wind power, photovoltaic power and gas turbines to establish a purchase and sale optimization model for virtual power plant considering the deep interaction of diversified flexible loads. In addition, with the development of clean energy and the challenge of uncertainty brought by photovoltaic and wind power, a scenario data-driven distributional robust model with two-stage variable for power purchase and sale optimization of virtual power plant is proposed, which is solved by column and constraint generation algorithm. The effectiveness of the optimization model is verified by the analysis of examples.

Key words: virtual power plant, diversified flexible load, distributionally robust operation, coordinated optimization

中图分类号:

TM73

叶圣永, 魏俊, 阮贺彬, 刘洁颖, 刘旭娜, 高红均. 多样化柔性负荷深度互动下的虚拟电厂分布鲁棒购售电策略[J]. 电力建设, 2021, 42(1): 59-66.

YE Shengyong, WEI Jun, RUAN Hebin, LIU Jieying, LIU Xuna, GAO Hongjun. Distributional Robust Electricity Trading Strategy of Virtual Power Plant with Deep Interaction of Diversified Flexible Loads[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 59-66.

图/表 11

图1 虚拟电厂内部组成

Fig.1 Internal composition of a virtual power plant

表1 燃气轮机运行参数

Table 1 Parameters of gas turbines

表2 燃气轮机排污参数

Table 2 Blowdown parameters of gas turbines

表3 市场电价

Table 3 Market electricity price

图2 负荷与风电光伏的预测值

Fig.2 Prediction values of load, photovoltaic and wind power

图3 柔性负荷参与虚拟电厂优化时各发电机组出力

Fig.3 Output of each generating unit when flexible load participates in virtual power plant optimization

图4 柔性负荷参与优化前后风电光伏出力对比

Fig.4 Comparison of photovoltaic and wind power output with or without flexible load optimization

图5 不同类型柔性负荷有效利用对比

Fig.5 Comparison of effective utilization of different types of flexible loads

表4 负荷调用成本对不同类型负荷利用占比的影响

Table 4 The influence of load calling cost on the proportion of different types of load utilization

图6 柔性负荷有效利用曲线

Fig.6 Effective utilization curve of flexible loads

表5 不同算法概率分布总收益期望

Table 5 Comparison of results of different algorithms

参考文献 18

[1]	RUIZ N, COBELO I, OYARZABAL J . A direct load control model for virtual power plant management[J]. IEEE Transactions on Power Systems, 2009,24(2):959-966.
[2]	卫志农, 余爽, 孙国强 , 等. 虚拟电厂的概念与发展[J]. 电力系统自动化, 2013,37(13):1-9.
	WEI Zhinong, YU Shuang, SUN Guoqiang , et al. Concept and development of virtual power plant[J]. Automation of Electric Power Systems, 2013,37(13):1-9.
[3]	艾欣, 周树鹏, 赵阅群 . 含虚拟发电厂的电力系统优化运行与竞价策略研究[J]. 中国电机工程学报, 2016,36(23):6351-6362, 6600.
	AI Xin, ZHOU Shupeng, ZHAO Yuequn . Research on optimal operation and bidding strategy of power system with virtual power plants[J]. Proceedings of the CSEE, 2016,36(23):6351-6362, 6600.
[4]	刘吉臻, 李明扬, 房方 , 等. 虚拟发电厂研究综述[J]. 中国电机工程学报, 2014,34(29):5103-5111.
	LIU Jizhen, LI Mingyang, FANG Fang , et al. Review on virtual power plants[J]. Proceedings of the CSEE, 2014,34(29):5103-5111.
[5]	HROPKO D, IVANECKY J, TURCEK J . Optimal dispatch of renewable energy sources included in virtual power plant using accelerated particle swarm optimization [C]// 2012 ELEKTRO. Rajeck Teplice, Slovakia: IEEE, 2012.
[6]	SHROPSHIRE D, PURVINS A, PAPAIOANNOU I , et al. Benefits and cost implications from integrating small flexible nuclear reactors with off-shore wind farms in a virtual power plant[J]. Energy Policy, 2012,46:558-573.
[7]	袁桂丽, 陈少梁, 刘颖 , 等. 基于分时电价的虚拟电厂经济性优化调度[J]. 电网技术, 2016,40(3):826-832.
	YUAN Guili, CHEN Shaoliang, LIU Ying , et al. Economic optimal dispatch of virtual power plant based on time-of-use power price[J]. Power System Technology, 2016,40(3):826-832.
[8]	牛文娟, 李扬, 王蓓蓓 . 考虑不确定性的需求响应虚拟电厂建模[J]. 中国电机工程学报, 2014,34(22):3630-3637.
	NIU Wenjuan, LI Yang, WANG Beibei . Demand response based virtual power plant modeling considering uncertainty[J]. Proceedings of the CSEE, 2014,34(22):3630-3637.
[9]	卢志刚, 王荟敬, 赵号 , 等. 含V2G的虚拟电厂双层逆鲁棒优化调度策略[J]. 电网技术, 2017,41(4):1245-1252.
	LU Zhigang, WANG Huijing, ZHAO Hao , et al. Strategy of bilevel inverse robust optimization dispatch of virtual power plant containing V2G[J]. Power System Technology, 2017,41(4):1245-1252.
[10]	邹云阳, 杨莉 . 基于经典场景集的风光水虚拟电厂协同调度模型[J]. 电网技术, 2015,39(7):1855-1859. doi: 10.13335/j.1000-3673.pst.2015.07.014 URL
	ZOU Yunyang, YANG Li . Synergetic dispatch models of a wind/PV/hydro virtual power plant based on representative scenario set[J]. Power System Technology, 2015,39(7):1855-1859.
[11]	周任军, 闵雄帮, 童小娇 , 等. 电力环保经济调度矩不确定分布鲁棒优化方法[J]. 中国电机工程学报, 2015,35(13):3248-3256.
	ZHOU Renjun, MIN Xiongbang, TONG Xiaojiao , et al. Distributional robust optimization under moment uncertainty of environmental and economic dispatch for power system[J]. Proceedings of the CSEE, 2015,35(13):3248-3256.
[12]	CHEN Y, WEI W, LIU F , et al. Distributionally robust hydro-thermal-wind economic dispatch[J]. Applied Energy, 2016,173:511-519.
[13]	DARIVIANAKIS G, EICHLER A, SMITH R S , et al. A data-driven stochastic optimization approach to the seasonal storage energy management[J]. IEEE Control Systems Letters, 2017,1(2):394-399.
[14]	BAGHERI A, WANG J H, ZHAO C Y . Data-driven stochastic transmission expansion planning[J]. IEEE Transactions on Power Systems, 2017,32(5):3461-3470.
[15]	PAN K, GUAN Y P . Data-driven risk-averse stochastic self-scheduling for combined-cycle units[J]. IEEE Transactions on Industrial Informatics, 2017,13(6):3058-3069.
[16]	任文诗, 高红均, 刘友波 , 等. 智能建筑群电能日前优化共享[J]. 电网技术, 2019,43(7):2568-2577.
	REN Wenshi, GAO Hongjun, LIU Youbo , et al. Optimal day-ahead electricity scheduling and sharing for smart building cluster[J]. Power System Technology, 2019,43(7):2568-2577.
[17]	刘小聪, 王蓓蓓, 李扬 , 等. 智能电网下计及用户侧互动的发电日前调度计划模型[J]. 中国电机工程学报, 2013,33(1):30-38.
	LIU Xiaocong, WANG Beibei, LI Yang , et al. Day-ahead generation scheduling model considering demand side interaction under smart grid paradigm[J]. Proceedings of the CSEE, 2013,33(1):30-38.
[18]	阮贺彬, 高红均, 刘俊勇 , 等. 考虑DG无功支撑和开关重构的主动配电网分布鲁棒无功优化模型[J]. 中国电机工程学报, 2019,39(3):685-695.
	RUAN Hebin, GAO Hongjun, LIU Junyong , et al. A distributionally robust reactive power optimization model for active distribution network considering reactive power support of DG and switch reconfiguration[J]. Proceedings of the CSEE, 2019,39(3):685-695.

多样化柔性负荷深度互动下的虚拟电厂分布鲁棒购售电策略

Distributional Robust Electricity Trading Strategy of Virtual Power Plant with Deep Interaction of Diversified Flexible Loads

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 18

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李凌昊, 邱晓燕, 张浩禹, 赵有林, 张楷. 电力市场下的虚拟电厂风险厌恶模型与利益分配方法[J]. 电力建设, 2021, 42(1): 67-75.
[2]	胡嘉凯, 蒋传文, 李磊. 用户侧利益主体虚拟电厂联盟行为及交易机制[J]. 电力建设, 2020, 41(9): 1-9.
[3]	李红霞, 樊伟, 李楠, 谭彩霞, 许德操, 鞠立伟. 考虑风光不确定性的电气互联虚拟电厂近零碳调度优化模型[J]. 电力建设, 2020, 41(9): 10-19.
[4]	韩帅, 吴宛潞, 郭小璇, 孙乐平. 含多类型资源虚拟电厂鲁棒竞标模型研究[J]. 电力建设, 2020, 41(9): 20-29.
[5]	王瀚琳, 刘洋, 许立雄, 陈贤邦, 谭思维. 考虑风电消纳的区域多微网分层协调优化模型[J]. 电力建设, 2020, 41(8): 87-98.
[6]	安麒，王剑晓，李庚银，王宣元，刘蓁. 基于均衡理论的虚拟电厂市场参与模式及方法[J]. 电力建设, 2020, 41(6): 1-8.
[7]	谭忠富，谭彩霞，蒲雷，杨佳澄. 基于协同免疫量子粒子群优化算法的虚拟电厂双层博弈模型[J]. 电力建设, 2020, 41(6): 9-17.
[8]	练小林，李晓露，陆一鸣，丁一，李聪利. 可交易能源框架下的微网群动态电能交易策略[J]. 电力建设, 2020, 41(6): 18-27.
[9]	叶飞，邵平，王宣元，左娟，王俊，窦迅，张鑫. 考虑购售风险的虚拟电厂双层竞标策略[J]. 电力建设, 2020, 41(6): 28-35.
[10]	赵晨，何宇俊，罗钢，龚超，赵越，张轩，陈启鑫. 基于泛在互联的虚拟电厂参与实时市场模型[J]. 电力建设, 2020, 41(6): 36-43.
[11]	艾欣，杨子豪，胡寰宇，王智冬，彭冬，赵朗. 基于改进k-means算法的VPP负荷曲线聚类方法及应用[J]. 电力建设, 2020, 41(5): 28-36.
[12]	陆秋瑜，杨银国，王中冠，谭嫣，夏天，伍双喜. 基于次梯度投影分布式控制法的虚拟电厂经济性一次调频[J]. 电力建设, 2020, 41(3): 79-85.
[13]	张军六，樊伟，谭忠富，鞠立伟，德格吉日夫，杨莘博，孙婧霞. 计及需求响应的气电互联虚拟电厂多目标调度优化模型[J]. 电力建设, 2020, 41(2): 1-10.
[14]	朱誉，仪忠凯，陆秋瑜，杨银国，李博，许银亮. 基于典型场景集的虚拟电厂与配电网协同定价策略[J]. 电力建设, 2019, 40(6): 74-85.
[15]	何奇琳，艾芊. 售电侧放开环境下含需求响应虚拟电厂的电力市场竞价策略[J]. 电力建设, 2019, 40(2): 1-10.