Method and apparatus, system, storage medium for ordered charging dynamic guidance

Abstract

The application discloses an orderly charging dynamic guiding method and device, system and storage medium, introduces a dynamic robust pricing strategy and a charging station gravity function model, significantly optimizes operation efficiency and resource allocation of an electric vehicle charging station, adjusts a charging price in real time, effectively guides electric vehicle users to select a non-crowded charging station, thereby relieving congestion of part of the charging stations, reducing user waiting time, and improving charging experience of the users.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle charging technology, specifically relating to an orderly charging dynamic guidance method, device, system, and storage medium. Background Technology

[0002] In research on electric vehicle charging stations, traditional pricing strategies often rely on fixed prices or simple time-of-use pricing models. These methods cannot effectively address the randomness of electric vehicle user charging behavior and the intermittent fluctuations in charging demand. In particular, due to the strong uncertainty in electric vehicle charging behavior, existing charging pricing strategies often lead to congestion at some charging stations, while others suffer from resource waste due to a lack of demand. Furthermore, many traditional pricing strategies fail to consider the spatial distribution of electric vehicle users from charging stations and the actual attractiveness of charging stations, further limiting the rational utilization of charging station resources and the balance of grid load. In contrast, the dynamic robust pricing strategy proposed in this paper, combined with a gravity function model, can dynamically adjust prices based on real-time demand, the number of remaining charging plugs, and electric vehicle charging behavior to guide electric vehicle users to choose less congested charging stations, thereby alleviating charging station congestion and improving operational efficiency and user satisfaction. This technical solution, by introducing robust optimization, can effectively handle the uncertainty of electric vehicle charging demand, providing a more flexible and efficient charging pricing mechanism with significant technological advancement and practical application value. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides an orderly charging dynamic guidance method, device, system, and storage medium. By combining a gravity function with a dynamic robust pricing strategy, it solves the shortcomings of traditional pricing methods in effectively coping with fluctuations in electric vehicle charging demand and congestion.

[0004] To achieve the above objectives, the present invention provides the following solution: The present invention also provides an ordered charging dynamic guidance method, comprising: Step S1: Obtain charging station data; Step S2: Construct a gravity function model for the charging station based on the charging station data; Step S3: Based on the electricity market price and photovoltaic power generation data, which are uncertain sets, construct a dynamic pricing model for charging stations; Step S4: Model and manage the electric vehicle charging process based on the charging station gravity function model and the charging station dynamic pricing model.

[0005] As a preferred option, in step S3, optimization will be performed under extreme scenarios of the uncertainty set, and a robustness factor constraint will be introduced to control the size of the uncertainty set in order to construct a dynamic pricing model for charging stations.

[0006] Preferably, the charging station data includes: the location of the charging station, the number of charging plugs, the time distribution of electric vehicle charging demand, and the parameters of photovoltaic power generation and energy storage equipment.

[0007] The present invention also provides an orderly charging dynamic guidance device, comprising: The first processing module is used to acquire charging station data; The second processing module is used to construct a gravity function model of the charging station based on the charging station data; The third processing module is used to construct a dynamic pricing model for charging stations based on electricity market prices and photovoltaic power generation data, which are considered as an uncertain set. The fourth processing module is used to model and manage the electric vehicle charging process based on the charging station's gravity function model and dynamic pricing model.

[0008] As a preferred option, the third processing module will be optimized under extreme scenarios of uncertainty set and a robustness factor constraint will be introduced to control the size of uncertainty set in order to construct a dynamic pricing model for charging stations.

[0009] Preferably, the charging station data includes: the location of the charging station, the number of charging plugs, the time distribution of electric vehicle charging demand, and the parameters of photovoltaic power generation and energy storage equipment.

[0010] The present invention also provides an ordered charging dynamic guidance system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes an ordered charging dynamic guidance method when executed by the processor.

[0011] The present invention also provides a storage medium storing a computer program, which executes an ordered charging dynamic guidance method during runtime.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: By introducing a dynamic robust pricing strategy and a charging station gravity function model, the operational efficiency and resource allocation of electric vehicle charging stations are significantly optimized. Real-time adjustments to charging prices effectively guide electric vehicle users to choose less congested charging stations, thereby alleviating congestion at some stations, reducing user waiting times, and improving the user charging experience. Simultaneously, charging station profits are effectively increased, and charging resources are utilized more rationally. Furthermore, this invention can cope with fluctuations in electricity market prices and the uncertainties of photovoltaic power generation, providing charging station operators with a flexible and reliable pricing mechanism, promoting more efficient integration and interaction between electric vehicles and the power grid, and contributing to the achievement of sustainable development goals. Attached Figure Description

[0013] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the ordered charging dynamic guidance method according to an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Example 1 like Figure 1 As shown, the present invention provides an ordered charging dynamic guidance method, comprising: Step S1: Data Collection In the initial stage of the experiment, it is necessary to collect data on charging stations, including their locations, the number of charging plugs, the temporal distribution of electric vehicle charging demand, and the parameters of photovoltaic power generation and energy storage devices. Taking four geographically adjacent charging stations in Boulder, Colorado, as an example, the data includes charging demand at different times and the parameters of the photovoltaic and energy storage units at each charging station.

[0018] Step S2: Construction of the Gravitational Function Model for Charging Station The attractiveness of charging stations is simulated using a gravity function model. The attractiveness of a charging station is related not only to factors such as charging price, number of charging plugs, charging power, and the distance from the user to the charging station, but also to the photovoltaic power generation and energy storage systems of the charging station.

[0019] charging station CS i The gravitational function is: In the formula: To enhance the attractiveness of charging stations to electric vehicles, S i This represents the total number of charging plugs in the charging station. P i The rated charging capacity corresponds to the charging level of the charging station. For the electricity price of charging, Let j be the distance from the electric vehicle to the charging station.

[0020] The boundary point between two charging stations that attracts electric vehicles is the midpoint where their attraction to electric vehicle users is equal. This boundary point can be calculated using the following formula: In the formula: l a and l b They are respectively CS a and CS b Distance to the attraction boundary point.

[0021] Once charging stations are equipped with monitoring devices, electric vehicle users can check the availability of different charging station plugs at any time and make a selection. Based on this, the gravity function of charging stations is improved: . In the formula: For charging stations The number of charging plugs available in the system To enhance the attractiveness of charging stations to electric vehicles, S i This represents the total number of charging plugs in the charging station. P i The rated charging capacity corresponds to the charging level of the charging station. For the electricity price of charging, Let j be the distance from the electric vehicle to the charging station.

[0022] Furthermore, the boundary points of the charging station can be calculated using the following formula: In the formula:l a and l b They are respectively CS a and CS b Distance to the attraction boundary point.

[0023] The attractiveness of each charging station changes in real time with fluctuations in charging demand and price adjustments, influencing the choices of electric vehicle users. For example, charging stations 1 and 3 have larger photovoltaic and energy storage capacities, enabling them to store electricity when electricity prices are low and offer lower prices to attract users during periods of high demand. Charging station 2, on the other hand, has a smaller photovoltaic output and experiences greater price fluctuations, but it can still attract users during peak demand periods by adjusting its prices in real time.

[0024] Step S3: Setting the dynamic pricing model for charging stations To dynamically adjust charging prices, the dynamic pricing model for this charging station employs a robust optimization method to handle the impact of uncertainties such as electricity market prices and photovoltaic power output. By setting uncertainty sets (such as the range of electricity market price fluctuations and the randomness of photovoltaic power generation), the optimization model makes pricing decisions under worst-case scenarios to reduce the operational risks of the charging station.

[0025] Robust optimization will be performed under extreme scenarios with uncertain sets, and a robustness factor will be introduced. and Constraints are used to control the size of the uncertainty set in order to reduce the conservatism of the optimization results.

[0026] External electricity market price uncertainty set: In the formula, λ D This indicates the uncertainty of electric vehicle charging demand. λ h This represents the expected value of electric vehicle charging demand. and It is a robustness factor.

[0027] Uncertain set of photovoltaic output: In the formula, and These are the midpoints of the confidence intervals for random factors. and Each is half the length of the confidence interval for the random factor. PV m This represents the amount of photovoltaic power generated at a specific moment. PV h This represents the expected value of photovoltaic power generation. and It is a robustness factor.

[0028] The robustness factor can be determined by the following formula: In the formula, T This is a runtime segment value. β The confidence level.

[0029] Charging stations dynamically adjust prices to respond to varying electric vehicle charging demands. During peak demand periods (9-15 AM), stations raise prices to attract more electric vehicles to less congested stations. Conversely, during off-peak periods (after 4 PM), prices are appropriately reduced to increase the attractiveness of the charging stations.

[0030] Step S4: Charging process modeling and queue management During the charging process, the usage of each charging plug needs to be monitored. If a charging plug is already occupied, electric vehicle users need to wait or choose another charging station. The implementation of a gravity function and dynamic pricing strategy can effectively reduce waiting time. The gravity function is as follows: In the formula: For charging stations The number of charging plugs available in the system To enhance the attractiveness of charging stations to electric vehicles, S i This represents the total number of charging plugs in the charging station. P i The rated charging capacity corresponds to the charging level of the charging station. For the electricity price of charging, Let j be the distance from the electric vehicle to the charging station.

[0031] Electric vehicle users choose the most suitable charging station based on charging prices and plug availability. In summary, the queuing management process at charging stations is based on dynamic pricing and a gravity function model. By adjusting charging prices in real time, optimizing resource allocation at charging stations, and guiding users to distribute their demand, efficient queuing management is achieved, ultimately reducing queuing time, improving charging station operational efficiency, and increasing user satisfaction.

[0032] When modeling the charging process of electric vehicles, the first step is to examine the charging stations. Whether an electric vehicle is charging among all the charging plugs in the system is indicated by the following formula: In the formula: For connection to the charging plug k The final state of charge of the electric vehicle's battery. For connection to the charging plug k The initial state of charge of the battery in the electric vehicle. For the battery capacity of electric vehicles, P i For the first i One charging station CS i The average charging power.

[0033] The charging station pricing decision model is constructed, and the charging station revenue function is: In the formula: For the first i The revenue obtained by each charging station For the first i Each charging station has set up [a certain number of] [units / stations]. t The price of charging at any time; For the first i One charging station t The number of charging plugs occupied at any given time. For the first i The average charging energy of each charging station.

[0034] Charging station operating costs: In the formula: For the first i The cost of a charging station The electricity price that charging stations purchase from the electricity market. This refers to the maintenance cost coefficient per unit of electricity used for charging facilities. This represents the maintenance cost coefficient for energy storage equipment. and The first i The charging and discharging power of the energy storage equipment at each charging station. This represents the maintenance cost coefficient for photovoltaic equipment. For the first i The amount of electricity generated by photovoltaic equipment in each charging station.

[0035] Charging station profit function: In the formula: For the first i The total revenue of each charging station, and i∈I .

[0036] Furthermore, a robust optimization model is introduced, and a dynamic robust pricing decision model for charging stations is constructed. The objective of the robust optimization model is to make the optimal decision under the worst-case scenario of uncertainty variables. Based on this, the objective function of the robust optimization model for dynamic pricing decisions of charging stations can be expressed as follows: In the formula, R CS Indicates charging station i The payoff function Indicates charging station i At any moment t The set charging price, λ D This indicates the uncertainty of electric vehicle charging demand. This indicates the degree to which electric vehicle charging demand depends on photovoltaic power generation.

[0037] Charging price decision constraints: In the formula, For the first i Each charging station sets a lower limit for charging prices. For the first i Each charging station sets an upper limit on charging prices.

[0038] Energy storage battery operating constraints: In the formula: For the first i The energy storage capacity of the energy storage battery equipment in each charging station This represents the upper limit of the capacity of energy storage battery devices. This represents the lower limit of the capacity of energy storage battery devices; and These refer to the charging and discharging efficiencies of energy storage battery devices, respectively. For the first i The maximum charging capacity of the energy storage battery equipment in each charging station. For the first i The maximum discharge capacity of the energy storage battery equipment in each charging station; and The first iThe charging and discharging states of the energy storage battery equipment in each charging station are binary variables.

[0039] Constraints on purchasing electricity from external electricity markets: In the formula, To purchase the maximum amount of electricity from the power grid, Electricity purchased from the power grid.

[0040] System power balance constraints: In the formula, Electricity purchased from the power grid, Indicates the first i A charging station at a time t The power of the energy storage device's discharge. Indicates the first i A charging station at a time t The power output of the photovoltaic system Indicates the first i A charging station at a time t The charging power of energy storage devices, Indicates the first i A charging station at a time t Total power requirements.

[0041] Uncertainty Variable Constraints: In the formula, λ D This indicates the uncertainty of electric vehicle charging demand. This indicates the degree to which electric vehicle charging demand depends on photovoltaic power generation.

[0042] At this point, the dynamic robust pricing decision model for electric vehicles regarding charging stations has been completed.

[0043] During testing, the super-fast charging plugs at charging stations 1 and 3 were over 80% occupied during peak demand, while the price at charging station 2 fluctuated significantly, leading to increased waiting times for users at certain times. However, with dynamic pricing implemented, users tended to choose stations with lower charging prices and available charging plugs, thus reducing waiting times.

[0044] Three different pricing strategies were compared: fixed pricing, peak-valley time-of-use pricing, and dynamic robust pricing. The operating revenue of charging stations, the number of electric vehicles served, and user wait times were evaluated under each strategy, as shown in Table 1. Table 1 From Table 1, we know that: Fixed pricing strategy (strategy 3): The total profit of the charging station operator is 6,480 yuan, with a total of 439 electric vehicles charging, and users have a long waiting time.

[0045] Peak-valley time-of-use pricing strategy (strategy two): Charging station operators' profits increased by 34.6%, the number of electric vehicles served reached 616, and the waiting time was reduced by 9 minutes.

[0046] Dynamic Robust Pricing Strategy (Strategy 1): The charging station's profit reached 11,070 yuan, the number of electric vehicles served increased to 758, and the user waiting time was further reduced to 8 minutes. Compared with the other two strategies, the dynamic pricing strategy significantly improved the attractiveness and operational efficiency of the charging station.

[0047] Dynamic robust pricing strategies have demonstrated significant advantages in alleviating charging station congestion, increasing charging station profitability, and reducing waiting times for electric vehicle users. This strategy can adjust prices in real time based on changes in charging demand, the attractiveness of charging stations, and fluctuations in the electricity market, thereby achieving optimal resource allocation.

[0048] Example 2 The present invention also provides an orderly charging dynamic guidance device, comprising: The first processing module is used to acquire charging station data; The second processing module is used to construct a gravity function model of the charging station based on the charging station data; The third processing module is used to construct a dynamic pricing model for charging stations based on electricity market prices and photovoltaic power generation data, which are considered as an uncertain set. The fourth processing module is used to model and manage the electric vehicle charging process based on the charging station's gravity function model and dynamic pricing model.

[0049] As one embodiment of the present invention, the third processing module will optimize under extreme scenarios of uncertainty set and introduce robustness factor constraints to control the size of uncertainty set in order to construct a dynamic pricing model for charging stations.

[0050] As one embodiment of the present invention, the charging station data includes: the location of the charging station, the number of charging plugs, the time distribution of electric vehicle charging demand, and the parameters of photovoltaic power generation and energy storage equipment.

[0051] Example 3 The present invention also provides an ordered charging dynamic guidance system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes an ordered charging dynamic guidance method when executed by the processor.

[0052] Example 4 The present invention also provides a storage medium storing a computer program, which executes an ordered charging dynamic guidance method during runtime.

[0053] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for guiding orderly charging dynamically, characterized in that, include: Step S1: Obtain charging station data; Step S2: Construct a gravity function model for the charging station based on the charging station data; Step S3: Based on the electricity market price and photovoltaic power generation data, which are uncertain sets, construct a dynamic pricing model for charging stations; Step S4: Model and manage the electric vehicle charging process based on the charging station gravity function model and the charging station dynamic pricing model.

2. The ordered charging dynamic guidance method as described in claim 1, characterized in that, In step S3, optimization will be performed under extreme scenarios of the uncertainty set, and a robustness factor constraint will be introduced to control the size of the uncertainty set in order to construct a dynamic pricing model for charging stations.

3. The ordered charging dynamic guidance method as described in claim 2, characterized in that, The charging station data includes: the location of the charging station, the number of charging plugs, the time distribution of electric vehicle charging demand, and the parameters of photovoltaic power generation and energy storage equipment.

4. An orderly charging dynamic guidance device, characterized in that, include: The first processing module is used to acquire charging station data; The second processing module is used to construct a gravity function model of the charging station based on the charging station data; The third processing module is used to construct a dynamic pricing model for charging stations based on electricity market prices and photovoltaic power generation data, which are considered as an uncertain set. The fourth processing module is used to model and manage the electric vehicle charging process based on the charging station's gravity function model and dynamic pricing model.

5. The ordered charging dynamic guidance device as described in claim 4, characterized in that, The third processing module will optimize under extreme scenarios of uncertainty set and introduce robustness factor constraints to control the size of uncertainty set in order to build a dynamic pricing model for charging stations.

6. The ordered charging dynamic guidance device as described in claim 5, characterized in that, The charging station data includes: the location of the charging station, the number of charging plugs, the time distribution of electric vehicle charging demand, and the parameters of photovoltaic power generation and energy storage equipment.

7. An orderly charging dynamic guidance system, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program performing the ordered charging dynamic boot method as described in any one of claims 1-3 when executed by the processor.

8. A storage medium, characterized in that, The storage medium stores a computer program, which executes the ordered charging dynamic guidance method as described in any one of claims 1-3 when it runs.

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