Optimization of Day-Ahead Dispatch Time Resolution in Power System with a High Proportion of Climate-Sensitive Renewable Energy Sources

doi:10.16183/j.cnki.jsjtu.2022.277

Abstract

Abstract:

The time resolution of scheduling plan refers to the length of each time interval in the scheduling plan. With the increasing proportion of climate-sensitive renewable energy sources (RES), the volatility of net load during the dispatch interval is significantly enhanced, resulting in an insufficient system climbing capacity and abnormal frequency. Therefore, the setting of time resolution at different renewable energy penetration rates becomes an urgent problem to be solved at present. A global sensitivity-based day-ahead dispatch time resolution selection optimization method is proposed to select the time resolution for different volatility grids. To achieve a balance between the degree of refinement and the accuracy of load prediction, the global sensitivity analysis method based on Sobol' method and sparse chaos expansion is used to evaluate the impact of net load volatility and uncertainty on dispatch at different time resolutions, and an appropriate time resolution is chosen to optimize the scheduling effect. The analysis and simulation results show that the choice of time resolution is mainly determined by the net load fluctuation rate. The appropriate time granularity is chosen according to the net load fluctuation rate, which minimizes the unbalanced power, and the purpose of improving the optimal scheduling effect and reducing the dispatching cost is achieved.

Key words: power system dispatch, scheduling plan, renewable energy sources (RES), global sensitivity

CLC Number:

TM732

YE Zhiliang, LI Canbing, ZHANG Yongjun, LI Licheng, XIAO Yinjing, WU Yuhang, TAI Nengling. Optimization of Day-Ahead Dispatch Time Resolution in Power System with a High Proportion of Climate-Sensitive Renewable Energy Sources[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 781-790.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Data for the system

机组	$P m m a x$ /MW	$P m m i n$ /MW	a_m/[美元· (MW²·h)^-1]	b_m/[美元· (MW·h)^-1]	c_m/ [美元·h^-1]	$R m +$ /MW	$R m -$ /MW	$c m s u$ /美元	$c m s d$ /美元
1	470	150	4.3×10^-4	21.60	958.20	120	-120	3000	3000
2	460	130	6.3×10^-4	21.05	1313.60	120	-120	3000	3000
3	340	73	5.9×10^-4	20.81	604.97	120	-120	2200	2200
4	300	60	7.0×10^-4	22.90	471.60	100	-100	1800	1800
5	243	73	7.9×10^-4	21.62	480.29	100	-100	900	900
6	160	57	5.6×10^-4	17.87	601.75	100	-100	900	900
7	130	20	2.1×10^-3	16.51	502.70	50	-50	260	260
8	120	47	4.8×10^-3	23.23	639.40	50	-50	260	260
9	80	20	10.9×10^-1	19.58	455.60	50	-50	30	30
10	55	20	9.5×10^-3	22.54	629.40	50	-50	30	30

Tab.1

Fig.5

Tab.2

Tab.3

Fig.6

Tab.4

Fig.7

References 27

[1]	国家发展和改革委员会能源研究所. 中国2050高比例可再生能源发展情景暨路径研究[R]. 北京: 国家发展和改革委员会能源研究所, 2015.
	Energy Research Institute of National Development and Reform Commission. China 2050 high percentage renewable energy development scenario and pathway study[R]. Beijing: Energy Research Institute of the National Development and Reform Commission, 2015.
[2]	PANDŽIĆ H, DVORKIN Y, WANG Y S, et al. Effect of time resolution on unit commitment decisions in systems with high wind penetration[C]// 2014 IEEE PES General Meeting \| Conference & Exposition. National Harbor, MD, USA: IEEE, 2014: 1-5.
[3]	艾小猛, 韩杏宁, 文劲宇, 等. 考虑风电爬坡事件的鲁棒机组组合[J]. 电工技术学报, 2015, 30(24): 188-195.
	AI Xiaomeng, HAN Xingning, WEN Jinyu, et al. Robust unit commitment considering wind power ramp events[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 188-195.
[4]	MILLIGAN M, KIRBY B, et al. Combining balancing areas’ variability: Impacts on wind integration in the western interconnection. (2010-05-23)[2022-06-26]. https://www.osti.gov/biblio/986254.
[5]	GUY J D. Security constrained unit commitment[J]. IEEE Transactions on Power Apparatus & Systems, 1971, 90(3): 1385-1390.
[6]	CONTAXIS G C, KABOURIS J. Short term scheduling in a wind/diesel autonomous energy system[J]. IEEE Transactions on Power Systems, 1991, 6(3): 1161-1167. doi: 10.1109/59.119261 URL
[7]	陈之栩, 谢开, 张晶, 等. 电网安全节能发电日前调度优化模型及算法[J]. 电力系统自动化, 2009, 33(1): 10-13.
	CHEN Zhixu, XIE Kai, ZHANG Jing, et al. Optimal model and algorithm for day-ahead generation scheduling of transmission grid security constrained convention dispatch[J]. Automation of Electric Power Systems, 2009, 33(1): 10-13.
[8]	GANGAMMANAVAR H, SEN S, ZAVALA V M. Stochastic optimization of sub-hourly economic dispatch with wind energy[J]. IEEE Transactions on Power Systems, 2016, 31(2): 949-959. doi: 10.1109/TPWRS.2015.2410301 URL
[9]	CHE L, LIU X, ZHU X, et al. Intra-interval security assessment in power systems with high wind penetration[J]. IEEE Transactions on Sustainable Energy, 2019, 10(4): 1890-1903. doi: 10.1109/TSTE.5165391 URL
[10]	CHE L, LIU X, ZHU X, et al. Intra-interval security based dispatch for power systems with high wind penetration[J]. IEEE Transactions on Power Systems, 2019, 34(2): 1243-1255. doi: 10.1109/TPWRS.2018.2870667 URL
[11]	SIOSHANSI R, O’NEILL R, OREN S S. Economic consequences of alternative solution methods for centralized unit commitment in day-ahead electricity markets[J]. IEEE Transactions on Power Systems, 2008, 23(2): 344-352. doi: 10.1109/TPWRS.2008.919246 URL
[12]	MORALES-ESPAÑA G, RAMÍREZ-ELIZONDO L, HOBBS B F. Hidden power system inflexibilities imposed by traditional unit commitment formulations[J]. Applied Energy, 2017, 191: 223-238. doi: 10.1016/j.apenergy.2017.01.089 URL
[13]	BAKIRTZIS E A, BISKAS P N, LABRIDIS D P, et al. Multiple time resolution unit commitment for short-term operations scheduling under high renewable penetration[J]. IEEE Transactions on Power Systems, 2014, 29(1): 149-159. doi: 10.1109/TPWRS.2013.2278215 URL
[14]	BAKIRTZIS E A, SIMOGLOU C K, BISKAS P N, et al. Comparison of advanced power system operations models for large-scale renewable integration[J]. Electric Power Systems Research, 2015, 128: 90-99. doi: 10.1016/j.epsr.2015.06.025 URL
[15]	BAKIRTZIS E A, BISKAS P N. Multiple time resolution stochastic scheduling for systems with high renewable penetration[J]. IEEE Transactions on Power Systems, 2017, 32(2): 1030-1040.
[16]	ELA E, O’MALLEY M. Studying the variability and uncertainty impacts of variable generation at multiple timescales[J]. IEEE Transactions on Power Systems, 2012, 27(3): 1324-1333. doi: 10.1109/TPWRS.2012.2185816 URL
[17]	WAN Y H. Analysis of wind power ramping behavior in ERCOT[R]. USA: Office of Scientific and Technical Information, 2011.
[18]	梁双, 胡学浩, 张东霞, 等. 光伏发电置信容量的研究现状与发展趋势[J]. 电力系统自动化, 2011, 35(19): 101-107.
	LIANG Shuang, HU Xuehao, ZHANG Dongxia, et al. Current status and development trend on capacity credit of photovoltaic generation[J]. Automation of Electric Power Systems, 2011, 35(19): 101-107.
[19]	邬超, 朱桂萍, 钱敏慧. 基于信息熵的历史数据选取对超短期风电功率预测精度影响研究[J]. 电网技术, 2021, 45(5): 1767-1772.
	WU Chao, ZHU Guiping, QIAN Minhui. Impact of historical data selection on accuracy of ultra-short-term wind power prediction based on prediction information entropy[J]. Power System Technology, 2021, 45(5): 1767-1772.
[20]	PINEDA S, FERNÁNDEZ-BLANCO R, MORALES J M. Time-adaptive unit commitment[J]. IEEE Transactions on Power Systems, 2019, 34(5): 3869-3878. doi: 10.1109/TPWRS.59 URL
[21]	金国彬, 潘狄, 陈庆, 等. 考虑自适应实时调度的多电压等级直流配电网能量优化方法[J]. 电网技术, 2021, 45(10): 3906-3917.
	JIN Guobin, PAN Di, CHEN Qing, et al. Energy optimization method of multi-voltage-level DC distribution network considering adaptive real-time scheduling[J]. Power System Technology, 2021, 45(10): 3906-3917.
[22]	AGENCY I E. Empowering variable renewables-options for flexible electricity systems[M]. Paris: OECD Publishing, 2009: 13-14.
[23]	楚成博. 含可调控负荷系统的调度灵活性研究[D]. 济南: 山东大学, 2013.
	CHU Chengbo. Studies on flexibility of power system dispatch with controllable loads[D]. Jinan: Shandong University, 2013.
[24]	NI F, NIJHUIS M, NGUYEN P H, et al. Variance-based global sensitivity analysis for power systems[J]. IEEE Transactions on Power Systems, 2018, 33(2): 1670-1682. doi: 10.1109/TPWRS.2017.2719046 URL
[25]	孙鑫, 王博, 陈金富, 等. 基于稀疏多项式混沌展开的可用输电能力不确定性量化分析[J]. 中国电机工程学报, 2019, 39(10): 2904-2914.
	SUN Xin, WANG Bo, CHEN Jinfu, et al. Sparse polynomial chaos expansion based uncertainty quantification for available transfer capability[J]. Proceedings of the CSEE, 2019, 39(10): 2904-2914.
[26]	KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proceedings of ICNN’95-International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
[27]	王皓, 艾芊, 甘霖, 等. 基于多场景随机规划和MPC的冷热电联合系统协同优化[J]. 电力系统自动化, 2018, 42(13): 51-58.
	WANG Hao, AI Qian, GAN Lin, et al. Collaborative optimization of combined cooling heating and power system based on multi-scenario stochastic programming and model predictive control[J]. Automation of Electric Power Systems, 2018, 42(13): 51-58.

γ/min	Δβ	P_sum/(MW·h)	D/%
5	0.21	3 230	90.74
15	0.10	3 187	90.87
30	0.27	3 247	90.70
60	0.46	3 281	90.60

γ/min	f₁/美元	f₂/美元	f₃/美元	F/%
5	826723	83915	910640	2.02
15	826555	82794	909350	1.93
30	825736	84352	910090	2.09
60	824864	85249	910110	2.17

γ/min	Δβ	F/%	D/%	f₃/美元
5	0.07	2.19	89.17	932860
15	0.11	2.30	88.87	932900
30	0.31	2.45	88.79	933270
60	0.54	2.52	88.69	933480