Realization method and system for supporting electric power spot goods through ordered charging

Abstract

The invention relates to the technical field of electric vehicle charging, and discloses an implementation method and system for supporting electric power spot goods through ordered charging, and the method comprises the steps: constructing a cooperative regulation and control system with a load aggregator as a core, integrating the data of an intelligent charging pile, a user side management system and an electric power spot goods market, and dynamic optimization scheduling of the charging behavior of the electric vehicle is realized. The system collects multi-dimensional data such as user vehicle information, travel plans, battery states, court transformer loads and electricity market prices, establishes a multi-target optimization model including charging cost, battery loss, transformer safe operation and user time constraints, and generates an optimal charging strategy by dynamically adjusting weight coefficients to adapt to different operation scenes. The strategy participates in electric power spot market transaction through a load aggregator and then is issued to a charging pile to be executed, minute-level power adjustment is supported, and on the premise that the vehicle using requirement of a user is met, peak load shifting is achieved, new energy consumption is promoted, and the charging cost of the user and the operation risk of a power grid are reduced.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and more specifically to a method and system for realizing orderly charging to support electricity spot supply. Background Technology

[0002] With the rapid growth in the number of electric vehicles, disorderly charging behavior has significantly impacted the power system. Especially during peak evening hours in residential areas, concentrated charging leads to a surge in local grid load, widening the peak-to-valley difference, exacerbating voltage fluctuations, and even causing equipment aging and safety hazards. Meanwhile, users often ignore electricity price signals and charge during periods of high electricity prices, increasing their own electricity costs.

[0003] On the other hand, my country is accelerating its power market reform, and the electricity spot market is gradually improving, forming a time-of-use pricing mechanism that reflects supply and demand. However, electric vehicles have not yet effectively participated in market transactions, and there is a lack of technical means to link flexible load resources on the user side with spot market prices.

[0004] Most existing charging stations are passive power supply devices, unable to actively adjust charging power based on grid conditions or electricity price changes. Furthermore, they lack a multi-objective collaborative optimization mechanism that comprehensively considers user needs, battery lifespan, power distribution safety, and market profitability.

[0005] Therefore, there is an urgent need for a comprehensive and orderly charging method that can connect electric vehicle users, smart charging piles, power distribution networks, and the electricity spot market. Summary of the Invention

[0006] In view of this, the present invention provides a method and system for realizing orderly charging to support the electricity spot market, solving the problems of disorderly charging of electric vehicles exacerbating grid pressure, high charging costs for users, insufficient absorption of new energy sources, and low market participation in the prior art. By constructing a four-in-one control system of "user-charging pile-load aggregator-electricity market", it achieves precise scheduling of charging load, closed-loop feedback of economic incentives, and coordinated optimization of grid operation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for implementing orderly charging to support electricity spot market includes:

[0009] S1: Obtain vehicle information, charging preferences and travel plans of electric vehicle users through the user-side management system, and collect battery status and charging data in real time through smart charging piles;

[0010] S2: Load aggregators integrate the adjustable charging loads of multiple electric vehicles to form a controllable load resource pool;

[0011] S3: Based on the day-ahead and real-time electricity prices in the electricity spot market, the current load of the transformers in the distribution area, historical load curves and rated capacity, and the data collected by S1, the load aggregator builds an intelligent optimization model for the load resource pool and determines the objective function and constraints.

[0012] S4: Dynamically adjust the weight coefficients of each penalty term in the objective function according to the current operating scenario, and solve for the optimal charging power sequence of each electric vehicle in the load resource pool at each time period;

[0013] S5: The optimal charging power sequence is sent to the corresponding smart charging pile through the user-side management system, and the electric vehicles in the load resource pool are controlled to charge according to the strategy.

[0014] S6: During the charging process, monitor the grid status, electricity price fluctuations, changes in user demand, or equipment malfunctions in real time. If a preset condition is triggered, return to S3, reconstruct the intelligent optimization model based on the updated information, and solve it to generate a new optimal charging power sequence.

[0015] Preferably, vehicle information includes battery capacity and rated power, travel plans include planned usage time and target charge, charging preferences include whether to participate in the electricity spot market and whether to accept charging during off-peak hours, and battery status and charging data include current SOC, charging voltage, current, and temperature.

[0016] Preferably, the objective function of the intelligent optimization model is:

[0017]

[0018]

[0019]

[0020]

[0021] Where T represents the total time period, c t P represents the electricity price during time period t. t For optimal charging power, L is the rate of change of SOC, reflecting the charging rate; μ is the weighting coefficient of the battery loss penalty term, balancing spot market load fluctuations and battery loss. t Let γ be the transformer load rate during time period t, and γ be the transformer economic operation weighting coefficient. β represents the penalty coefficient for overload and light load, respectively, and P t * Based on power optimization, χ is the spatiotemporal coupling influence coefficient, PriceGrad t Let LoadFactor be the electricity price gradient for time period t. tAs the load impact factor, The average electricity price for the same historical period. This represents the average electricity price for the next adjacent period in history for the same time period. To predict load, L max L min These are the historical maximum and minimum loads, respectively; T use For users to plan their vehicle usage time, SOC represents the percentage of the battery's current remaining charge relative to its rated capacity. req ω represents the minimum percentage of battery power required when using the vehicle, δ represents the time constraint weight, and δ represents the user priority coefficient.

[0022] The constraints include:

[0023] Power constraint: 0≤P t ≤P max , where P max The rated power of the charging pile;

[0024] Time constraints: in, SOC(T) is the safety margin factor. use (T) represents the user's vehicle usage time point. use The current battery percentage; Indicates the time point T when using the vehicle. use Corresponding load impact factors for the transformer area;

[0025] Battery constraints: SOC min ≤SOC(t)≤SOC max Among them, SOC min SOC max These represent the minimum and maximum charging currents of the battery, respectively.

[0026] Transformer load constraints: Among them, L max To reserve a safety margin; L base S represents the transformer base load rate during time period t; transformer This refers to the rated capacity of the transformer.

[0027] Preferably, the specific logic for dynamically adjusting the weight coefficients in S4 includes:

[0028] When the available charging time is less than the first preset threshold, the time constraint weight ω is increased;

[0029] When the current load of the transformer in the distribution area approaches the safety threshold of its rated capacity, the weighting coefficient γ for economic operation of the transformer is increased.

[0030] When the battery temperature exceeds a certain temperature threshold or the battery health status is lower than a second preset threshold, the weighting coefficient μ of the battery loss penalty term is increased.

[0031] Preferably, in S5, the electric vehicles in the load resource pool are charged according to a strategy, including:

[0032] The charging station limits its maximum output power based on its rated power.

[0033] The charging process continuously monitors the battery's state of charge (SOC) to ensure that the set target capacity is reached.

[0034] The output power is adjusted at minute-level frequency. During periods of low electricity prices in the spot market, the maximum output power is used for charging. During periods of high electricity prices, the charging power of non-emergency vehicles is reduced or charging is suspended, and priority is given to ensuring that vehicles that have been booked for use during the morning rush hour the next day can complete charging.

[0035] Preferably, the conditions for triggering policy recalculation in S6 include:

[0036] Users can modify their planned car usage time through the user-side management system;

[0037] The real-time electricity price on the power grid fluctuated beyond the preset range.

[0038] The transformer in the distribution area has triggered an overload alarm;

[0039] The smart charging station experienced a communication interruption or malfunction.

[0040] Preferably, it also includes S7: After the load aggregator receives revenue from participating in the electricity spot market on behalf of users, it will allocate a portion of the incentives to the user-side management system according to the user's contribution in the load resource pool, so that users can redeem charging services or electricity discounts; among them, incentive allocation is only made to users who are set to participate in the electricity spot market in S1.

[0041] A system for realizing orderly charging to support electricity spot market, comprising:

[0042] The user-side management system is used to obtain vehicle information, charging preferences and travel plans of electric vehicle users, and to receive battery status and charging data collected in real time from smart charging piles.

[0043] Multiple smart charging piles are connected to electric vehicles to collect battery status and charging data in real time, and adjust the output power according to the received control commands.

[0044] The load aggregator system communicates with the user-side management system and multiple smart charging piles, including:

[0045] The load integration module is used to integrate the adjustable charging loads of multiple electric vehicles to form an adjustable load resource pool.

[0046] The optimization modeling module is used to build an intelligent optimization model for the load resource pool based on the day-ahead and real-time electricity prices in the electricity spot market, the current load of the transformers in the distribution area, historical load curves and rated capacity, as well as data collected by the user-side management system, and to determine the objective function and constraints.

[0047] The weight adjustment and solution module is used to dynamically adjust the weight coefficients of each penalty term in the objective function according to the current operating scenario, and solve for the optimal charging power sequence of each electric vehicle in the load resource pool at each time period.

[0048] The strategy distribution module is used to distribute the optimal charging power sequence to the corresponding smart charging pile through the user-side management system, and control the electric vehicles in the load resource pool to perform charging according to the strategy.

[0049] The dynamic monitoring and recalculation module is used to monitor the grid status, electricity price fluctuations, changes in user demand, or equipment abnormalities in real time during the charging process. If a preset condition is triggered, the optimization modeling module is notified to rebuild the intelligent optimization model based on the updated information and solve it to generate a new optimal charging power sequence.

[0050] Preferably, the load aggregator system also includes:

[0051] The market trading and incentive allocation module is used to represent users in electricity spot market transactions. After obtaining benefits, a portion of the incentives is allocated to the user-side management system according to the user's contribution to the load resource pool, which can be used by the user to redeem charging services or electricity discounts. Incentive allocation is only performed on users who are set to participate in the electricity spot market in the user-side management system.

[0052] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for implementing orderly charging to support electricity spot trading, which has the following beneficial effects:

[0053] 1) Reduce the peak-to-valley difference in the power grid, improve the stability of the power grid system, and reduce the risk of voltage drops in local power grids.

[0054] 2) Stabilize the power grid load and reduce the probability of sudden increases in power grid load and the risk of power outages.

[0055] 3) Ensure the economical and reliable operation of the power distribution facilities in the transformer area, reduce equipment wear and upgrade costs, and indirectly reduce electricity costs.

[0056] 4) Balance the utilization rate of smart charging piles in certain areas to ensure that users make full use of resources.

[0057] 5) Remind users to charge at low prices during peak green energy periods.

[0058] 6) Provide users with the best charging solutions, protect users' wallets, and extend the battery life of users' cars.

[0059] 7) Reduce the risks for power generation companies and user markets, promote win-win results, and accelerate the market-oriented reform of the power sector. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0061] Figure 1 The present invention provides a flowchart of a method for implementing orderly charging to support electricity spot trading.

[0062] Figure 2 This invention provides a system block diagram for realizing orderly charging support for spot power.

[0063] Figure 3 This is a schematic diagram illustrating the application of an orderly charging support system for spot electricity provided by the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] This invention discloses a method for implementing ordered charging to support electricity spot trading, such as... Figure 1 As shown, it includes:

[0066] S1: Obtain vehicle information, charging preferences, and travel plans of electric vehicle users through the user-side management system, and collect battery status and charging data in real time through smart charging piles; it should be noted here that electric vehicle users first register an account in the user-side management system, then fill in the relevant information of their electric vehicles, and then obtain vehicle information, charging preferences, and travel plans of electric vehicle users through the user-side management system.

[0067] S2: Load aggregators integrate the adjustable charging loads of multiple electric vehicles to form a controllable load resource pool. The load resource pool has two main functions: peak shaving and valley filling: During periods of low spot market electricity prices (such as early morning when renewable energy generation is high and load is low), the resource pool guides the loads within the pool (such as electric vehicles) to increase electricity consumption, absorbing excess energy; during peak electricity price periods (such as 7-10 pm when load surges), it reduces electricity consumption, alleviating grid pressure. Balance deviation: In the real-time market, if there are sudden fluctuations in power generation output (such as a sharp drop in wind power), the resource pool can quickly adjust the load (such as suspending some charging) to fill the supply-demand gap and prevent a sharp rise in electricity prices.

[0068] S3: Based on the day-ahead and real-time electricity prices in the electricity spot market, the current load of the transformers in the distribution area, historical load curves and rated capacity, and the data collected by S1, the load aggregator builds an intelligent optimization model for the load resource pool and determines the objective function and constraints.

[0069] S4: Dynamically adjust the weight coefficients of each penalty term in the objective function according to the current operating scenario, and solve for the optimal charging power sequence of each electric vehicle in the load resource pool at each time period;

[0070] S5: The optimal charging power sequence is sent to the corresponding smart charging pile through the user-side management system, and the electric vehicles in the load resource pool are controlled to charge according to the strategy.

[0071] S6: During the charging process, monitor the grid status, electricity price fluctuations, changes in user demand, or equipment malfunctions in real time. If a preset condition is triggered, return to S3, reconstruct the intelligent optimization model based on the updated information, and solve it to generate a new optimal charging power sequence.

[0072] Furthermore, to deeply integrate with the electricity market and address the lack of an economic incentive loop, this invention also includes S7: After the load aggregator, representing users, participates in electricity spot market transactions and earns revenue, a portion of the incentive is allocated to the user-side management system based on the user's contribution to the load resource pool. This incentive can then be redeemed by the user for charging services or electricity discounts. Specifically, incentive allocation is only applied to users designated in S1 to participate in the electricity spot market. The calculation of user contribution is based on: the user's actual response to valley filling or peak shaving; the duration of continuous participation; and the contribution is used to generate corresponding points, which are then pushed to the user-side management system. Users can use these points to redeem charging services, electricity discount coupons, etc., during their next charging session, thereby promoting orderly charging participation and improving user satisfaction.

[0073] After charging is complete, users can view the charging bill through the charging app, including electricity prices and charging power at different times during the charging process, incentive income from participating in the spot market, charging points, electricity usage analysis, and optimal electricity usage plan.

[0074] In this embodiment, vehicle information includes battery capacity and rated power, travel plan includes planned usage time and target battery level, charging preferences include whether to participate in the electricity spot market and whether to accept charging during off-peak hours, and battery status and charging data include current SOC, charging voltage, current and temperature.

[0075] In this embodiment, the objective function is "charging cost c". t *P t +Battery degradation penalty +Transformer load penalty γ*f(L t The objective function of the intelligent optimization model is to minimize the sum of "+user time constraint penalty δ*g(t)".

[0076]

[0077]

[0078]

[0079] Where T represents the total time period, c t P represents the electricity price during time period t. t For optimal charging power, L is the rate of change of SOC (State of Charge), reflecting the charging rate; μ is the weighting coefficient of the battery loss penalty term, balancing spot market load fluctuations and battery losses. t The relationship between the transformer load rate and charging power during time period t is as follows: Where L base Based on the base load rate (excluding charging load), S transformer γ represents the rated capacity of the transformer (unit: kVA), and γ is the weighting coefficient for the economic operation of the transformer. This coefficient can be adjusted according to the importance of the transformer (e.g., a higher value for main transformers in residential areas and a lower value for auxiliary transformers in industrial areas) to control the importance of this term in the objective function. β represents the penalty coefficient for overload and light load, respectively. And β can be obtained through training on historical data, for example:

[0080] P t * Based on the optimized power (ignoring historical data and load forecasts), χ is the spatiotemporal coupling influence coefficient, controlling the adjustment intensity, with a value of 0.2-0.5. PriceGrad tThe load factor represents the electricity price gradient (the rate of change of electricity prices during the same historical time period) over time period t. t Load impact factor (based on transformer area load forecast), The average electricity price for the same historical period. To predict load, L max L min These are the historical maximum and minimum loads, respectively; T use SOC plans user car usage time req ω is the minimum SOC required when using the vehicle, δ is the time constraint weight, and δ is the user priority coefficient.

[0081] The constraints include:

[0082] Power constraint: 0 ≤ P t ≤P max , where P max The rated power of the charging station, such as 7kW;

[0083] Time constraints: in, This is a safety margin factor, for example, with a value of 0.05-0.1;

[0084] Battery constraints: SOC min ≤SOC(t)≤SOC max Among them, SOC min SOC max These represent the minimum and maximum charging currents of the battery, respectively.

[0085] Transformer load constraints: Among them, L max To reserve a safety margin, for example, a value of 90%;

[0086] In this embodiment, when the above parameters do not meet the constraints, the weighting coefficients are adjusted. The specific logic for dynamically adjusting the weighting coefficients in S4 includes:

[0087] When the available charging time is less than the first preset threshold, the time constraint weight ω is increased;

[0088] When the current load of the transformer in the distribution area approaches the safety threshold of its rated capacity, the transformer load penalty weighting coefficient γ is increased.

[0089] When the battery temperature is too high or the battery health status is below the second preset threshold, the weight coefficient μ of the battery loss penalty term is increased.

[0090] Specifically, when the first preset threshold is set to 1 hour, that is, when the user has insufficient remaining available charging time (such as less than 1 hour), the system determines it as a 'user demand priority' scenario and automatically increases the time constraint weight coefficient ω of the time constraint penalty item to ensure that the vehicle is charged before use.

[0091] When the safety threshold of rated capacity is set to 85%, that is, when the load rate of the transformer in the distribution area exceeds 85%, the 'grid stability priority' mode is entered, the weight coefficient γ of economic operation of the transformer is increased, and the charging power is actively reduced to avoid equipment overload.

[0092] When the temperature threshold is set to 45℃ or the second preset threshold is set to 80%, that is, when the battery temperature is detected to be >45℃ or SOH <80%, the 'device protection priority' mechanism is triggered, the weight coefficient μ of the battery loss penalty item is increased, fast charging is restricted, and the battery life is extended.

[0093] When the real-time electricity price in the spot market fluctuates, the system responds quickly to price signals and locks in low-priced electricity based on the 'optimal charging cost priority' strategy.

[0094] When all constraints are met, the charging strategy is dynamically adjusted by the load aggregator and then sent to the user-side management system. The user-side management system will then adjust the output power of each charging pile at a frequency of minutes. For example:

[0095] Off-peak hours (23:00-6:00 the next day): All charging stations charge at maximum power;

[0096] Peak hours (18:00-21:00): Power of non-emergency charging vehicles will be reduced to 30%, with priority given to vehicles that have been reserved for use during the morning rush hour the next day.

[0097] This method constructs a collaborative control system centered on load aggregators, integrating data from smart charging piles, user-side management systems, and the electricity spot market to achieve dynamic optimization scheduling of electric vehicle charging behavior. The system collects multi-dimensional data such as user vehicle information, travel plans, battery status, transformer load in the distribution area, and electricity market prices. It establishes a multi-objective optimization model encompassing charging costs, battery losses, transformer safe operation, and user time constraints. By dynamically adjusting weight coefficients to adapt to different operating scenarios, it generates the optimal charging strategy. The strategy is then distributed to charging piles for execution after the load aggregator participates in electricity spot market transactions, supporting minute-level power adjustments. While meeting user needs, it achieves peak shaving and valley filling, promotes the consumption of renewable energy, and reduces user charging costs and grid operation risks. This invention also designs an incentive allocation mechanism to enhance user participation and promote electricity market reform.

[0098] This invention provides a system for implementing ordered charging to support electricity spot trading, such as... Figure 2 and Figure 3 As shown, it includes:

[0099] The user-side management system is used to obtain vehicle information, charging preferences and travel plans of electric vehicle users, and to receive battery status and charging data collected in real time from smart charging piles.

[0100] Multiple smart charging piles are connected to electric vehicles to collect battery status and charging data in real time. Based on the received control commands, the output power is adjusted. Specifically, during periods of low grid load, the charging power is increased to speed up the charging process; during periods of high grid load or high electricity prices, the charging power is reduced or charging is suspended to achieve orderly charging control.

[0101] The load aggregator system communicates with the user-side management system and multiple smart charging piles, including:

[0102] The load integration module is used to integrate the adjustable charging loads of multiple electric vehicles to form an adjustable load resource pool.

[0103] The optimization modeling module is used to build an intelligent optimization model for the load resource pool based on the day-ahead and real-time electricity prices in the electricity spot market, the current load of the transformers in the distribution area, historical load curves and rated capacity, as well as data collected by the user-side management system, and to determine the objective function and constraints.

[0104] The weight adjustment and solution module is used to dynamically adjust the weight coefficients of each penalty term in the objective function according to the current operating scenario, and solve for the optimal charging power sequence of each electric vehicle in the load resource pool at each time period.

[0105] The strategy distribution module is used to distribute the optimal charging power sequence to the corresponding smart charging pile through the user-side management system, and control the electric vehicles in the load resource pool to perform charging according to the strategy.

[0106] The dynamic monitoring and recalculation module is used to monitor the grid status, electricity price fluctuations, changes in user demand, or equipment abnormalities in real time during the charging process. If a preset condition is triggered, the optimization modeling module is notified to rebuild the intelligent optimization model based on the updated information and solve it to generate a new optimal charging power sequence.

[0107] In this embodiment, the load aggregator system further includes:

[0108] The market trading and incentive allocation module is used to represent users in electricity spot market transactions. After obtaining benefits, a portion of the incentives is allocated to the user-side management system according to the user's contribution to the load resource pool, which can be used by the user to redeem charging services or electricity discounts. Incentive allocation is only performed on users who are set to participate in the electricity spot market in the user-side management system.

[0109] The specific implementation process of the system of the present invention is the same as that of the above method, and will not be repeated here. Please refer to the description in the invention section.

[0110] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0111] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for implementing an ordered charging support for spot electricity market, characterized in that, Comprise: S1: Obtain the vehicle information, charging preference and travel plan of the electric vehicle user through the user side management system, and collect the battery state and charging data in real time through the intelligent charging pile; S2: The load aggregator integrates the adjustable charging load of multiple electric vehicles to form a controllable load resource pool; S3: Based on the day-ahead price and real-time price of the electricity spot market, the current load of the transformer, the historical load curve and the rated capacity of the transformer, and the data collected in S1, the load aggregator constructs an intelligent optimization model for the load resource pool, determines the objective function and the constraint condition; S4: Dynamically adjust the weight coefficients of each penalty term in the objective function according to the current running scenario, and solve to obtain the optimal charging power sequence of each electric vehicle in the load resource pool at each time period; S5: The optimal charging power sequence is sent to the corresponding intelligent charging pile through the user side management system to control the electric vehicles in the load resource pool to perform charging according to the strategy; S6: Real-time monitoring of power grid state, electricity price fluctuation, user demand change or equipment abnormality, if the preset condition is triggered, return to S3, based on the updated information, reconstruct the intelligent optimization model and solve, generate a new optimal charging power sequence.

2. The method of claim 1, wherein the method is implemented by an ordered charging support power spot. The vehicle information includes battery capacity and rated power, the travel plan includes planned vehicle time and target power, the charging preference includes whether to participate in the electricity spot market and whether to accept charging in the low valley period, and the battery state and charging data include current SOC, charging voltage, current and temperature.

3. The method of claim 2, wherein the method is implemented by an ordered charging support power spot. The objective function of the intelligent optimization model is: P t = P t * *(1 + χ * PriceGrad t *LoadFactor t ), where T represents the total time period, c t represents the electricity price at time period t, P t is the optimal charging power, is the change rate of SOC, reflecting the charging rate, μ is the weight coefficient of battery loss penalty term, balancing spot market load fluctuation and battery loss, L t is the transformer load rate at time period t, γ is the transformer economic operation weight coefficient, β are the penalty coefficients of overload and light load, respectively, is the basic optimization power, χ is the space-time coupling influence coefficient, PriceGrad t is the electricity price gradient at time period t, LoadFactor t is the load influence factor, is the average electricity price of the same time period in history, is the average electricity price of the next adjacent time period of the same time period in history, is the predicted load, L max , L min are the historical maximum load and minimum load, respectively; T use is the user's planned driving time, SOC represents the current remaining capacity of the battery as a percentage of its rated capacity, SOC req is the minimum percentage of electricity required by the user when driving, ω is the time constraint weight, and δ is the user priority coefficient; The constraint conditions include: Power constraint: 0 ≤ P t ≤ P max where P max is the charging station rated power; Time constraint: wherein, is a safety margin coefficient; SOC(T use ) is the percentage of battery charge at the user's time of use T use ; represents the corresponding substation load impact factor at the user's time of use T use ; Battery constraints: SOC min ≤ SOC(t) ≤ SOC max where SOC min , SOC max denote the minimum and maximum percentage of charge of the battery, respectively. Transformer load constraint: where L max is the reserved safety margin; L base denotes the transformer base load rate for time period t; S transformer is the transformer rated capacity.

4. The method of claim 3, wherein the method is implemented by an ordered charging support power spot. The specific logic of dynamically adjusting the weight coefficients in S4 includes: When the available charging time is less than the first preset threshold, increase the time constraint weight ω; When the current load of the transformer is close to the safety threshold of its rated capacity, increase the transformer economic operation weight coefficient γ; When the battery temperature exceeds the temperature threshold or the battery health state is lower than the second preset threshold, increase the weight coefficient μ of the battery loss penalty term.

5. The method of claim 2, wherein the method is implemented by an ordered charging support power spot. In S5, the electric vehicles in the load resource pool are controlled to perform charging according to the strategy, including: The charging pile limits the maximum output power according to the rated power; The charging process continuously monitors the SOC in the battery state to ensure that the target power is reached; Adjust the output power at a minute level, charge at the maximum output power during the low price period of the electricity spot market, reduce the charging power of non-emergency vehicles or suspend charging during the peak price period, and prioritize the charging of vehicles that have reserved the next day's early peak vehicle.

6. The method of claim 2, wherein the method is implemented by an ordered charging support power spot. The conditions for triggering the strategy recalculation in S6 include: The user modifies the planned vehicle time through the user side management system; The real-time electricity price of the power grid suddenly changes by more than the preset amplitude; The transformer of the transformer area appears overload alarm; The intelligent charging pile has communication interruption or failure.

7. The method of claim 2, wherein the method is implemented by an ordered charging support power spot. Further comprising S7: After the load aggregator obtains the income by participating in the electricity spot market on behalf of the user, part of the incentive is distributed to the user side management system according to the contribution of the user in the load resource pool, for the user to exchange charging service or electricity discount; Wherein, only the users who set to participate in the electricity spot market in S1 are incentivized.

8. A system for implementing an orderly charging support spot power market, characterized by, Comprise: A user-side management system is configured to acquire vehicle information, charging preferences and travel plans of electric vehicle users, and receive battery status and charging data collected by intelligent charging piles in real time. A plurality of intelligent charging piles are respectively connected to electric vehicles, configured to collect battery status and charging data in real time, and adjust output power according to received control instructions. A load aggregator system is communicatively connected to the user-side management system and the plurality of intelligent charging piles, and includes: A load integration module is configured to integrate adjustable charging loads of the plurality of electric vehicles to form a controllable load resource pool. An optimization modeling module is configured to construct an intelligent optimization model for the load resource pool based on day-ahead and real-time electricity prices of a power spot market, current load of a transformer, historical load curve and rated capacity of a transformer, and data collected by the user-side management system, determine a target function and constraint conditions. A weight adjustment and solution module is configured to dynamically adjust weight coefficients of each penalty term in the target function according to a current operating scenario, and solve to obtain an optimal charging power sequence of each electric vehicle in the load resource pool at each time period. A strategy issuing module is configured to issue the optimal charging power sequence to corresponding intelligent charging piles through the user-side management system, and control the electric vehicles in the load resource pool to perform charging according to the strategy. A dynamic monitoring and recalculation module is configured to monitor power grid state, electricity price fluctuation, user demand change or equipment abnormality in real time during the charging process, and if a preset condition is triggered, notify the optimization modeling module to reconstruct the intelligent optimization model based on updated information and solve, to generate a new optimal charging power sequence.

9. The implementation system of an ordered charging support power spot according to claim 8, characterized in that, The load aggregator system further includes: A market transaction and incentive distribution module is configured to represent users to participate in power spot market transactions, and after obtaining a profit, distribute part of the incentive to the user-side management system according to the contribution of the users in the load resource pool, for the users to exchange charging services or electricity fee concessions; wherein only users set to participate in the power spot market in the user-side management system are incentivized.

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