Coordinated Day-Ahead Scheduling and Real-Time Dispatch of a Wind-Thermal-Storage Energy Base Considering Flexibility Interval

doi:10.16183/j.cnki.jsjtu.2023.509

Abstract

Abstract:

Large-scale new energy bases in desert, Gobi, and arid regions are key components of new-type power systems in China. Considering factors such as construction cost and carbon emissions, the capacities of thermal power and energy storage in these bases are limited, resulting in constrained flexibility. Consequently, the scheduling and operation of these large bases face significant challenges. This paper proposes a coordinated day-ahead and real-time scheduling method for wind-thermal-storage integrated bases. In the day-ahead stage, the startup/shutdown plans and adjustable output ranges of thermal units are determined based on a rough prediction of wind power. Then, it constructs a wind power accommodation interval based on the adjustable range of thermal power output and the operational constraints of energy storage. In the real-time stage, dispatch strategies are generated using a quantile-based rule according to current wind and solar power output, eliminating the need for high-precision forecasts. It is further demonstrated that the dispatch strategies generated by the quantile rule inherently satisfy system operational constraints. The case study validates the effectiveness of the proposed method for wind-thermal-storage systems. The results demonstrate that the proposed method, which does not rely on point prediction, outperforms rolling optimization methods when the three-step prediction error exceeds 10%. Moreover, the performance of operational scheduling can be improved by enhancing the accuracy of day-ahead or intraday short-term forecasts. The proposed method provides valuable reference for the operation of large-scale new energy bases.

Key words: wind-thermal-storage system, day-ahead scheduling, real-time operation, uncertainty, energy storage

CLC Number:

TM732
TM614

YANG Yinguo, FENG Yinying, WEI Wei, XIE Pingping, CHEN Yue. Coordinated Day-Ahead Scheduling and Real-Time Dispatch of a Wind-Thermal-Storage Energy Base Considering Flexibility Interval[J]. Journal of Shanghai Jiao Tong University, 2025, 59(9): 1270-1280.

Figures/Tables 9

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References 26

[1]	韩肖清, 李廷钧, 张东霞, 等. 双碳目标下的新型电力系统规划新问题及关键技术[J]. 高电压技术, 2021, 47(9): 3036-3046.
	HAN Xiaoqing, LI Tingjun, ZHANG Dongxia, et al. New issues and key technologies of new power system planning under double carbon goals[J]. High Voltage Engineering, 2021, 47(9): 3036-3046.
[2]	黄强, 郭怿, 江建华, 等. “双碳” 目标下中国清洁电力发展路径[J]. 上海交通大学学报, 2021, 55(12): 1499-1509. doi: 10.16183/j.cnki.jsjtu.2021.272
	HUANG Qiang, GUO Yi, JIANG Jianhua, et al. Development pathway of China’s clean electricity under carbon peaking and carbon neutrality goals[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1499-1509.
[3]	王骞, 张学广, 朱玲, 等. 含整体煤气化燃料电池-碳捕集电厂的风火储系统分布鲁棒调度方法[J]. 中国电机工程学报, 2024, 44(9): 3573-3588.
	WANG Qian, ZHANG Xueguang, ZHU Ling, et al. Robust scheduling method for wind-fire-energy storage system with integrated gasification fuel cell and carbon capture power plant[J]. Proceedings of the CSEE, 2024, 44(9): 3573-3588.
[4]	黄远明, 张玉欣, 夏赞阳, 等. 考虑需求响应资源和储能容量价值的新型电力系统电源规划方法[J]. 上海交通大学学报, 2023, 57(4): 432-441. doi: 10.16183/j.cnki.jsjtu.2021.477
	HUANG Yuanming, ZHANG Yuxin, XIA Zanyang, et al. Power system planning considering demand response resources and capacity value of energy storage[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 432-441.
[5]	LÓPEZ-GRAJALES A M, GONZÁLEZ-SANCHEZ J W, CARDONA-RESTREPO H A, et al. Economy, financial, and regulatory method for the integration of electrical energy storage in a power network[J]. Journal of Energy Storage, 2023, 58: 106433.
[6]	ABDALLA A N, NAZIR M S, TAO H, et al. Integration of energy storage system and renewable energy sources based on artificial intelligence: An overview[J]. Journal of Energy Storage, 2021, 40: 102811.
[7]	BAFRANI H A, SEDIGHIZADEH M, DOWLATSHAHI M, et al. Reliability and reserve in day ahead joint energy and reserve market stochastic scheduling in presence of compressed air energy storage[J]. Journal of Energy Storage, 2021, 43: 103194.
[8]	汪超群, 韦化, 吴思缘. 计及风电不确定性的随机安全约束机组组合[J]. 电网技术, 2017, 41(5): 1419-1427.
	WANG Chaoqun, WEI Hua, WU Siyuan. Stochastic-security-constrained unit commitment considering uncertainty of wind power[J]. Power System Technology, 2017, 41(5): 1419-1427.
[9]	WU H Y, SHAHIDEHPOUR M, LI Z Y, et al. Chance-constrained day-ahead scheduling in stochastic power system operation[J]. IEEE Transactions on Power Systems, 2014, 29(4): 1583-1591.
[10]	BERTSIMAS D, LITVINOV E, SUN X A, et al. Adaptive robust optimization for the security constrained unit commitment problem[J]. IEEE Transactions on Power Systems, 2013, 28(1): 52-63.
[11]	ZEYNALI S, ROSTAMI N, AHMADIAN A, et al. Robust multi-objective thermal and electrical energy hub management integrating hybrid battery-compressed air energy storage systems and plug-in-electric-vehicle-based demand response[J]. Journal of Energy Storage, 2021, 35: 102265.
[12]	李建林, 张则栋, 梁策, 等. 计及源-荷不确定性的综合能源系统多目标鲁棒优化调度[J]. 上海交通大学学报, 2025, 59(2): 175-185. doi: 10.16183/j.cnki.jsjtu.2023.238
	LI Jianlin, ZHANG Zedong, LIANG Ce, et al. Multi-objective robust optimization dispatch for integrated energy systems considering source-load uncertainty[J]. Journal of Shanghai Jiao Tong University, 2025, 59(2): 175-185.
[13]	XIONG P, JIRUTITIJAROEN P, SINGH C. A distributionally robust optimization model for unit commitment considering uncertain wind power generation[J]. IEEE Transactions on Power Systems, 2017, 32(1): 39-49.
[14]	ZHENG X D, QU K P, LV J Q, et al. Addressing the conditional and correlated wind power forecast errors in unit commitment by distributionally robust optimization[J]. IEEE Transactions on Sustainable Energy, 2021, 12(2): 944-954.
[15]	马洪艳, 贠靖洋, 严正. 基于分布鲁棒优化的灵活爬坡备用调度方法[J]. 中国电机工程学报, 2020, 40(19): 6121-6132.
	MA Hongyan, YUN Jingyang, YAN Zheng. Distributionally robust optimization based dispatch methodology of flexible ramping products[J]. Proceedings of the CSEE, 2020, 40(19): 6121-6132.
[16]	杨策, 孙伟卿, 韩冬, 等. 考虑风电出力不确定的分布鲁棒经济调度[J]. 电网技术, 2020, 44(10): 3649-3655.
	YANG Ce, SUN Weiqing, HAN Dong, et al. Distributionally-robust economic dispatch considering uncertain wind power output[J]. Power System Technology, 2020, 44(10): 3649-3655.
[17]	TANG Y, ZHAI Q Z, ZHAO J X. Multi-stage robust economic dispatch with virtual energy storage and renewables based on a single level model[J]. IEEE Transactions on Automation Science & Engineering, 2024, 21(4): 5490-5502.
[18]	LIN S J, FAN G S, JIAN G Y, et al. Stochastic economic dispatch of power system with multiple wind farms and pumped-storage hydro stations using approximate dynamic programming[J]. IET Renewable Power Generation, 2020, 14(13): 2507-2516.
[19]	韦化, 龙丹丽, 黎静华. 求解大规模机组组合问题的策略迭代近似动态规划[J]. 中国电机工程学报, 2014, 34(25): 4420-4429.
	WEI Hua, LONG Danli, LI Jinghua. Policy iteration-approximate dynamic programming for large scale unit commitment problems[J]. Proceedings of the CSEE, 2014, 34(25): 4420-4429.
[20]	XIONG H B, SHI Y H, CHEN Z, et al. Multi-stage robust dynamic unit commitment based on pre-extended-fast robust dual dynamic programming[J]. IEEE Transactions on Power Systems, 2023, 38(3): 2411-2422.
[21]	包宇庆, 王蓓蓓, 李扬, 等. 考虑大规模风电接入并计及多时间尺度需求响应资源协调优化的滚动调度模型[J]. 中国电机工程学报, 2016, 36(17): 4589-4600.
	BAO Yuqing, WANG Beibei, LI Yang, et al. Rolling dispatch model considering wind penetration and multi-scale demand response resources[J]. Proceedings of the CSEE, 2016, 36(17): 4589-4600.
[22]	KHALID M, SAVKIN A V. A model predictive control approach to the problem of wind power smoothing with controlled battery storage[J]. Renewable Energy, 2010, 35(7): 1520-1526.
[23]	DI GIORGIO A, LIBERATI F, LANNA A, et al. Model predictive control of energy storage systems for power tracking and shaving in distribution grids[J]. IEEE Transactions on Sustainable Energy, 2017, 8(2): 496-504.
[24]	ZHAO Z L, GUO J T, LUO X, et al. Distributed robust model predictive control-based energy management strategy for islanded multi-microgrids considering uncertainty[J]. IEEE Transactions on Smart Grid, 2022, 13(3): 2107-2120.
[25]	LIN M H, LIU Z H, WIERMAN A, et al. Online algorithms for geographical load balancing[C]// 2012 International Green Computing Conference. San Jose, USA: IEEE, 2012: 1-10.
[26]	ZHANG J, QIN D, YE Y C, et al. Multi-time scale economic scheduling method based on day-ahead robust optimization and intraday MPC rolling optimization for microgrid[J]. IEEE Access, 2021, 9: 140315-140324.

参数	数值
风电额定功率/GW	10
负荷峰值/GW	8
N	8
单台火电机组出力上限/MW	500
单台火电机组出力下限/MW	125
单台火电机组开/关机爬坡/(MW·h^-1)	250
储能SOC范围	[0.2,0.95]

算法	总成本× 10^-6/元	运行成本× 10^-6/元	优化间隙/%
B1	2.4732	2.4732
B2	3.0222	2.8243	14.20
B3(10%)	2.9530	2.8538	15.39
B3(20%)	2.9585	2.8574	15.53
所提方法	2.8381	2.8298	14.42

SOC范围	总成本× 10^-6/元	运行成本× 10^-6/元	优化间隙/%
[0.2,0.8]	2.8442	2.8398	15.00
[0.2,0.95]	2.8381	2.8298	14.76
[0.15,0.8]	2.8473	2.8367	15.13