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

doi:10.19799/j.cnki.2095-4239.2025.0027

Abstract

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

CLC Number:

TM 73

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.

Figures/Tables 23

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Table 1

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Table 2

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Table 4

Table A1

Table A2

Pollutant emission parameters of thermal power units"

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

Table A2

Table 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