考虑风电不确定性的电力现货市场联合出清模型

doi:10.3969/j.issn.2097-0706.2025.05.008

摘要/Abstract

摘要：

在高风电占比的电力系统中，由于风电预测具有较强的不确定性，电能量和备用联合优化现货市场按照最大负荷优化备用出清的传统模式已不能兼顾系统运行安全性和经济性的要求。在风电预测误差分析的基础上构建了考虑风电不确定性的现货市场联合出清模型。提出了基于正态云模型的混合偏态分布模型以准确描述风电短期出力预测误差的分布特性；在联合出清模型中考虑了风电出力预测误差对功率平衡约束及系统旋转备用的影响，建立了基于机会约束规划的联合市场出清模型；对风电出力预测误差分布模型进行多次采样，并运用采样结果将市场出清模型进行确定性转化，以使模型能够快速求解。以某高风电占比地区的实际数据进行仿真分析，结果表明，所提模型能够在保障系统运行安全性的前提下，有效提高系统运行的经济性。

关键词: 风电预测误差, 误差分布模型, 旋转备用, 现货市场, 联合出清模型

Abstract:

In a power system with a high wind power share， due to the strong uncertainty of wind power forecasts， the traditional model of joint optimization of the spot market for electric energy and standby in accordance with the maximum load optimization of standby clearing is no longer able to take into account the requirements of security and economy of system operation. Therefore， this paper constructs a joint spot market clearing model considering wind power uncertainty on the basis of wind power forecast error analysis. Firstly， a hybrid skewed distribution model based on the normal cloud model is proposed to accurately characterize the distribution of short-term wind power output forecast errors； secondly， the impact of wind power output forecast errors on power balance constraints and system spinning standby is taken into account in the joint clearing model， and a joint market clearing model based on the opportunity constraints planning is established； subsequently， the wind power output forecast error distribution model is sampled several times and the sampling result is used to match the real-time supply and demand in the market clearing model with the wind power output forecast errors. Subsequently， the wind power forecast error distribution model is sampled several times and the sampling results are used to transform the market clearing model deterministically so that the model can be solved quickly. Finally， a simulation analysis is carried out with the actual data of a region with high wind power share， and the results show that the model proposed in this paper can effectively improve the economy of system operation under the premise of guaranteeing the safety of system operation.

Key words: wind power prediction error, Error distribution modeling, rotating spares, spot market, joint clearing model

中图分类号:

TK01：TM614

白留星, 吴飞宇, 叶露阳, 蔡长林, 雷霞. 考虑风电不确定性的电力现货市场联合出清模型[J]. 综合智慧能源, 2025, 47(5): 73-83.

BAI Liuxing, WU Feiyu, YE Luyang, CAI Changlin, LEI Xia. A joint clearing model of electricity spot market considering wind power uncertainty[J]. Integrated Intelligent Energy, 2025, 47(5): 73-83.

图/表 17

图1

图2

图3

图4

表1

表2

模型参数

参数	单场站	风电集群
$α 1$	0.425 6	0.514 8
$μ 1$	0.135 2	0.159 4
$σ 1$	0.147 9	0.051 5
$λ 1$	-1.128 3	-1.190 1
$α 2$	0.574 4	0.485 2
$μ 2$	0.343 5	0.586 9
$σ 2$	0.346 2	0.194 5
$λ 2$	-1.396 7	-15.889 3
$E X$	0.740 7	1.295 6
$S N$	0.736 6	1.270 4
$S H E$	0.358 2	0.707 1

表2

图5

图6

表3

不同模型对不同场景的拟合效果评价对比

模型	评价指标	单场站	风电集群
Gaussian	$e M A E$	0.113 2	0.913 4
	$e R M S E$	0.142 3	1.174 2
	$I c o s$	0.018 6	0.024 3
MSN	$e M A E$	0.046 1	0.104 6
	$e R M S E$	0.081 5	0.147 1
	$I c o s$	0.003 1	0.004 3
MSN-CLOUD	$e M A E$	0.023 7	0.029 2
	$e R M S E$	0.069 2	0.059 8
	$I c o s$	0.002 1	0.003 5

表3

图7

图8

图9

图10

图11

表4

图12

图13

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机组编号	机组类型	接入节点	出力上限/MW	出力下限/MW	爬坡功率/(MW·h)
G1	火电	3	600.00	140	190.00
G2	火电	9	1 200.00	285	375.00
G3	火电	20	330.00	75	100.00
G4	火电	29	800.00	200	250.00
G5	火电	32	270.00	65	100.00
W6	风电	1	198.30	0	198.30
W7	风电	4	100.00	0	100.00
W8	风电	6	1 249.00	0	1 249.00
W9	风电	7	498.00	0	498.00
W10	风电	16	293.35	0	293.35
W11	风电	17	295.50	0	295.50
W12	风电	19	297.00	0	297.00
W13	风电	22	300.15	0	300.15
W14	风电	23	198.00	0	198.00
W15	风电	26	305.70	0	305.70
W16	风电	30	249.00	0	249.00
W17	风电	33	247.50	0	247.50
W18	风电	36	48.00	0	48.00
W19	风电	37	49.50	0	49.50
W20	风电	38	99.00	0	99.00
W21	风电	39	198.50	0	198.50
W22	风电	43	448.50	0	448.50

方案	总运行费用/万元	各时刻所需正旋转备用容量总和/MW	各时刻所需负旋转备用容量总和/MW
1	4 809.406	28 453.359	28 453.359
2	4 806.453	28 453.359	28 453.359
3	4 803.884	24 627.146	19 045.912
4	4 799.442	22 467.379	17 346.556