Intelligent cell electric vehicle charging dynamic pricing method and system

Abstract

The application discloses a kind of intelligent cell electric vehicle charging dynamic pricing method and system, the method includes: the data of electric vehicle served in future dispatching period is collected by cell operator, and the lower response model is constructed with the minimum electric cost of electric vehicle owner as target;Operator considers future photovoltaic output and electric vehicle charging response and arranges energy storage system, constructs the upper dynamic price design model with the minimum equivalent load curve fluctuation, and the limit to price upper limit and mean value is considered;The lower response model and the upper dynamic price design model are solved using genetic algorithm, and the dynamic price parameter with the highest fitness is obtained and released at the initial moment of future dispatching period.The system includes lower model construction module, upper model construction module and solving module.The application solves the problem that expected response is inconsistent with actual charging behavior in existing electric vehicle charging behavior regulation model.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power systems, and particularly relates to a smart community electric vehicle charging dynamic pricing method and system. BACKGROUND

[0002] With the popularity of electric vehicles, power grid load management becomes more complex. The charging behavior of electric vehicles brings new challenges to the stability of the power grid and the scheduling of power supply. Especially during peak electricity consumption periods, the disorderly concentrated charging of electric vehicles can lead to a sharp rise in power demand, which in turn can harm the safe operation of distribution facilities. On the other hand, the matching degree of photovoltaic output and daily load electricity consumption characteristics of smart communities is low, causing the peak-valley difference of the community net load curve to further widen.

[0003] In view of the limitations of existing community distribution facility reconstruction and expansion, and the difficulty of providing dedicated transformers for electric vehicle charging piles, smart community operators hope to guide electric vehicle owners to charge in periods of low grid load by equipping energy storage systems and using price signals to achieve orderly charging of electric vehicles and coordinated scheduling of photovoltaic output, thereby improving the system load curve. However, existing models fail to fully consider the interest game between electric vehicle owners and operators, resulting in unsatisfactory load management. At the same time, the counteraction of load response on electricity prices is ignored, leading to a mismatch between actual charging behavior and expected response, and a lack of deep understanding and control of electric vehicle charging behavior.

[0004] Therefore, it is necessary to implement a new pricing mechanism for community public charging piles, which can guide reasonable charging behavior and balance the power grid load while considering the interests of community operators and electric vehicle owners, thereby minimizing power costs. To this end, in order to ensure that the charging behavior of electric vehicles is consistent with the expected response, a smart community electric vehicle charging dynamic pricing method is proposed, a community operator double-layer dynamic pricing model is established, the charging behavior of electric vehicle owners is considered, and the equivalent load curve fluctuation is minimized as the target, and the energy storage output is arranged. The operator releases dynamic electricity price parameters for each period in the future scheduling period to guide electric vehicle owners to independently select appropriate charging periods. In this way, the owners can choose to charge at a lower electricity price, thereby reducing the cost of charging. Through the application of the double-layer master-slave game model and dynamic electricity price, the interests of the agent and the electric vehicle owner are balanced. In addition, in the actual response, both parties can maximize their returns within their respective interests, avoiding potential losses due to unexpected results. SUMMARY

[0005] This invention provides a dynamic pricing method and system for electric vehicle (EV) charging in smart communities, aiming to solve the problem of inconsistency between expected and actual charging behavior in existing EV charging behavior control models. Existing models neglect the impact of load response on electricity prices, thus failing to consider the interaction behavior of EVs dependent on dynamic electricity prices from a fundamental perspective. This leads to unexpected response peaks, causing losses for operators. To address this, this invention constructs a pricing model for community operators at the upper level. Community operators consider photovoltaic output and allocate energy storage output to minimize the fluctuation of the equivalent load curve, disseminating dynamic electricity price parameters for each time period within the future scheduling cycle to EV owners. At the lower level, EV owners, guided by dynamic electricity prices, autonomously choose charging times to minimize charging costs. The introduction of dynamic electricity prices incorporates the impact of load response on electricity prices, ensuring no potential high electricity prices harm their interests. The relationship between community operators and EVs is established through a two-layer master-slave game model. At equilibrium, the interests of both parties are maximized in the actual response, achieving an incentive-compatible state and avoiding inconsistencies between actual and expected EV responses, thus preventing new response peaks and potential high electricity prices that could harm the interests of both community operators and EV owners.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A dynamic pricing method for electric vehicle charging in a smart community includes:

[0008] Step 1: The community operator collects data on the electric vehicles served during the future scheduling cycle and builds a lower-level response model with the goal of minimizing the electricity cost for electric vehicle owners;

[0009] Step 2: Operators consider future photovoltaic output and electric vehicle charging response and arrange energy storage systems to construct an upper-level dynamic electricity price design model that minimizes the fluctuation of the equivalent load curve, taking into account the upper and lower limits and average limits of the electricity price.

[0010] Step 3: Use a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model to obtain the dynamic electricity price parameters with the highest fitness and publish them at the initial moment of the future scheduling period.

[0011] A further improvement of this invention is that, in step one: the community operator collects data on the electric vehicles served during future scheduling cycles, including:

[0012] Arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle.

[0013] (1)

[0014] In the formula: EV aIndicates electric vehicles a Data; Indicates electric vehicles a Time of arrival at the charging station; Indicates electric vehicles a Time spent away from the charging station; Indicates electric vehicles a Battery level upon entering the charging station; Indicates electric vehicles a Battery level when leaving the charging station; Indicates electric vehicles a The upper limit of charging power.

[0015] A further improvement of this invention lies in step one: constructing a lower-level response model with the objective of minimizing the electricity cost for electric vehicle owners, as follows:

[0016] (2)

[0017] The constraints of the lower-level model are as follows:

[0018] (3)

[0019] In the formula: t Indicates a time index; Indicates a time interval; This indicates the cost of charging electric vehicles; Indicates electric vehicles a exist t The charging power at that time.

[0020] A further improvement of this invention is that, in step two: the operator considers future photovoltaic output and electric vehicle charging response and arranges the energy storage system, including: the community operator predicts the photovoltaic output and base load during the future scheduling cycle and calculates the community's equivalent load curve. ,

[0021] (2)

[0022] (3)

[0023] In the formula: express t Total load at that time; Indicates energy storage system t The charging and discharging power at that time; express t Photovoltaic output at that time; express t The base load at that time; express t The charging power of all electric vehicles at that time.

[0024] A further improvement of this invention lies in step two: constructing an upper-level dynamic electricity price design model that minimizes the fluctuation of the equivalent load curve, taking into account restrictions on the upper and lower limits and the average value of the electricity price, wherein the objective function of the upper-level dynamic electricity price design model is,

[0025] (4)

[0026] (5)

[0027] In the formula: T Indicates the scheduling period; Represents the variance of the equivalent load curve; This represents the average value of the equivalent load within the scheduling period;

[0028] The upper-level dynamic electricity pricing design model has the following constraints regarding dynamic electricity pricing:

[0029] (6)

[0030] In the formula: express t The electricity price for charging at that time; express t Dynamic electricity price sensitivity coefficient at that time; express t Dynamic electricity price benchmark coefficient at that time; express t The lower limit of the charging electricity price at that time; express t The upper limit of the charging electricity price at that time;

[0031] The upper-level dynamic electricity pricing design model has the following constraints regarding the energy storage system:

[0032] (7)

[0033] In the formula: This indicates the maximum output of the energy storage system; express t Energy storage system power at that time; This indicates the initial state of the energy storage system; This indicates the maximum amount of electricity that the energy storage system can store.

[0034] A further improvement of this invention lies in step three: using a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtaining the dynamic electricity price parameters with the highest fitness, and publishing them at the initial moment of the future scheduling period, including:

[0035] Step 1: Set the population size, number of iterations, and population mutation rate;

[0036] Step 2: Using the dynamic electricity price sensitivity coefficient and benchmark coefficient set by the community operator as decision variables, randomly generate the initial population. m Group;

[0037] Step 3: Electric vehicle delivery to the community operator m The dynamic electricity price is used to calculate the charging volume for each time period within the scheduling cycle with the lowest charging cost, and then the result is returned to the community operator.

[0038] Step 4: The community operator calculates the equivalent load curve fluctuation value based on the electricity consumption returned during the scheduling cycle, arranges the output of energy storage equipment, and records the optimal result;

[0039] Step 5: The community operator generates a new dynamic electricity price population by cross-referencing and varying the dynamic electricity price coefficients;

[0040] Step 6: Repeat steps 3-4 until the termination condition is met, and output the dynamic electricity price corresponding to the one with the smallest fluctuation in the equivalent load curve of the community operator.

[0041] A further improvement of this invention is that, at the initial moment of the actual scheduling cycle, the community operator publishes the dynamic electricity price that it has ultimately decided upon.

[0042] A smart community electric vehicle charging dynamic pricing system includes:

[0043] The lower-level model building module collects data on electric vehicles served in future scheduling cycles and builds a lower-level response model with the goal of minimizing the electricity cost for electric vehicle owners.

[0044] The upper-level model construction module considers future photovoltaic output and electric vehicle charging response and arranges energy storage systems to construct an upper-level dynamic electricity price design model with minimal fluctuations in the equivalent load curve, taking into account the upper and lower limits and average limits of the electricity price.

[0045] The solution module uses a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtains the dynamic electricity price parameters with the highest fitness, and publishes them at the initial moment of the future scheduling period.

[0046] A further improvement of this invention is that the lower-level model construction module collects data on electric vehicles served during future scheduling cycles, including:

[0047] Arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle.

[0048] (1)

[0049] In the formula: EV aIndicates electric vehicles a Data; Indicates electric vehicles a Time of arrival at the charging station; Indicates electric vehicles a Time spent away from the charging station; Indicates electric vehicles a Battery level upon entering the charging station; Indicates electric vehicles a Battery level when leaving the charging station; Indicates electric vehicles a The upper limit of charging power.

[0050] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned dynamic pricing method for electric vehicle charging in a smart community.

[0051] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0052] Compared to existing traditional master-slave game-theoretic static electricity pricing design methods, this invention addresses the problem of the detrimental effects on the interests of both parties when the actual load response deviates from the expected response. For operators, after the traditional electricity price is released, smart terminals concentrate on charging during low-price periods based on electric vehicle owners' settings, resulting in unexpected load peaks. For electric vehicle owners, these unexpected load peaks present the potential for higher electricity prices, increasing charging costs. Compared to traditional electricity pricing designs, the dynamic electricity price proposed in this invention considers the impact of load response on electricity prices during the load response phase, avoiding load peaks. First, a response to the dynamic electricity price is introduced at the lower level to avoid the potential for high electricity prices, ensuring that electric vehicle owners' charging behavior aligns with the expected response. Furthermore, operators consider photovoltaic output and the load response behavior of lower-level electric vehicles; the designed dynamic electricity price meets the constraints of retail electricity prices, and the output of the energy storage system is arranged to minimize the fluctuation of the equivalent load curve. Finally, a genetic algorithm is used to solve for the optimal dynamic electricity price. Attached Figure Description

[0053] Figure 1 This is a flowchart of a dynamic pricing method for electric vehicle charging in a smart community, according to the present invention.

[0054] Figure 2 This is a schematic diagram of the base load and photovoltaic output.

[0055] Figure 3 This is the load curve under disordered charging mode.

[0056] Figure 4 This is the load curve under ordered charging mode.

[0057] Figure 5 A comparison chart of charging costs for electric vehicles.

[0058] Figure 6 This is a structural block diagram of a dynamic pricing system for electric vehicle charging in a smart community, according to the present invention. Detailed Implementation

[0059] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0060] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0061] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0062] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0063] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0064] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0065] Example 1

[0066] like Figure 1 As shown, the present invention provides a dynamic pricing method for electric vehicle charging in smart communities, comprising:

[0067] Step 1: The community operator collects data on the electric vehicles served during the future scheduling cycle and builds a lower-level response model with the goal of minimizing the electricity cost for electric vehicle owners;

[0068] Step 2: Operators consider future photovoltaic output and electric vehicle charging response and arrange energy storage systems to construct an upper-level dynamic electricity price design model that minimizes the fluctuation of the equivalent load curve, taking into account the upper and lower limits and average limits of the electricity price.

[0069] Step 3: Use a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model to obtain the dynamic electricity price parameters with the highest fitness and publish them at the initial moment of the future scheduling period.

[0070] In this embodiment, step one: The community operator collects data on the electric vehicles served during future scheduling cycles, including:

[0071] Arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle.

[0072] (1)

[0073] In the formula: EV a Indicates electric vehicles a Data; Indicates electric vehicles a Time of arrival at the charging station; Indicates electric vehicles a Time spent away from the charging station; Indicates electric vehicles a Battery level upon entering the charging station; Indicates electric vehicles a Battery level when leaving the charging station; Indicates electric vehicles a The upper limit of charging power.

[0074] In this embodiment, step one: construct the lower-level response model with the goal of minimizing the electricity cost for electric vehicle owners, as follows:

[0075] (2)

[0076] The constraints of the lower-level model are as follows:

[0077] (3)

[0078] In the formula: t Indicates a time index; Indicates a time interval; This indicates the cost of charging electric vehicles; Indicates electric vehicles a exist t The charging power at that time.

[0079] In this embodiment, step two: The operator considers future photovoltaic output and electric vehicle charging response and arranges the energy storage system, including: the community operator predicts the photovoltaic output and base load in the future scheduling cycle and calculates the community equivalent load curve. ,

[0080] (2)

[0081] (3)

[0082] In the formula: express t Total load at that time; Indicates energy storage system t The charging and discharging power at that time; express t Photovoltaic output at that time; express t The base load at that time; express t The charging power of all electric vehicles at that time.

[0083] In this embodiment, step two: Construct an upper-level dynamic electricity price design model that minimizes the fluctuation of the equivalent load curve, taking into account the constraints on the upper and lower limits and the average value of the electricity price. The objective function of the upper-level dynamic electricity price design model is:

[0084] (4)

[0085] (5)

[0086] In the formula: T Indicates the scheduling period; Represents the variance of the equivalent load curve; This represents the average value of the equivalent load within the scheduling period;

[0087] The upper-level dynamic electricity pricing design model has the following constraints regarding dynamic electricity pricing:

[0088] (6)

[0089] In the formula: express t The electricity price for charging at that time; express t Dynamic electricity price sensitivity coefficient at that time; express t Dynamic electricity price benchmark coefficient at that time; expresst The lower limit of the charging electricity price at that time; express t The upper limit of the charging electricity price at that time;

[0090] The upper-level dynamic electricity pricing design model has the following constraints regarding the energy storage system:

[0091] (7)

[0092] In the formula: This indicates the maximum output of the energy storage system; express t Energy storage system power at that time; This indicates the initial state of the energy storage system; This indicates the maximum amount of electricity that the energy storage system can store.

[0093] In this embodiment, step three: using a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtaining the dynamic electricity price parameters with the highest fitness, and publishing them at the initial time of the future scheduling period, including:

[0094] Step 1: Set the population size, number of iterations, and population mutation rate;

[0095] Step 2: Using the dynamic electricity price sensitivity coefficient and benchmark coefficient set by the community operator as decision variables, randomly generate the initial population. m Group;

[0096] Step 3: Electric vehicle delivery to the community operator m The dynamic electricity price is used to calculate the charging volume for each time period within the scheduling cycle with the lowest charging cost, and then the result is returned to the community operator.

[0097] Step 4: The community operator calculates the equivalent load curve fluctuation value based on the electricity consumption returned during the scheduling cycle, arranges the output of energy storage equipment, and records the optimal result;

[0098] Step 5: The community operator generates a new dynamic electricity price population by cross-referencing and varying the dynamic electricity price coefficients;

[0099] Step 6: Repeat steps 3-4 until the termination condition is met, and output the dynamic electricity price corresponding to the one with the smallest fluctuation in the equivalent load curve of the community operator.

[0100] At the beginning of the actual scheduling cycle, the community operator publishes the dynamic electricity price that it ultimately decides on.

[0101] Example 2

[0102] The implementation process of this method is illustrated with a case study. A smart community has 500 housing units, an electric vehicle penetration rate of 40%, and 200 electric vehicles. The photovoltaic output and base load forecasts for future scheduling cycles are as follows: Figure 2 As shown, the energy storage system has a battery capacity of 1400kWh and a maximum charging and discharging power of 400kW.

[0103] This paper compares three scenarios: disordered charging mode, ordered charging mode, and dynamic electricity price. Disordered charging mode refers to charging immediately after the last trip; ordered charging mode refers to charging at the lowest possible cost for electric vehicles; static electricity price refers to the price independent of load response; and dynamic electricity price refers to the price proposed in this invention. Figure 3 The load curve under disordered charging mode is given; Figure 4 Load curves under static and dynamic electricity prices in an ordered charging mode are presented; Table 1 shows a performance comparison of the equivalent load curves. The charging costs of electric vehicles under static and dynamic electricity prices are compared, as shown in Table 1. Figure 5 As shown in Table 2, the proposed two-tier dynamic pricing strategy not only balances the interests of community operators and electric vehicle owners, but also avoids unintended outcomes such as new load peaks under static electricity prices. It effectively guides the orderly charging of electric vehicles and the scheduling of renewable energy output systems, thus improving the load curve.

[0104] Table 1. Comparison of equivalent load curve performance under different scenarios

[0105]

[0106] Table 2 Comparison of total charging costs for electric vehicles in different scenarios

[0107]

[0108] Example 3

[0109] like Figure 6 As shown, the present invention provides a dynamic pricing system for electric vehicle charging in a smart community, comprising:

[0110] The lower-level model building module collects data on electric vehicles served in future scheduling cycles and builds a lower-level response model with the goal of minimizing the electricity cost for electric vehicle owners.

[0111] The upper-level model construction module considers future photovoltaic output and electric vehicle charging response and arranges energy storage systems to construct an upper-level dynamic electricity price design model with minimal fluctuations in the equivalent load curve, taking into account the upper and lower limits and average limits of the electricity price.

[0112] The solution module uses a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtains the dynamic electricity price parameters with the highest fitness, and publishes them at the initial moment of the future scheduling period.

[0113] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a dynamic pricing method for electric vehicle charging in a smart community.

[0114] 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.

[0115] This application is described with reference to flowchart illustrations and / or block diagrams of methods, 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 system that specifies functions in one or more boxes.

[0116] 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 function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment 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.

[0118] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0119] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A dynamic pricing method for electric vehicle charging in a smart community, characterized in that, include: Step 1: The community operator collects data on the electric vehicles served during the future scheduling cycle and constructs a lower-level response model with the goal of minimizing the electricity cost for electric vehicle owners; the lower-level response model constructed with the goal of minimizing the electricity cost for electric vehicle owners is as follows: (2) The constraints of the lower-level model are as follows: (3) In the formula: express t The electricity price for charging at that time; t Indicates a time index; Indicates a time interval; This indicates the cost of charging electric vehicles; Indicates electric vehicles a exist t The charging power at that time; Indicates electric vehicles a Time of arrival at the charging station; Indicates electric vehicles a Time spent away from the charging station; Indicates electric vehicles a Battery level upon entering the charging station; Indicates electric vehicles a Battery level when leaving the charging station; Indicates electric vehicles a The upper limit of charging power; Step 2: Operators consider future photovoltaic output and electric vehicle charging response and arrange energy storage systems to construct an upper-level dynamic electricity price design model that minimizes the fluctuation of the equivalent load curve, and consider the upper and lower limits and average limits of the electricity price. Operators consider future solar power output and electric vehicle charging response and arrange energy storage systems, including: forecasting solar power output and base load for future dispatch cycles and calculating the equivalent load curve for the community. , (2) (3) In the formula: express t Total load at that time; Indicates energy storage system t The charging and discharging power at that time; express t Photovoltaic output at that time; express t The base load at that time; express t All electric vehicle charging power at that time; The objective function of the upper-level dynamic electricity price design model is: (4) (5) In the formula: T Indicates the scheduling period; Represents the variance of the equivalent load curve; This represents the average value of the equivalent load within the scheduling period; The upper-level dynamic electricity pricing design model has the following constraints regarding dynamic electricity pricing: (6) In the formula: express t Dynamic electricity price sensitivity coefficient at that time; express t Dynamic electricity price benchmark coefficient at that time; express t The lower limit of the charging electricity price at that time; express t The upper limit of the charging electricity price at that time; The upper-level dynamic electricity pricing design model has the following constraints regarding the energy storage system: (7) In the formula: This indicates the maximum output of the energy storage system; express t Energy storage system power at that time; This indicates the initial state of the energy storage system; This indicates the maximum amount of electricity that the energy storage system can store; Step 3: Use a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model to obtain the dynamic electricity price parameters with the highest fitness and publish them at the initial moment of the future scheduling period.

2. The method for dynamic pricing of electric vehicle charging in a smart community according to claim 1, characterized in that, Step 1: The community operator collects data on the electric vehicles served during the future scheduling cycle, including: Arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle. (1) In the formula: EV a Indicates electric vehicles a The data.

3. The method for dynamic pricing of electric vehicle charging in a smart community according to claim 1, characterized in that, Step 3: Use a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model to obtain the dynamic electricity price parameters with the highest fitness, and publish them at the initial time of the future scheduling period, including: Step 1: Set the population size, number of iterations, and population mutation rate; Step 2: Using the dynamic electricity price sensitivity coefficient and benchmark coefficient set by the community operator as decision variables, randomly generate the initial population. m Group; Step 3: Electric vehicle delivery to the community operator m The dynamic electricity price is used to calculate the charging volume for each time period within the scheduling cycle with the lowest charging cost, and then the result is returned to the community operator. Step 4: The community operator calculates the equivalent load curve fluctuation value based on the electricity consumption returned during the scheduling cycle, arranges the output of energy storage equipment, and records the optimal result; Step 5: The community operator generates a new dynamic electricity price population by cross-referencing and varying the dynamic electricity price coefficients; Step 6: Repeat steps 3-4 until the termination condition is met, and output the dynamic electricity price corresponding to the one with the smallest fluctuation in the equivalent load curve of the community operator.

4. The method for dynamic pricing of electric vehicle charging in a smart community according to claim 3, characterized in that, At the beginning of the actual scheduling cycle, the community operator publishes the dynamic electricity price that it ultimately decides on.

5. A dynamic pricing system for electric vehicle charging in a smart community, characterized in that, include: The lower-level model building module collects data on electric vehicles served during future scheduling cycles and constructs a lower-level response model with the goal of minimizing the electricity cost for electric vehicle owners. The lower-level response model, constructed with the goal of minimizing the electricity cost for electric vehicle owners, is as follows: (2) The constraints of the lower-level model are as follows: (3) In the formula: express t The electricity price for charging at that time; t Indicates a time index; Indicates a time interval; This indicates the cost of charging electric vehicles; Indicates electric vehicles a exist t The charging power at that time; The upper-level model construction module considers future photovoltaic output and electric vehicle charging response, and arranges energy storage systems to construct an upper-level dynamic electricity price design model that minimizes the fluctuation of the equivalent load curve, while also considering upper and lower limits and average limits on the electricity price. The operator considers future photovoltaic output and electric vehicle charging response and arranges energy storage systems, including: the community operator predicts photovoltaic output and base load for future dispatch cycles and calculates the community's equivalent load curve. , (2) (3) In the formula: express t Total load at that time; Indicates energy storage system t The charging and discharging power at that time; express t Photovoltaic output at that time; express t The base load at that time; express t All electric vehicle charging power at that time; Indicates electric vehicles a Time of arrival at the charging station; Indicates electric vehicles a Time spent away from the charging station; Indicates electric vehicles a Battery level upon entering the charging station; Indicates electric vehicles a Battery level when leaving the charging station; Indicates electric vehicles a The upper limit of charging power; The objective function of the upper-level dynamic electricity price design model is: (4) (5) In the formula: T Indicates the scheduling period; Represents the variance of the equivalent load curve; This represents the average value of the equivalent load within the scheduling period; The upper-level dynamic electricity pricing design model has the following constraints regarding dynamic electricity pricing: (6) In the formula: express t Dynamic electricity price sensitivity coefficient at that time; express t Dynamic electricity price benchmark coefficient at that time; express t The lower limit of the charging electricity price at that time; express t The upper limit of the charging electricity price at that time; The upper-level dynamic electricity pricing design model has the following constraints regarding the energy storage system: (7) In the formula: This indicates the maximum output of the energy storage system; express t Energy storage system power at that time; This indicates the initial state of the energy storage system; This indicates the maximum amount of electricity that the energy storage system can store; The solution module uses a genetic algorithm to solve the lower-level response model and the upper-level dynamic electricity price design model, obtains the dynamic electricity price parameters with the highest fitness, and publishes them at the initial moment of the future scheduling period.

6. The intelligent community electric vehicle charging dynamic pricing system according to claim 5, characterized in that, The lower-level model building module collects data on electric vehicles served during future scheduling cycles, including: Arrival time, departure time, battery level at arrival, battery level at departure, and maximum charging power of the electric vehicle. (1) In the formula: EV a Indicates electric vehicles a The data.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the dynamic pricing method for electric vehicle charging in a smart community according to any one of claims 1-4.

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