Low-carbon economical charging pile power dynamic regulation and control method, equipment and medium

Abstract

The invention relates to the technical field of intelligent power grid and new energy automobile charging control, and discloses a low-carbon economical charging pile power dynamic regulation and control method, system and device and a medium, and the method comprises the steps: obtaining user charging parameters and preference weights, combining historical travel data to predict a departure probability, and screening an adjustable and controllable time period; collecting electricity price, carbon intensity and power grid state data in real time to calculate a dynamic regulation and control coefficient; performing standardized processing on external data and fusing user weights to construct a time period scoring function; selecting high-quality time periods to form a candidate set according to score sorting; solving an optimal power distribution scheme meeting target electric quantity and power constraint in the candidate set by taking minimization of carbon emission and power cost as a target; and finally, converting the scheme into a control instruction sequence to execute dynamic power regulation and control in real time. The method is used for optimizing electricity-carbon collaboration and user experience in the charging process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart grid and new energy vehicle charging control technology, and particularly relates to a low-carbon economic charging pile power dynamic regulation method, device and medium. BACKGROUND

[0002] With the rapid growth of the number of new energy vehicles, charging load has become an important factor affecting the stability and operating efficiency of the distribution network. Most current charging systems are mainly controlled based on price guidance or simple timing strategies, lacking comprehensive consideration of power system operating state, carbon intensity and user behavior patterns. Under the "double carbon" goal, how to achieve green and low-carbon and economic and efficient charging process while ensuring user travel demand has become a key problem to be solved.

[0003] However, the prior art generally has the following shortcomings: first, it lacks the fusion and use of dynamic information such as power market prices and regional carbon intensity, and cannot achieve true low-carbon charging regulation; second, the user setting flexibility is insufficient, and cannot fully express individualized charging preferences (such as the latest departure time, the minimum acceptable power, etc.); third, the power regulation granularity of the charging pile is relatively coarse, and it is difficult to dynamically adjust according to external signals. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a low-carbon economic charging pile power dynamic regulation method, which can achieve the dual optimization goals of energy system friendly and user experience friendly.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a low-carbon economic charging pile power dynamic regulation method, comprising: obtaining target parameters and preference parameters set by a user; based on historical travel time data of the user, generating departure probabilities of each period within a charging time window through a time series prediction model, and screening periods with departure probabilities lower than a safety threshold to form a set of adjustable periods; collecting external environmental information in real time, calculating a dynamic regulation coefficient in combination with power grid load state and renewable energy proportion; standardizing price and carbon intensity data, and constructing a period scheduling scoring function by fusing weight parameters set by the user; sorting the adjustable periods according to the scoring function to generate a priority queue, and selecting a predetermined proportion of low-score periods to form a candidate scheduling set; within the range of the candidate scheduling set, solving an optimal power allocation scheme under the conditions of meeting target charging power and power constraints, with the goal of minimizing carbon emissions and power cost; converting the power allocation scheme into a charging control instruction sequence, and executing charging power dynamic regulation according to time resolution.

[0007] As a kind of low-carbon economy of the charging pile power dynamic regulation method described in the application, a preferred scheme, wherein: the target parameter and the preference parameter set by user include that user sets target parameter through terminal system, including start time t s With the latest completion time t e , target charging capacity E target , power upper limit P max And minimum power threshold P min , time resolution Δt, price sensitive preference weight α and carbon emission sensitive weight β, wherein α+β=1, as the source of system input, determine the regulation space and optimization weight.

[0008] As a kind of low-carbon economy of the charging pile power dynamic regulation method described in the application, a preferred scheme, wherein: the trigger probability of each period in the charging time window is generated, including according to the time window Combined with user's historical travel data Y=[y1,y2,...,y7] in recent 7 days, behavior prediction is constructed by autoregressive moving average model ARIMA, and effective adjustable period set is dynamically generated:

[0009] Extract user's historical departure time sequence in recent 7 days as the basic data of ARIMA modeling, determine ω autoregressive order and η moving average order by autocorrelation function and partial autocorrelation function, and the model form is as follows:

[0010]

[0011] Wherein, d is difference order, Is d order difference operator, normal is constant term, φ i Is autoregressive coefficient, θ j Is moving average coefficient, ∈ t Is white noise;

[0012] Using the trained ARIMA model, the trigger probability of each period t s In time window [t e ] of user is predicted i Trigger probability density function The generation logic of adjustable period set is as follows:

[0013]

[0014] Wherein σ is safety threshold.

[0015] As a kind of low-carbon economy of the charging pile power dynamic regulation method described in the application, a preferred scheme, wherein: the real-time acquisition of external environment information includes, in Time acquisition information, including real-time price vector P=p (t i), regional carbon intensity vector C = c(t i );

[0016] Define the electricity price response coefficient λ(t i ) as the grid load rate, and the carbon intensity correction coefficient μ(t i ) as the product of the collected regional renewable energy generation proportion and the real-time fluctuation coefficient of the carbon market;

[0017] When λ(t i ) is greater than 80%, the power allocation priority of the charging pile in the period is reduced; if λ(t i ) is less than 30%, the charging power is increased;

[0018] When the region is in a high green power proportion, μ(t i ) is triggered to be reduced, the current period carbon emission 'quality' is improved, and the charging power is preferentially allocated; if an emergency carbon emission reduction order is issued due to over-generation of thermal power caused by extreme weather, μ(t i ) is amplified, and the current period charging power allocation is forcibly reduced.

[0019] As a preferred scheme of the low-carbon economic charging pile power dynamic regulation method, wherein: the standardization processing includes calculating the normalized electricity price and carbon intensity

[0020]

[0021] Where min is the minimum value, max is the maximum value, and a scheduling merit score function s(t i ) is constructed for each time period:

[0022]

[0023] Where α is the electricity price sensitive preference weight, and β is the carbon emission sensitive weight.

[0024] As a preferred scheme of the low-carbon economic charging pile power dynamic regulation method, wherein: the candidate scheduling set includes sorting all time periods according to the score function s(t i ) to generate a regulation time period priority queue

[0025]

[0026] And select the first k = m + n low-score high-quality time periods to form a candidate time period set Where m = 8 is the number of safety redundancy time periods, and

[0027] As a preferred scheme of the low-carbon economic charging pile power dynamic regulation method, the optimal power allocation scheme comprises, in an optimization model is constructed within the range,

[0028]

[0029] The objective function is essentially to minimize the cumulative value of carbon intensity, charging power and time, that is, to reduce the power allocation in high-carbon intensity period, and to mathematically force the model to preferentially select the power allocation in low-carbon intensity period;

[0030] The constraint condition is:

[0031] ∑q(t i )·Δt≥E target ,P min ≦P(t i )≤P max

[0032] The cumulative value of power and time of all periods is greater than or equal to the target charging power E target set by the user; and the power P(t i ) of each period is within the range set by the user, and the optimal power allocation q * (t i ) is solved.

[0033] As a preferred scheme of the low-carbon economic charging pile power dynamic regulation method, the optimal power allocation scheme comprises, in * (t i ) is mapped into a control instruction set, and a scheduling plan table is constructed to provide clear scheduling instruction flow for the charging pile end control module;

[0034] Wherein, the control instruction set is:

[0035]

[0036] The system checks whether the current time enters the instruction time period t i every δt time unit, and executes the corresponding charging power q * (t i );

[0037] At the same time, the actual power E(t) is collected in real time and updated as follows:

[0038] E(t+δt)=E(t)+P actual (t)·δt

[0039] Wherein, P actual is the actual power; if E(t)≥E target, terminate all scheduling tasks, form a closed loop control.

[0040] It should be noted that the generation of the adjustable period set avoids the risk of charging delay users, and increases the behavior prediction element. By quantifying the user behavior law through the ARIMA model, the period in which the user is extremely unlikely to start is accurately identified, and these periods are included in the adjustable set. Compared with the fuzzy "historical behavior statistics", ARIMA is based on time series modeling, which can more dynamically and accurately capture the user travel mode (such as the weekly regularity and sudden fluctuations), and ensure that the adjustable period is strongly adapted to the actual travel behavior of the user from the mathematical model level, thereby avoiding the delay caused by charging occupation of the start period from the root. The behavior prediction element includes the user's daily travel time (such as '7:30', '8:15') and the number of travel days.

[0041] It is used as a rigid screening condition (probability < only included), solves the problem of "how to apply behavior prediction", and the confidence interval can be used as a supplement (if the interval is too wide, manual verification or additional redundant periods can be triggered), which improves the robustness of the scheme and upgrades the behavior prediction from "experience judgment" to "mathematical model driven accurate decision".

[0042] The application provides a computer device, including a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the low-carbon economy charging pile power dynamic regulation method when executing the computer program.

[0043] The application provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the low-carbon economy charging pile power dynamic regulation method when being executed by a processor.

[0044] The application has the beneficial effects that: the application realizes dynamic optimization and regulation of charging power by fusing user multi-dimensional preference setting, historical travel behavior prediction and multi-source environment information perception, the user can independently weigh the cost and environmental protection demand, the system guarantees zero delay of travel based on behavior prediction; the system synchronously responds to electricity price and carbon emission signal, improves decision efficiency through standardized scoring and heuristic optimization strategy; realizes minute-level flexible control through continuous power distribution, and combines real-time rescheduling mechanism to respond to sudden changes, finally achieves the three goals of user experience optimization, stable power grid load and reduced charging carbon emission, forms an intelligent charging solution that is economic and environmentally friendly. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creating labor.

[0046] Figure 1 A low-carbon economy charging pile power dynamic regulation method flow chart is provided for an embodiment of the application.

[0047] Figure 2 A low-carbon economy charging pile power dynamic regulation method price and carbon emission change schematic diagram is provided for an embodiment of the application.

[0048] Figure 3 A low-carbon economy charging pile power dynamic regulation method time period comprehensive score schematic diagram is provided for an embodiment of the application.

[0049] Figure 4 A low-carbon economy charging pile power dynamic regulation method optimized power scheduling schematic diagram is provided for an embodiment of the application.

[0050] Figure 5 A low-carbon economy charging pile power dynamic regulation method cost and carbon emission accumulation schematic diagram is provided for an embodiment of the application. DETAILED DESCRIPTION

[0051] In order to make the above objectives, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0052] Embodiment 1, refer to Figure 1 , the first embodiment of the present application, the embodiment provides a low-carbon economy charging pile power dynamic regulation method, comprising:

[0053] S1: obtaining the target parameters and preference parameters set by the user.

[0054] S2: based on the user historical travel time data, generating the departure probability of each period in the charging time window through the time series prediction model, and screening the periods with departure probability lower than the safety threshold to form the adjustable period set.

[0055] S3: real-time acquisition of external environment information, combined with the power grid load state and the proportion of renewable energy to calculate the dynamic regulation coefficient.

[0056] S4: standardizing the price and carbon intensity data, and constructing the period scheduling score function by fusing the weight parameters set by the user.

[0057] S5: Sort the controllable time periods according to the score function to generate a priority queue, and select a predetermined proportion of low-score time periods to form a candidate scheduling set;

[0058] S6: Within the range of the candidate scheduling set, solve the optimal power allocation scheme to minimize carbon emissions and power cost, while meeting the target charging power and power constraints;

[0059] S7: Convert the power allocation scheme into a charging control instruction sequence, and execute the charging power dynamic regulation according to the time resolution.

[0060] Embodiment 2, refer to Figures 2-5 For an embodiment of the present application, based on the above embodiment, a low-carbon economic charging pile power dynamic regulation method is provided.

[0061] Further, in the embodiment of the present application, step S1 obtains the target parameters and preference parameters set by the user, and the specific steps include:

[0062] The user sets the target parameters through the terminal system, including the start time t s and the latest completion time t e , the target charging power E target , the power upper limit P max , the minimum power threshold P min , the time resolution Δt, the price sensitivity preference weight α and the carbon emission sensitivity weight β, where α+β=1. This step is the source of system input, which determines the regulation space and optimization weight.

[0063] Further, in the embodiment of the present application, step S2 generates the departure probability of each period within the charging time window based on the user's historical travel time data through the time series prediction model, and selects the periods with departure probability below the safety threshold to form the controllable time period set, and the specific steps include:

[0064] According to the user-set time window Combined with the user's historical travel data Y=[y1, y2,..., y7] in the past 7 days, the behavior prediction is constructed through the autoregressive moving average model (ARIMA), and the effective controllable time period set is dynamically generated:

[0065] Step S2.1: Extract the user's historical departure time series in the past 7 days as the basic data for ARIMA modeling, and determine ω (autoregressive order) and η (moving average order) using the autocorrelation function and the partial autocorrelation function. The model form is as follows:

[0066]

[0067] Where d is the difference order, is the dth-order difference operator, normal is the constant term, and φ i is the autoregressive coefficient, and θ j is the moving average coefficient, and ∈ t is white noise.

[0068] Using the trained ARIMA model, the probability density function of triggering is calculated for each time period t s in the time window [t e ]. i

[0069] Step S2.2: The generation logic of the adjustable time period set is as follows:

[0070]

[0071] where σ is a safety threshold, and the value is 0.9, indicating that only time periods with a user departure probability lower than 10% are retained.

[0072] Furthermore, in the embodiments of the present application, step S3 collects external environmental information in real time, and calculates a dynamic regulation coefficient in combination with the power grid load state and the proportion of renewable energy, and the specific steps include:

[0073] In time, information is collected, including the real-time electricity price vector P=p(t i ), and the regional carbon intensity vector C=c(t i ). The electricity price response coefficient λ(t i ) is defined as the power grid load rate, and the carbon intensity correction coefficient μ(t i ) is the product of the collected regional renewable energy generation proportion and the carbon market real-time fluctuation coefficient.

[0074] When λ(t i ) is greater than 80%, the power distribution priority of the charging pile in this time period is reduced; if λ(t i ) is less than 30%, the charging pile power is increased.

[0075] When the region is in a high green energy proportion, μ(t i ) is triggered to be reduced, the 'quality' of carbon emission in this period is improved, and the charging power is preferentially distributed; if the thermal power is over-released due to extreme weather and an emergency carbon emission reduction order is issued, μ(t i ) is amplified, and the charging power distribution in this period is forcibly reduced.

[0076] This step is used to realize the dynamic linkage with the power grid state and the carbon emission factor, and has external perception capability.

[0077] ​Furthermore, in this embodiment, step S4 standardizes the electricity price and carbon intensity data, and integrates user-defined weight parameters to construct a time-slot scheduling scoring function. Specific steps include:

[0078] Calculate the normalized electricity price With carbon strength

[0079]

[0080] Here, min refers to taking the minimum value, and max refers to taking the maximum value. Then, a scheduling performance evaluation function s(t) is constructed for each time period. i ):

[0081]

[0082] This scoring function is used for subsequent power allocation decisions and is the core computational indicator for intelligent regulation.

[0083] Furthermore, in this embodiment, step S5 generates a priority queue by sorting the adjustable time periods according to the scoring function, and selects a predetermined proportion of low-scoring time periods to form a candidate scheduling set. The specific steps include:

[0084] According to the scoring function s(t) i ) for all Time period sorting, generating a priority queue for control periods.

[0085]

[0086] The top k = m + n low-scoring high-quality time periods are selected to form a candidate time period set. Where m = 8 is the number of safe redundancy periods, and This step introduces heuristic search thinking, simplifying the difficulty of global optimization.

[0087] Furthermore, in this embodiment, step S6, within the candidate scheduling set, aims to minimize carbon emissions and electricity costs, and solves for the optimal power allocation scheme while satisfying the target charging capacity and power constraints. The specific steps include:

[0088] exist Construct the following optimization model within the scope:

[0089]

[0090] The objective function is essentially to minimize the cumulative value of carbon intensity, charging power, and time, that is, to allocate as little power as possible during periods of high carbon intensity, thus mathematically forcing the model to prioritize allocating power during periods of low carbon intensity.

[0091] The constraint is:

[0092] ∑q(t i )·Δt≥E target ,P min ≦P(t i )≤P max

[0093] The meaning of the constraint is that the power and time cumulative value of all time periods is greater than or equal to the target charging capacity E set by the user target ; and the power P(t i ) of each time period is within the range set by the user, and the optimal power distribution q * (t i ) is solved. This step ensures the unity of green low carbon and target capacity completion through mathematical optimization.

[0094] Further, in the embodiments of the present application, step S7 converts the power distribution scheme into a charging control instruction sequence, and performs dynamic regulation of charging power according to the time resolution, and the specific steps include:

[0095] Mapping q * (t i ) to the control instruction set, and constructing a scheduling plan table:

[0096]

[0097] Table 1: Scheduling plan table

[0098] Time period t i ]] Instructions Power q * (t i )]]> ​ Start charging 2.5kW [ t2 ] Hold 2.5kW … … …

[0099] Provide clear scheduling instruction flow for the charging pile end control module.

[0100] The system checks whether the current time enters the instruction time period t i every δt time unit, and executes the corresponding charging power q * (t i ).

[0101] At the same time, the actual capacity E(t) is collected in real time, and is updated as follows:

[0102] E(t+δt)=E(t)+P actual (t)·δt

[0103] If E(t)≥E target , terminate all scheduling tasks, and form a closed loop control.

[0104] If the following events are found in the monitoring process: user manually terminates in advance, external electricity price or carbon intensity fluctuates greatly, and the power grid sends an emergency load response request. Trigger the dispatch interruption and model re-solution, that is, return to step five to regenerate Q and execute the subsequent process. This step ensures that the system is adaptive to dynamic changes in the scene.

[0105] Embodiment 3 is the third embodiment of the present application, which is different from the first two embodiments:

[0106] The embodiment also provides an electronic device suitable for the case of dynamic power regulation and control of charging piles in a low-carbon economy, comprising a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the method for dynamic power regulation and control of charging piles in a low-carbon economy proposed in the above embodiments.

[0107] The embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to realize the method for dynamic power regulation and control of charging piles in a low-carbon economy proposed in the above embodiments.

[0108] The storage medium proposed in the embodiment and the method for dynamic power regulation and control of charging piles in a low-carbon economy proposed in the above embodiments belong to the same inventive concept, and the technical details not described in detail in the embodiment can be referred to the above embodiments, and the embodiment and the above embodiments have the same beneficial effects.

[0109] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware. Of course, it can also be realized by hardware. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH memory, a hard disk, or an optical disc, etc., including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

[0111] Embodiment 2, which is an embodiment of the present application, based on the above embodiment, provides a low-carbon economy charging pile power dynamic regulation method.

[0112] The following takes a home charging of an electric vehicle at night as an example to illustrate the actual application process and optimization effect of the method.

[0113] The user configures the following target parameters through the App, wherein the starting time t s is 2025-07-1818:00, the latest completion time t e is 2025-07-1907:00, the target charging capacity E target is 20kWh, the power upper limit P max is 7.0kW, the minimum power threshold P min is 1.0kW, the electricity price sensitive preference weight a is 0.6, the carbon emission sensitive weight b is 0.4, and the time resolution At is 15 minutes (0.25 time units), wherein a+b=1. This step is the source of system input, which determines the regulation space and optimization weight.

[0114] Based on the analysis of the user's travel behavior in the past 30 days, the model predicts that the earliest morning travel time is 06:30 with a probability of 85%, and the system sets the safe departure time as 06:30. Therefore, the effective scheduling time window is:

[0115]

[0116] A total of 39 available time periods.

[0117] Through the interface of the power market and the power grid dispatching center, 12 hours of data are collected: the electricity price is P=p(t1), p(t2),..., p(t 39 ), and the regional carbon emission vector is C=c(t1), c(t2),..., c(t 39 ). Part of the data is shown in Table 2:

[0118] Table 2 Data Collection Table

[0119] Time <![CDATA[p(t i ) / (yuan / kWh)]]> [CDATA[c(t i ) / (gCO2 / kWh)]]> 18:00 1.30 820 21:00 1.00 710 01:00 0.75 600 03:00 0.65 540

[0120] The electricity price response coefficient l(t i ) is defined as the grid load rate, and the carbon intensity correction coefficient m(t i ) is the product of the collected regional renewable energy generation proportion and the real-time fluctuation coefficient of the carbon market.

[0121] When l(t i ) is greater than 80%, the power allocation priority of the charging pile in this period is reduced; if l(t i ) is less than 30%, the charging pile power is increased.

[0122] When the region is in high green electricity ratio, trigger μ(t i ) to reduce, improve the carbon emissions 'quality' of this period, priority allocation of charging power; if due to extreme weather resulting in thermal power over issue emergency carbon emission reduction instructions, amplify μ(t i ), forced to reduce the charging power allocation.

[0123] The price of electricity, carbon intensity, etc. are normalized:

[0124]

[0125] Further construct the scheduling score function of each time period:

[0126]

[0127] According to the score function s(t i ) to sort all periods, generate control period priority queue

[0128]

[0129] And select the first k = m + n low score quality period to form the candidate period set Where m = 8 is the number of safety redundancy period, and Through the calculation, k is 20. For power allocation. That is:

[0130]

[0131] In the range of , build the following optimization model:

[0132]

[0133] The constraint conditions are:

[0134] ∑q(t i )·Δt≥20,1.0≦q(t i )≤7.0

[0135] Solve the optimal power allocation q * (t i ).

[0136] Map q * (t i ) to control instruction set, and build the scheduling plan table:

[0137]

[0138] Table 3 scheduling plan table

[0139] Time period t i ]] Instructions Power q * (t i )]]> ​ Start charging 7kW [t2] Hold 7kW … … …

[0140] The controller converts the instructions into IEC 61851 or OCPP control signals and sends them to the smart charging pile.

[0141] The system checks every 0.5 time unit whether the current time enters the instruction time period t i , and executes the corresponding charging power q * (t i ).

[0142] At the same time, the actual power E(t) is collected in real time, and the update is as follows:

[0143] E(t+δt)=E(t)+P actual (t)·δt

[0144] If E(t)≥20kWh, terminate all scheduling tasks, and form a closed-loop control.

[0145] If the following events are found during monitoring: user manually terminates in advance, external electricity price or carbon intensity fluctuates greatly, and the power grid issues an emergency load response request, the scheduling interruption and model re-solution are triggered, that is, step five is returned to regenerate and execute the subsequent process.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A low-carbon economy charging pile power dynamic regulation method, characterized in that: Comprising, obtaining target parameters and preference parameters set by a user; based on user historical travel time data, generating departure probability of each period within the charging time window through a time series prediction model, and screening periods with departure probability lower than a safety threshold to form a set of controllable periods; real-time acquisition of external environmental information, combined with power grid load state and renewable energy proportion to calculate dynamic regulation coefficient; standardizing the price and carbon intensity data, and constructing a period scheduling score function by fusing the weight parameters set by the user; According to the scoring function, the controllable periods are sorted to generate a priority queue, and a predetermined proportion of low-score periods are selected to form a candidate scheduling set; In the range of the candidate scheduling set, the minimum carbon emission and power cost is taken as the target to solve the optimal power allocation scheme under the condition of meeting the target charging power and power constraints; Convert the power allocation scheme into a charging control instruction sequence, and execute the charging power dynamic regulation according to the time resolution.

2. The low-carbon economy charging pile power dynamic regulation method of claim 1, wherein: The target parameter and the preference parameter set by the user include that the user sets the target parameter through the terminal system, including the start time t s and the latest completion time t e , the target charging electric quantity E target , the power upper limit P max , the minimum power threshold P min , the time resolution Δt, the price sensitive preference weight α and the carbon emission sensitive weight β, wherein α+β=1, as the source of the system input, determine the regulation space and the optimization weight.

3. The low-carbon economy charging pile power dynamic regulation method of claim 2, wherein: The generating the trigger probability of each time period in the charging time window comprises: according to the user setting time window W u = [t s ,t e ], in combination with the user's historical travel data Y = [y1, y2,..., y7] in the last 7 days, an autoregressive moving average model ARIMA is used to construct a behavior prediction, and a dynamically generated effective controllable time period set is obtained. Extract the user's historical departure time sequence in the past 7 days as the basic data for ARIMA modeling, use the autocorrelation function and partial autocorrelation function to determine the ω autoregressive order and η moving average order, and the model form is as follows: where d is the difference order, is a dth order difference operator, normal is a constant term, and φ i is an autoregressive coefficient, θ j is a moving average coefficient, ∈ t is white noise; With the trained ARIMA model, predict the probability density function of the user's behavior in each time period t s ,t e ] within the time window [t i Trigger the probability density function The generation logic of the controllable time period set is as follows: Where σ is the safety threshold.

4. The low-carbon economy charging pile power dynamic regulation method of claim 3, wherein: The real-time acquisition of external environment information includes, in acquiring information within a time, including a real-time electricity price vector P = p(t i ), a regional carbon intensity vector C = c(t i ); The electricity price response coefficient λ(t i ) is defined as the grid load rate, and the carbon intensity correction coefficient μ(t i ) is the product of the collected regional renewable energy power generation proportion and the real-time fluctuation coefficient of the carbon market. When λ(t i ) is greater than 80%, the power allocation priority of the charging pile in the period is reduced; if λ(t i ) is less than 30%, the power of the charging pile is increased. When the region is in high green electricity ratio, trigger μ(t i ) to reduce, improve the current period carbon emission 'quality', and allocate charging power preferentially; If the thermal power over-releases due to extreme weather and an emergency carbon reduction order is issued, amplify μ(t i ) to force the current period of charging power allocation to be reduced.

5. The low-carbon economy charging pile power dynamic regulation method of claim 4, wherein: The performing the standardization process includes calculating a normalized electricity price with carbon intensity where min is the minimum and max is the maximum, and the scheduling merit score function s(t i ) is constructed for each time period Where, α is the price sensitive preference weight, β is the carbon emission sensitive weight.

6. The low-carbon economy charging pile power dynamic regulation method of claim 5, wherein: The composition candidate scheduling set includes, according to the score function s(t i ) for all Time period sorting, generating a regulatory time period priority queue and select the top k = m + n low-scored high-quality time period to form the candidate time period set where m = 8 is the number of safety redundancy time periods, and 7. The low-carbon economy charging pile power dynamic regulation method of claim 6, wherein: The optimal power allocation scheme includes, in constructing an optimization model within the range The objective function is essentially to minimize the cumulative value of carbon intensity, charging power and time, that is, to reduce the allocation of power in high-carbon intensity periods, and to mathematically force the model to prefer low-carbon intensity periods for power allocation; The constraint condition is: ∑q(t i )·Δt≥E target ,P min ≦P(t i )≤P max for all time periods, the power-time cumulative value is greater than or equal to the user-set target charging power E target ; and the power P(t i ) of each time period is within the user-set range, to solve the optimal power allocation q * (t i ).

8. The low-carbon economy charging pile power dynamic regulation method of claim 7, wherein: The executing the charging power dynamic regulation includes mapping q * (t i ) into a control instruction set, and constructing a scheduling plan table to provide clear scheduling instruction flow for the charging pile end control module. Where, the control instruction set is: The system checks every δt time unit whether the current time enters the instruction time period t i and executes the corresponding charging power q * (t i ); At the same time, the actual power E(t) is collected in real time, and the update is as follows: E(t + δt) = E(t) + P actual (t) · δt where P actual is the actual power; if E(t) ≥ E target , terminate all scheduled tasks, forming a closed loop control. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the low-carbon economic charging pile power dynamic regulation method in any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the low-carbon economic charging pile power dynamic regulation method in any one of claims 1 to 8.

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