Low-Carbon Economic Dispatch of Power Grid with Electric Vehicles Considering Wind Power Accommodation

Liangliang Sun; Xingguo Tan; Chaomeng Li

doi:10.54691/2h39dc37

Authors

Liangliang Sun
Xingguo Tan
Chaomeng Li

DOI:

https://doi.org/10.54691/2h39dc37

Keywords:

Wind power accommodation, electric vehicle, charging/swapping station, NSGA-II.

Abstract

To mitigate the impact of uncoordinated charging of electric vehicle (EV) clusters on grid load and improve wind power accommodation, this paper proposes an ordered charging scheduling strategy for EVs considering wind power consumption. First, a charging load model for EVs is established, and the impact of uncoordinated charging of EV clusters of different scales on grid load is analyzed. Second, an ordered charging control model for EV clusters is developed, and a time-of-use (TOU) electricity price strategy based on grid-connected wind power output is proposed to effectively regulate the charging load of EVs and battery charging and swapping stations (BCSS), thereby enhancing wind power accommodation. Finally, considering carbon trading and wind curtailment penalty costs, a low-carbon economic dispatch model for the power grid is established, aiming to minimize both the load peak-valley difference and the comprehensive operating cost. The NSGA-II algorithm is employed to solve the model. Simulation results demonstrate that the proposed strategy can increase wind power accommodation while achieving low-carbon and economic operation of the grid.

Downloads

Download data is not yet available.

References

[1] Wang X J, Liu P, Li R C, et al. Research progress and prospect of advanced power generation technology under the goal of carbon peak and carbon neutrality. Thermal Power Generation, 2022, 51(01): 52-59.

[2] Cui Y, Guo F Y, Fu X B, et al. Source-load coordinated optimal dispatch of integrated energy system promoting wind power accommodation by supply-use conversion. Power System Technology, 2022, 46(04): 1437-1447.

[3] Malika K B, Pattanaik V, Panda S, et al. A critical review of distribution system planning: Optimal placement and sizing of distributed generation and energy storage devices in microgrids. Energy Strategy Reviews, 2025, 62: 101947.

[4] Ma Y P. Optimal capacity configuration of hybrid energy storage in microgrid considering electric vehicle dispatch. Power System Protection and Control, 2017, 45(23): 98-107.

[5] Niu M, Ji R, Pang H, et al. A two-stage optimization framework for microgrid operation with EV ordered charging and hydrogen-battery co-storage. Journal of Energy Storage, 2025, 129: 117165.

[6] Wang H, Li X Y, Wang B Q, et al. Reliability evaluation of distribution network with distributed generation and electric vehicles. Journal of Chongqing University, 2024, 47(01): 115-126.

[7] Bahmani H M, Shayan E M, Mishra K D. Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2024, 46(1): 4524-4542.

[8] Wang S, Liu B, Hua Y, et al. Dispatchable capability of aggregated electric vehicle charging in distribution systems. Energy Engineering, 2024, 122(1): 129-152.

[9] Guo Y N, Liao K, Yang J W, et al. Capacity optimization method for electric vehicle battery swapping station considering distribution network resilience enhancement. Power System Technology, 2025, 49(02): 470-480.

[10] Kong S F, Hu Z J, Xie S Y, et al. Two-stage robust planning model and its solution method for active distribution network with electric vehicle charging stations. Transactions of China Electrotechnical Society, 2020, 35(5): 1093-1105.

[11] Ma M, Ren Z W, Liu L C, et al. Ordered charging control strategy for electric vehicles considering renewable energy accommodation. Acta Energiae Solaris Sinica, 2024, 45(08): 94-103.

[12] Ji X, Li M, Yue Z, et al. Renewable energy consumption strategies for electric vehicle aggregators based on a two-layer game. Energies, 2024, 18(1): 80-80.

[13] Cui Y, Jiang T, Zhong W Z, et al. Economic dispatch method of regional integrated energy system considering electric vehicle and heat pump for promoting wind power consumption. Electric Power Automation Equipment, 2021, 41(02): 1-7.

[14] Shu Z Y, Liu W C, Li H Q, et al. Ordered charging and discharging strategy for electric vehicles based on cooperative game and dynamic time-of-use electricity price. Electric Power Engineering Technology, 2025, 44(03): 179-187.

[15] Yang J X, Zhou L, Zhang Y J, et al. Ordered EV charging considering time-varying price and transition probability under dedicated transformer sharing mode. Electric Power Automation Equipment, 2020, 40(10): 173-180, 193.

[16] Zeng X K, Yang P, Liu L Y, et al. Optimal regulation strategy of EV charging and swapping station in electricity spot market environment. Electric Power Automation Equipment, 2022, 42(10): 38-45.

[17] Wang Y F, Wang X H, Lei X S. Optimal operation strategy under integrated service mode of prefabricated substation and EV charging/swapping station. Southern Power System Technology, 2022, 16(05): 61-70.

[18] Yue X, Fang M Q, Liu J Y, et al. Distributed dispatch of multiple energy systems considering carbon trading. CSEE Journal of Power and Energy Systems, 2023, 9(02): 459-469.

[19] Jin J, Zhou P, Li C, et al. Low-carbon power dispatch with wind power based on carbon trading mechanism. Energy, 2019, 170: 250-260.

[20] Wang J, Huang K R, Xu X, et al. Orderly charging of electric vehicles based on stepped carbon price and adaptive time-of-use electricity price. Electric Power Automation Equipment, 2024, 44(02): 64-71.

[21] Cui Y, Zeng P, Zhong W Z, et al. Low-carbon economic dispatch of electricity-gas-heat integrated energy system based on stepped carbon trading. Electric Power Automation Equipment, 2021, 41(03): 10-17.

[22] Xiao Q Y, Yang K, Song Z X. Dispatch strategy of integrated energy system in industrial park considering carbon trading and EV charging load. High Voltage Engineering, 2023, 49(04): 1392-1401.

[23] Huang Z, Wang Y, Liu C, et al. Low-carbon and optimized dispatching of regional integrated energy systems, taking into account the uncertainties of wind–solar power and dynamic hydrogen prices. Energies, 2025, 18(23): 6265-6265.

[24] Wu X, Liang K X, Jiao D, et al. Group coordination method of air conditioners for clean energy tracking. Automation of Electric Power Systems, 2020, 44(11): 68-77.

[25] Zhang C, Kuang Y, Zou F M, et al. Low-carbon economic dispatch of integrated energy system considering wind and solar uncertainty and electric vehicles. Electric Power Automation Equipment, 2022, 42(10): 236-244.

[26] Liu R L, Yu Y, Liu J, et al. Electric vehicle charging load prediction based on improved Monte Carlo algorithm. Zhejiang Electric Power, 2025, 44(08): 15-23.

[27] Wang S Q, Duan J D, Duan Z Y. Collaborative optimization strategy of “wind power-grid-EV charging and swapping station” considering wind power accommodation. Thermal Power Generation, 2023, 52(03): 112-120.

[28] Zhu Y S, Chang W, Wu D Y, et al. Master-slave game optimization dispatch strategy considering interaction between integrated charging-swapping-storage station and electric vehicles. Power System Protection and Control, 2024, 52(07): 157-167.

[29] Chen J, Zhang X D, Li L Y, et al. Two-stage game collaborative dispatch strategy for shared EV charging based on electricity-carbon joint mechanism. Power System Technology, 2024, 48(09): 3584-3594.

[30] Yu Y F, Liu B L. Environmental economic dispatch optimization of power load based on improved NSGA-II algorithm. Jiangxi Electric Power, 2024, 48(03): 8-11.