Optimization method for charging power scheduling of electric vehicle cluster considering expected charging curve

Abstract

The application discloses a kind of electric vehicle cluster charging power scheduling optimization methods considering expected charging curve.First, the electric vehicle ECC curve is combined to construct electric vehicle cluster charging power scheduling optimization model;Then, the predicted charging load distribution and power grid load condition are obtained, and the user charging demand of charging area is obtained;Based on the predicted charging load distribution and power grid load condition, the user charging demand of charging area is solved to the electric vehicle cluster charging power scheduling optimization model, and the charging power plan of each charging pile in the charging area is obtained;If new vehicle is accessed in the charging area, update the user charging demand, and then update the charging power plan of each charging pile;The charging power plan of each charging pile is issued to electric vehicle charging management device, and the orderly charging of all vehicles in the charging area is realized.The application realizes the integration of charging control, collection and communication under cluster charging scene.

Description

TECHNICAL FIELD

[0001] The present application relates to an electric vehicle charging power scheduling optimization method in the field of electric vehicle charging management, and in particular to an electric vehicle cluster charging power scheduling optimization method and system considering expected charging curves. BACKGROUND

[0002] User charging demand has a high spatiotemporal distribution characteristic, which makes the traditional power distribution network bear greater pressure. For example, concentrated charging during peak hours can cause voltage deviation in local areas, line overload, and even system instability. In addition, the connection of charging equipment will also affect power quality, such as harmonic and power factor problems. Therefore, how to effectively manage the charging demand of electric vehicles has become an important topic in current power system research.

[0003] In recent years, many studies have proposed different electric vehicle charging management methods. For example, distributed charging control can effectively alleviate the burden on the power grid while improving the economic efficiency of the system. On the other hand, intelligent charging methods based on reinforcement learning and deep learning can more accurately respond to user diversification needs and uncertain loads. In addition, vehicle-to-grid technology provides new possibilities for electric vehicle charging and discharging, further enhancing the flexibility of the power system and the ability to accommodate renewable energy.

[0004] In practical applications, the optimization model of electric vehicle charging has also gradually developed. For example, models based on time electricity prices and battery state of charge (SoC) curves can effectively reduce user charging costs and power grid operating costs. In addition, probabilistic models that consider spatial and temporal dynamics provide theoretical support for regional charging demand forecasting.

[0005] However, current research and applications still face many challenges, including user behavior uncertainty during charging, and few methods and devices for centralized control of electric vehicle charging. Therefore, designing a comprehensive solution that balances grid stability and user charging experience has become a focus of current research. SUMMARY

[0006] In order to solve the negative impact on the power grid during the charging process of electric vehicles, especially in residential areas, problems such as transformer aging, power grid overload, and power quality decline caused by electric vehicle charging loads,

[0007] The application provides an optimization method and system for electric vehicle cluster charging power scheduling considering expected charging curves, to solve the problem that existing electric vehicle charging optimization methods cannot effectively coordinate the contradiction between user charging demand and power grid operation stability, resulting in the technical problems of intensified power grid peak-valley difference and low renewable energy utilization efficiency.

[0008] The technical scheme of the application is as follows:

[0009] One kind is an optimization method for electric vehicle cluster charging power scheduling considering expected charging curves

[0010] First, an electric vehicle cluster charging power scheduling optimization model is constructed in combination with an electric vehicle ECC curve; then, a predicted charging load distribution and a power grid load condition are obtained, and user charging demand of a charging area is obtained; after the electric vehicle cluster charging power scheduling optimization model is solved based on the predicted charging load distribution and the power grid load condition, the user charging demand of the charging area, the charging power plan of each charging pile in the charging area is obtained; if a new vehicle is connected to the charging area, the user charging demand is updated, and then the charging power plan of each charging pile is updated; the charging power plan of each charging pile is sent to an electric vehicle charging management device, and orderly charging of all vehicles in the charging area is realized.

[0011] In the electric vehicle cluster charging power scheduling optimization model, the optimization objective function satisfies the following formula:

[0012]

[0013] Wherein, F represents the objective function of the optimization model, T i represents the user charging demand time set of the electric vehicle i, represents the actual charging power of the electric vehicle i in the time period t, C t represents the charging electricity fee in the time period t, and Δt represents the length of the time period; k load is the load pressure conversion coefficient of the transformer, η t represents the load rate of the transformer in the time period t, P t respre represents the predicted resident load in the time period t, P t carpre represents the predicted vehicle charging load in the time period t, P stationRepresenting the transformer area public variable capacity;

[0014] The optimization constraints include an ECC curve-charging power constraint, a maximum charging power constraint, a charging energy constraint, a charging start-stop constraint, and a charging time period constraint.

[0015] The ECC curve-charging power constraint satisfies the following formula:

[0016]

[0017] u it ∈{0,1}

[0018] Wherein, is the maximum charging power of the electric vehicle i in the time period t at a given state of charge; f i () represents a linear function model of the expected charging curve of the electric vehicle i; S it represents the SoC of the electric vehicle i in the time period t; represents the actual charging power of the electric vehicle i in the time period t; u it is a binary variable describing whether the electric vehicle i is charging in the time period t; alpha t is a load reduction coefficient; eta t represents the load rate of the transformer in the time period t; T i represents a set of user charging demand times of the electric vehicle i.

[0019] The electric vehicle charging management device includes a concentrator and an electric vehicle charging controller, the concentrator is used for concentrating charging data of each electric vehicle in a charging area and generating user charging demand, sending the user charging demand to the cloud, and distributing charging power plans of each charging pile issued by the cloud to the corresponding electric vehicle charging controller; each electric vehicle charging controller is used for changing the duty cycle of the CP signal communicated with the vehicle according to the charging plan, adjusting the charging power of the corresponding vehicle, and collecting the real-time power and total energy consumption of the vehicle and feeding back to the concentrator.

[0020] II. An electric vehicle cluster charging power scheduling optimization system considering an expected charging curve

[0021] An electric vehicle charging management device is used for obtaining user charging demand of a charging area and controlling charging of a vehicle according to received charging power plans of each charging pile;

[0022] A charging load distribution prediction unit is used for predicting a charging load distribution;

[0023] A power grid load condition acquisition unit is used for obtaining a power grid load condition;

[0024] A real-time scheduling optimization unit is configured to solve an electric vehicle cluster charging power scheduling optimization model based on the predicted charging load distribution, the power grid load condition, and the user charging demand of the charging area, to obtain a charging power plan of each charging pile and send the charging power plan to the electric vehicle charging management device.

[0025] Three, a computer device

[0026] The device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the electric vehicle cluster charging power scheduling optimization method considering the expected charging curve when executing the computer program.

[0027] Four, a computer readable storage medium

[0028] The medium stores a computer program, and the computer program implements the steps of the electric vehicle cluster charging power scheduling optimization method considering the expected charging curve when executed by the processor.

[0029] Five, a computer program product

[0030] The product comprises a computer program / instruction, which implements the steps of the electric vehicle cluster charging power scheduling optimization method considering the expected charging curve when executed by the processor.

[0031] The present application has the following beneficial effects:

[0032] On the one hand, the present application adopts a power optimization model considering the ECC curve, which is based on the constant current and constant voltage method of electric vehicle charging, obtains a linearized ECC curve of electric vehicle charging, and uses the curve as a constraint condition of electric vehicle charging power, so that the model output can be as close as possible to the actual power of electric vehicle charging, while realizing fast charging, avoiding the influence of thermal stress, and reducing the wear of the battery.

[0033] On the other hand, the present application comprehensively considers user electricity charges and transformer pressure. In the present application, the objective function of the optimization model includes the electricity charges generated by electric vehicle charging within a certain time under the background of peak-valley electricity price, and the conversion cost of charging load and resident load on transformer pressure, and the target is set as the minimum cost, which can reduce user charging consumption, relieve the load pressure of public transformer, and realize the effect of common benefits of user side and power grid side. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a method flowchart provided by an embodiment of the present application;

[0035] Figure 2 is a vehicle ECC segmented function diagram provided by an embodiment of the present application;

[0036] Figure 3 is a single-vehicle 24-hour power planning diagram obtained by the optimization model considering the ECC provided by the embodiment of the application;

[0037] Figure 4 is a comparison diagram of daily load curves obtained by the ordered charging method and the unordered charging method;

[0038] Figure 5 is a structural diagram of the electric vehicle charging management device provided by the method provided by the embodiment of the application. DETAILED DESCRIPTION

[0039] Embodiments of the application will be described in more detail below with reference to the drawings. Although some embodiments of the application are shown in the drawings, the application can be implemented in various forms, and should not be interpreted as being limited to the embodiments described herein, on the contrary, these embodiments are provided to make the application more thorough and complete. It should be understood that the drawings and embodiments of the application are only for exemplary purposes, and are not intended to limit the scope of protection of the application.

[0040] The application proposes a method for optimizing charging power scheduling of an electric vehicle cluster considering an expected charging curve, as shown in Figure 1 The method comprises the following steps:

[0041] First, on the basis of the predicted charging load distribution and user charging demand, an electric vehicle cluster charging power scheduling optimization model is constructed in combination with an electric vehicle ECC curve; then, 24 hours are divided into 96 time periods, the predicted charging load distribution and power grid load condition are obtained, and the user charging demand of the charging area is obtained by using an electric vehicle charging management device, the user charging demand including the start time, end time and required power; after the electric vehicle cluster charging power scheduling optimization model is solved based on the predicted charging load distribution and power grid load condition and the user charging demand of the charging area, the charging power plan of each charging pile in the charging area is obtained, specifically, the electric vehicle charging power in 96 time periods in a day is obtained; if a new vehicle is connected to the charging area, the user charging demand is updated, and then the charging power plan of each charging pile is updated, so that the optimal charging power plan can be continuously updated, and the change of the planning result can adapt to the change of the charging actual situation; the charging power plan of each charging pile is sent to the electric vehicle charging management device, the electric vehicle charging management device sets the electric vehicle charging power in real time, and realizes the ordered charging of all vehicles in the charging area. The electric vehicle charging management device can obtain the charging power plan value from the cloud, and control the electric vehicle to charge at the preset power; at the same time, the electric vehicle real-time charging power can be collected and reported to the cloud.

[0042] In the optimization model of the charging power of the electric vehicle cluster, the objective function satisfies the following formula:

[0043]

[0044] Wherein, F represents the objective function of the optimization model, T i represents the set of user charging demand time of the electric vehicle i, represents the actual charging power of the electric vehicle in the time period t, C t represents the charging electricity fee in the time period t, and Δt represents the length of the time period; k load represents the load pressure conversion coefficient of the transformer, η t represents the load rate of the transformer in the time period t, P t respre represents the predicted resident load in the time period t, P t carpre represents the predicted vehicle charging load in the time period t, P station represents the public transformer capacity.

[0045] In combination with the peak-valley electricity price, the peak-valley electricity price is included in the objective function, the user is guided to charge in the low electricity price period or the low valley period of the power grid, the electricity fee spent by the same user in the charging period is obtained; the real-time load of the transformer is obtained, the real-time pressure of the transformer is included in the objective function, the equivalent loss of the resident load and the electric vehicle charging load on the transformer pressure is obtained, so as to avoid the overload of the transformer. represents the electricity cost of a single time interval, and describes the cost of the user side, represents the consumption cost of the load conversion of the electric vehicle to the transformer. Taking this as the objective function, the balanced win-win effect of reducing the pressure of the transformer and reducing the user's expenditure can be achieved, which is optimal for the user and the power grid.

[0046] On the basis of the predicted load value and the actual user charging demand value, the electric vehicle ECC curve is emphatically considered, and the optimization constraint is established. The optimization constraint includes the ECC curve-charging power constraint, the maximum charging power constraint, the charging energy constraint, the charging start-stop constraint and the charging time period constraint.

[0047] Wherein, the ECC curve-charging power constraint satisfies the following formula:

[0048]

[0049] u it ∈{0, 1}

[0050] Wherein, is the maximum charging power of the electric vehicle i in time period t at given state of charge (SoC); f i () represents a linear function model of the expected charging curve (ECC curve) of the electric vehicle i; S it represents the SoC of the electric vehicle i in time period t; represents the actual charging power of the electric vehicle i in time period t; u it is a binary variable describing whether the electric vehicle i is charging in time period t; a t is a load shedding coefficient, which reduces the charging power of the vehicle when the transformer pressure is large; η t represents the load rate of the transformer in time period t; T i represents a set of time periods representing the user charging demand of the electric vehicle i. This constraint expression can take into account both the ECC curve and the need for transformer pressure load shedding. Considering the ECC curve is one of the important focuses of the present application. In the case of considering the ECC, the generated charging plan can be made as close as possible to the constant current constant voltage (CC-CV) charging mode commonly used by current electric vehicle lithium batteries, thereby reducing battery wear and reducing the impact of problems such as thermal stress under the premise of fast charging. In the present application, the ECC curve is replaced by a linearized model, as shown in Figure 2 , which is a three-segment linear function.

[0051] The maximum charging power constraint satisfies the following formula:

[0052]

[0053] where β is the future load reserve energy coefficient, which leaves a margin for possible future vehicle access to charging, and is set to 0.15. This expression combines the current charging power and the future possible charging power of the electric vehicle.

[0054] The charging energy constraint satisfies the following formula:

[0055]

[0056] where, is the initial SoC state, is obtained from the user input charging demand, is the charging efficiency, Q cap is the rated battery capacity of the electric vehicle, Q cap = 42 kWh, S it is the SoC state of the electric vehicle in the current time period. T i arris the time period when the electric vehicle i arrives; l is the time variable from the time period when the electric vehicle i arrives to the time period t. In the two expressions, the former calculates the current SoC by using the initial SoC and the charging power, and the latter considers the error possibly existing in the 15-minute discrete time planning and sets a certain tolerance for the full charging state.

[0057] The charging start-stop constraint satisfies the following formula:

[0058]

[0059] wherein M it is a binary variable indicating whether the charging time is being changed; u i,t-1 is a binary variable indicating whether the electric vehicle i is being charged in the time period t-1. The variable is constrained, so that the number of times of starting charging of a single vehicle is constrained, and multiple starting charging of a single vehicle is avoided.

[0060] The charging time period constraint satisfies the following formula:

[0061]

[0062] If it is not in the charging time period set by the user, the charging power is constant 0. The time period is set by the user.

[0063] The electric vehicle charging management device comprises a concentrator and an electric vehicle charging controller, the concentrator is used for concentrating charging data of each electric vehicle in a charging area and generating user charging demand, and sending the user charging demand to the cloud, and distributing charging power plans of each charging pile issued by the cloud to the corresponding electric vehicle charging controller; each electric vehicle charging controller is used for changing the duty cycle of the CP signal communicated with the vehicle according to the charging plan, adjusting the charging power of the corresponding vehicle, and collecting the real-time power and total energy consumption of the vehicle and feeding back to the concentrator, each electric vehicle charging controller independently controls the electric vehicle charging power between the charging pile and the electric vehicle, and can communicate with the charging pile and the vehicle, so that the charging pile is correctly guided and the power is output to the electric vehicle.

[0064] The application further provides an electric vehicle cluster charging power scheduling optimization system considering an expected charging curve, comprising:

[0065] The electric vehicle charging management device is used for acquiring user charging demand of a charging area and controlling charging of a vehicle according to received charging power plans of each charging pile;

[0066] The charging load distribution prediction unit is used for predicting the charging load distribution;

[0067] The power grid load condition acquisition unit is used for acquiring the power grid load condition;

[0068] A real-time scheduling optimization unit is configured to solve an electric vehicle cluster charging power scheduling optimization model based on the predicted charging load distribution, the power grid load condition and the user charging demand of the charging area, obtain the charging power plan of each charging pile and send the charging power plan to the electric vehicle charging management device.

[0069] The application further provides a computer device comprising a memory and a processor, and the memory stores a computer program.

[0070] The application further provides a computer readable storage medium, which stores a computer program.

[0071] The application further provides a computer program product comprising a computer program / instruction.

[0072] The model solver used in the embodiment is Gurobi12.0.0, and the solver is called by a pyomo modeling environment. Figure 3 The total power distribution curve obtained by solving is compared with the power distribution curve in the unordered charging condition, as shown in Figure 4 It can be found from the results that the embodiment can better play the role of the ordered charging of the electric vehicles, relieve the peak pressure of the transformer and realize the ordered distribution of the load.

[0073] As an embodiment of the application, the process of issuing the plan to the device corresponding to the method and controlling the output power by using the device comprises that the plan issuing can be based on the MQTT protocol, cloud edge communication is realized by using a communication chip based on the 4G LTE network; the device required by the method mainly comprises a concentrator and an electric vehicle charging controller, and the device architecture is as shown in Figure 5The concentrator can be a computer with cloud edge communication and field bus communication function, can be a hardware composed of a Raspberry Pi and a communication module, or can be an edge computing device with cloud edge and field bus communication function, and has the characteristics of having the ability to complete cloud edge communication and the ability to communicate with various controllers in the field, and a certain local computing capability to deploy the concentrator program. The charging controller controls the maximum power of the electric vehicle charging by controlling the duty cycle of the CP signal in the process of controlling the electric vehicle charging. Therefore, the charging controller needs to be able to generate a specific duty cycle of the bipolar PWM signal, and be able to accurately change the duty cycle according to the charging plan, so as to accurately control the charging power of the electric vehicle. The charging controller also needs to provide charging guidance for the charging pile, and plays the role of allowing output, which requires the controller to simulate the resistance of the electric vehicle CP communication circuit. In terms of collection, the charging controller needs to have the function of collecting charging data, and the collected data mainly includes real-time charging power, cumulative charging energy, etc.

[0074] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A method for optimizing charging power scheduling of an electric vehicle cluster considering expected charging profiles, characterized in that, The method comprises the following steps: First, an electric vehicle cluster charging power scheduling optimization model is constructed in combination with an electric vehicle charging curve; then, predicted charging load distribution and power grid load conditions are obtained, and user charging demand of the charging area is obtained; after the electric vehicle cluster charging power scheduling optimization model is solved based on the predicted charging load distribution and power grid load conditions and the user charging demand of the charging area, charging power plans of each charging pile in the charging area are obtained; If a new vehicle is connected to the charging area, the user charging demand is updated, and the charging power plans of each charging pile are updated; the charging power plans of each charging pile are sent to the electric vehicle charging management device, so that orderly charging of all vehicles in the charging area is realized; In the electric vehicle cluster charging power scheduling optimization model, the optimization objective function satisfies the following formula: wherein, represents the objective function of the optimization model, T i represents the user charging demand time set of the electric vehicle i, represents the charging electricity fee in the time period t, and Δt represents the length of the time period; is the load pressure conversion coefficient of the transformer, represents the load rate of the transformer in the time period t, represents the predicted resident load in the time period t, represents the predicted vehicle charging load in the time period t, represents the transformer capacity of the transformer area. The optimization constraints include ECC curve-charging power constraints, maximum charging power constraints, charging energy constraints, charging start-stop constraints, and charging time period constraints; The ECC curve-charging power constraints satisfy the following formula: wherein, is the maximum charging power of the electric vehicle i in the time period t at a given state of charge; represents a linear function model of the expected charging profile of the electric vehicle i; represents the SoC of the electric vehicle i in the time period t; represents the actual charging power of the electric vehicle i in the time period t; is a binary variable describing whether the electric vehicle i is charging in the time period t or not; is the load shedding factor. 2.The method of claim 1, wherein, The electric vehicle charging management device comprises a concentrator and an electric vehicle charging controller; the concentrator is configured to concentrate charging data of each electric vehicle in the charging area, generate user charging demand, send the user charging demand to the cloud, and distribute charging power plans of each charging pile sent by the cloud to corresponding electric vehicle charging controllers; each electric vehicle charging controller is configured to change a duty cycle of a CP signal communicated with a vehicle according to the charging plan, adjust charging power of the corresponding vehicle, and collect real-time power and total energy consumption of the vehicle and feed back to the concentrator.

3. A cluster charging power scheduling optimization system for implementing the method of claim 1, considering the expected charging profile, characterized in that, The method comprises: An electric vehicle charging management device is configured to obtain user charging demand of a charging area and control charging of a vehicle according to received charging power plans of each charging pile; A charging load distribution prediction unit is configured to predict charging load distribution; A power grid load condition acquisition unit is configured to obtain power grid load conditions; A real-time scheduling optimization unit is configured to solve an electric vehicle cluster charging power scheduling optimization model based on predicted charging load distribution, power grid load conditions, and user charging demand of the charging area, obtain charging power plans of each charging pile, and send the charging power plans to the electric vehicle charging management device.

4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the electric vehicle cluster charging power scheduling optimization method considering expected charging curves in any one of claims 1 to 2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the electric vehicle cluster charging power scheduling optimization method considering expected charging curves in any one of claims 1 to 2.

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