Electric vehicle charging station pricing method and system, electronic device and storage medium

Abstract

The application discloses an electric vehicle charging station pricing method and system, an electronic device and a storage medium. The electric vehicle charging station pricing method comprises the following steps: obtaining historical operation data of a charging station and regional market electricity price information; inputting the historical operation data and the regional market electricity price information into an intelligent pricing model, outputting a recommended time-of-use electricity price based on the historical operation data and the regional market electricity price information by the intelligent pricing model; and configuring the recommended time-of-use electricity price to an operation system of the charging station. Thus, the recommended time-of-use electricity price is generated by the intelligent pricing model based on the historical operation data and the regional market electricity price information instead of simply relying on artificial experience, data-driven pricing and tooling of pricing capabilities are realized, and charging pile utilization, charging income and user satisfaction are improved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and in particular to a pricing method and system for electric vehicle charging stations, electronic equipment, and storage medium. Background Technology

[0002] Currently, when charging stations sell or purchase electricity, existing technologies mostly use fixed prices or simple time-of-use pricing, without dynamically adjusting based on real-time supply and demand, photovoltaic capacity, energy storage status, and market price fluctuations. This results in low utilization rates during off-peak hours and unattractive prices during peak hours, making it difficult to achieve optimal overall profitability. Furthermore, manually setting prices is often based on experience or simple rules, without fully considering the price elasticity of user demand and the actual operational capacity of the charging station, which can easily lead to low adoption rates or losses for the charging station. Operators need to manually summarize load curves, electricity price trends, and energy storage status before making decisions, a cumbersome process that is difficult to continuously optimize. Summary of the Invention

[0003] The present invention aims to provide a pricing method and system for electric vehicle charging stations, as well as electronic devices and storage media, to solve the problem that existing charging station pricing does not dynamically adjust based on real-time supply and demand and market electricity price fluctuations, resulting in low utilization rates during off-peak hours and unattractive electricity prices during peak hours, making it difficult to optimize overall revenue.

[0004] To address the aforementioned technical problems, a first aspect of the present invention provides a pricing method for electric vehicle charging stations, comprising: Obtain historical operational data of charging stations and regional market electricity price information; The historical operating data and the regional market electricity price information are input into the smart pricing model, and the smart pricing model outputs a recommended time-of-use electricity price based on the historical operating data and the regional market electricity price information; The recommended time-of-use electricity price will be configured into the charging station's operating system.

[0005] Accordingly, a second aspect of the present invention provides an electric vehicle charging station pricing system, applied to the electric vehicle charging station pricing method described in the first aspect of the present invention. The electric vehicle charging station pricing system includes: an acquisition module, a processing module, and a configuration module. The acquisition module is used to acquire historical operating data of the charging station and regional market electricity price information; The processing module is used to input the historical operating data and the regional market electricity price information into the smart pricing model, and the smart pricing model outputs a recommended time-of-use electricity price based on the historical operating data and the regional market electricity price information; The configuration module is used to configure the recommended time-of-use electricity price to the charging station's operation system.

[0006] Accordingly, a third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program, when executed by the processor, implements the electric vehicle charging station pricing method described in the first aspect of the present invention.

[0007] Accordingly, a fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the electric vehicle charging station pricing method described in the first aspect of the present invention.

[0008] This invention provides a pricing method and system for electric vehicle charging stations, as well as electronic devices and storage media. The pricing method acquires historical operating data and regional market electricity price information from the charging station, inputs this data into an intelligent pricing model, and then outputs a recommended time-of-use (TOU) price based on this data. This recommended TOU price is then configured into the charging station's operating system. This invention generates recommended TOU prices based on historical operating data and regional market electricity price information through an intelligent pricing model, rather than relying solely on human experience. This achieves data-driven pricing and the tooling of pricing capabilities, thereby improving charging pile utilization, charging revenue, and user satisfaction. It addresses the problems of inflexible fixed electricity prices, lack of data support for pricing decisions, difficulty in balancing demand elasticity and operational capabilities with manual pricing, and lack of readily available tools in charging station pricing. Attached Figure Description

[0009] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0010] Figure 1 This is a flowchart illustrating a pricing method for electric vehicle charging stations provided by the present invention. Figure 2 This is another flowchart illustrating a pricing method for electric vehicle charging stations provided by the present invention; Figure 3 This is a schematic diagram of the structure of an electric vehicle charging station pricing system provided by the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0011] To facilitate understanding of the present invention, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. It should be noted that when an element is described as being "fixed to" another element, it can be directly on the other element, or one or more intermediate elements may exist between them. When an element is described as being "electrically connected" to another element, it can be directly connected to the other element, or one or more intermediate elements may exist between them. The terms "upper," "lower," "inner," "outer," "bottom," etc., used in this specification indicate orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0012] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0013] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0014] In one embodiment, please refer to Figure 1 This invention provides a pricing method for electric vehicle charging stations based on operational data, comprising: S1. Obtain historical operational data of charging stations and regional market electricity price information; S2. Input historical operating data and regional market electricity price information into the smart pricing model, and the smart pricing model outputs recommended time-of-use electricity prices based on historical operating data and regional market electricity price information; S3. The recommended time-of-use pricing will be configured in the charging station's operating system.

[0015] This embodiment provides a pricing method for electric vehicle charging stations based on operational data. This method acquires historical operational data and regional market electricity price information from the charging station, inputs this data into an intelligent pricing model, and then outputs a recommended time-of-use (TOU) price based on this data. This recommended TOU price is then configured into the charging station's operating system. This invention generates recommended TOU prices based on historical operational data and regional market electricity price information through an intelligent pricing model, rather than solely relying on human experience. This achieves data-driven pricing and the tooling of pricing capabilities, thereby improving charging pile utilization, charging revenue, and user satisfaction. This addresses the problems in charging station pricing, such as inflexible fixed electricity prices, lack of data support for pricing decisions, difficulty in balancing demand elasticity and operational capabilities with manual pricing, and a lack of readily available tools.

[0016] In one embodiment, in step S1, historical operating data of the charging station and regional market electricity price information are obtained.

[0017] Specifically, in response to a pricing recommendation request, the system retrieves historical operating data of the selected charging station from the charging station's operating system. The pricing recommendation request is initiated by the operating system to the electric vehicle charging station pricing system based on the selected charging station and the effective date range.

[0018] Based on the charging stations and effective date range selected by the operator in the operating system, the operating system sends a pricing recommendation request to the electric vehicle charging station pricing system. This pricing recommendation request is structured and standardized, including at least the charging station identifier and effective date range, and optionally the statistical period, current electricity price configuration, and constraints. This structured and standardized approach allows charging station operators to obtain recommended time-of-use pricing and its explanation simply by initiating a pricing recommendation request, eliminating the need for manual aggregation of multi-source data and complex calculations, thus improving decision-making efficiency.

[0019] In the charging pricing scenario at charging stations, historical operational data related to pricing includes the following categories of indicators: photovoltaic (PV) power generation and consumption data, battery energy storage (BESS) charging and discharging data, grid electricity price and demand data, charging load and charging pile operation data, energy dispatch data, and cost and revenue pricing data; among which: I. Data related to photovoltaic (PV) power generation and consumption. This category is related to and affects the self-consumption electricity price and income.

[0020] Photovoltaic (PV) is short for Photovoltaic Generation System. Data related to photovoltaic (PV) power generation and consumption includes: Photovoltaic power generation, cumulative photovoltaic power generation; Photovoltaic self-consumption and photovoltaic grid connection (photovoltaic surplus); Photovoltaic power generation time distribution (hourly power generation curve during the day); Photovoltaic module temperature, light intensity, and power generation efficiency; On-grid electricity price (desulfurized coal-fired power price / local purchase price), revenue per kilowatt-hour from self-consumption; Photovoltaic losses, inverter losses, and line losses.

[0021] II. Data related to energy storage (BESS) charging and discharging, which is related to pricing and serves as the core basis for pricing.

[0022] Battery Energy Storage System (BESS) is a shorthand for Battery Energy Storage System. Data related to BESS charging and discharging includes: Energy storage SOC (State of Charge). Energy storage charging sources: photovoltaic charging / grid off-peak electricity charging / hybrid charging; Energy storage charging power, charging capacity, and charging time period (off-peak / slow-peak / peak); Energy storage discharge power, discharge capacity, discharge efficiency (round-trip efficiency); Energy storage charge / discharge cycles, cycle life, and State of Health (SOH); Energy storage peak-valley arbitrage opportunity (charging at off-peak prices and discharging at peak prices); Maximum discharge power of energy storage and ability to support charging load; Discharge quantity for demand regulation and peak shaving / valley filling.

[0023] III. Grid electricity price and demand data category. This category is related to costs and determines costs, including: Time-of-use pricing for electricity: real-time electricity prices for peak, off-peak, and low-peak periods. Different unit prices are set for different time periods (peak, off-peak, and low-peak periods) and are used for charging stations or industrial and commercial enterprises to set prices for selling or purchasing electricity. The time period division and unit price are determined by the operator or system based on the type of charging station and the market environment.

[0024] Basic electricity fee, transformer capacity / maximum demand electricity fee; Peak demand, penalties for exceeding demand, and cost savings through peak shaving; Electricity purchased from the grid and electricity sold from the grid (surplus electricity sold to the grid); Power factor, power factor penalty, transformer loss and line loss.

[0025] IV. Charging Load and Charging Pile Operation Data: This category is related to dynamic pricing and determines dynamic pricing, including: Real-time charging power (load), total charging load, and simultaneous utilization rate (proportion of multiple charging piles charging at the same time, maximum simultaneous charging power). Time-of-use charging power (time-of-use charging power, total load; charging kWh per hour, per day, per week; peak congestion periods, idle periods), charging order distribution (average daily number of orders, single charging kWh, single charging duration, vehicle charging power, initial SOC (State of Charge), final SOC); Charging pile utilization rate (average daily charging time and duty cycle of a single charging pile; peak / off-peak / off-peak utilization rate); The proportion of photovoltaic self-consumption, energy storage discharge, and grid power supply; Discount / Promotion Costs: Coupons, Member Prices, Time-Based Discounts, New Customer Acquisition Subsidies.

[0026] V. Energy Dispatch Related Data: This category is related to the coordinated pricing of photovoltaic, energy storage, and charging systems, and determines the pricing of these systems. It includes: Real-time power allocation ratio between photovoltaic, energy storage, charging, and grid; The proportion of vehicles directly powered by photovoltaic power; the proportion of energy storage charging by photovoltaic power. The power and amount of energy stored during discharge support the charging process; Forecast data: Photovoltaic power generation forecast (short-term / ultra-short-term), charging load forecast, time-of-use electricity price forecast; Energy dispatch costs and the benefits of dispatch strategies.

[0027] VI. Cost & Revenue Pricing Data: This category is related to service fees and determines service fees, including: Cost per kilowatt-hour: the weighted cost of grid electricity, photovoltaic electricity, and energy storage electricity; Energy storage depreciation, photovoltaic depreciation, charging pile depreciation; Peak-valley arbitrage profits, profits from reduced demand, and profits from self-consumption; Venue rental, operation and maintenance costs, monitoring fees, cloud platform service fees; Gross profit per kilowatt-hour, target gross profit margin, and service fee range. Gross profit per kilowatt-hour = Charging unit price - Electricity purchase cost - Line loss / transformer loss - Depreciation and operating cost per unit of electricity.

[0028] Among the above indicators, those directly used for dynamic pricing and / or time-of-use pricing of photovoltaic, energy storage, and charging include, but are not limited to: photovoltaic power generation, photovoltaic self-consumption, photovoltaic grid connection, energy storage SOC, charging and discharging costs, available discharge capacity, grid time-of-use tariff, maximum demand tariff, real-time charging load, charging pile utilization rate, photovoltaic / energy storage / grid power supply ratio, load forecast, power generation forecast, time-of-use tariff forecast, comprehensive cost per kilowatt-hour, peak shaving revenue, and arbitrage revenue.

[0029] Specifically, the electric vehicle charging station pricing system responds to the pricing recommendation request by obtaining the historical operating data of the selected charging station from the charging station's operating system. That is, the electric vehicle charging station pricing system responds in a structured manner according to the effective date range in the pricing recommendation request, and automatically obtains the historical operating data of the selected charging station from the charging station's operating system according to the effective date.

[0030] In this embodiment, the charging station uses historical operating data from sources such as photovoltaics, energy storage, and / or charging piles to structure pricing recommendation requests. The electric vehicle charging station pricing system automatically selects historical operating data within the effective date range and provides material input for the subsequent intelligent pricing model to achieve structured response, which facilitates integration with existing charging station operation system platforms and edge systems.

[0031] In one embodiment, in step S2, historical operating data and regional market electricity price information are input into the smart pricing model, and the smart pricing model outputs a recommended time-of-use electricity price based on the historical operating data and regional market electricity price information.

[0032] S21. Input historical operating data and regional market electricity price information into the smart pricing model.

[0033] The smart pricing model is obtained by pre-training a general-purpose large model with a large amount of historical operating data and regional market electricity price information.

[0034] The pricing strategy of the smart pricing model for recommended time-of-use electricity prices includes: during the charging period, the recommended time-of-use electricity price is the base time-of-use electricity price multiplied by a price adjustment ratio obtained by training the smart pricing model using historical operating data. The recommended time-of-use electricity price is subject to the allowable electricity price range. The recommended time-of-use electricity price is restricted to the allowable electricity price range by a cutoff function. The allowable electricity price range is the minimum and maximum electricity price allowed during the charging period.

[0035] Specifically, the formula for calculating the recommended time-of-use electricity price in the smart pricing model is as follows: in: To recommend time-of-use electricity pricing; The benchmark time-of-use electricity price for charging period t; This refers to the charging pile price adjustment ratio calculated based on the charging pile utilization rate. This is the photovoltaic price adjustment ratio calculated based on photovoltaic output and surplus / fluctuation. This refers to the electricity price adjustment percentage calculated based on the regional market electricity price. This is the energy storage price adjustment ratio calculated based on the SOC of the energy storage battery; This is a truncation function that limits the intermediately calculated temporary charging price to within an allowed range; The lowest allowable electricity price for the charging period t; The highest allowable electricity price for the charging period t; This refers to the price adjustment percentage, specifically the price adjustment percentage for charging piles. Photovoltaic price adjustment ratio item Electricity price adjustment ratio item And the proportion of energy storage price adjustment These are all obtained by training intelligent pricing models using historical operational data.

[0036] The meaning of the above formula (1) is: the recommended time-of-use electricity price of the smart pricing model during a certain charging period t. It is the base time-of-use electricity price multiplied by a price adjustment ratio obtained by training a smart pricing model using historical operating data. The recommended time-of-use electricity price Limited by the allowable range of electricity prices, a truncation function is used. Recommended time-of-use electricity pricing The charging period t is limited to the allowable electricity price range, which is the lowest allowable electricity price for that period. and the highest electricity price That is, the charging allowable range is .

[0037] Based on a pre-trained smart pricing model, historical operating data and regional market electricity price information are used as inputs to the smart pricing model.

[0038] S22. The intelligent pricing model analyzes the historical load curves and charging pile utilization rates of charging stations by time period based on historical operating data, and identifies periods of low charging pile utilization, periods of high photovoltaic power generation, and periods of high charging demand.

[0039] Specifically, the intelligent pricing model analyzes historical load curves and charging pile utilization rates of charging stations by time period based on historical operational data, identifying periods of low charging pile utilization, periods of high solar power generation, and periods of peak charging demand. This includes: the intelligent pricing model statistically analyzes charging pile utilization, solar power capacity, and energy storage status by time period based on historical operational data, identifying periods of low charging pile utilization, periods of high solar power generation, and periods of peak charging demand. Specifically, this includes: Based on historical operational data (such as historical load curves of charging stations, charging pile utilization rate, photovoltaic output, and energy storage SOC), indicators such as charging pile utilization rate, photovoltaic peak generation, and charging demand are calculated at preset time granularities (e.g., 15 minutes, 30 minutes, or 60 minutes) to identify periods of low charging pile utilization, peak photovoltaic generation, and peak charging demand. Specifically, periods of low charging pile utilization are defined as charging pile utilization rates below a preset threshold; peak photovoltaic generation periods are characterized by high photovoltaic PV output and relatively insufficient load and / or charging demand; and peak charging demand periods are characterized by peak load, high charging pile utilization, or simultaneous queuing and / or limited charging power.

[0040] S23. Based on periods of low utilization of charging piles, periods of high photovoltaic power generation, and periods of high charging demand, combined with regional market electricity price information, output recommended time-of-use electricity prices.

[0041] Specifically, the recommended time-of-use pricing includes lower prices during off-peak hours, higher prices during peak hours, increased price reductions during periods of high solar power generation and low utilization of charging stations, and / or increased price reductions during periods of negative electricity prices, among which: Off-peak pricing refers to the suggestion to reduce charging prices during off-peak hours. During off-peak hours, due to (1) weak demand: the average utilization rate of charging piles is low; and (2) surplus load: the load margin is sufficient. Therefore, it is recommended to reduce charging prices during off-peak hours to improve the utilization rate of charging piles during off-peak hours and optimize the overall revenue of charging stations.

[0042] Peak-hour price increases refer to the suggestion to raise charging prices during peak hours. During peak hours, (1) the utilization rate of charging piles is very high: for example, the after-get off work rush hour, and sudden peaks during holidays; (2) the price is aligned with the market peak price: when the external market or catalog electricity price enters the peak period, the terminal peak period will be adjusted accordingly to avoid being out of touch with the market, thereby increasing the utilization rate of charging piles and the attractiveness of electricity prices during peak hours, so that the overall revenue of the charging station can reach the optimal level.

[0043] During periods of high solar power generation and low charging pile utilization, it is recommended to increase the price reduction. Specifically, it is suggested to reduce the price (i.e., reduce the price on top of the basic time-of-use electricity price) during periods of high solar power generation and low charging pile utilization, and to increase the price reduction of charging stations to improve the utilization rate of charging piles during these periods, so as to optimize the overall revenue of charging stations.

[0044] During periods of negative electricity prices, it is recommended to increase price reductions, specifically by lowering prices on top of the base time-of-use (TOU) price, and further increasing the reduction in charging prices. Negative electricity prices typically occur during low-load periods in the electricity spot market, particularly during holidays, industrial off-peak hours, and peak periods for renewable energy sources such as solar and wind power. Typical periods include the Spring Festival, May Day holiday, and midday off-peak hours. During periods of negative electricity prices, it is feasible to allocate more of the price difference to the charging side by lowering prices on top of the base TOU price. This would stimulate charging, promote the consumption of renewable energy, improve the utilization rate of charging piles and the attractiveness of electricity prices, and ultimately optimize the overall profitability of charging stations.

[0045] In this embodiment, historical operating data and regional market electricity price information are input into a pre-trained intelligent pricing model. The intelligent pricing model then outputs recommended time-of-use (TOU) prices based on this data. This allows charging station pricing to be dynamically adjusted in conjunction with real-time supply and demand and market electricity price fluctuations, improving the utilization rate of charging piles during off-peak hours and the attractiveness of electricity prices during peak hours, thus optimizing the overall revenue of the charging station. Therefore, by generating recommended TOU prices based on historical operating data and regional market electricity price information, rather than relying solely on human experience, the intelligent pricing model achieves data-driven pricing and tool-based pricing capabilities, thereby improving charging pile utilization, charging revenue, and user satisfaction.

[0046] In one embodiment, in step S3, the recommended time-of-use electricity price is configured to the charging station's operating system.

[0047] Specifically, after the intelligent pricing model outputs a recommended time-of-use (TOU) price based on historical operating data and regional market electricity price information, the electric vehicle charging station pricing system configures the recommended TOU price into the charging station's operating system. Simultaneously, the operating system also configures accompanying textual explanations for the recommended TOU price, ensuring that the charging station operator clearly understands the rationale behind the intelligent pricing module's generation of the recommended TOU price. Once the operator adopts the recommended TOU price, the operating system writes it into the TOU price configuration and can optionally synchronize it to the edge computing system.

[0048] This system standardizes pricing recommendation requests and responses. Pricing recommendation requests are structured, including at least the charging station identifier and effective date range, and optionally include the statistical period, current electricity price configuration, and constraints. Responses are also structured, including at least a list of recommended time-of-use (TOU) prices, and optionally include statistical indicators for each time period, textual descriptions, and a summary of the price adjustment reasons. Pricing rules and parameters are configurable and auditable, facilitating integration with the operating system platform and edge execution, and supporting auditing and traceability. This allows charging stations to obtain recommended TOU prices and their descriptions simply by initiating a pricing recommendation request, eliminating the need for manual aggregation of multi-source data and complex calculations. This improves decision-making efficiency, enables data-driven pricing, and tool-based pricing capabilities, thereby increasing charging pile utilization, charging revenue, and user satisfaction.

[0049] In one embodiment, please refer to Figure 2 The pricing method for electric vehicle charging stations also includes: S4, obtaining the actual operating data of the operating system after configuring the recommended time-of-use electricity price, and optimizing the intelligent pricing model based on the actual operating data.

[0050] Specifically, after the operator adopts the recommended time-of-use pricing, the operating system writes the recommended time-of-use pricing into the time-of-use pricing configuration and can optionally synchronize it to the edge. In actual operation, the electric vehicle charging station pricing system obtains actual operating data from the operating system after configuring the recommended time-of-use pricing, such as charging pile utilization, operating revenue, and user feedback. Based on the actual operating data, it evaluates the pricing effectiveness of the recommended time-of-use pricing and optimizes the smart pricing model (e.g., adjusting various price adjustment parameters of the smart pricing model, minimum price, and maximum price).

[0051] In this embodiment, by acquiring actual operational data from the operating system after configuring recommended time-of-use pricing, the pricing effect of the operating system after configuring the recommended time-of-use pricing generated by the intelligent pricing model is evaluated. Based on the actual operational data, the intelligent pricing model is optimized, forming a closed-loop iteration of "recommendation-adoption-evaluation-optimization." This enables the intelligent pricing model to possess closed-loop optimization capabilities, allowing its pricing strategy to be continuously iteratively improved. The intelligent pricing model generates recommended time-of-use pricing based on historical operational data and regional market electricity price information, rather than relying solely on human experience. This achieves data-driven pricing and the tooling of pricing capabilities. Furthermore, it supports the collection of actual operational data after adopting recommended time-of-use pricing for effect pricing evaluation and intelligent pricing model optimization, forming a closed loop of "recommendation-adoption-evaluation-optimization." This improves charging pile utilization, charging revenue, and user satisfaction, or increases industrial and commercial electricity sales profits. This addresses the problems in charging station pricing, such as inflexible fixed electricity prices, lack of data support for pricing decisions, difficulty in balancing demand elasticity and operational capabilities with manual pricing, and lack of tooling and closed-loop optimization capabilities.

[0052] Based on the same concept, please refer to Figure 3The present invention also provides an electric vehicle charging station pricing system 600, which is applied to the electric vehicle charging station pricing method described in any of the above embodiments. The electric vehicle charging station pricing system 600 includes: an acquisition module 610, a processing module 620 and a configuration module 630. The acquisition module 610 is used to acquire historical operating data of the charging station and regional market electricity price information; Processing module 620 is used to input historical operating data and regional market electricity price information into the smart pricing model, and the smart pricing model outputs recommended time-of-use electricity prices based on historical operating data and regional market electricity price information; Configuration module 630 is used to configure the recommended time-of-use electricity price to the charging station's operating system.

[0053] This embodiment provides an electric vehicle charging station pricing system based on operational data. The system acquires historical operational data and regional market electricity price information from the charging station through an acquisition module. A processing module inputs this data into an intelligent pricing model, which then outputs a recommended time-of-use (TOU) price based on the data. A configuration module configures this recommended TOU price into the charging station's operational system. This invention generates recommended TOU prices based on historical operational data and regional market electricity price information through an intelligent pricing model, rather than relying solely on human experience. This achieves data-driven pricing and tool-based pricing capabilities, thereby improving charging pile utilization, charging revenue, and user satisfaction. It addresses the problems of inflexible fixed electricity prices, lack of data support for pricing decisions, difficulty in balancing demand elasticity and operational capabilities with manual pricing, and lack of readily available tools in charging station pricing.

[0054] In one embodiment, the acquisition module 610 is used to acquire historical operating data of the charging station and regional market electricity price information.

[0055] Specifically, module 610 responds to the pricing recommendation request and obtains the historical operating data of the selected charging station from the charging station's operating system. The pricing recommendation request is initiated by the operating system to the electric vehicle charging station pricing system based on the selected charging station and the effective date range.

[0056] Based on the charging stations and effective date range selected by the operator in the operating system, the operating system sends a pricing recommendation request to the electric vehicle charging station pricing system. This pricing recommendation request is structured and standardized, including at least the charging station identifier and effective date range, and optionally the statistical period, current electricity price configuration, and constraints. This structured and standardized approach allows charging station operators to obtain recommended time-of-use pricing and its explanation simply by initiating a pricing recommendation request, eliminating the need for manual aggregation of multi-source data and complex calculations, thus improving decision-making efficiency.

[0057] In the charging pricing scenario of charging stations, the historical operational data related to pricing includes the following categories of indicators: photovoltaic (PV) power generation and consumption data, energy storage (BESS) charging and discharging data, grid electricity price and demand data, charging load and charging pile operation data, energy dispatch data, and cost and revenue pricing data.

[0058] The acquisition module 610 responds to the pricing recommendation request and obtains the historical operating data of the selected charging station from the charging station's operation system. That is, the electric vehicle charging station pricing system responds in a structured manner according to the effective date range in the pricing recommendation request and automatically obtains the historical operating data of the selected charging station from the charging station's operation system according to the effective date.

[0059] In this embodiment, the charging station uses historical operating data from sources such as photovoltaics, energy storage, and / or charging piles to structure pricing recommendation requests. The electric vehicle charging station pricing system automatically selects historical operating data within the effective date range and provides material input for the subsequent intelligent pricing model to achieve structured response, which facilitates integration with existing charging station operation system platforms and edge systems.

[0060] In one embodiment, the processing module 620 is used to input historical operating data and regional market electricity price information into the smart pricing model, and the smart pricing model outputs a recommended time-of-use electricity price based on the historical operating data and regional market electricity price information; specifically including: Historical operating data and regional market electricity price information are input into the smart pricing model. The smart pricing model is obtained by pre-training a general-purpose model with a large amount of historical operating data and regional market electricity price information. The smart pricing model's recommended time-of-use (TOU) pricing strategy includes: during charging periods, the recommended TOU price is the base TOU price multiplied by a price adjustment ratio obtained by training the smart pricing model using historical operating data. This recommended TOU price is subject to a price allowance range, which is restricted to this range using a truncation function. This allowance range consists of the minimum and maximum allowable electricity prices for that charging period.

[0061] The intelligent pricing model analyzes historical load curves and charging pile utilization rates of charging stations by time period based on historical operational data, identifying periods of low charging pile utilization, periods of high solar power generation, and periods of peak charging demand. Specifically, the intelligent pricing model uses historical operational data to statistically analyze charging pile utilization, solar power capacity, and energy storage status by time period, identifying periods of low charging pile utilization, periods of high solar power generation, and periods of peak charging demand. Based on historical operational data (such as historical load curves of charging stations, charging pile utilization rate, photovoltaic output, and energy storage SOC), indicators such as charging pile utilization rate, photovoltaic peak generation, and charging demand are calculated at preset time granularities (e.g., 15 minutes, 30 minutes, or 60 minutes) to identify periods of low charging pile utilization, peak photovoltaic generation, and peak charging demand. Specifically, periods of low charging pile utilization are defined as charging pile utilization rates below a preset threshold; peak photovoltaic generation periods are characterized by high photovoltaic PV output and relatively insufficient load and / or charging demand; and peak charging demand periods are characterized by peak load, high charging pile utilization, or simultaneous queuing and / or limited charging power.

[0062] Based on periods of low charging pile utilization, peak solar power generation, and high charging demand, and combined with regional market electricity price information, a recommended time-of-use (TOU) electricity price is generated. Specifically, the recommended TOU electricity price includes price reductions during off-peak hours, price increases during peak hours, larger price reductions during peak solar power generation periods and periods of low charging pile utilization, and / or larger price reductions during periods of negative electricity prices. Off-peak pricing refers to the suggestion to reduce charging prices during off-peak hours. During off-peak hours, due to (1) weak demand: the average utilization rate of charging piles is low; and (2) surplus load: the load margin is sufficient. Therefore, it is recommended to reduce charging prices during off-peak hours to improve the utilization rate of charging piles during off-peak hours and optimize the overall revenue of charging stations.

[0063] Peak-hour price increases refer to the suggestion to raise charging prices during peak hours. During peak hours, (1) the utilization rate of charging piles is very high: for example, the after-get off work rush hour, and sudden peaks during holidays; (2) the price is aligned with the market peak price: when the external market or catalog electricity price enters the peak period, the terminal peak period will be adjusted accordingly to avoid being out of touch with the market, thereby increasing the utilization rate of charging piles and the attractiveness of electricity prices during peak hours, so that the overall revenue of the charging station can reach the optimal level.

[0064] During periods of high solar power generation and low charging pile utilization, it is recommended to increase the price reduction. Specifically, it is suggested to reduce the price (i.e., reduce the price on top of the basic time-of-use electricity price) during periods of high solar power generation and low charging pile utilization, and to increase the price reduction of charging stations to improve the utilization rate of charging piles during these periods, so as to optimize the overall revenue of charging stations.

[0065] During periods of negative electricity prices, it is recommended to increase price reductions, specifically by lowering prices on top of the base time-of-use (TOU) price, and further increasing the reduction in charging prices. Negative electricity prices typically occur during low-load periods in the electricity spot market, particularly during holidays, industrial off-peak hours, and peak periods for renewable energy sources such as solar and wind power. Typical periods include the Spring Festival, May Day holiday, and midday off-peak hours. During periods of negative electricity prices, it is feasible to allocate more of the price difference to the charging side by lowering prices on top of the base TOU price. This would stimulate charging, promote the consumption of renewable energy, improve the utilization rate of charging piles and the attractiveness of electricity prices, and ultimately optimize the overall profitability of charging stations.

[0066] In this embodiment, historical operating data and regional market electricity price information are input into a pre-trained intelligent pricing model. The intelligent pricing model then outputs recommended time-of-use (TOU) prices based on this data. This allows charging station pricing to be dynamically adjusted in conjunction with real-time supply and demand and market electricity price fluctuations, improving utilization during off-peak hours and enhancing the attractiveness of peak-hour prices, thus optimizing the overall revenue of the charging station. By generating recommended TOU prices based on historical operating data and regional market electricity price information, rather than relying solely on human experience, the intelligent pricing model achieves data-driven pricing and tool-based pricing capabilities, thereby improving charging pile utilization, charging revenue, and user satisfaction.

[0067] In one embodiment, the configuration module 630 is used to configure the recommended time-of-use electricity price to the charging station's operating system.

[0068] Specifically, after the intelligent pricing model outputs a recommended time-of-use (TOU) price based on historical operating data and regional market electricity price information, the electric vehicle charging station pricing system configures the recommended TOU price into the charging station's operating system. Simultaneously, the operating system also configures accompanying textual explanations for the recommended TOU price, ensuring that the charging station operator clearly understands the rationale behind the intelligent pricing module's generation of the recommended TOU price. Once the operator adopts the recommended TOU price, the operating system writes it into the TOU price configuration and can optionally synchronize it to the edge computing system.

[0069] This system standardizes pricing recommendation requests and responses. Pricing recommendation requests are structured, including at least the charging station identifier and effective date range, and optionally include the statistical period, current electricity price configuration, and constraints. Responses are also structured, including at least a list of recommended time-of-use (TOU) prices, and optionally include statistical indicators for each time period, textual descriptions, and a summary of the price adjustment reasons. Pricing rules and parameters are configurable and auditable, facilitating integration with the operating system platform and edge execution, and supporting auditing and traceability. This allows charging stations to obtain recommended TOU prices and their descriptions simply by initiating a pricing recommendation request, eliminating the need for manual aggregation of multi-source data and complex calculations. This improves decision-making efficiency, enables data-driven pricing, and tool-based pricing capabilities, thereby increasing charging pile utilization, charging revenue, and user satisfaction.

[0070] In one embodiment, the electric vehicle charging station pricing system 600 further includes: an acquisition module 610 is also used to acquire the actual operating data of the operating system after configuring the recommended time-of-use electricity price, and optimize the intelligent pricing model based on the actual operating data.

[0071] Specifically, after the operator adopts the recommended time-of-use pricing, the operating system writes the recommended time-of-use pricing into the time-of-use pricing configuration and can optionally synchronize it to the edge. During actual operation, the acquisition module 610 acquires actual operating data from the operating system after configuring the recommended time-of-use pricing, such as the utilization rate of charging piles, operating revenue, and user feedback. Based on the actual operating data, it evaluates the pricing effect of the recommended time-of-use pricing and optimizes the smart pricing model (e.g., adjusting various price adjustment ratio parameters of the smart pricing model, minimum electricity price, and maximum electricity price).

[0072] In this embodiment, the acquisition module obtains actual operational data from the operating system after configuring recommended time-of-use pricing, evaluates the pricing effect of the recommended time-of-use pricing generated by the intelligent pricing model, and optimizes the intelligent pricing model based on actual operational data, forming a closed-loop iteration of "recommendation-adoption-evaluation-optimization." This enables the intelligent pricing model to have closed-loop optimization capabilities, and its pricing strategy can be continuously iterated and improved. The intelligent pricing model generates recommended time-of-use pricing based on historical operational data and regional market electricity price information, rather than relying solely on human experience. This achieves data-driven pricing and the toolization of pricing capabilities. Furthermore, it supports the collection of actual operational data after adopting recommended time-of-use pricing for effect pricing evaluation and intelligent pricing model optimization, forming a closed loop of "recommendation-adoption-evaluation-optimization." This improves charging pile utilization, charging revenue, and user satisfaction, or increases industrial and commercial electricity sales profits. This addresses the problems in charging station pricing, such as inflexible fixed electricity prices, lack of data support for pricing decisions, difficulty in balancing demand elasticity and operational capabilities with manual pricing, and lack of toolization and closed-loop optimization capabilities.

[0073] Based on the same concept, the present invention also provides an electronic device 900, please refer to... Figure 4The electronic device 900 can be a server, desktop computer, tablet computer, or interactive flat panel. The electronic device 900 includes a memory 920, a processor 910, and one or more computer programs stored in the memory 920 and capable of running on the processor 910. The memory 920 and the processor 910 are coupled together through a bus system 930. When one or more computer programs are executed by the processor 910, they implement the electric vehicle charging station pricing method provided in this embodiment of the invention.

[0074] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 910. Processor 910 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the hardware of processor 910 or by instructions in software form. Processor 910 may be a general-purpose processor, DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 910 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of the present invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, specifically memory 920. Processor 910 reads information from memory 920 and, in conjunction with its hardware, completes the steps of the aforementioned methods.

[0075] It is understood that the memory 920 in this embodiment of the invention can be a volatile memory or a non-volatile memory, or it can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory or other memory technologies, compact disk read-only memory (CD-ROM), digital video disk (DVD) or other optical disc storage, magnetic cartridges, magnetic tapes, disk storage or other magnetic storage devices; the volatile memory can be random access memory (RAM). By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). Memory, Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0076] It should be noted that the above-described electronic device embodiments and method embodiments belong to the same concept. For details of their implementation process, please refer to the method embodiments. Furthermore, the technical features in the method embodiments are all applicable to the electronic device embodiments, and will not be repeated here.

[0077] In addition, in an exemplary embodiment, the present invention also provides a computer-readable storage medium, such as a memory 920 storing a computer program. The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor 910, the processor 910 executes a pricing method for an electric vehicle charging station provided by the present invention.

[0078] It should be noted that the above-mentioned computer-readable storage medium-based electric vehicle charging station pricing method program embodiment and method embodiment belong to the same concept. For details of its specific implementation process, please refer to the method embodiment. Furthermore, the technical features in the method embodiment are all applicable to the above-mentioned computer-readable storage medium embodiment, and will not be repeated here.

[0079] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pricing method for electric vehicle charging stations, characterized in that, include: Obtain historical operational data of charging stations and regional market electricity price information; The historical operating data and the regional market electricity price information are input into the smart pricing model, and the smart pricing model outputs a recommended time-of-use electricity price based on the historical operating data and the regional market electricity price information; The recommended time-of-use electricity price will be configured into the charging station's operating system.

2. The pricing method for electric vehicle charging stations according to claim 1, characterized in that, The acquisition of historical operating data of charging stations and regional market electricity price information includes: In response to a pricing recommendation request, the system obtains historical operating data of the selected charging station from the charging station's operating system. The pricing recommendation request is initiated by the operating system to the electric vehicle charging station pricing system based on the selected charging station and the effective date range.

3. The pricing method for electric vehicle charging stations according to claim 1, characterized in that, The step of inputting the historical operating data and the regional market electricity price information into the smart pricing model, and having the smart pricing model output a recommended time-of-use electricity price based on the historical operating data and the regional market electricity price information, includes: The historical operating data and the regional market electricity price information are input into the smart pricing model; The intelligent pricing model analyzes the historical load curves and charging pile utilization rates of charging stations by time period based on the historical operating data, and identifies periods of low charging pile utilization, periods of high photovoltaic power generation, and periods of high charging demand. Based on the periods of low utilization of the charging piles, the periods of high photovoltaic power generation, and the periods of high charging demand, combined with the regional market electricity price information, a recommended time-of-use electricity price is output.

4. The pricing method for electric vehicle charging stations according to claim 3, characterized in that, The pricing strategy of the smart pricing model for recommended time-of-use electricity prices includes: during the charging period, the recommended time-of-use electricity price is the base time-of-use electricity price multiplied by a price adjustment ratio obtained by training the smart pricing model using historical operating data. The recommended time-of-use electricity price is restricted by the allowable electricity price range. The recommended time-of-use electricity price is restricted to the allowable electricity price range by a truncation function. The allowable electricity price range is the minimum and maximum electricity price allowed during the charging period.

5. The pricing method for electric vehicle charging stations according to claim 3, characterized in that, The recommended time-of-use pricing includes price reductions during off-peak hours, price increases during peak hours, increased price reductions during periods of high photovoltaic power generation and low utilization of charging piles, and / or increased price reductions during periods of negative electricity prices.

6. The pricing method for electric vehicle charging stations according to claim 1, characterized in that, The operating system for configuring the recommended time-of-use electricity price to charging stations includes: The recommended time-of-use electricity price is configured in the charging station's operation system, and a corresponding text description of the recommended time-of-use electricity price is also configured in the operation system.

7. The pricing method for electric vehicle charging stations according to claim 1, characterized in that, The electric vehicle charging station pricing method further includes: obtaining actual operating data of the operating system after configuring the recommended time-of-use electricity price, and optimizing the intelligent pricing model based on the actual operating data.

8. A pricing system for electric vehicle charging stations, characterized in that, The electric vehicle charging station pricing method according to any one of claims 1 to 7, wherein the electric vehicle charging station pricing system comprises: an acquisition module, a processing module, and a configuration module; The acquisition module is used to acquire historical operating data of the charging station and regional market electricity price information; The processing module is used to input the historical operating data and the regional market electricity price information into the smart pricing model, and the smart pricing model outputs a recommended time-of-use electricity price based on the historical operating data and the regional market electricity price information; The configuration module is used to configure the recommended time-of-use electricity price to the charging station's operation system.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program, when executed by the processor, implements the electric vehicle charging station pricing method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the electric vehicle charging station pricing method according to any one of claims 1 to 8.

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