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Lu, D.; Lu, Y.; Zhang, K.; Zhang, C.; Ma, S. Guiding the Coordinated Charging of Electric Vehicles. Encyclopedia. Available online: https://encyclopedia.pub/entry/46972 (accessed on 27 July 2024).
Lu D, Lu Y, Zhang K, Zhang C, Ma S. Guiding the Coordinated Charging of Electric Vehicles. Encyclopedia. Available at: https://encyclopedia.pub/entry/46972. Accessed July 27, 2024.
Lu, Dingyi, Yunqian Lu, Kexin Zhang, Chuyuan Zhang, Shaochao Ma. "Guiding the Coordinated Charging of Electric Vehicles" Encyclopedia, https://encyclopedia.pub/entry/46972 (accessed July 27, 2024).
Lu, D., Lu, Y., Zhang, K., Zhang, C., & Ma, S. (2023, July 19). Guiding the Coordinated Charging of Electric Vehicles. In Encyclopedia. https://encyclopedia.pub/entry/46972
Lu, Dingyi, et al. "Guiding the Coordinated Charging of Electric Vehicles." Encyclopedia. Web. 19 July, 2023.
Guiding the Coordinated Charging of Electric Vehicles
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This entry is adapted from the peer-reviewed paper 10.3390/su151410758

Guiding the coordinated charging of electric vehicles can alleviate the load fluctuation of power systems within a local area caused by uncoordinated charging of electric vehicles and greatly reduce the cost of power system operation. This will become an inevitable development trend of future energy system transformation.

electric vehicles renewable energy utilization coordinated charging

1. Introduction

The vigorous development of electric vehicles (EVs) can get rid of the environmental damage and dependence on petroleum resources caused by conventional petrol vehicles [1]. Without the guidance of basic policies or related regulations to guide the charging behavior of EV owners, the time and space required for charging EVs will be uncertain under the influence of seasonal and geographical factors [2]. Uncoordinated charging behavior can directly affect the magnitude of electric load fluctuations in local areas, increase the cost of power system operation [3], and reduce the utilization of fossil fuels.

The rapid development of wind and photovoltaic (PV) power generation on the supply side of the power system has become a common development trend worldwide. However, the energy supply side, whether wind or PV power generation technology, can be equally affected by seasonal and geographical factors, etc. Electricity supply has inherent characteristics, such as unstable supply, low energy density, and difficulty in accurate prediction. These uncertainties can put great pressure on the grid’s supply-side dispatching capabilities [4][5].

In the future, if we want to make the power system run smoothly and realize the steady transition of energy production methods from traditional thermal power generation to wind and PV power generation, we must solve the above two kinds of uncertainties. One is the uncertainty of the charging time on the electricity consumption side, and the other is the uncertainty of the power generation capacity on the generation side affected by environmental factors. In essence, it is to solve the mismatch between the supply and demand time of the two, which can improve energy utilization and reduce power storage and transportation costs [6].

Vehicle-to-grid (V2G) is the inevitable trend of low-carbon energy transition and the key to solving the above two problems. The current research focuses on unidirectional V2G, i.e., coordinated charging mode. Unidirectional scheduling of EVs’ charging to consume excess power can help the grid shave peaks and fill valleys, achieving a win-win situation.

The demand-side energy management strategies include pricing approaches [7]. Specifically, the aggregators or EV owners can shift their load according to the announced electricity price mechanism designed by the utility grid, and the total load curves can then be regulated accordingly [8][9]. The application of time-of-use (TOU) charging pricing to guide EV owners’ charging behavior in the world is mostly at the stage of static TOU prices [8][10]. In China, for example, most of the current urban residential electricity consumption is charged by sectional tiered prices. The industrial sector divides prices by seasons and fixed hours, dividing different periods of different seasons into peak hours, flat hours, and valley hours. However, it cannot respond quickly to the problem of load fluctuations in the power system due to the weather-related effects of renewable energy generation.

To address the above issues, this study designed a mobile application for cell phones. This application was designed to match the fluctuating load curve of the renewable energy generation system by guiding the charging behavior of EV owners through real-time fluctuating price changes. This means that the renewable energy generation power signal is converted into a price signal in real-time to provide EV owners, guiding them to assist in peak and valley reduction of the power system.

The application connects the beginning of the data stream to the power plant, which outputs the electricity price every two hours based on the weather and the amount of electricity generated. It is published on the application platform as quickly as possible to achieve a real-time presentation of the electricity price for the users. At the same time, the power plant can also give a price forecast based on the forecast weather conditions of the coming week, allowing consumers to choose their future charging times. This format satisfies the new charging model of the electricity system.

2. EV Charging Price Mechanism

The effectiveness of coordinating the charging time of EVs through a price mechanism, thus reducing the load on the power system, has achieved a consensus in most research-based papers [11]. However, multiple research theories exist on specific price-setting methodologies.

Current methods of mainstream charging price setting include dynamic pricing and static TOU pricing [12]. Dynamic pricing mainly constructs an algorithm model to calculate the electricity price by collecting the charging information of EVs and the load power of the grid with an aggregator, also including the charging demand habits of vehicle owners [13][14]. Some of the common algorithmic models are the interior point method [15], the particle swarm optimization (PSO) algorithm [16][17], and the genetic algorithm. The grid indirectly coordinates the charging behavior of all EVs through a real-time power variation pricing scheme [18] to minimize the charging cost. Furthermore, a unidirectional communication network is necessary to ensure the price information can be broadcast to EV owners [13]. TOU charging models reduce EV owners’ electricity bills by shifting charging times from peak load periods to valley load periods [19][20][21]. The charging price depends on time only, and its range and corresponding period are predetermined according to consumer behavior and the objectives to be achieved through TOU pricing [22]. From the perspective of grid load management, Ma et al. [23] constructed an optimized rates model and showed that TOU prices show great advantages in reducing costs and flattening the grid load curve. The regional TOU price model can effectively reduce the charging cost of customers and mitigate peak-valley load differences and network losses [24].

3. Application of V2G

The charging demand of EVs will increase the peak load of the power grid [25], and their large-scale uncoordinated charging will put enormous pressure on the power supply, thus affecting the safety and stability of the whole power system. To solve this problem, it is necessary to optimize the charging of large EVs [26]. From a technical point of view, the V2G scheme is an important transformation path [27]. In V2G, the aggregated power from a group of EVs can be used to support the grid by providing regulation services (to stabilize voltage and frequency) or reserve services (to meet sudden increases in demand or generator set outages). V2G modes can be divided into bidirectional V2G and unidirectional V2G. Bidirectional V2G means that while conventional charging piles supply power to the car, the EV power battery is also regarded as a decentralized energy storage unit of the power system. Reasonable utilization of EVs’ battery energy to achieve reverse power supply will alleviate the load shock of the grid. Thus, the EV is not only a movable load, but also a distributed energy source. This mode can be used to provide services such as frequency regulation or peak shaving of power grids [12]. However, due to technical and cost problems, it has not been possible to promote it on a large scale for the time being.

Unidirectional V2G is used to guide EVs to coordinated charging by cooperating with the grid operation rules. There are no reserve services. What’s more, the unidirectional V2G services can help consume the abundant renewable energy sources, such as solar and wind energy, by the coordinated charging strategy [28]. Unidirectional V2G can reduce power consumption during peak hours, improve power utilization during valley hours, and alleviate the impact of random charging demand on the grid [5]. Although the unidirectional V2G services still face some obstacles, solutions have been in process. Additionally, unidirectional V2G would build a solid foundation to implement bidirectional V2G in the future [28].

References

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  2. Zhao, P.; Lu, Z.; Fang, J.; Paramati, S.R.; Jiang, K. Determinants of renewable and non-renewable energy demand in China. Struct. Chang. Econ. Dynam. 2022, 54, 202–209.
  3. Muratori, M. Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nat. Energy 2018, 3, 193–201.
  4. Wolinetz, M.; Axsen, J.; Peters, J.; Crawford, C. Simulating the value of electricvehicle-grid integration using a behaviourally realistic model. Nat. Energy 2018, 3, 132–139.
  5. Ma, S.-C.; Yi, B.-W.; Fan, Y. Research on the valley-filling pricing for EV charging considering renewable power generation. Energy Econ. 2022, 106, 105781.
  6. Rupp, M.; Rieke, C.; Handschuh, N.; Kuperjans, I. Economic and ecological optimization of electric bus charging considering variable electricity prices and CO2eq intensities. Transp. Res. Part D Transp. Environ. 2020, 81, 102293.
  7. Wu, Y.; Ravey, A.; Chrenko, D.; Miraoui, A. Demand side energy management of EV charging stations by approximate dynamic programming. Energy Convers. Manag. 2019, 196, 878–890.
  8. Hu, Z.; Zhan, K.; Zhang, H.; Song, Y. Pricing mechanisms design for guiding electric vehicle charging to fill load valley. Appl. Energy 2016, 178, 155–163.
  9. Dong, X.; Mu, Y.; Xu, X.; Jia, H.; Wu, J.; Yu, X.; Qi, Y. A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks. Appl. Energy 2018, 225, 857–868.
  10. Hu, J.; Morais, H.; Sousa, T.; Lind, M. Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects. Renew. Sust. Energy Rev. 2016, 56, 1207–1226.
  11. Kim, J.D. Insights into residential EV charging behavior using energy meter data. Energy Policy 2019, 129, 610–618.
  12. Limmer, S. Dynamic pricing for electric vehicle charging—A literature review. Energies 2019, 12, 3574.
  13. Jian, L.; Zheng, Y.; Shao, Z. High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles. Appl. Energy 2017, 186, 46–55.
  14. Bai, X.; Qiao, W.; Wei, H.; Huang, F.; Chen, Y. Bidirectional coordinating dispatch of large-scale V2G in a future smart grid using complementarity optimization. Int. J. Electr. Power Energy Syst. 2015, 68, 269–271.
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  17. Lunz, B.; Walz, H.; Sauer, D. Optimizing vehicle-to-grid charging strategies using genetic algorithms under the consideration of battery aging. 2011 IEEE Vehicle Power and Propulsion Conference., Chicago, IL, USA, 6–9 September 2011; pp. 1–7.
  18. Zhang, K.; Xu, L.; Ouyang, M.; Wang, H.; Lu, L.; Li, J.; Li, Z. Optimal decentralized valley-filling charging strategy for electric vehicles. Energy Convers. Manag. 2014, 78, 537–550.
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Subjects: Computer Science, Software Engineering
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Dingyi Lu
,
Yunqian Lu
,
Kexin Zhang
,
Chuyuan Zhang
,
Shaochao Ma
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Revisions: 3 times (View History)
Update Date: 20 Jul 2023
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