考虑光伏发电不确定性的日前火电-光伏经济调度

doi:10.16182/j.issn1004731x.joss.21-0336

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (8): 1874-1884.doi: 10.16182/j.issn1004731x.joss.21-0336

考虑光伏发电不确定性的日前火电-光伏经济调度

刘兴华¹(), 耿晨¹, 谢胜寒¹, 田佳强¹(), 曹晖²

^1.西安理工大学电气工程学院，陕西西安 710054
^2.西安交通大学电气工程学院，陕西西安 710049

收稿日期:2021-04-20 修回日期:2021-05-27 出版日期:2022-08-30 发布日期:2022-08-15
通讯作者: 田佳强 E-mail:liuxh@xaut.edu.cn;tjq1992@mail.ustc.edu.cn
作者简介:刘兴华(1984-)，男，博士，教授，研究方向为智能电网运行优化与控制。E-mail：liuxh@xaut.edu.cn
基金资助:
国家自然科学基金(61903296);陕西省教育厅重点实验室项目(20JS110);陕西省青年科技新星项目(2020KJXX-094)

Day Ahead Thermal-photovoltaic Economic Dispatch Considering Uncertainty of Photovoltaic Power Generation

Xinghua Liu¹(), Chen Geng¹, Shenghan Xie¹, Jiaqiang Tian¹(), Hui Cao²

^1.School of Electrical Engineering, Xi'an University of Technology, Xi'an 710054, China
^2.School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

Received:2021-04-20 Revised:2021-05-27 Online:2022-08-30 Published:2022-08-15
Contact: Jiaqiang Tian E-mail:liuxh@xaut.edu.cn;tjq1992@mail.ustc.edu.cn

摘要/Abstract

摘要：

针对光伏发电受季节天气因素影响产生的不确定性和随机性的问题，建立了考虑光伏发电不确定性的日前火电-光伏经济调度的数学模型。以传统火电机组的运行成本、光伏发电成本、旋转备用成本，以及受到季节天气因素影响的光伏发电预测误差成本为经济目标函数；以火电机组的二氧化硫排放为环保目标函数。为提高光伏出力预测的准确性，采用加入季节天气因素的长短时记忆神经网络对于光伏发电进行预测，通过Cplex对所构建的模型进行求解，结果表明了所提模型的有效性和可行性。

关键词: 经济调度, 光伏发电, 预测误差成本, 长短时记忆神经网络

Abstract:

Aiming at the uncertainty and randomness of photovoltaic power generation affected by weather factors, a mathematical model of day ahead thermal-photovoltaic economic dispatch considering seasonal weather factors is established. The mathematical model takes the operation cost of thermal power units, the cost of photovoltaic power generation, the cost of spinning reserve and the forecast error cost of photovoltaic power generation affected by weather factors as the economic objective function, and the sulfur dioxide emission of thermal power units as the environmental objective function. In order to improve the accuracy of photovoltaic output prediction, the long short term memory neural network with seasonal weather factors is used to predict the photovoltaic power generation. The model is solved by Cplex, and the effectiveness and feasibility of the proposed model are proved by case simulation.

Key words: economic dispatch, photovoltaic power generation, prediction error cost, long short term memory neural network

中图分类号:

刘兴华, 耿晨, 谢胜寒, 田佳强, 曹晖. 考虑光伏发电不确定性的日前火电-光伏经济调度[J]. 系统仿真学报, 2022, 34(8): 1874-1884.

Xinghua Liu, Chen Geng, Shenghan Xie, Jiaqiang Tian, Hui Cao. Day Ahead Thermal-photovoltaic Economic Dispatch Considering Uncertainty of Photovoltaic Power Generation[J]. Journal of System Simulation, 2022, 34(8): 1874-1884.

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时间/h	负荷/MW	时间/h	负荷/MW	时间/h	负荷/MW
1	400	9	520	17	490
2	420	10	555	18	520
3	430	11	570	19	530
4	440	12	585	20	540
5	450	13	520	21	560
6	480	14	500	22	540
7	490	15	490	23	500
8	500	16	580	24	405

季节	晴天	阴天	雨天
冬季	0.076	0.381	0.543
春季	0.506	0.325	0.169
夏季	0.541	0.320	0.139
秋季	0.065	0.607	0.328

指标	考虑季节天气因素		不考虑季节天气因素
指标	LSTM	BP	LSTM	BP
RMES	17.250 3	21.508 4	20.286 3	28.697 5
MAE	0.101 9	0.192 1	0.183 6	0.274 4

场景	案例1调度结果	案例2调度结果
1	175 741	175 207
2	184 283	183 719
3	190 547	185 751
4	175 922	182 076
5	189 118	187 374
6	184 227	182 180
7	186 717	181 451
8	173 242	169 967
9	185 728	187 051
10	183 679	170 365

场景	案例2	案例3
1	315 421	335 603
2	321 266	337 204
3	323 424	335 442
4	318 108	335 948
5	323 862	335 526
6	320 397	324 865
7	318 940	334 052
8	310 452	335 446
9	325 452	334 981
10	310 896	334 747

考虑光伏发电不确定性的日前火电-光伏经济调度

Day Ahead Thermal-photovoltaic Economic Dispatch Considering Uncertainty of Photovoltaic Power Generation

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时间/h	负荷/MW	时间/h	负荷/MW	时间/h	负荷/MW
1	400	9	520	17	490
2	420	10	555	18	520
3	430	11	570	19	530
4	440	12	585	20	540
5	450	13	520	21	560
6	480	14	500	22	540
7	490	15	490	23	500
8	500	16	580	24	405

场景	案例2	案例4
1	175 207	161 384
2	183 719	161 407
3	185 751	161 141
4	182 076	161 310
5	187 374	161 301
6	182 180	161 236
7	181 451	161 295
8	169 967	161 357
9	187 051	161 269
10	170 365	161 336

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时间/h	负荷/MW	时间/h	负荷/MW	时间/h	负荷/MW
1	400	9	520	17	490
2	420	10	555	18	520
3	430	11	570	19	530
4	440	12	585	20	540
5	450	13	520	21	560
6	480	14	500	22	540
7	490	15	490	23	500
8	500	16	580	24	405

时间/h	负荷/MW	时间/h	负荷/MW	时间/h	负荷/MW
1	400	9	520	17	490
2	420	10	555	18	520
3	430	11	570	19	530
4	440	12	585	20	540
5	450	13	520	21	560
6	480	14	500	22	540
7	490	15	490	23	500
8	500	16	580	24	405