计及激励型需求侧响应的发输电鲁棒规划策略

doi:10.12204/j.issn.1000-7229.2022.07.007

电力建设 ›› 2022, Vol. 43 ›› Issue (7): 63-72.doi: 10.12204/j.issn.1000-7229.2022.07.007

计及激励型需求侧响应的发输电鲁棒规划策略

孙磊¹^,²(), 王一均¹^,²(), 丁江¹^,²(), 丁明¹^,²(), 王芃³()

1.合肥工业大学电气与自动化工程学院, 合肥市 230009
2.新能源利用与节能安徽省重点实验室, 合肥市 230009
3.国网能源研究院有限公司, 北京市 102209

收稿日期:2021-12-13 出版日期:2022-07-01 发布日期:2022-06-30
通讯作者: 孙磊 E-mail:leisun@hfut.edu.cn;wangyijun2325@163.com;jiangding@mail.hfut.edu.cn;mingding56@126.com;wangpeng@sgeri.sgcc.com.cn
作者简介:王一均(1999), 女, 本科生, 主要研究方向为电力系统规划, E-mail: wangyijun2325@163.com;
丁江(1998), 男, 硕士研究生, 主要研究方向为电力系统恢复、综合能源系统规划与运行, E-mail: jiangding@mail.hfut.edu.cn;
丁明(1956), 男, 教授, 博士生导师, 主要研究方向为电力系统规划及其可靠性、新能源技术及其应用, E-mail: mingding56@126.com;
王芃(1989), 男, 博士, 高级工程师, 主要研究方向为能源电力规划、能源经济分析和政策评估, E-mail: wangpeng@sgeri.sgcc.com.cn。
基金资助:
国家自然科学基金资助项目(51907043);中央高校基本科研业务费专项资金资助项目(JZ2020HGTB0040)

Robust Generation and Transmission Expansion Planning Model Considering Incentive-Based Demand Response

SUN Lei¹^,²(), WANG Yijun¹^,²(), DING Jiang¹^,²(), DING Ming¹^,²(), WANG Peng³()

1. School of Electrical Engineer and Automation, Hefei University of Technology, Hefei 230009, China
2. Anhui Province Key Laboratory of Renewable Energy Utilization and Energy Saving, Hefei 230009, China
3. State Grid Energy Research Institute Co., Ltd., Beijing 102209, China

Received:2021-12-13 Online:2022-07-01 Published:2022-06-30
Contact: SUN Lei E-mail:leisun@hfut.edu.cn;wangyijun2325@163.com;jiangding@mail.hfut.edu.cn;mingding56@126.com;wangpeng@sgeri.sgcc.com.cn
Supported by:
National Natural Science Foundation of China(51907043);Fundamental Research Funds for the Central Universities(JZ2020HGTB0040)

摘要/Abstract

摘要：

大规模风电等可再生能源广泛接入电网将成为新型电力系统发展的重要方向之一, 通过需求侧响应机制调整用户负荷需求, 可有效提高电力系统运行的灵活性并促进风电的消纳。在此背景下, 提出了一种计及激励型需求侧响应的发输电鲁棒规划策略。首先, 引入基于价格激励的需求侧响应机制, 构建基于分段激励价格的需求侧响应模型。其次, 为了有效应对风电的不确定性对规划方案的影响, 提出了基于信息间隙决策理论的计及激励型需求侧响应的发输电鲁棒规划模型, 并将所提出的模型构造为混合整数线性规划模型。最后, 以Garver 6节点系统和改进的IEEE 118系统为例证明所提方法的可行性与有效性。

关键词: 发输电规划, 需求侧响应, 风电, 信息间隙决策理论, 混合整数线性规划

Abstract:

The extensive access of the large-scale renewable energy, such as wind power, to power systems will be one of the important directions for the development of the new-type power systems. The power demands of customers can be adjusted by employing the demand response mechanism to improve the flexibility of power system operation and promote the consumption of wind power. In this context, a generation and transmission expansion planning model is proposed taking the incentive-based demand response into account. Firstly, the incentive-based demand response mechanism is introduced, and a demand response model based on the segmented price incentive is proposed. Secondly, a robust generation and transmission expansion planning model based on the information gap decision theory is proposed considering incentive-based demand response, and formulated as a mixed integer linear model. Finally, the Garver 6-bus system and the improved IEEE 118-bus system are served for demonstrating the feasibility and validity of the proposed method.

Key words: generation and transmission expansion planning, demand response, wind power, information gap decision theory, mixed integer linear programming

中图分类号:

TM715

孙磊, 王一均, 丁江, 丁明, 王芃. 计及激励型需求侧响应的发输电鲁棒规划策略[J]. 电力建设, 2022, 43(7): 63-72.

SUN Lei, WANG Yijun, DING Jiang, DING Ming, WANG Peng. Robust Generation and Transmission Expansion Planning Model Considering Incentive-Based Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(7): 63-72.

图/表 13

图1 用户参与DR时单位响应量的费用

Fig.1 Cost for per responsive power when customers participate in DR

图2 Garver 6节点系统拓扑图

Fig.2 Topology of the Garver 6-bus system

图3 各场景中的风电预测出力

Fig.3 Predicted power generation of wind turbines in each scenario

表1 Garver 6节点系统的候选机组的参数

Table 1 Parameters of candidate units in Garver 6-bus system

表2 Garver 6节点系统的侯选线路参数

Table 2 Parameters of candidate lines in Garver 6-bus system

图4 Garver 6节点系统的规划方案

Fig.4 Planning scheme of the Garver 6-bus system

表3 部分场景的需求响应结果

Table 3 DR results in partial selected scenarios

表4 考虑DR与否的发输电规划方案对比

Table 4 Comparison of generation and transmission expansion planning schemes with and without DR in case 1

表5 考虑分段与恒定激励价格的发输电规划方案对比 104美元

Table 5 Comparison of generation and transmission expansion planning schemes with segmented and fixed price incentive.

表6 单位响应量费用的变化对发输电规划结果的影响

Table 6 The impact of cost changes of per responsive power on transmission system planning solutions

表7 投资与运行费用阈值的变化对风电最大扰动的影响

Table 7 The effect of changes of budget threshold on the maximum fluctuation of wind power

表8 IEEE 118系统中候选机组的参数

Table 8 Parameters of candidate units in the IEEE 118-bus power system

表9 IEEE 118系统中考虑DR与否的发输电规划方案对比

Table 9 Comparison of generation and transmission expansion planning schemes with and without DR in the IEEE 118-bus power system

参考文献 23

[1]	BARINGO L, BARINGO A. A stochastic adaptive robust optimization approach for the generation and transmission expansion planning[J]. IEEE Transactions on Power Systems, 2018, 33(1): 792-802. doi: 10.1109/TPWRS.2017.2713486 URL
[2]	于永军, 王利超, 张明远, 等. 基于阻抗特性多项式拟合的直驱风电机组次同步振荡稳定判据[J]. 发电技术, 2020, 41(4): 429-436.
	YU Yongjun, WANG Lichao, ZHANG Mingyuan, et al. Stability criterion of subsynchronous oscillation of direct drive permanent magnet synchronous generator based on impedance polynomial fitting[J]. Power Generation Technology, 2020, 41(4): 429-436.
[3]	周计晨, 吕胤杰, 杨诚之, 等. 考虑风电出力不确定性的分布鲁棒主备协同优化调度[J]. 电力系统保护与控制, 2020, 48(20): 66-73.
	ZHOU Jichen, LÜ Yinjie, YANG Chengzhi, et al. Distributionally robust co-optimization of energy and reserve dispatch considering uncertain wind power output[J]. Power System Protection and Control, 2020, 48(20): 66-73.
[4]	许志恒, 张勇军, 余兆荣, 等. 考虑激励型需求侧响应的电-气互联系统两阶段随机优化调度[J]. 电力建设, 2020, 41(4): 81-87.
	XU Zhiheng, ZHANG Yongjun, YU Zhaorong, et al. Two-stage stochastic optimal scheduling for integrated power and natural-gas system considering incentive demand response[J]. Electric Power Construction, 2020, 41(4): 81-87.
[5]	梁子鹏, 陈皓勇, 郑晓东, 等. 考虑风电极限场景的输电网鲁棒扩展规划[J]. 电力系统自动化, 2019, 43(16): 58-67.
	LIANG Zipeng, CHEN Haoyong, ZHENG Xiaodong, et al. Robust expansion planning of transmission network considering extreme scenario of wind power[J]. Automation of Electric Power Systems, 2019, 43(16): 58-67.
[6]	曾鸣, 吕春泉, 邱柳青, 等. 风电并网时基于需求侧响应的输电规划模型[J]. 电网技术, 2011, 35(4): 129-134.
	ZENG Ming, LÜ Chunquan, QIU Liuqing, et al. A demand-side response-based transmission planning model with grid-connected wind farms[J]. Power System Technology, 2011, 35(4): 129-134.
[7]	张衡, 程浩忠, 张建平, 等. 高比例风电背景下计及N-1安全网络约束的发输电优化规划[J]. 中国电机工程学报, 2018, 38(20): 5929-5936.
	ZHANG Heng, CHENG Haozhong, ZHANG Jianping, et al. Generation and transmission expansion planning considering N-1 security constraints with high penetration of wind power[J]. Proceedings of the CSEE, 2018, 38(20): 5929-5936.
[8]	郑静, 文福拴, 李力, 等. 计及风电场和储能系统联合运行的输电系统扩展规划[J]. 电力系统自动化, 2013, 37(1): 135-142.
	ZHENG Jing, WEN Fushuan, LI Li, et al. Transmission system expansion planning considering combined operation of wind farms and energy storage systems[J]. Automation of Electric Power Systems, 2013, 37(1): 135-142.
[9]	黄启航, 王秀丽, 齐世雄, 等. 考虑负荷和风电相关性的多场景鲁棒输电网规划[J]. 电力建设, 2018, 39(6): 63-70.
	HUANG Qihang, WANG Xiuli, QI Shixiong, et al. Multi-scenario robust transmission planning considering correlation between wind power and load demand[J]. Electric Power Construction, 2018, 39(6): 63-70.
[10]	LIANG R H, LIAO J H. A fuzzy-optimization approach for generation scheduling with wind and solar energy systems[J]. IEEE Transactions on Power Systems, 2007, 22(4): 1665-1674. doi: 10.1109/TPWRS.2007.907527 URL
[11]	张立波, 程浩忠, 曾平良, 等. 基于不确定理论的输电网规划[J]. 电力系统自动化, 2016, 40(16): 159-167.
	ZHANG Libo, CHENG Haozhong, ZENG Pingliang, et al. Transmission network planning approaches based on uncertainty theories[J]. Automation of Electric Power Systems, 2016, 40(16): 159-167.
[12]	张衡, 程浩忠, 曾平良, 等. 考虑经济性与安全性的发输电联合优化规划[J]. 电力系统自动化, 2017, 41(21): 62-69.
	ZHANG Heng, CHENG Haozhong, ZENG Pingliang, et al. Generation and transmission expansion planning considering economy and safety[J]. Automation of Electric Power Systems, 2017, 41(21): 62-69.
[13]	高红均, 刘俊勇, 魏震波, 等. 主动配电网分层鲁棒规划模型及其求解方法[J]. 中国电机工程学报, 2017, 37(5): 1389-1401.
	GAO Hongjun, LIU Junyong, WEI Zhenbo, et al. A Bi-level robust planning model of active distribution network and its solution method[J]. Proceedings of the CSEE, 2017, 37(5): 1389-1401.
[14]	李昀昊, 王建学, 曹晓宇, 等. 面向风电场-储能-输电网联合规划的机会约束IGDT模型[J]. 电网技术, 2019, 43(10): 3715-3724.
	LI Yunhao, WANG Jianxue, CAO Xiaoyu, et al. A chance-constrained IGDT model for joint planning of wind farm, energy storage and transmission[J]. Power System Technology, 2019, 43(10): 3715-3724.
[15]	魏大钧, 张承慧, 孙波, 等. 基于分时电价的电动汽车充放电多目标优化调度[J]. 电网技术, 2014, 38(11): 2972-2977.
	WEI Dajun, ZHANG Chenghui, SUN Bo, et al. A time-of-use price based multi-objective optimal dispatching for charging and discharging of electric vehicles[J]. Power System Technology, 2014, 38(11): 2972-2977.
[16]	CONEJO A J, MORALES J M, BARINGO L. Real-time demand response model[J]. IEEE Transactions on Smart Grid, 2010, 1(3): 236-242. doi: 10.1109/TSG.2010.2078843 URL
[17]	ZHONG H W, XIE L, XIA Q. Coupon incentive-based demand response: Theory and case study[J]. IEEE Transactions on Power Systems, 2013, 28(2): 1266-1276. doi: 10.1109/TPWRS.2012.2218665 URL
[18]	刘洪, 郑楠, 葛少云, 等. 内嵌需求响应与优化运行策略的主动配电系统源网协同规划[J]. 电力系统自动化, 2020, 44(3): 89-97.
	LIU Hong, ZHENG Nan, GE Shaoyun, et al. Coordinated planning of source and network in active distribution system with demand response and optimized operation strategy[J]. Automation of Electric Power Systems, 2020, 44(3): 89-97.
[19]	曾鸣, 杨雍琦, 向红伟, 等. 计及需求侧响应的电力系统鲁棒优化规划模型[J]. 电力系统自动化, 2016, 40(17): 137-145.
	ZENG Ming, YANG Yongqi, XIANG Hongwei, et al. Robust optimization planning model of power system considering demand response[J]. Automation of Electric Power Systems, 2016, 40(17): 137-145.
[20]	HAIM Y B. Info-gap decision theory[M]. San Diego, CA: Academic, 2006: 267-296
[21]	PARK H, BALDICK R, MORTON D P. A stochastic transmission planning model with dependent load and wind forecasts[J]. IEEE Transactions on Power Systems, 2015, 30(6): 3003-3011. doi: 10.1109/TPWRS.2014.2385861 URL
[22]	DEHGHAN S, AMJADY N, CONEJO A J. Reliability-constrained robust power system expansion planning[J]. IEEE Transactions on Power Systems, 2016, 31(3): 2383-2392. doi: 10.1109/TPWRS.2015.2464274 URL
[23]	ZIAEE O, ALIZADEH-MOUSAVI O, CHOOBINEH F F. Co-optimization of transmission expansion planning and TCSC placement considering the correlation between wind and demand scenarios[J]. IEEE Transactions on Power Systems, 2018, 33(1): 206-215. doi: 10.1109/TPWRS.2017.2690969 URL

计及激励型需求侧响应的发输电鲁棒规划策略

Robust Generation and Transmission Expansion Planning Model Considering Incentive-Based Demand Response

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