Two-layer Optimization Strategy for Unit Commitment with Transient Frequency Constraint Considering Optimized Reserve of Renewable Energy

doi:10.12204/j.issn.1000-7229.2023.02.007

Abstract

Abstract:

The inertia reduction caused by the integration of large-scale wind power, PV, and other renewable energy sources has brought new challenges to the safe operation of new type power systems, especially transient frequency safety. In this paper, on the basis of making full use of the frequency support of new energy sources, a two-layer optimization strategy for unit combinations with transient frequency constraints is proposed. A new frequency-response model of the power system considering the frequency support of the new energy station is constructed, and the analytical expression of the transient frequency characteristic quantity is deduced. Then, on the basis of the traditional unit combination model, a unit combination optimization model considering the dynamic frequency constraint is constructed. Introducing an atomic search algorithm, synergistically considering frequency support for optimal load shedding optimization of new energy sources, and unit combination optimization, a two-layer optimization strategy is established. Taking a 10-machine system including PV and wind power as an example, the calculation and analysis are carried out, and the results verify the effectiveness and feasibility of the method in this paper.

Key words: renewable energy, transient frequency, optimal load shedding, unit commitment, atomic search optimization

CLC Number:

TM73

YANG Deyou, MENG Zhen, WANG Bo, DUAN Fangwei. Two-layer Optimization Strategy for Unit Commitment with Transient Frequency Constraint Considering Optimized Reserve of Renewable Energy[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(2): 74-82.

Figures/Tables 16

Fig.1 Output power curve of wind power generator

Fig.2 Schematic diagram of load shedding of photovoltaic power

Fig.3 Universal single-machine frequency-response model

Fig.4 Frequency-response model of multi-machine

Fig.5 Flow chart of two-layer optimization

Fig.6 Curves of load and new energy output

Fig.7 Unit combination of scheme a

Fig.8 Frequency distribution of the lowest point of scheme a

Fig.9 Unit combination of scheme b

Fig.10 Unit combination of scheme c

Table 1 Optimal load shedding of new energy field/station under scheme c

Fig.11 Frequency distribution of the lowest point of schemes b and c

Fig.12 New energy power generation of schemes b and c

Fig.13 Startup number of thermal power units in schemes a, b and c

Table 2 Comparison of acceptance capacity of schemes b and c

Table 3 Comparison of permeability and power generation cost under different load shedding schemes

References 20

[1]	魏震波, 马新如, 郭毅, 等. 碳交易机制下考虑需求响应的综合能源系统优化运行[J]. 电力建设, 2022, 43(1): 1-9. doi: 10.12204/j.issn.1000-7229.2022.01.001
	WEI Zhenbo, MA Xinru, GUO Yi, et al. Optimized operation of integrated energy system considering demand response under carbon trading mechanism[J]. Electric Power Construction, 2022, 43(1): 1-9. doi: 10.12204/j.issn.1000-7229.2022.01.001
[2]	胡泽春, 罗浩成. 大规模可再生能源接入背景下自动发电控制研究现状与展望[J]. 电力系统自动化, 2018, 42(8): 2-15.
	HU Zechun, LUO Haocheng. Research status and prospect of automatic generation control with integration of large-scale renewable energy[J]. Automation of Electric Power Systems, 2018, 42(8): 2-15.
[3]	FERNÁNDEZ-GUILLAMÓN A, GÓMEZ-LÁZARO E, MULJADI E, et al. Power systems with high renewable energy sources: a review of inertia and frequency control strategies over time[J]. Renewable and Sustainable Energy Reviews, 2019, 115: 109369. doi: 10.1016/j.rser.2019.109369 URL
[4]	曾辉, 孙峰, 李铁, 等. 澳大利亚“9·28”大停电事故分析及对中国启示[J]. 电力系统自动化, 2017, 41(13): 1-6.
	ZENG Hui, SUN Feng, LI Tie, et al. Analysis of “9·28” blackout in south Australia and its enlightenment to China[J]. Automation of Electric Power Systems, 2017, 41(13): 1-6.
[5]	胥国毅, 胡家欣, 郭树锋, 等. 超速风电机组的改进频率控制方法[J]. 电力系统自动化, 2018, 42(8): 39-44.
	XU Guoyi, HU Jiaxin, GUO Shufeng, et al. Improved frequency control strategy for over-speed wind turbines[J]. Automation of Electric Power Systems, 2018, 42(8): 39-44.
[6]	张雪丽, 杨洪耕, 王杨, 等. 考虑线路分布参数的并网系统稳定性分析[J]. 智慧电力, 2021, 49(4): 44-50.
	ZHANG Xueli, YANG Honggeng, WANG Yang, et al. Stability analysis of grid connected system considering line distribution parameters[J]. Smart Power, 2021, 49(4): 44-50.
[7]	DE BRABANDERE K, BOLSENS B, VAN DEN KEYBUS J, et al. A voltage and frequency droop control method for parallel inverters[J]. IEEE Transactions on Power Electronics, 2007, 22(4): 1107-1115. doi: 10.1109/TPEL.2007.900456 URL
[8]	郑天文, 陈来军, 陈天一, 等. 虚拟同步发电机技术及展望[J]. 电力系统自动化, 2015, 39(21): 165-175.
	ZHENG Tianwen, CHEN Laijun, CHEN Tianyi, et al. Review and prospect of virtual synchronous generator technologies[J]. Automation of Electric Power Systems, 2015, 39(21): 165-175.
[9]	蔡葆锐, 杨蕾, 黄伟. 基于惯性/下垂控制的变速型风电机组频率协调控制方法[J]. 电力系统保护与控制, 2021, 49(15): 169-177.
	CAI Baorui, YANG Lei, HUANG Wei. Frequency coordination control of a variable speed wind turbine based on inertia/droop control[J]. Power System Protection and Control, 2021, 49(15): 169-177.
[10]	张昭遂, 孙元章, 李国杰, 等. 超速与变桨协调的双馈风电机组频率控制[J]. 电力系统自动化, 2011, 35(17): 20-25, 43.
	ZHANG Zhaosui, SUN Yuanzhang, LI Guojie, et al. Frequency regulation by doubly fed induction generator wind turbines based on coordinated overspeed control and pitch control[J]. Automation of Electric Power Systems, 2011, 35(17): 20-25, 43.
[11]	ZHAO J J, LYU X, FU Y, et al. Coordinated microgrid frequency regulation based on DFIG variable coefficient using virtual inertia and primary frequency control[J]. IEEE Transactions on Energy Conversion, 2016, 31(3): 833-845. doi: 10.1109/TEC.2016.2537539 URL
[12]	钟诚, 周顺康, 严干贵, 等. 基于变减载率的光伏发电参与电网调频控制策略[J]. 电工技术学报, 2019, 34(5): 1013-1024.
	ZHONG Cheng, ZHOU Shunkang, YAN Gangui, et al. A new frequency regulation control strategy for photovoltaic power plant based on variable power reserve level control[J]. Transactions of China Electrotechnical Society, 2019, 34(5): 1013-1024.
[13]	王若谷, 陈果, 王秀丽, 等. 计及风电与电动汽车随机性的两阶段机组组合研究[J]. 电力建设, 2021, 42(8): 63-70. doi: 10.12204/j.issn.1000-7229.2021.08.008
	WANG Ruogu, CHEN Guo, WANG Xiuli, et al. Two-stage stochastic unit commitment considering the uncertainty of wind power and electric vehicle travel patterns[J]. Electric Power Construction, 2021, 42(8): 63-70. doi: 10.12204/j.issn.1000-7229.2021.08.008
[14]	王博, 杨德友, 蔡国伟. 大规模风电并网条件下考虑动态频率约束的机组组合[J]. 电网技术, 2020, 44(7): 2513-2519.
	WANG Bo, YANG Deyou, CAI Guowei. Dynamic frequency constraint unit commitment in large-scale wind power grid connection[J]. Power System Technology, 2020, 44(7): 2513-2519.
[15]	SHI Q X, LI F X, CUI H T. Analytical method to aggregate multi-machine SFR model with applications in power system dynamic studies[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6355-6367. doi: 10.1109/TPWRS.2018.2824823 URL
[16]	JU P, ZHENG Y, JIN Y Q, et al. Analytic assessment of the power system frequency security[J]. IET Generation, Transmission & Distribution, 2021, 15(15): 2215-2225. doi: 10.1049/gtd2.12171 URL
[17]	欧阳金鑫, 袁毅峰, 李梦阳, 等. 考虑风电减载调频的高比例风电电力系统优化调度方法[J]. 电网技术, 2021, 45(6): 2192-2201.
	OUYANG Jinxin, YUAN Yifeng, LI Mengyang, et al. Optimal dispatching method of high-proportion wind power systems considering wind power reserve for frequency adjustment[J]. Power System Technology, 2021, 45(6): 2192-2201.
[18]	葛晓琳, 郝广东, 夏澍, 等. 高比例风电系统的优化调度方法[J]. 电网技术, 2019, 43(2): 390-400.
	GE Xiaolin, HAO Guangdong, XIA Shu, et al. An optimal system scheduling method with high proportion of wind power[J]. Power System Technology, 2019, 43(2): 390-400.
[19]	叶婧, 林涛, 张磊, 等. 考虑动态频率约束的含高渗透率光伏电源的孤立电网机组组合[J]. 电工技术学报, 2017, 32(13): 194-202.
	YE Jing, LIN Tao, ZHANG Lei, et al. Isolated grid unit commitment with dynamic frequency constraint considering photovoltaic power plants participating in frequency regulation[J]. Transactions of China Electrotechnical Society, 2017, 32(13): 194-202.
[20]	YANG W S, CHEN L, WANG Y, et al. Multi/many-objective particle swarm optimization algorithm based on competition mechanism[J]. Computational Intelligence and Neuroscience, 2020, 2020: 5132803.