Time-division and domain-division electricity price formulating method and device considering charging pressure-state-behavior

Abstract

The invention discloses a time-division and domain-division electricity price formulating method and device considering charging pressure-state-behavior. According to the method, a PSR model is introduced, based on historical data analysis, the charging demand is regarded as a pressure factor, the optimal charging pile and the charging busy degree of a user are regarded as state factors, the charging behavior is regarded as a behavior factor, and the parameter weight of the PSR-FM model is deduced reversely; and predicting a user charging behavior by using the PSR-FM model under the determined time scale in combination with a new charging demand, the optimal charging pile of the user and the charging busy degree, and finally, introducing a dynamic correction mechanism combining long-time scale prediction and short-time scale correction to formulate the electricity prices of different time periods and different regions. According to the invention, the travel track of the electric vehicle is incorporated into the charging pricing strategy, so that the user experience and the user stickiness can be remarkably improved. Therefore, the method has remarkable superiority, economical efficiency, feasibility and commercial transformation potential.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method and device for formulating time-based and domain-based electricity prices taking charging pressure, state and behavior into consideration. Background Art

[0002] In the context of the current booming electric vehicle market, the research on charging electricity price strategies is no longer limited to the traditional time-of-use pricing model, but is gradually developing in a more refined and intelligent direction. Although the traditional time-of-use pricing strategy can effectively regulate the grid load, it ignores the uneven spatial distribution of electric vehicle charging demand and the profound impact of electric vehicle travel trajectories on charging behavior.

[0003] At present, the research direction of electric vehicle charging price strategy formulation is mainly to find the optimal pricing scheme that takes into account both user costs and load peak regulation in the time dimension (Ma Pengbo, Huang Zhao, Guo Zhiwei, et al. Optimal scheduling of electric vehicles taking into account load peak regulation and user costs [J]. Electrical Automation, 2024, 46(05): 1-3.). The general research idea is to use user electricity costs and grid load peak-valley difference as optimization targets for solution, and guide electric vehicle users to use electricity during valley hours through prices. Although this model can regulate grid load to a certain extent, on the one hand, it ignores the uneven spatial distribution of electric vehicle charging demand and the profound impact of electric vehicle travel trajectory on charging behavior. On the other hand, time-of-use electricity prices have already set the electricity price for each period in advance based on forecast data, and cannot be adjusted dynamically in real time for emergencies such as special weather and market changes. Summary of the invention

[0004] The present application provides a method and device for formulating time-sharing and domain-based electricity prices taking into account charging pressure-state-behavior, which is suitable for formulating time-sharing and domain-based pricing strategies and provides technical support for the construction of post-smart grids.

[0005] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0006] The time-divided and domain-divided electricity price formulation method considering charging pressure-state-behavior includes the following steps: S1. Collect the battery state of charge (SOC) data of electric vehicles, charging behavior data of electric vehicles, occupancy data of each charging pile and queue data at each time in the set time period of the research area; S2. Calculate the charging demand of electric vehicles based on the battery charging status data of electric vehicles, calculate the optimal charging pile for users based on the user's location and the time-based and domain-based electricity price in the electric vehicle charging behavior data, and calculate the busyness of the charging piles based on the occupancy data and queuing data of each charging pile; S3. Taking the user's charging demand as the pressure factor, the user's optimal charging pile and the busyness of the charging pile as the state factor, and the historical charging behavior as the behavior factor, the PSR-FM model is constructed, and the formula for obtaining the weight parameters of the PSR-FM model is obtained; S4. Setting a long time scale prediction-short time scale correction mode, obtaining the user's historical charging behavior, obtaining weight parameters of the PSR-FM model at two time scales, the long time scale and the short time scale, and obtaining the PSR-FM model at two time scales; S5. Use the PSR-FM model under two time scales to predict the user's charging behavior, and determine the time scale for prediction in the current time period based on the prediction error of the previous time period; S6. Take the time-sharing and domain-based electricity prices as variables, and the electric vehicle battery charging status data, charging pile occupancy data and queuing data as initial values. Use the PSR-FM model under the time scale determined in step S5 to predict the user's charging behavior. Set the sum of the charging benefit and the electricity selling benefit as the objective function. Use the particle swarm algorithm to iteratively calculate the time-sharing and segmented electricity prices for each time period in the study area to ensure the user's charging benefit and the charging pile's electricity selling benefit. Complete the electricity price setting for the current time period and return to step S5.

[0007] Furthermore, in step S2, the charging demand is calculated according to the charging status data of the electric vehicle battery, and the charging demand is quantitatively evaluated and normalized. The specific calculation formula is as follows: ; in, is the required calculation time, Divide the time into intervals, for Moment The charging needs of the vehicle, The closer it is to 1, the greater the car's charging demand. for All vehicles at the moment SOC The maximum value in for All vehicles at the moment SOC The minimum value in for Moment Car SOC .

[0008] Furthermore, in step S2, the optimal charging pile for the user is calculated according to the user's location and the time-sharing and domain-based electricity price in the electric vehicle charging behavior data, and the optimal charging pile for the user is quantitatively evaluated and normalized. The specific calculation formula is as follows: ; in, for Moment The charging cost performance of a vehicle at the optimal charging station is used to indicate the economic benefits of charging. The closer it is to 1, the more cost-effective the car charging is; for Moment Car SOC ; for Moment The price of charging the vehicle; for Moment The distance from the vehicle to its optimal charging station, Travel expenses per unit distance.

[0009] Furthermore, in step S2, the busyness of the charging piles is calculated according to the occupancy and queuing conditions of each charging pile, and a quantitative evaluation and normalization process is performed on the busyness of the charging piles. The specific calculation formula is as follows: ; in, Moment The optimal charging station selected by the vehicle is Charging stations; for Moment The busyness of the optimal charging pile selected by the vehicle is used to indicate the busyness of the charging pile. The closer it is to 1, the more idle the charging station is; for The total number of vehicles queuing at the charging station at any given moment; for Moment Car SOC ; for Moment The charging power of each charging station; For the The maximum charging power of each charging pile; For the The total number of vehicles in the queue when the charging power of a charging pile is maximum.

[0010] Furthermore, in step S3, a PSR-FM model is constructed based on the PSR evaluation system and the FM algorithm model, taking the user charging demand as the pressure factor, i.e., the disaster-causing factor, and the vehicle and charging pile location distribution, time-sharing and domain-sharing electricity prices, and the busyness of the charging piles as the state factors, i.e., the disaster-prone environment, and combining the historical charging behavior, finally obtaining the time-sharing and domain-sharing electricity price pricing strategy; In order to better explore the impact of pressure factors and state factors on user charging behavior, the state factors include charging economic benefits and charging pile busyness. The pressure factors and state factors expressed in numerical values ​​are converted into vectors existing in the first quadrant. The specific calculation formula is as follows: ; in, for Moment The pressure factor and state factor set of the vehicle, when When it is 1, 2, and 3, it corresponds to the pressure factor, the charging economic benefit in the state factor, and the busyness of the charging pile in the state factor respectively; In the PSR-FM model, according to the pressure factor , Charging economic benefits in state factors , the busyness of the charging pile in the state factor , get the predicted Moment Vehicle user charging behavior , as follows: ; According to the user's historical charging demand, the historical busyness of the charging pile, and the user's historical charging behavior, the prediction parameters of the PSR-FM model can be reversely derived. and ;in for The charging behavior error parameter, for The charging behavior and pressure state interact with each other.

[0011] Further, in step S4, a long time scale prediction-short time scale correction mode is set; The time period corresponding to the long time scale is one day, that is, 96 15-minute periods. The time period corresponding to the short time scale is 1 15-minute period. In the long time scale prediction model, The value is 96, in the short time scale correction mode, The value is 1; Since the prediction periods used in the two time scales are different, the weight parameters of the PSR-FM model in the two time scales are and They are also different; Get the user's historical charging behavior: ; Weight parameters of the PSR-FM model at two time scales; and obtaining the PSR-FM model at two time scales.

[0012] Furthermore, in step S5, the day-ahead data and the real-time data are used to predict the charging behavior of the user in the previous time period using the PSR-FM model at two time scales. and , and according to the prediction errors of the PSR-FM model at two time scales and , determine which time scale to use for prediction in the current time period. The specific calculation formula is as follows: ; in, is the prediction error of the PSR-FM model on a long time scale, For the current moment, for Moment The actual charging behavior of each user, is the prediction error of the PSR-FM model at short time scale; The charging behavior of users predicted by the PSR-FM model in a long time scale. The user's charging behavior predicted by the PSR-FM model in a short time scale.

[0013] Furthermore, in step S5, if the current time scale is a long time scale, one time period is one day, that is, 96 15-minute periods; if the current time scale is a short time scale, one time period is 15 minutes.

[0014] Furthermore, in step S6, the time-based and domain-based electricity prices are used as variables, the battery charging status data of electric vehicles, the occupancy data of each charging pile and the queue data are used as initial values, and the PSR-FM model under the time scale determined in step S5 is used to Moment Vehicle user charging behavior Make predictions; The sum of charging benefit and electricity selling benefit is taken as the objective function, and the particle swarm algorithm is used to iteratively calculate the time-sharing and segmented electricity prices in each period of the study area. The objective function calculation formula is as follows: ; ; ; in, and are the number of charging piles and the number of electric vehicle users, is the charging pile pricing coefficient, for Moment The electricity price of a charging station, for Moment The charging price for each user, The total amount of charging consumption for electric vehicle users, Optimize the objective function for the particle swarm.

[0015] Compared with the prior art, the advantages of the present invention are: The present invention proposes a method for formulating electricity prices that considers charging pressure-state-behavior in time and domain, introduces the PSR model, and based on historical data analysis, regards charging demand as a pressure factor, the user's optimal charging pile and charging busyness as state factors, and the charging behavior as a behavior factor, and reversely derives the parameter weights of the PSR-FM model. The trained PSR-FM model is then used to predict the user's charging behavior in combination with the new charging demand, the user's optimal charging pile and charging busyness. Finally, a dynamic correction mechanism combining long-term prediction with short-term correction is introduced to formulate electricity prices in different regions at different time periods.

[0016] It can be seen that compared with the prior art, the present invention perfectly solves the shortcomings of the prior art. On the one hand, the present invention introduces the busyness of charging piles and the location of users to formulate electricity prices in different regions, which can balance the supply and demand distribution of charging resources and alleviate the peak pressure of charging stations in urban centers. It can also guide users to reasonably choose charging time and location to improve the overall utilization rate of charging facilities. On the other hand, the present invention introduces a dynamic correction mechanism of -15min correction of the previous forecast, which is helpful to deal with various emergencies, adjust the charging electricity price in time, and ensure user experience. In addition, the present invention incorporates the travel trajectory of electric vehicles into the charging pricing strategy, which can significantly improve the user experience and enhance user stickiness. It can be seen that the present invention has significant superiority, economy, feasibility and commercial transformation potential.

[0017] The present invention incorporates the travel trajectory of electric vehicles as a key factor into the charging pricing strategy, aiming to further improve and optimize the existing pricing system. Considering that the charging demand of electric vehicle users not only fluctuates over time, but is also deeply affected by geographical location, by analyzing the vehicle's driving path and charging habits, the charging demand at different locations can be accurately predicted. This innovative idea enables the charging price to be flexibly adjusted according to actual needs, and can not only provide price discounts during periods of low grid load or low-utilization charging stations, guide users to reasonably choose charging time and location, thereby improving the overall utilization rate of charging facilities and reducing resource waste; it can also balance the supply and demand distribution of charging resources through differentiated pricing strategies, alleviate the peak pressure of charging stations in urban centers, and reduce traffic congestion caused by charging. In addition, incorporating the travel trajectory of electric vehicles into the charging pricing strategy can also significantly improve user experience and enhance user stickiness. Through personalized and intelligent charging services, the actual needs and preferences of different users can be met, such as providing cross-regional charging discount packages for long-distance travel users, and providing convenient and preferential charging services for short-distance commuting users at their frequented locations, thereby improving user satisfaction and loyalty to electric vehicles and charging services. A reasonable charging price system can not only attract more consumers to buy electric vehicles and promote the further expansion of the new energy vehicle market, but also promote the rational layout and efficient operation of charging infrastructure, laying a solid foundation for the long-term prosperity of the new energy vehicle industry. At the same time, it will also help to achieve the coordinated optimization of energy, transportation and the environment, and contribute to the construction of a green, low-carbon and sustainable development society.

[0018] In summary, incorporating electric vehicle travel trajectories into charging pricing strategies is an important measure to meet the current challenges of the electric vehicle market, improve grid operation efficiency, optimize resource allocation, enhance user experience, and promote the sustainable development of the new energy vehicle industry. The implementation of this innovative idea will open a new chapter in electric vehicle charging services and inject new impetus into the popularization and green development of new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a method for formulating time-divided and domain-divided electricity prices taking into account charging pressure-state-behavior in an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of the PSR-FM model in an embodiment of the present invention; Figure 3 A schematic diagram of the distribution of charging power in time and space before optimization in an embodiment of the present invention; Figure 4 Schematic diagram of the distribution of charging power in time and space after optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the specific implementation of the present invention is described in detail below with reference to the accompanying drawings and examples.

[0021] Example: The time-of-day and domain-based electricity price formulation method considering charging pressure, state and behavior, such as Figure 1 As shown, the following steps are included: S1. Collect the battery state of charge (SOC) data of electric vehicles, charging behavior data of electric vehicles, occupancy data of each charging pile and queue data at each time in the set time period of the research area; S2. Calculate the charging demand of electric vehicles based on the battery charging status data of electric vehicles, calculate the optimal charging pile for users based on the user's location and the time-based and domain-based electricity price in the electric vehicle charging behavior data, and calculate the busyness of the charging piles based on the occupancy data and queuing data of each charging pile; The charging demand is calculated based on the battery charging status data of the electric vehicle, and the charging demand is quantitatively evaluated and normalized. The specific calculation formula is as follows: ; in, is the required calculation time, Divide the time into intervals, for Moment The charging needs of the vehicle, The closer it is to 1, the greater the car's charging demand. for All vehicles at the moment SOC The maximum value in for All vehicles at the moment SOC The minimum value in for Moment Car SOC .

[0022] The optimal charging pile for users is calculated based on the user's location and the time-sharing and region-based electricity price in the electric vehicle charging behavior data, and the optimal charging pile for users is quantitatively evaluated and normalized. The specific calculation formula is as follows: ; in, for Moment The charging cost performance of a vehicle at the optimal charging station is used to indicate the economic benefits of charging. The closer it is to 1, the more cost-effective the car charging is; for Moment Car SOC ; for Moment The price of charging the vehicle; for Moment The distance from the vehicle to its optimal charging station, Travel expenses per unit distance.

[0023] The busyness of the charging piles is calculated based on the occupancy and queuing conditions of each charging pile, and is quantitatively evaluated and normalized. The specific calculation formula is as follows: ; in, Moment The optimal charging station selected by the vehicle is Charging stations; for Moment The busyness of the optimal charging pile selected by the vehicle is used to indicate the busyness of the charging pile. The closer it is to 1, the more idle the charging station is; for The total number of vehicles queuing at the charging station at any given moment; for Moment Car SOC ; for Moment The charging power of each charging station; For the The maximum charging power of each charging pile; For the The total number of vehicles in the queue when the charging power of a charging pile is maximum.

[0024] S3. Taking the user's charging demand as the pressure factor, the user's optimal charging pile and the busyness of the charging pile as the state factor, and the historical charging behavior as the behavior factor, the PSR-FM model is constructed, and the formula for obtaining the weight parameters of the PSR-FM model is obtained; In one embodiment, Figure 2As shown, based on the PSR evaluation system (see: Xu Bailu, Wang Kuang. Comprehensive evaluation of soil environmental quality of railway vehicle depot based on PSR model [J / OL]. Railway Standard Design, 1-8 [2024-12-04]. https: / / doi.org / 10.13238 / j.issn.1004-2954.202405280004.) and the FM algorithm model (see: Li Xingbing, Xie Jun, Xu Xinying, et al. Deep click rate prediction based on improved FM algorithm and attention mechanism Model[J]. Journal of Nanjing University of Science and Technology, 2021, 45(04):429-438.DOI:10.14177 / j.cnki.32-1397n.2021.45.04.006.) The PSR-FM model is constructed. Compared with the traditional charging price strategy, the user charging demand is taken as the pressure factor, that is, the disaster-causing factor, and the vehicle and pile location distribution, time-sharing and domain-sharing electricity prices and the busyness of the charging piles are taken as the state factors, that is, the disaster-prone environment. The historical charging behavior is integrated, and finally the time-sharing and domain-sharing electricity price pricing strategy is obtained; In order to better explore the impact of pressure factors and state factors on user charging behavior, the state factors include charging economic benefits and charging pile busyness. The pressure factors and state factors expressed in numerical values ​​are converted into vectors existing in the first quadrant. The specific calculation formula is as follows: ; in, for Moment The pressure factor and state factor set of the vehicle, when When it is 1, 2, and 3, it corresponds to the pressure factor, the charging economic benefit in the state factor, and the busyness of the charging pile in the state factor respectively; In the PSR-FM model, according to the pressure factor , Charging economic benefits in state factors , the busyness of the charging pile in the state factor , get the predicted Moment Vehicle user charging behavior , as follows: ; According to the user's historical charging demand, the historical busyness of the charging pile, and the user's historical charging behavior, the prediction parameters of the PSR-FM model can be reversely derived. and ;in for The charging behavior error parameter, for The charging behavior and pressure state interact with each other.

[0025] S4. Setting a long time scale prediction-short time scale correction mode, obtaining the user's historical charging behavior, obtaining weight parameters of the PSR-FM model at two time scales, the long time scale and the short time scale, and obtaining the PSR-FM model at two time scales; Set the long time scale prediction-short time scale correction mode; The time period corresponding to the long time scale is one day, that is, 96 15-minute periods. The time period corresponding to the short time scale is 1 15-minute period. In the long time scale prediction model, The value is 96, in the short time scale correction mode, The value is 1; Since the prediction periods used in the two time scales are different, the weight parameters of the PSR-FM model in the two time scales are and They are also different; Get the user's historical charging behavior: ; Weight parameters of the PSR-FM model at two time scales; and obtaining the PSR-FM model at two time scales.

[0026] S5. Use the PSR-FM model under two time scales to predict the user's charging behavior, and determine the time scale for prediction in the current time period based on the prediction error of the previous time period; Using the day-ahead data and real-time data, the PSR-FM model at two time scales is used to predict the user's charging behavior in the previous time period. and , and according to the prediction errors of the PSR-FM model at two time scales and , determine which time scale to use for prediction in the current time period. The specific calculation formula is as follows: ; in, is the prediction error of the PSR-FM model on a long time scale, For the current moment, for Moment The actual charging behavior of each user, is the prediction error of the PSR-FM model at short time scale; The charging behavior of users predicted by the PSR-FM model in a long time scale. The user's charging behavior predicted by the PSR-FM model in a short time scale.

[0027] If the current time scale is a long time scale, a time period is one day, that is, 96 15 minutes. If the current time scale is a short time scale, a time period is 15 minutes.

[0028] S6, taking the time-sharing and domain-based electricity prices as variables, the electric vehicle battery charging status data, the charging pile occupancy data and the queuing data as initial values, using the PSR-FM model under the time scale determined in step S5 to predict the user's charging behavior, setting the sum of the charging benefit and the electricity selling benefit as the objective function, and using the particle swarm algorithm to iteratively calculate the time-sharing and segmented electricity prices for each time period in the study area, ensuring the user's charging benefit and the charging pile's electricity selling benefit, completing the electricity price formulation for the current time period, and returning to step S5; The time-based and domain-based electricity prices are used as variables, and the battery charging status data of electric vehicles, the occupancy data of each charging pile, and the queue data are used as initial values. The PSR-FM model under the time scale determined in step S5 is used to Moment Vehicle user charging behavior Make predictions; The sum of charging benefit and electricity selling benefit is taken as the objective function, and the particle swarm algorithm is used to iteratively calculate the time-sharing and segmented electricity prices in each period of the study area. The objective function calculation formula is as follows: ; ; ; in, and are the number of charging piles and the number of electric vehicle users, is the charging pile pricing coefficient, for Moment The electricity price of a charging station, for Moment The charging price for each user, The total amount of charging consumption for electric vehicle users, Optimize the objective function for the particle swarm.

[0029] In one embodiment, considering that the battery industry is developing rapidly and its capacity is increasing, the capacity of the EV battery is 80kW·h, the power consumption per 100km is 20kW·h, and the purchase price of the battery is 80,000 yuan. The EV initial SOC follows a normal distribution with a mean of 0.5 and a standard deviation of 0.1, the user's travel time follows a normal distribution with a mean of 480 and a standard deviation of 60, and the user's return time follows a normal distribution with a mean of 1080 and a standard deviation of 60. Based on the probability function obtained by fitting, the EV travel situation is simulated, and the charging and feeding behavior of the EV is obtained by using the Monte Carlo sampling method.

[0030] The smart charging device uses a conventional charging method, adopting a constant power charging and feeding mode, with a charging and feeding power of 8kW and a charging and feeding efficiency of 90%. Considering safety, the upper and lower limits of SOC are set to 90% and 10% respectively.

[0031] The charging (discharging) efficiency of EV batteries is set to 90%.

[0032] The simulation scenario is a residential community-company park with 200 EVs, which is powered by 6 transformers with a capacity of 1500 kV·A.

[0033] In one embodiment, the IEEE33 model is used for simulation, and the distribution of charging power in time and space before optimization is as follows: Figure 3 As shown; the distribution of charging power in time and space after optimization is as follows Figure 4 As shown. It can be seen that before optimization, the spatial distribution of charging power is obviously unreasonable, the curve fluctuates greatly, the charging load is concentrated in a small number of nodes, the peak-to-valley difference is significant, and the maximum charging power is 49kW. At this time, the variance of the spatial charging power is 45.77. After optimization, the spatial distribution of charging power is reasonable, the charging load is relatively evenly distributed on each node, the curve fluctuates little, the maximum charging power is reduced to 31kW, and the variance of the spatial charging power is 22.91. It can be seen that the present invention is effective in regulating the spatial distribution of charging power.

[0034] The preferred embodiments of the present application disclosed above are only used to help understand the present invention and its core ideas. For those skilled in the art, according to the ideas of the present invention, there will be changes in specific application scenarios and implementation operations, and this specification should not be interpreted as limiting the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A time-based and domain-based electricity price formulation method considering charging pressure, state, and behavior, characterized in that: The following steps are involved: S1. Collect the battery charging status data of electric vehicles, the charging behavior data of electric vehicles, the occupancy data of each charging pile and the queuing data at each time in the set time period of the research area; S2. Calculate the charging demand of electric vehicles based on the battery charging status data of electric vehicles, calculate the optimal charging pile for users based on the user's location and the time-based and domain-based electricity price in the electric vehicle charging behavior data, and calculate the busyness of the charging piles based on the occupancy data and queuing data of each charging pile; S3. Taking the user's charging demand as the pressure factor, the user's optimal charging pile and the busyness of the charging pile as the state factor, and the historical charging behavior as the behavior factor, the PSR-FM model is constructed, and the formula for obtaining the weight parameters of the PSR-FM model is obtained; S4. Setting a long time scale prediction-short time scale correction mode, obtaining the user's historical charging behavior, obtaining weight parameters of the PSR-FM model at two time scales, the long time scale and the short time scale, and obtaining the PSR-FM model at two time scales; S5. Use the PSR-FM model under two time scales to predict the user's charging behavior, and determine the time scale for prediction in the current time period based on the prediction error of the previous time period; S6. Take the time-sharing and domain-based electricity prices as variables, and the electric vehicle battery charging status data, charging pile occupancy data and queuing data as initial values. Use the PSR-FM model under the time scale determined in step S5 to predict the user's charging behavior. Set the sum of the charging benefit and the electricity selling benefit as the objective function. Use the particle swarm algorithm to iteratively calculate the time-sharing and segmented electricity prices for each time period in the study area to ensure the user's charging benefit and the charging pile's electricity selling benefit. Complete the electricity price setting for the current time period and return to step S5.

2. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to claim 1 is characterized in that: In step S2, the charging demand is calculated according to the charging status data of the electric vehicle battery, and the charging demand is quantitatively evaluated and normalized. The specific calculation formula is as follows: ， in, is the required calculation time, Divide the time into intervals, for Moment The charging needs of the vehicle, The closer it is to 1, the greater the car's charging demand. for All vehicles at the moment SOC The maximum value in for All vehicles at the moment SOC The minimum value in for Moment Car SOC .

3. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to claim 1, characterized in that: In step S2, the optimal charging pile for the user is calculated according to the user's location and the time-sharing and domain-sharing electricity price in the electric vehicle charging behavior data, and the optimal charging pile for the user is quantitatively evaluated and normalized. The specific calculation formula is as follows: ， in, for Moment The charging cost performance of a vehicle at the optimal charging station is used to indicate the economic benefits of charging. The closer it is to 1, the more cost-effective the car charging is; for Moment Car SOC ; for Moment The price of charging the vehicle; for Moment The distance from the vehicle to its optimal charging station, Travel expenses per unit distance.

4. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to claim 1, characterized in that: In step S2, the busyness of the charging piles is calculated according to the occupancy and queuing conditions of each charging pile, and a quantitative evaluation and normalization process is performed on the busyness of the charging piles. The specific calculation formula is as follows: ； in, Moment The optimal charging station selected by the vehicle is Charging stations; for Moment The busyness of the optimal charging pile selected by the vehicle is used to indicate the busyness of the charging pile. The closer it is to 1, the more idle the charging station is; for The total number of vehicles queuing at the charging station at any given moment; for Moment Car SOC ; for Moment The charging power of each charging station; For the The maximum charging power of each charging pile; For the The total number of vehicles in the queue when the charging power of the charging pile is maximum.

5. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to any one of claims 2 to 4, characterized in that: In step S3, a PSR-FM model is constructed based on the PSR evaluation system and the FM algorithm model, taking the user charging demand as the pressure factor, i.e., the disaster-causing factor; taking the vehicle and charging pile location distribution, the time-sharing and domain-sharing electricity prices, and the busyness of the charging piles as the state factors, i.e., the disaster-prone environment; and combining the historical charging behavior, finally obtaining the time-sharing and domain-sharing electricity price pricing strategy; The state factor includes the economic benefits of charging and the busyness of the charging pile. The pressure factor and state factor expressed in numerical values ​​are converted into vectors existing in the first quadrant. The specific calculation formula is as follows: ； in, for Moment The pressure factor and state factor set of the vehicle, when When it is 1, 2, and 3, it corresponds to the pressure factor, the charging economic benefit in the state factor, and the busyness of the charging pile in the state factor respectively; In the PSR-FM model, according to the pressure factor , Charging economic benefits in state factors , the busyness of the charging pile in the state factor , get the predicted Moment Vehicle user charging behavior , as follows: ， According to the user's historical charging demand, the historical busyness of the charging pile, and the user's historical charging behavior, the prediction parameters of the PSR-FM model can be reversely derived. and ;in for The charging behavior error parameter, for The charging behavior and pressure state interact with each other.

6. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to claim 5, characterized in that: In step S4, a long time scale prediction-short time scale correction mode is set; The time period corresponding to the long time scale is one day, that is, 96 15-minute periods. The time period corresponding to the short time scale is 1 15-minute period. In the long time scale prediction model, The value is 96, in the short time scale correction mode, The value is 1; Since the prediction periods used in the two time scales are different, the weight parameters of the PSR-FM model in the two time scales are and They are also different; Get the user's historical charging behavior: ； Weight parameters of the PSR-FM model at two time scales; and obtaining the PSR-FM model at two time scales.

7. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to claim 6, characterized in that: In step S5, the day-ahead data and real-time data are used to predict the user's charging behavior in the previous time period using the PSR-FM model at two time scales. and , and according to the prediction errors of the PSR-FM model at two time scales and , determine which time scale to use for prediction in the current time period. The specific calculation formula is as follows: ； ； in, is the prediction error of the PSR-FM model on a long time scale, For the current moment, for Moment The actual charging behavior of each user, is the prediction error of the PSR-FM model at short time scale; The charging behavior of users predicted by the PSR-FM model in a long time scale. The user's charging behavior predicted by the PSR-FM model in a short time scale.

8. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to claim 1, characterized in that: In step S5, if the current time scale is a long time scale, one time period is one day, that is, 96 15-minute periods; if the current time scale is a short time scale, one time period is 15 minutes.

9. The method for formulating time-based and domain-based electricity prices considering charging pressure-state-behavior according to claim 7, characterized in that: In step S6, the time-based and domain-based electricity prices are used as variables, the battery charging status data of electric vehicles, the occupancy data of each charging pile and the queue data are used as initial values, and the PSR-FM model under the time scale determined in step S5 is used to Moment Vehicle user charging behavior Make predictions; The sum of charging benefit and electricity selling benefit is taken as the objective function, and the particle swarm algorithm is used to iteratively calculate the time-sharing and segmented electricity prices in each period of the study area. The objective function calculation formula is as follows: ； ； ； in, and are the number of charging piles and the number of electric vehicle users, is the charging pile pricing coefficient, for Moment The electricity price of a charging station, for Moment The charging price for each user, The total amount of charging consumption for electric vehicle users, Optimize the objective function for the particle swarm.

10. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.

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