Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment
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Yusong Zhou
Abstract
This paper puts forward the dynamic load prediction of charging piles of energy storage electric vehicles based on time and space constraints in the Internet of Things environment, which can improve the load prediction effect of charging piles of electric vehicles and solve the problems of difficult power grid control and low power quality caused by the randomness of charging loads in time and space. After constructing a traffic road network model based on the Internet of Things, a travel chain model with different complexity and an electric vehicle charging model, the travel chain is randomly extracted. With the shortest travel time as a constraint, combined with the traffic road network model based on the Internet of Things, the travel route and travel time are determined. According to the State of Charge (SOC) and the travel destination, the location and charging time of the energy storage electric vehicle charging pile are determined. After obtaining the time-space distribution information of the energy storage electric vehicle charging pile at different times and in different regions, it is used as the input of the deep multi-step time-space dynamic neural network, and the network output is the dynamic electric vehicle charging pile. The experimental results show that this method can realize the dynamic load prediction of electric vehicle charging piles. When the number of stacking units is 11, the indexes of Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are the lowest and the index of R 2 is the largest. The load of charging piles in residential areas and work areas exists in the morning and evening peak hours, while the load fluctuation of charging piles in other areas presents a decentralized change law; The higher the complexity of regional traffic network, the greater the load of electric vehicle charging piles in the morning rush hour.
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Research ethics: Not applicable.
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Author contributions: The article was written independently by the author.
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Competing interests: The authors declare that they have no competing financial interests.
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Research funding: Not applicable.
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Data availability: The data are available from the corresponding author on reasonable request.
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Articles in the same Issue
- Frontmatter
- Research Articles
- A seven level fault tolerant hybrid cascaded inverter for renewable energy applications
- Optimal layout scheme design of distribution network micro PMU based on information entropy theory
- Current sensorless model predictive control for LC-filtered voltage source inverters based on sliding mode observer
- BCLM: a novel chaotic map for designing cryptography-based security mechanism for IEEE C37.118.2 PMU communication in smart grid
- Design and control of utility grid-tied bipolar DC microgrid
- Network dynamics in hybrid microgrid and its implications on stability analysis
- Electrical modelling, design, and implementation of a hardware PEM electrolyzer emulator for smart grid testing
- A hybrid search space reduction algorithm and Newton–Raphson based selective harmonic elimination for an asymmetric cascade H-bridge multi-level inverter
- Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment
- Power coordination control method for AC/DC hybrid microgrid considering demand response
- Performance analysis and effective modeling of a solar photovoltaic module based on field tests
- Unleashing the economic potential of wind power for ancillary services
