Dynamic pricing method and system for electricity price of charging pile

Abstract

The invention discloses a dynamic pricing method and system for the electricity price of a charging pile, and relates to the technical field of charging pile operation management and electric power pricing, and the method comprises the steps: obtaining charging order data, traffic flow data and the time-of-use electricity price of a power grid, dividing the time period, and calculating the traffic flow proportion; calculating a reference service charge based on the fixed cost and the target profit; determining a demand adjustment coefficient according to the traffic flow proportion, and calculating a dynamic service fee; generating an initial predicted electricity price in combination with the power grid basic electricity price; and comprehensively correcting the initial electricity price by using the pressure, state and behavior factors, constraining the initial electricity price between the cost and the upper limit of the power grid through a truncation function, and outputting a final execution electricity price. According to the invention, through multi-source data fusion and multi-factor dynamic correction, automatic and accurate regulation and control of the electricity price are realized, and the operation income of the charging pile and the operation efficiency of the power grid are effectively improved.

Description

Technical Field

[0001] This invention relates to the fields of charging pile operation management and electricity pricing technology, and in particular to a dynamic pricing method and system for charging pile electricity prices. Background Technology

[0002] With the rapid development of the new energy vehicle industry, charging piles, as a key supporting facility, have seen continuous expansion in construction scale. However, the current layout and operation of charging piles still face many challenges, such as uneven distribution and an imbalance between fast and slow charging pile structures. For charging pile operators, how to dynamically adjust their operating strategies based on the usage of charging piles in different locations to maximize return on investment in a complex market environment has become a focus of industry attention.

[0003] Currently, most mainstream charging pile operation platforms in the market adopt a pricing model where operators manually set charging electricity and service fees. Specifically, after the operator sets the electricity price strategy on the platform, the platform distributes the strategy to the charging piles for execution. The advantage of this model is its flexibility; operators can adjust it at any time based on experience. However, its disadvantages are also significant: the formulation and modification of pricing strategies rely entirely on manual decision-making, are highly subjective, lack scientific and real-time data support, and are difficult to accurately respond to changes in supply and demand.

[0004] Specifically, existing technical solutions have the following objective defects:

[0005] 1. Delayed response and lack of foresight: Existing pricing models are mostly static or semi-static adjustments, which cannot formulate electricity pricing strategies in advance based on forecast data such as traffic flow and grid load in future periods, resulting in an inability to effectively guide user behavior and balance peak and valley loads in the power grid.

[0006] 2. Limited adjustment dimensions and neglect of comprehensive factors: Manually set strategies often only consider basic electricity costs or simple profit margins, failing to systematically incorporate multi-dimensional dynamic factors such as traffic volume, real-time grid pressure, the health status of charging piles, supporting energy storage, and user price elasticity into the pricing model.

[0007] 3. Difficulty in maximizing revenue: Due to the subjective and singular nature of the pricing process, it is impossible to dynamically adjust service fees based on real-time supply and demand while ensuring cost recovery. This results in missed opportunities to increase profits during peak demand periods and attract users during off-peak demand periods to increase equipment utilization, leading to overall revenue not reaching its optimal level.

[0008] Therefore, there is an urgent need in this field for a solution that can automatically and intelligently integrate multiple real-time and predictive data to dynamically generate and correct charging electricity prices, in order to overcome the shortcomings of existing technologies that rely on manual labor, have slow response, and have a single dimension, thereby achieving dual optimization of charging pile operation revenue and grid operation efficiency. Summary of the Invention

[0009] To address this, embodiments of the present invention provide a dynamic pricing method and system for charging pile electricity prices, which solves the problems of lagging regulation and single dimension caused by relying on manual decision-making and static pricing in the prior art, and the inability to achieve optimal balance between charging station revenue and grid load.

[0010] To address the aforementioned technical problems, this invention provides a dynamic pricing method for charging pile electricity prices, comprising the following steps:

[0011] Acquire historical and real-time data for the target area, including at least charging order data, vehicle traffic data, and time-of-use electricity pricing; based on the charging order data and vehicle traffic data, divide the day into multiple time periods and calculate the vehicle traffic ratio for each time period;

[0012] The benchmark service fee is calculated based on the fixed operating cost and target profit margin of the charging pile.

[0013] Based on the traffic flow ratio for each time period, a corresponding demand adjustment coefficient is determined, and the base service fee is multiplied by the demand adjustment coefficient to obtain the dynamic service fee for each time period.

[0014] The grid base price for each time period is calculated based on the grid time-of-use price, and the grid base price for each time period is added to the corresponding dynamic service fee to generate the initial predicted price for that time period.

[0015] The initial predicted electricity price is comprehensively corrected based on preset pressure factors, state factors, and behavioral factors. The corrected electricity price is then constrained between the cost price and the grid-specified upper limit price using a truncation function to obtain the final executed electricity price.

[0016] Preferably, a day is divided into four periods: peak, high, flat, and low; the traffic flow ratio is the percentage of traffic flow in that period relative to the total traffic flow for the whole day; the demand adjustment coefficient is determined based on the range of the traffic flow ratio, wherein the higher the traffic flow ratio, the larger the value of the demand adjustment coefficient.

[0017] Preferably, the rule for determining the demand adjustment coefficient is as follows:

[0018] when Peak hours and traffic volume ratio At that time, the demand adjustment coefficient It ranges from 1.3 to 1.5;

[0019] when During peak hours and hour, It is between 1.1 and 1.2;

[0020] when For flat periods and hour, It is 1.0;

[0021] when Low period hour, It ranges from 0.7 to 0.9.

[0022] Preferably, the benchmark service fee is calculated using the following formula:

[0023] ;

[0024] in, The base service fee; For fixed operating costs; Target profit.

[0025] Preferably, the method of comprehensively correcting the initial predicted electricity price based on preset pressure factors, state factors, and behavioral factors, and constraining the corrected electricity price between the cost price and the grid-specified upper limit price using a cutoff function is as follows:

[0026] ;

[0027] in, For the final implementation of the electricity price, For the initial predicted electricity price, This is a correction term for the stress factor. For state factor correction term, For behavioral factor correction terms, The interaction coefficient and , This is a truncation function used to ensure that the final implemented electricity price meets the requirements. ,in This is the operating cost price of the charging pile. This refers to the time-limited electricity price cap set by the power company.

[0028] Preferably, the pressure factor correction term Calculated using the following formula:

[0029] ;

[0030] in, For time period Predicted charging amount, For time period A reasonable charging capacity benchmark, For time period Real-time load of the power grid For time period The upper limit of the power grid load and are weight coefficients and .

[0031] Preferably, the state factor correction term is calculated by the following formula:

[0032] ;

[0033] where is the health degree of the charging pile and , is the state of charge of the energy storage device supporting the charging pile and , is the target state of charge of the energy storage, and are weight coefficients and .

[0034] Preferably, the behavior factor correction term is calculated by the following formula:

[0035] ;

[0036] where is the user price elasticity during the time period , is the acceptable elasticity threshold, is the initial predicted electricity price of the previous adjacent time period during the time period , is the maximum acceptable price difference ratio between adjacent time periods, and are weight coefficients and .

[0037] Preferably, when , the truncation function corrects the final executed electricity price to ; when , the truncation function corrects the final executed electricity price to .

[0038] An embodiment of the present invention further provides a dynamic pricing system for the charging pile electricity price. This system is used to implement the dynamic pricing method for the charging pile electricity price described above, and includes the following modules:

[0039] The data acquisition and preprocessing module is used to acquire historical and real-time data of the target area, including at least charging order data, vehicle flow data, and time-of-use electricity price; based on the charging order data and vehicle flow data, the day is divided into multiple time periods, and the proportion of vehicle flow in each time period is calculated;

[0040] The benchmark service fee calculation module is used to calculate the benchmark service fee based on the fixed operating cost and target profit margin of the charging pile.

[0041] The dynamic service fee calculation module is used to determine the corresponding demand adjustment coefficient based on the traffic flow ratio of each time period, and multiply the base service fee by the demand adjustment coefficient to obtain the dynamic service fee for each time period.

[0042] The initial electricity price determination module is used to calculate the basic electricity price of the power grid for each time period based on the time-of-use electricity price of the power grid, and add the basic electricity price of the power grid for each time period to the corresponding dynamic service fee to generate the initial predicted electricity price for that time period.

[0043] The electricity price correction module is used to comprehensively correct the initial predicted electricity price based on preset pressure factors, state factors and behavior factors, and use a truncation function to constrain the corrected electricity price between the cost price and the upper limit price stipulated by the power grid to obtain the final executed electricity price.

[0044] As can be seen from the above technical solutions, this invention application has the following beneficial effects:

[0045] (1) Existing technologies rely on operators manually setting electricity prices, resulting in slow response and strong subjectivity. This invention automatically collects and analyzes multi-source data such as traffic flow and orders through a data acquisition and preprocessing module, and automatically generates and optimizes the next day's electricity price based on a pricing model and electricity price correction, using a preset algorithm model (such as demand adjustment coefficient and three-factor correction model). This completely liberates people from repetitive and subjective decision-making, enabling the pricing process to respond to changes in market supply and demand in real time and proactively, providing objective and quantitative data support for accurate pricing.

[0046] (2) Existing technologies have a single adjustment dimension, often only considering basic electricity fees or simple profits. This invention innovatively introduces a multi-factor correction model composed of pressure factors, state factors, and behavioral factors. Among them, the pressure factor ensures that pricing is linked to grid load and charging demand, which helps to "shave peaks and fill valleys" and improve grid operation efficiency. The state factor considers the health of charging piles and the status of energy storage. When equipment fails, it can guide traffic diversion through price to ensure service reliability; when energy storage is insufficient, it can reasonably raise prices to ensure the efficiency of energy storage system. The behavioral factor focuses on user price elasticity and price difference between adjacent time periods, avoiding user churn due to excessive price fluctuations and improving user experience and stickiness. This multi-dimensional collaborative mechanism realizes comprehensive optimization that takes into account grid stability, equipment availability, and user satisfaction while ensuring operator revenue.

[0047] (3) This invention does not adjust electricity prices indefinitely. Through a core truncation function, the final electricity price is strictly limited to between the operating cost price and the grid's stipulated upper limit. This ensures the operator's basic profit margin, avoids vicious competition and loss-making operations, and guarantees the sustainability of the business model. At the same time, it also ensures the compliance of electricity prices, strictly adhering to the power company's pricing regulations and avoiding policy risks. This method enables dynamic pricing to actively pursue profit maximization while steadily maintaining cost limits and policy red lines in a complex market environment, achieving a balance between proactive expansion and prudent operation. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Referring to the drawings will make the features and advantages of the present invention clearer. The drawings are illustrative and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0049] Figure 1 This is a flowchart of a dynamic pricing method for charging pile electricity provided by the present invention;

[0050] Figure 2 This is a block diagram of a dynamic pricing system for charging pile electricity provided by the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1: To address the issues of lag in regulation and limited perspective caused by reliance on manual decision-making and static pricing in existing technologies, thus failing to achieve optimal balance between charging station revenue and grid load, as follows: Figure 1 As shown, this invention proposes a dynamic pricing method for charging pile electricity prices, which includes the following steps:

[0053] S1: Obtain historical and real-time data for the target area, including at least charging order data, vehicle traffic data, and time-of-use electricity pricing; based on the charging order data and vehicle traffic data, divide the day into multiple time periods and calculate the vehicle traffic ratio for each time period;

[0054] S2: The benchmark service fee is calculated based on the fixed operating cost and target profit margin of the charging pile;

[0055] S3: Determine the corresponding demand adjustment coefficient based on the traffic flow ratio of each time period, and multiply the base service fee by the demand adjustment coefficient to obtain the dynamic service fee for each time period;

[0056] S4: Calculate the grid base price for each time period based on the grid time-of-use price, add the grid base price for each time period to the corresponding dynamic service fee, and generate the initial predicted price for that time period.

[0057] S5: Based on preset pressure factors, state factors and behavior factors, the initial predicted electricity price is comprehensively corrected, and the corrected electricity price is constrained between the cost price and the grid-specified upper limit price using a cutoff function to obtain the final executed electricity price.

[0058] As can be seen from the above technical solution, the present invention proposes a dynamic pricing method for charging pile electricity prices. This method first lays a scientific data foundation for accurate pricing through data acquisition and time period division (S1), achieving a quantitative perception of demand heat; then calculates the benchmark service fee (S2) to ensure the coverage of operating costs and the realization of the basic profit target; on this basis, introduces a dynamic service fee based on traffic flow (S3) to automatically adjust revenue according to the supply-demand relationship and initially optimize the profit structure; then generates an initial predicted electricity price (S4) to complete the initial combination of grid basic costs and dynamic revenue. Finally, the core of this method lies in the comprehensive correction and constraint based on multiple factors (S5), which accurately captures and responds to multi-dimensional dynamic information such as grid load, equipment health, and user behavior through three major factors of pressure, state, and behavior, and uses a truncation function to ensure the feasibility and compliance of electricity prices, thereby achieving the maximization of charging pile operation revenue, efficiency optimization, and risk control in a complex and changeable market environment in all aspects.

[0059] In step S1, data acquisition and preprocessing are carried out. This step aims to provide a comprehensive and standardized data foundation for the subsequent pricing model. The following specific operations are performed:

[0060] Data acquisition: Communicate with the cloud platform database, power grid data platform, etc. through application programming interfaces to obtain historical and real-time multi-source data of the target area. The data specifically includes:

[0061] 1. Grid-side data: local grid time-of-use electricity price , grid real-time load and grid load upper limit .

[0062] 2. Charging pile body status data: charging pile health degree (the value range is 0 to 1, 1 means completely normal) and the state of charge of the energy storage device supporting the charging pile (the value range is 0 to 1).

[0063] 3. User-side and market data: historical and real-time charging order data (at least including the charging start time), traffic flow data, fixed operating costs , target profit rate and user price elasticity .

[0064] Data preprocessing: Based on the obtained charging order data and traffic flow data, analyze their time distribution rules, and refer to the peak-valley time period division of the power grid to divide a day into four core time periods: peak period, high peak period, flat period, and low valley period. Calculate the proportion of traffic flow in each time period to the total traffic flow of the whole day , which is used to quantify the demand heat in different time periods.

[0065] In step S2, the base service fee is calculated. It forms the basis of the pricing model, designed to cover fixed costs and achieve target profits. Its calculation formula is as follows:

[0066] ;

[0067] in, To fix operating costs, The target profit margin is defined by this formula. This formula ensures that the expected profit target can be achieved during flat periods (demand-neutral periods).

[0068] In step S3, the dynamic service fee is calculated. This step dynamically adjusts the service fee based on the demand in different time periods, which is key to maximizing revenue. Specifically:

[0069] Based on the traffic flow ratios for each time period calculated in step S1 The corresponding demand adjustment coefficient is determined according to pre-set rules. The preferred rule for determining this coefficient is:

[0070] when During peak hours and hour, Take a value of 1.3 to 1.5;

[0071] when During peak hours and hour, Take a value of 1.1 to 1.2;

[0072] when For flat periods and hour, Set the value to 1.0;

[0073] when It is a low period and hour, Take a value between 0.7 and 0.9.

[0074] Then, the base service fee is multiplied by the demand adjustment factor to obtain the dynamic service fee for each time period:

[0075] .

[0076] In step S4, an initial predicted electricity price is generated, which includes the grid base price for each time period. With the corresponding dynamic service fee Add them together to generate the initial predicted electricity price for that period. :

[0077] ;

[0078] Among them, basic electricity fee Depending on local power grid policies, time-of-use pricing on the power grid can be implemented. Based on the consideration of transmission and distribution cost coefficient Perform calculations, that is .

[0079] In step S5, the final executed electricity price is comprehensively corrected and output. This step is the core innovation of this invention. It uses a multi-factor model to refine the initial predicted electricity price, ensuring its economic viability and feasibility. The correction formula is as follows:

[0080] ;

[0081] in, For the final implementation of the electricity price, The interaction coefficient (ranging from 0 to 0.1) is used to capture the interaction effects between multiple factors. This is a truncation function used to ensure that the final implemented electricity price meets the requirements. ,in This is the operating cost price of the charging pile. This refers to the time-limited electricity price cap set by the power company.

[0082] The calculation methods for each correction item are as follows:

[0083] 1. Stress factor correction item This reflects charging demand and grid load pressure.

[0084] ;

[0085] in, For time period Predicted charging amount, For time period A reasonable charging capacity benchmark (e.g., 70% to 80% of the historical average capacity). and The weighting coefficients and (Primarily driven by demand pressure) When demand exceeds the benchmark or load exceeds the upper limit, this item is positive, causing electricity prices to rise; conversely, it is negative (electricity prices to fall).

[0086] 2. State factor correction term This reflects the status of the charging pile equipment and its supporting energy storage.

[0087] ;

[0088] in, For the health of charging piles, The target state of charge for energy storage is (e.g., 0.5). and The weighting coefficients and (Based on equipment status, then) When the energy health is low, this item is positive, indicating a tendency to lower prices to balance availability; when the energy storage SOC is lower than the target, this item is negative, indicating a tendency to raise prices to reduce competition for electricity between users and energy storage.

[0089] 3. Behavioral Factor Modification Items This reflects users' price sensitivity and market acceptance.

[0090] ;

[0091] in, For time period User price elasticity (percentage change in charging volume for every 1% change in price). This is an acceptable elasticity threshold (e.g., -30%, meaning a 1% increase in price should result in a ≤30% decrease in demand). This is the initial forecast electricity price for the adjacent previous time period. This represents the maximum acceptable price difference percentage between adjacent time periods (e.g., 20%). and These are the weighting coefficients, and (Prioritizing user flexibility) > When users are overly sensitive to prices, this item is negative, prompting a price reduction; when the price difference between adjacent time periods is too large, this item is forcibly corrected to smooth the electricity price curve.

[0092] Example 2: This example uses the daily electricity price setting of a public charging pile (with a 10kW energy storage system) in a commercial area of ​​a city as an example to explain in detail the execution process of the dynamic pricing method.

[0093] I. Data Preprocessing

[0094] 1. Data Acquisition

[0095] The data acquisition module obtains all charging order data for the day (T day) from the charging pile controller, totaling 1200 orders. Each order includes "order start time" (e.g., 10:23, 19:45), "vehicle identifier" (used to distinguish different vehicles), and "charging amount". At the same time, it obtains the local power grid peak and valley time division suggestions from the power grid dispatch platform: peak time (10:00-14:00, 18:00-22:00), peak time (8:00-10:00, 14:00-18:00), flat time (6:00-8:00, 22:00-24:00), and low time (0:00-6:00).

[0096] 2. Time Period Division and Traffic Flow Ratio Calculation

[0097] Based on the aforementioned power grid peak-valley recommendations, day T is divided into four core time periods, and the traffic flow ratio is calculated using two methods. :

[0098] Method 1 (by order quantity): Count the number of orders in each time period and calculate... =Number of orders in a certain period / Total number of orders for the whole day (1200);

[0099] Method 2 (by number of vehicles): Count the number of charging vehicles in each time period (800 vehicles after deduplication), and calculate... =Number of vehicles in a certain period / Total number of vehicles in the whole day (800 vehicles).

[0100] This embodiment uses method 1, with the number of orders in each time period and... As shown in Table 1 below.

[0101] Table 1. Number of orders and traffic flow ratio for different time periods

[0102] Period type Specific time range Number of orders Traffic flow proportion Peak period 10:00-14:00、18:00-22:00 480 40%（480 / 1200） High peak period 8:00-10:00、14:00-18:00 300 25%（300 / 1200） Flat period 6:00-8:00、22:00-24:00 240 20%（240 / 1200） Low valley period 0:00-6:00 180 15%（180 / 1200）

[0103] Note: If a certain period of time If the calculation result is consistent with Method 2 (e.g., 320 vehicles during peak hours, 320 / 800 = 40%), then use it directly; if there is a difference (e.g., vehicles being recharged during off-peak hours), take the average of the two methods to ensure accuracy. Accurately reflects the level of demand.

[0104] II. Establishing an electricity pricing model and determining the initial projected electricity price

[0105] 1. Determine the basic electricity price for the power grid.

[0106] Obtain the local power grid time-of-use electricity price for each time period on T+1 day (the next day). The transmission and distribution cost coefficient is determined in accordance with local power grid policies. The city requires operators to bear the costs of power transmission and distribution, and sets... (If no liability is required) (Through the formula) The basic electricity price of the power grid was calculated, and the basic electricity price of the power grid for each time period is shown in Table 2 below.

[0107] Table 2 Basic Electricity Prices for Each Time Period

[0108] Period type (μA / °C) (μA / °C) Peak period 1.0 0.1 1.1（1.0×1.1） High peak period 0.8 0.1 0.88（0.8×1.1） Flat period 0.6 0.1 0.66（0.6×1.1） Low valley period 0.4 0.1 0.44（0.4×1.1）

[0109] 2. Calculate the base service fee

[0110] Based on charging pile operation data, set fixed operating costs. Yuan / degree (including amortized costs such as equipment depreciation, labor, and site rental), target profit (That is, an expected profit margin of 20%). Using the formula... Calculation (Base service fee is the service fee for a flat period):

[0111] Yuan / degree.

[0112] 3. Calculate dynamic service fees

[0113] Based on the above... The demand adjustment coefficient shall be determined according to the following rules. Through formula The dynamic service fees for each time period are calculated, and the resulting dynamic service fees for each time period are shown in Table 3 below.

[0114] Table 3 Dynamic service fees for different time periods

[0115] Period type (Regular value) (μA / °C) Peak period ≥25% 1.4 (1.3-1.5 interval) 1.05（0.75×1.4） High peak period [15%，25%) 1.2 (1.1-1.2 interval) 0.9（0.75×1.2） Flat period [10%，15%) 1.0 0.75（0.75×1.0） Low valley period ≤10% 0.8 (0.7-0.9 interval) 0.6（0.75×0.8）

[0116] 4. Calculate the initial forecast electricity price

[0117] Through formula The initial forecast electricity price for each time period was calculated, and the initial forecast electricity price for each time period is shown in Table 4 below.

[0118] Table 4 Initial Forecast Electricity Prices for Each Time Period

[0119] Period type (μA / °C) (μA / °C) (μA / °C) Peak period 1.1 1.05 2.15（1.1+1.05） High peak period 0.88 0.9 1.78（0.88+0.9） Flat period 0.66 0.75 1.41（0.66+0.75） Low valley period 0.44 0.75 1.04（0.44+0.6）

[0120] III. Three-Factor Correction and Interaction Regulation

[0121] Taking peak hours as an example (the correction logic is the same for other time periods), the three-factor correction process is explained in detail:

[0122] 1. Calculate the three major factor correction terms

[0123] (1) Pressure factor correction term

[0124] Input parameters:

[0125] Peak hour predicted charging amount = 120% (Demand exceeds benchmark);

[0126] The benchmark for reasonable charging during peak hours = 80% of the historical average charging amount (historical average is 1000 kWh, therefore...). =800 degrees);

[0127] Peak-hour real-time grid load = 90% (Load not exceeded the limit);

[0128] Peak-hour grid load limit = 1200kW;

[0129] Weighting coefficients: (Demand pressure weight) (Grid load weight), and .

[0130] Calculation process:

[0131] Yuan / degree;

[0132] Logical verification: Requirements exceed benchmark ( (Positive, electricity price increases), grid load does not exceed the upper limit (offsetting part of the increase), final The result is positive, which aligns with the weighting setting of "demand pressure as the primary factor".

[0133] (2) State factor correction term

[0134] Input parameters:

[0135] Charging station health rating = 0.85 (15% of charging stations have minor faults, such as reduced charging speed).

[0136] Energy storage state of charge = 0.3 (below target) );

[0137] Weighting coefficients: (Equipment status weight) (energy storage state weight), and .

[0138] Calculation process:

[0139] Yuan / degree;

[0140] Logical verification: The equipment health is low (a slight price increase is needed to balance availability), and the energy storage SOC is lower than the target (a price increase is needed to reduce competition for electricity between users and energy storage). Therefore... A positive value aligns with the weight setting of "device status as the primary factor".

[0141] (3) Behavioral factor modification items

[0142] Input parameters:

[0143] User price elasticity = -25% (for every 1% increase in price, the charging capacity decreases by 25%). (Acceptable lower bound);

[0144] Initial electricity price during peak hours = 1.78 yuan / kWh (the period preceding the peak hour);

[0145] The maximum acceptable price difference between adjacent time periods is 20%.

[0146] Weighting coefficients: (User elasticity weight) (Price difference weighting), and .

[0147] Calculation process:

[0148] Yuan / degree;

[0149] Logical verification: User elasticity did not exceed the threshold (-25% > -30%), but the adjacent price difference slightly exceeded 20% (0.2079 > 0.2), therefore... A negative value forces a reduction in the price difference, which aligns with the weighting setting that prioritizes user flexibility.

[0150] 2. Calculate the interaction effect term

[0151] Set the interaction impact coefficient ( (To avoid over-amplifying the interaction), through the formula calculate:

[0152] Yuan / degree;

[0153] The absolute value of this value is extremely small, and its impact on the final electricity price is negligible, which is in line with... The design purpose.

[0154] IV. Obtaining the final execution price by truncation constraints

[0155] Set the operating cost price of the charging station Yuan / kWh (bottom-line cost, not lower than this price), the power company stipulates the upper limit of electricity price during peak hours. Yuan / degree.

[0156] 1. Calculate the corrected sum.

[0157] :

[0158] 2.15 + 0.172 + 0.48375 - 0.222 - 0.0009 ≈ 2.58285 yuan / kWh.

[0159] 2. Truncation Constraint

[0160] Use truncation function judge:

[0161] The corrected total (2.58285 yuan / kWh) > (2.5 yuan / kWh), therefore the final electricity price will be [price missing]. Revised to 2.5 yuan / kWh;

[0162] If the corrected sum for a certain period is < (If the price during off-peak hours is calculated to be 0.5 yuan / kWh), then the price will be truncated to 0.5 yuan / kWh to ensure that the operation does not incur losses.

[0163] V. Final electricity price calculation for other time periods

[0164] The final electricity price for other time periods (peak hours, average hours, and off-peak hours) is calculated based on the above logic, and the results are shown in Table 5 below.

[0165] Table 5 Final Electricity Prices for Other Time Periods

[0166] Period type High peak period Flat period Low valley period (μA / °C) 1.78 1.41 1.04 (μA / °C) 0.089 0.021 -0.4576 (μA / °C) 0.352 0.129 -0.026 (μA / °C) 0.051 -0.035 0.0156 Interaction term (yuan / degree) 0.0006 -0.0001 0.000009 Corrected total (yuan / degree) 2.2726 1.5249 0.572 (μA / °C) 2.2 1.8 1.2 (μmol / min / mg) 0.6 0.6 0.6 (μmol / min / mg) 2.2 (upper limit of truncation) 1.52 (no need for truncation) 0.6 (truncated cost)

[0167] VI. Verification of Implementation Results

[0168] The T+1 electricity price obtained through the dynamic pricing method in this embodiment has the following advantages compared to the existing manual pricing model:

[0169] (1) More precise supply and demand matching: The peak electricity price is 2.5 yuan / kWh (higher than the initial price of 2.15 yuan), which not only utilizes high demand to increase profits, but also avoids excessive user churn (elasticity -25% is controllable); the adjusted final electricity price during off-peak hours is 0.6 yuan / kWh (lower than the initial price of 1.04 yuan), which attracts users to charge and improves the utilization rate of charging piles (it is expected that the charging volume during off-peak hours will increase by more than 30%).

[0170] (2) Balance between supply and demand and cost: During peak / peak hours, the upper limit of electricity price is controlled to ensure reasonable pricing, while during off-peak hours, cost is cut off to ensure that the operation does not suffer losses, which is in line with the goal of "maximizing profits".

[0171] (3) Dynamic response capability: If the power grid load suddenly increases the next day ( =110% ), stress factor This will increase the need to further raise electricity prices to balance the grid load; if the health of charging piles drops to 0.6 (40% failure rate), the state factor will... This will increase the likelihood of users choosing legitimate charging stations by raising prices, thus ensuring service quality.

[0172] Example 3: Figure 2 As shown, the present invention provides a dynamic pricing system for charging pile electricity prices. This system is used to implement the dynamic pricing method for charging pile electricity prices in Embodiment 1 above, and includes the following modules:

[0173] The data acquisition and preprocessing module 100 is used to acquire historical and real-time data of the target area. The data includes at least charging order data, vehicle flow data, and basic electricity price of the power grid. Based on the charging order data and vehicle flow data, the day is divided into multiple time periods, and the proportion of vehicle flow in each time period is calculated.

[0174] The benchmark service fee calculation module 200 is used to calculate the benchmark service fee based on the fixed operating cost and target profit margin of the charging pile.

[0175] The dynamic service fee calculation module 300 is used to determine the corresponding demand adjustment coefficient based on the traffic flow ratio of each time period, and multiply the base service fee by the demand adjustment coefficient to obtain the dynamic service fee for each time period.

[0176] The initial electricity price determination module 400 is used to calculate the basic electricity price of the power grid for each time period based on the time-of-use electricity price of the power grid, and add the basic electricity price of the power grid for each time period to the corresponding dynamic service fee to generate the initial predicted electricity price for that time period.

[0177] The electricity price correction module 500 is used to comprehensively correct the initial predicted electricity price based on preset pressure factors, state factors and behavior factors, and use a truncation function to constrain the corrected electricity price between the cost price and the upper limit price stipulated by the power grid to obtain the final executed electricity price.

[0178] This embodiment provides a dynamic pricing system for charging pile electricity prices, used to implement the aforementioned dynamic pricing method for charging pile electricity prices. Therefore, the specific implementation of the dynamic pricing system for charging pile electricity prices can be found in the previous embodiment section of the dynamic pricing method for charging pile electricity prices. For example, the data acquisition and preprocessing module 100, the benchmark service fee calculation module 200, the dynamic service fee calculation module 300, the initial electricity price determination module 400, and the electricity price correction module 500 are respectively used to implement steps S1, S2, S3, S4, and S5 in the aforementioned dynamic pricing method for charging pile electricity prices. Therefore, its specific implementation can be referred to the description of the corresponding embodiments. To avoid redundancy, it will not be repeated here.

[0179] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0181] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0182] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A dynamic pricing method for electricity prices at charging stations, characterized in that, Includes the following steps: Acquire historical and real-time data for the target area, including at least charging order data, vehicle traffic data, and time-of-use electricity pricing; based on the charging order data and vehicle traffic data, divide the day into multiple time periods and calculate the vehicle traffic ratio for each time period; The benchmark service fee is calculated based on the fixed operating cost and target profit margin of the charging pile. Based on the traffic flow ratio for each time period, a corresponding demand adjustment coefficient is determined, and the base service fee is multiplied by the demand adjustment coefficient to obtain the dynamic service fee for each time period. The grid base price for each time period is calculated based on the grid time-of-use price, and the grid base price for each time period is added to the corresponding dynamic service fee to generate the initial predicted price for that time period. The initial predicted electricity price is comprehensively corrected based on preset pressure factors, state factors, and behavioral factors. The corrected electricity price is then constrained between the cost price and the grid-specified upper limit price using a truncation function to obtain the final executed electricity price.

2. The dynamic pricing method for charging pile electricity prices according to claim 1, characterized in that, The day is divided into four periods: peak, high, flat, and low. The traffic flow ratio is the percentage of traffic flow in that period relative to the total traffic flow for the day. The demand adjustment coefficient is determined based on the range of the traffic flow ratio, with a higher traffic flow ratio resulting in a larger demand adjustment coefficient.

3. The dynamic pricing method for charging pile electricity prices according to claim 1 or 2, characterized in that, The rule for determining the demand adjustment coefficient is as follows: when Peak hours and traffic volume ratio At that time, the demand adjustment coefficient It ranges from 1.3 to 1.5; when During peak hours and hour, It is between 1.1 and 1.2; when For flat periods and hour, It is 1.0; when Low period hour, It ranges from 0.7 to 0.

9.

4. The dynamic pricing method for charging pile electricity prices according to claim 1, characterized in that, The benchmark service fee is calculated using the following formula: ； in, The base service fee; For fixed operating costs; Target profit.

5. The dynamic pricing method for charging pile electricity prices according to claim 1, characterized in that, The method of comprehensively correcting the initial predicted electricity price based on preset pressure factors, state factors, and behavioral factors, and constraining the corrected electricity price between the cost price and the grid-specified upper limit price using a cutoff function is as follows: ； in, For the final implementation of the electricity price, For the initial predicted electricity price, This is a correction term for the stress factor. For state factor correction term, For behavioral factor correction terms, The interaction coefficient and , This is a truncation function used to ensure that the final implemented electricity price meets the requirements. ,in This is the operating cost price of the charging pile. This refers to the time-limited electricity price cap set by the power company.

6. The dynamic pricing method for charging pile electricity prices according to claim 5, characterized in that, The pressure factor correction term Calculated using the following formula: ； in, For time period Predicted charging amount, For time period A reasonable charging capacity benchmark, For time period Real-time load of the power grid For time period The upper limit of the power grid load, and The weighting coefficients and .

7. The dynamic pricing method for charging pile electricity prices according to claim 5, characterized in that, The state factor correction term Calculated using the following formula: ； Among them, is the health degree of the charging pile and , is the state of charge of the energy storage supporting the charging pile and , is the target state of charge of the energy storage, and are weight coefficients and .

8. The dynamic pricing method for charging pile electricity prices according to claim 5, characterized in that, The behavioral factor modification item Calculated using the following formula: ； in, For time period User price elasticity, For an acceptable elasticity threshold, For time period The initial forecast electricity price for the adjacent previous time period, This represents the maximum acceptable price difference ratio between adjacent time periods. and The weighting coefficients and .

9. The dynamic pricing method for charging pile electricity prices according to claim 5, characterized in that, when When, truncation function The final electricity price will be implemented. Revised to ;when When, truncation function The final electricity price will be implemented. Revised to .

10. A dynamic pricing system for electricity prices at charging stations, characterized in that, The system is used to implement the dynamic pricing method for charging pile electricity prices as described in any one of claims 1 to 9, and includes the following modules: The data acquisition and preprocessing module is used to acquire historical and real-time data of the target area, including at least charging order data, vehicle flow data, and basic electricity price; based on the charging order data and vehicle flow data, the day is divided into multiple time periods, and the vehicle flow ratio of each time period is calculated; The benchmark service fee calculation module is used to calculate the benchmark service fee based on the fixed operating cost and target profit margin of the charging pile. The dynamic service fee calculation module is used to determine the corresponding demand adjustment coefficient based on the traffic flow ratio of each time period, and multiply the base service fee by the demand adjustment coefficient to obtain the dynamic service fee for each time period. The initial electricity price determination module is used to calculate the basic electricity price of the power grid for each time period based on the time-of-use electricity price of the power grid, and add the basic electricity price of the power grid for each time period to the corresponding dynamic service fee to generate the initial predicted electricity price for that time period. The electricity price correction module is used to comprehensively correct the initial predicted electricity price based on preset pressure factors, state factors and behavior factors, and use a truncation function to constrain the corrected electricity price between the cost price and the upper limit price stipulated by the power grid to obtain the final executed electricity price.

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