考虑电动汽车的新型电力系统源荷日前-日内低碳优化调度

doi:10.19799/j.cnki.2095-4239.2025.0027

摘要/Abstract

摘要：

电动汽车作为柔性负荷参与新型电力系统的优化调度可提高新能源消纳，为加快构建新型电力系统提供了一种新途径。针对电动汽车同时参与价格型需求响应与激励型需求响应的新型电力系统源荷低碳优化调度问题，本工作基于麻雀搜索算法优化卷积长短时记忆神经网络，给出一种考虑电动汽车需求响应特性的新型电力系统源荷日前-日内低碳优化调度方法。首先，对新能源和基本负荷历史数据利用麻雀搜索算法优化卷积长短时记忆神经网络进行预测，降低源荷两侧的不确定性对新型电力系统日前-日内优化调度的影响；其次，以电动汽车参与需求响应的充电特性，将电动汽车分为三类充电模式，考虑阶梯式碳交易的系统总成本和污染气体排放最优为目标构建源荷互动的日前-日内两阶段低碳环境经济调度模型；最后，利用改进多目标灰狼算法对模型进行求解。算例分析选取典型日的光伏、风电与负荷数据，并综合考虑了电动汽车不同充电模式的需求响应特性。通过4种运行场景下的优化调度结果可知，场景4与场景1相比，较传统方法总成本降低10.3%、污染物排放减少10.9%、新能源消纳提高4.2%，日前-日内低碳优化调度方法可有效提高新能源消纳和新型电力系统的低碳环境经济综合效益。

关键词: 电动汽车, 源荷预测, 新型电力系统, 日前-日内优化调度, 低碳优化调度

Abstract:

Electric vehicles (EVs), as flexible loads in the optimal scheduling of new power systems, can enhance the utilization of new energy and offer a new way to accelerating the development of next-generation power infrastructure. To address the low-carbon optimal scheduling challenge involving sources and loads, where EVs participate in price- and incentive-based demand response, this study proposes a new day-ahead-intraday low-carbon optimal scheduling method. The approach leverages the demand response characteristics of EVs and employs the sparrow search algorithm (SSA) to optimize a convolutional long short-term memory neural network (ConvLSTM NN). First, the historical data of new energy generation and base load are predicted using the SSA-optimized ConvLSTM NN, thereby reducing the influence of uncertainties on the supply and demand sides in the day-ahead-intraday scheduling of new power systems. Next, three types of EV charging modes are defined based on their charging characteristics observed under demand response participation. A two-stage low-carbon environmental-economic dispatch model for source-load interaction is then developed, incorporating the total system cost under ladder-type carbon trading and the optimal control of pollutant emissions. Finally, an improved multiobjective gray wolf algorithm is employed to solve the model. For validation, typical daily data on photovoltaic power, wind power, and loads are used in the case analysis, which also fully considers the demand response characteristics of the various EV charging modes. The optimal scheduling results across four operating scenarios show that, compared with Scenario 1, Scenario 4 achieves a 10.3% reduction in total cost, a 10.9% decrease in pollutant emissions, and a 4.2% increase in new energy consumption. Overall, the proposed day-ahead-intraday low-carbon optimal scheduling method effectively enhances the environmental and economic benefits of new energy utilization in modern power systems.

Key words: electric vehicles, source-load prediction, new power system, day-ahead-intraday optimal scheduling, low carbon optimal scheduling

中图分类号:

TM 73

李若琼, 司宇杰, 李欣. 考虑电动汽车的新型电力系统源荷日前-日内低碳优化调度[J]. 储能科学与技术, 2025, 14(8): 3170-3184.

Ruoqiong LI, Yujie SI, Xin LI. A novel day-ahead-intraday low-carbon optimal scheduling method for power system source-load dispatch based on the demand response characteristics of electric vehicles[J]. Energy Storage Science and Technology, 2025, 14(8): 3170-3184.

图/表 23

图1

图2

图3

图4

图5

表1

图6

图7

图8

图9

表2

图10

图11

图12

图13

表3

图14

图15

图 16

表4

表A1

表A2

火电机组的污染物排放参数"

机组编号

α

β

γ

ζ

λ

0.0465

0.0470

-3.9524

-3.9864

350.0056

360.0012

0.5475

0.0234

表A2

表A3

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调度结果	场景1	场景2	场景3	场景4
系统总成本/USD	64434	62850	60786	58929
启停成本/USD	320	380	290	290
运行成本/USD	63185	61761	59867	58344
弃光成本/USD	235	143	120	102
弃风成本/USD	694	461	198	39
碳交易成本/USD	0	105	0	-165
弃光率/%	6.13	3.75	3.13	2.84
弃风率/%	6.38	4.24	1.82	0.37
日前IDR成本/USD	0	0	311	319
污染物排放/lb	7932	7558	7292	7126

调度结果	场景1	场景2	场景3	场景4
系统总成本/USD	62963	60336	58511	56454
运行成本/USD	62339	59856	58162	56341
弃光成本/USD	49	22	15	10
弃风成本/USD	575	360	88	32
碳交易成本/USD	0	98	0	-187
弃光率/%	1.28	0.58	0.41	0.26
弃风率/%	5.17	3.25	0.79	0.29
日内IDR成本/USD	0	0	246	258
污染物排放/lb	7785	7456	7143	6937

评价指标	MAPE/%	RMSE/MW
BP神经网络	5.27	0.73
Elman神经网络	7.13	1.01
CNN-LSTM神经网络	4.22	0.7
GA-CNN-LSTM神经网络	2.91	0.38
SSA-CNN-LSTM神经网络	2.31	0.37

算法	目标	排放污染物/10³ lb	经济成本/10⁴ USD
传统MOGWO	经济最优	7.2612	5.5323
	环境最优	6.8445	5.8617
	折中解	6.9527	5.6593

改进MOGWO	经济最优	7.2858	5.5129
	环境最优	6.8356	5.8941
	折中解	6.9371	5.6454

机组编号	最大出力/MW	最小出力/MW	起停成本/USD	成本系数a/b/c/(USD/MW²)/(USD/MW)/USD	最小启停时间/h	机组爬坡/MW/h	碳排放强度/(t/MWh)
1	80	10	170	0.00712/22.3/480	3/3	20	1.05
2	40	5	30	0.00413/25.9/660	1/1	5	1.12
3	40	2	30	0.00222/27.3/665	1/1	5	1.15