Charging station energy scheduling method giving consideration to charging station income and battery attenuation compensation

Abstract

The invention belongs to the technical field of intelligent power grids, and discloses a charging station energy scheduling method giving consideration to charging station income and battery attenuation compensation, and the method comprises the steps: a scheduling center carries out the calculation of the energy of a charging station according to data collected by a charging station management system before each period starts, and the data comprises a battery state when an electric vehicle arrives and a battery state expected by a user when the electric vehicle leaves the station; establishing a battery operation constraint model; on the basis, reducing the total cost of the charging station and the degradation cost of the battery of the electric vehicle is taken as a target, constraints such as the battery capacity, the charging and discharging speed and the battery health condition are considered at the same time, a charging station energy scheduling model is constructed, an optimal scheduling scheme is determined by adopting a red-tail eagle optimization algorithm, and the scheme is issued to each charging pile for execution; and after a scheduling period is completed, the system updates the state information of the bicycle pile, collects data again and prepares to enter the energy scheduling of the next period, so that a continuously optimized cyclic process is formed. The key problems of unbalanced resource distribution, high operation cost, short battery life, low charging efficiency and the like of the charging station are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grids, and specifically relates to a charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation. Background Art

[0002] With the rapid expansion of the electric vehicle market, charging stations, as key infrastructure to support the popularization of electric vehicles, have a direct impact on the experience and satisfaction of electric vehicle users due to their operational efficiency and service quality. However, charging station operations face many challenges, including how to balance the relationship between charging service revenue and battery health maintenance, and how to efficiently schedule energy to cope with fluctuations in charging load. On the one hand, charging stations need to attract users and increase revenue by providing fast and convenient charging services; on the other hand, frequent charging and discharging operations will impose additional burdens on electric vehicle batteries, accelerate the attenuation of battery capacity, and thus affect the service life and performance of electric vehicles. Therefore, it is crucial to design an energy scheduling strategy for charging stations that can both guarantee the economic benefits of charging stations and effectively compensate for battery attenuation.

[0003] In view of the challenges faced in the operation of charging stations, namely how to balance the contradiction between the revenue growth brought by charging services and the maintenance of battery health, existing studies have proposed a variety of energy scheduling strategies. On the one hand, in order to improve the revenue of charging stations, researchers have proposed a variety of methods including dynamic pricing strategies, prediction models based on user behavior, and multi-objective optimization algorithms, aiming to attract users to charge during off-peak hours by flexibly adjusting charging prices and optimizing charging time allocation, thereby reducing the load on the power grid and increasing the revenue of charging stations. On the other hand, in response to the problem of battery degradation, researchers have developed an intelligent management system based on battery status monitoring, using big data analysis and machine learning technology to accurately predict the remaining service life of the battery, and formulate a reasonable charging plan based on this to reduce battery damage. In addition, some researchers have combined economic incentives to provide users with battery maintenance subsidies to encourage charging habits that are beneficial to battery health. Although the above methods have shown good results at the theoretical and experimental levels, they still face many challenges in practical applications, such as the unpredictability of user behavior, the complexity of battery models, and the control of system costs. Therefore, it is necessary to further explore more efficient and intelligent energy scheduling strategies, which should not only consider the economic benefits of charging stations, but also comprehensively consider multiple factors such as grid stability, user satisfaction and environmental protection, in order to achieve sustainable development of charging station operations. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation. First, the scheduling center sets the charging station scheduling cycle, collects the interaction data between each charging pile and the electric vehicle before the start of each cycle, and constructs a battery state of charge operation constraint model; secondly, with the goal of minimizing the total cost of the charging station and minimizing the degradation cost of the electric vehicle battery, the battery capacity, charging and discharging rate, battery health and other factors are comprehensively considered to construct a charging station energy scheduling model, and the red-tailed hawk optimization algorithm is used to formulate the optimal energy scheduling strategy; this strategy is sent to the charging pile for execution, and the vehicle-pile status information is updated after completing a scheduling cycle, and the data is collected again to enter the next scheduling cycle.

[0005] The charging station energy scheduling method of the present invention taking into account both charging station revenue and battery attenuation compensation comprises the following steps:

[0006] S1. The dispatch center sets the charging station dispatch cycle. Before the start of each dispatch cycle, the charging station management system collects the interaction data between each charging pile and the electric vehicle, and constructs the electric vehicle battery charge state operation constraint model based on the battery charge state when the electric vehicle arrives and the expected battery charge state when it leaves.

[0007] S2. Taking the minimization of the total cost of charging stations and the minimization of the degradation cost of electric vehicle batteries as the optimization goals, and taking battery capacity, charge and discharge rate, battery health and other factors as constraints, a charging station energy scheduling model is constructed, and the Red-tailed Hawk optimization algorithm is used to formulate the optimal energy scheduling strategy.

[0008] S3: Send the energy scheduling strategy of the charging station to the charging pile for execution. When the previous scheduling cycle is completed, the vehicle-pile status is updated synchronously, and then return to S1 to collect the interaction data between the charging pile and the electric vehicle, and then perform the energy scheduling of the charging station in the next scheduling cycle.

[0009] Furthermore, in S1, an electric vehicle battery state of charge operation constraint model is constructed according to the battery state of charge of the electric vehicle when it arrives and the expected battery state of charge when it leaves, specifically:

[0010] During the charging of electric vehicles, the scheduling system obtains the charging start time t of the electric vehicle from the charging pile arr,i , Battery state of charge SOC before charging arr,i and the owner's expected departure time t dep,i Compared with the expected departure state of charge SOC dep,i , calculate the charging demand of electric vehicle i:

[0011] The charge state of the i-th electric vehicle at time t is shown in formula (1):

[0012]

[0013] Where η i is the charging and discharging efficiency of electric vehicle i, is the battery capacity of electric vehicle i, Δt is the scheduling period, is the charging power of electric vehicle i at time t, specifically:

[0014]

[0015] Where η cha,i With η dis,i are the charging efficiency and discharging efficiency of electric vehicle i, and are the charging and discharging power of the charging station corresponding to electric vehicle i at time t, Indicates the charge and discharge status, 0 means discharging, and 1 means charging.

[0016] At any time, electric vehicles have three working modes: charging, idling and discharging, and the battery state of charge of electric vehicles must always be within its feasible domain:

[0017]

[0018] In the formula, and They are the upper and lower limits of the state of charge of the electric vehicle battery, specifically:

[0019]

[0020]

[0021] In the formula, and are the maximum charging and discharging power of electric vehicle i respectively.

[0022] In addition, equations (6) and (7) must be satisfied to ensure that the charging pile can complete charging within the user's expected charging cycle and achieve the user's expected SOC dep,i The battery state of charge of the i-th electric vehicle at time t is:

[0023]

[0024]

[0025] Furthermore, in S2, the optimization objectives are to minimize the total cost of the charging station and the degradation cost of the electric vehicle battery, specifically:

[0026] In the study of electric vehicle charging and discharging optimization, economic costs and battery health are considered at the same time. A method based on multi-objective optimization is proposed to construct the objective function by minimizing the total cost of the charging station and the degradation cost of the electric vehicle battery:

[0027]

[0028] In the formula, is the total cost, normalized by the factor Scale the total cost of the charging station TCC and the electric vehicle battery degradation cost BDC values ​​to have the same order of magnitude; τ is the Pareto weighting factor, and τ∈(0,1), the weights of the total cost of the charging station and the electric vehicle battery degradation cost can be adjusted according to actual needs.

[0029] Furthermore, in S2, the total cost of the charging station is:

[0030]

[0031] In the formula, is the system's dispatch demand for electric vehicle i at time t, N is the total dispatch period, is the charging power of electric vehicle i at time t, is the discharge power of electric vehicle i at time t; π t is the price signal based on the real-time electricity price at time t, C EG,NF The cost of purchasing electricity from the grid is defined as:

[0032]

[0033] In the formula, is the peak power purchased from the grid each year, is the peak price. Since taxes are added during the electricity purchase process, the electricity purchase price is higher than the price sold from the grid. Therefore, the price signal π needs to be adjusted. t Make adjustments:

[0034]

[0035] In the formula, is the total power consumption of the charging station, It means that the charging station buys electricity from the grid, otherwise it sells electricity; c EG,addon To fix energy prices, It is a dynamic real-time electricity price.

[0036] The electricity purchased and sold from the grid is not allowed to exceed the maximum installed capacity P EG,MIC ,Right now:

[0037]

[0038] Furthermore, in S2, the battery degradation cost is specifically:

[0039]

[0040] In the formula, The battery degradation cost generated when charging and discharging 1KWh, is the net amount of electricity exchanged by the i-th electric vehicle at time t.

[0041] Introducing a Boolean variable to indicate the available state of each electric vehicle and ensuring that the equation is applicable to the time period during which the vehicle is available, the battery degradation of each electric vehicle at time t is calculated as follows:

[0042]

[0043] Where C Bat is the battery cost, the denominator is the total charge and discharge capacity of the battery of electric vehicle i before it reaches the waste standard under certain charge and discharge conditions; DOD is the depth of discharge, is the battery degradation coefficient, and this coefficient is affected by external factors:

[0044]

[0045] Where, T Bat is the battery temperature of the electric vehicle, is the charging rate of the electric vehicle charger, β1~β8 are fitting parameters; is the average state of charge of the battery of the i-th electric vehicle during use.

[0046] Furthermore, in S2, factors such as battery capacity, charge and discharge rate, and battery health are used as constraints, specifically:

[0047] a. Energy management constraints:

[0048] Electric vehicle batteries have limited capacity, and it is necessary to accurately track the battery energy state at each point in time, while also considering natural discharge and energy conversion efficiency:

[0049]

[0050] In the formula, Indicates the maximum available battery capacity of an electric vehicle, is the energy state of electric vehicle i when it arrives at the charging station.

[0051] b. Charge and discharge power constraints

[0052] The charging and discharging power of electric vehicles is limited by the physical characteristics of the charging pile and the electric vehicle battery. It is necessary to appropriately limit the charging and discharging power of electric vehicles to ensure that the charging and discharging power of electric vehicles is within the tolerance range of the charging pile and the power grid:

[0053]

[0054]

[0055] In the formula, are the charging and discharging decision variables, is the discharge rate of the electric vehicle charger.

[0056] c. Minimum state of charge constraint

[0057] When an electric vehicle leaves a charging station, it must have enough power to complete the subsequent journey, ensuring that the vehicle reaches a certain minimum power requirement when leaving the charging station:

[0058]

[0059] d. Battery health constraints

[0060] The battery of an electric vehicle will gradually age during use. Therefore, when controlling the charging and discharging of the electric vehicle, a certain amount of spare capacity needs to be reserved to extend the battery life and avoid overcharging or over-discharging:

[0061]

[0062]

[0063] In the formula, λ min , max is the reserve capacity factor;

[0064] e. Mutually exclusive operation constraints,

[0065] In the process of controlling the charging and discharging of electric vehicles, only one operation is performed at the same time:

[0066]

[0067] Furthermore, in S2, the optimal energy scheduling strategy is formulated using the Red-tailed Hawk optimization algorithm, specifically:

[0068] The red-tailed hawk optimization algorithm consists of three stages: high-altitude hovering, low-altitude hovering, and diving for prey. It gradually transitions from global search to local optimization, with an iteration and feedback mechanism between them:

[0069] a. High-altitude hovering: Randomly distribute in the entire search space, and use global search to get a preliminary understanding of the general outline of the problem space. New candidate solutions are generated by the following formula:

[0070] X(q)=X best +Levy(d)[X mean -X(q-1)·TF(q)(23)

[0071]

[0072] Where X(q) is the position of the red-tailed hawk at the qth iteration; X best is the currently known global optimal position; X mean is the average position of the current population; Levy(d) is a random step size matrix that follows the Levy distribution, d is the variable dimension; Q max is the maximum number of iterations; TF(q) is the dynamic scaling factor.

[0073] b. Low-altitude hovering: Use a smaller step size to accurately search, and continuously perform fine-tuning and local search around the optimal solution to more accurately approach the optimal solution, as shown in the following formula:

[0074] X(q)=X best +[x(q)+y(q)]·[X(q)-X mean ] (25)

[0075] Where x and y represent random direction and length factor respectively. The random direction represents a random direction in two-dimensional or higher-dimensional space and the distance scaling ratio along that direction. The length factor uses the deviation of the individual position relative to the average position of the population to perform local search.

[0076] c. Diving predation: Based on the population position mean and the global optimal position, combined with the dynamic scaling factor TF, random direction, length factor, and ω and θ, the candidate direction of the individual's new position is calculated.

[0077]

[0078]

[0079]

[0080] Where ω and θ are the acceleration and gravity factor of the red-tailed hawk, respectively, which are used to control the degree of dependence on the global optimal solution and the contraction or expansion of the search range.

[0081] In the above three stages, if the cost of the new position is less than the cost of the original position, the new position is accepted and the position and cost of the corresponding individual in the population are updated. At the same time, it is checked whether it is better than the current global optimal solution. If so, the global optimal solution is updated. In addition, all new solutions are restricted to the search boundary, as shown in the following formula:

[0082]

[0083] In the formula They represent the upper and lower bounds of the search space respectively.

[0084] Furthermore, S3 is specifically as follows:

[0085] S3-1. The dispatch center sends the optimal energy dispatch strategy to each charging pile. After receiving the instruction, the charging pile charges or discharges the electric vehicle according to the requirements of the dispatch strategy.

[0086] S3-2. When a scheduling cycle ends, the charging station management system collects vehicle and charging pile information, including data such as the current state of charge of the electric vehicle, the health status of the battery, and the working status of the charging pile;

[0087] S3-3. The updated status information is synchronized to the charging station management system. The system restarts data collection and analysis and enters the next scheduling cycle based on the latest data.

[0088] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation is implemented.

[0089] A computer-readable storage medium having computer instructions stored thereon, characterized in that: when the computer instructions are executed by a processor, the charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation is implemented.

[0090] The present invention proposes a charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation. First, before the start of each scheduling cycle, the scheduling center collects data from each vehicle pile through the charging station management system, constructs an electric vehicle battery state of charge operation constraint model, and ensures the accurate matching of electric vehicle users' charging needs; secondly, the red-tailed hawk optimization algorithm is used to solve the optimal charging station energy scheduling, which not only greatly reduces operating costs, but also significantly extends battery life and improves the overall operating efficiency of the charging station; finally, the charging station energy scheduling strategy is sent to the charging pile for execution. When the previous scheduling cycle is completed, the vehicle pile status information is synchronously updated to prepare for the charging station energy scheduling of the next scheduling cycle. This method adopts a dynamic closed-loop feedback mechanism. The system can continuously adjust the optimization strategy according to real-time data, enhance adaptability and flexibility, and promote the effective allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 Flowchart of the energy scheduling method for charging stations that takes into account both charging station revenue and battery attenuation compensation. DETAILED DESCRIPTION

[0092] In order to make the contents of the present invention more clearly understood, the following is based on specific embodiments in combination with the accompanying drawings: The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, and cannot be used to limit the scope of protection of the present application.

[0093] Example: Figure 1 A flow chart of a charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation is provided as an example of the present invention; Figure 1 As shown, the method includes:

[0094] S1. The dispatch center sets the charging station dispatch cycle. Before the start of each dispatch cycle, the charging station management system collects the interaction data between each charging pile and the electric vehicle, and constructs the electric vehicle battery charge state operation constraint model based on the battery charge state when the electric vehicle arrives and the expected battery charge state when it leaves.

[0095] During the charging of electric vehicles, the scheduling system obtains the charging start time t of the electric vehicle from the charging pile arr,i , Battery state of charge SOC before charging arr,i and the owner's expected departure time t dep,i Compared with the expected departure state of charge SOC dep,i , calculate the charging demand of electric vehicle i:

[0096] The charge state of the i-th electric vehicle at time t is shown in formula (1):

[0097]

[0098] Where η i is the charging and discharging efficiency of electric vehicle i, is the battery capacity of electric vehicle i, Δt is the scheduling period, is the charging power of electric vehicle i at time t, specifically:

[0099]

[0100] Where η cha,i With η dis,i are the charging efficiency and discharging efficiency of electric vehicle i, and are the charging and discharging power of the charging station corresponding to electric vehicle i at time t, Indicates the charge and discharge status, 0 means discharging, and 1 means charging.

[0101] At any time, electric vehicles have three working modes: charging, idling and discharging, and the battery state of charge of electric vehicles must always be within its feasible domain:

[0102]

[0103] In the formula, and They are the upper and lower limits of the state of charge of the electric vehicle battery, specifically:

[0104]

[0105]

[0106] In the formula, and are the maximum charging and discharging power of electric vehicle i respectively.

[0107] In addition, equations (6) and (7) must be satisfied to ensure that the charging pile can complete charging within the user's expected charging cycle and achieve the user's expected SOC dep,i The battery state of charge of the i-th electric vehicle at time t is:

[0108]

[0109]

[0110] Step S2, taking minimization of the total cost of the charging station and minimization of the degradation cost of the electric vehicle battery as the optimization goals, and taking battery capacity, charge and discharge rate, battery health and other factors as constraints, constructing a charging station energy scheduling model, and using the Red-tailed Hawk optimization algorithm to formulate the optimal energy scheduling strategy.

[0111] First, in the study of electric vehicle charging and discharging optimization, considering both economic cost and battery health, a method based on multi-objective optimization is proposed. By minimizing the total cost of the charging station and the degradation cost of the electric vehicle battery, the objective function is constructed:

[0112]

[0113] In the formula, is the total cost, normalized by the factor Scale the total cost of the charging station TCC and the electric vehicle battery degradation cost BDC values ​​to have the same order of magnitude; τ is the Pareto weighting factor, and τ∈(0,1), the weights of the total cost of the charging station and the electric vehicle battery degradation cost can be adjusted according to actual needs.

[0114] Then, the total cost of the charging station and the degradation cost of the electric vehicle battery are determined respectively, as follows:

[0115] The total cost of the charging station is:

[0116]

[0117] In the formula, is the system's dispatch demand for electric vehicle i at time t, N is the total dispatch period, is the charging power of electric vehicle i at time t, is the discharge power of electric vehicle i at time t; π t is the price signal based on the real-time electricity price at time t, C EG,NF The cost of purchasing electricity from the grid is defined as:

[0118]

[0119] In the formula, is the peak power purchased from the grid each year, is the peak price. Since taxes are added during the electricity purchase process, the electricity purchase price is higher than the price sold from the grid. Therefore, the price signal π needs to be adjusted. t Make adjustments:

[0120]

[0121] In the formula, is the total power consumption of the charging station, It means that the charging station buys electricity from the grid, otherwise it sells electricity; c EG,addon To fix energy prices, It is a dynamic real-time electricity price.

[0122] The electricity purchased and sold from the grid is not allowed to exceed the maximum installed capacity P EG,MIC ,Right now:

[0123]

[0124] Battery degradation costs, specifically:

[0125]

[0126] In the formula, The battery degradation cost generated when charging and discharging 1KWh, is the net amount of electricity exchanged by the i-th electric vehicle at time t.

[0127] Introducing a Boolean variable to indicate the available state of each electric vehicle and ensuring that the equation is applicable to the time period during which the vehicle is available, the battery degradation of each electric vehicle at time t is calculated as follows:

[0128]

[0129] Where C Bat is the battery cost, the denominator is the total charge and discharge capacity of the battery of electric vehicle i before it reaches the waste standard under certain charge and discharge conditions; DOD is the depth of discharge, is the battery degradation coefficient, and this coefficient is affected by external factors:

[0130]

[0131] Where, T Bat is the battery temperature of the electric vehicle, is the charging rate of the electric vehicle charger, β1~β8 are fitting parameters; is the average state of charge of the battery of the i-th electric vehicle during use.

[0132] Then, the constraints of battery capacity, charge and discharge rate, and battery health are established respectively, as follows:

[0133] a. Energy management constraints:

[0134] Electric vehicle batteries have limited capacity, and it is necessary to accurately track the battery energy state at each point in time, while also considering natural discharge and energy conversion efficiency:

[0135]

[0136] In the formula, Indicates the maximum available battery capacity of an electric vehicle, is the energy state of electric vehicle i when it arrives at the charging station.

[0137] b. Charge and discharge power constraints

[0138] The charging and discharging power of electric vehicles is limited by the physical characteristics of the charging pile and the electric vehicle battery. It is necessary to appropriately limit the charging and discharging power of electric vehicles to ensure that the charging and discharging power of electric vehicles is within the tolerance range of the charging pile and the power grid:

[0139]

[0140]

[0141] In the formula, are the charging and discharging decision variables, is the discharge rate of the electric vehicle charger.

[0142] c. Minimum state of charge constraint

[0143] When an electric vehicle leaves a charging station, it must have enough power to complete the subsequent journey, ensuring that the vehicle reaches a certain minimum power requirement when leaving the charging station:

[0144]

[0145] d. Battery health constraints

[0146] The battery of an electric vehicle will gradually age during use. Therefore, when controlling the charging and discharging of the electric vehicle, a certain amount of spare capacity needs to be reserved to extend the battery life and avoid overcharging or over-discharging:

[0147]

[0148]

[0149] In the formula, λ min , max is the reserve capacity factor;

[0150] e. Mutually exclusive operation constraints,

[0151] In the process of controlling the charging and discharging of electric vehicles, only one operation is performed at the same time:

[0152]

[0153] Finally, the optimal energy scheduling strategy is formulated using the Red-tailed Hawk optimization algorithm, specifically:

[0154] The red-tailed hawk optimization algorithm consists of three stages: high-altitude hovering, low-altitude hovering, and diving for prey. It gradually transitions from global search to local optimization, with an iteration and feedback mechanism between them:

[0155] a. High-altitude hovering: Randomly distribute in the entire search space, and use global search to get a preliminary understanding of the general outline of the problem space. New candidate solutions are generated by the following formula:

[0156] X(q)=X best +Levy(d)[X mean -X(q-1)]·TF(q) (23)

[0157]

[0158] Where X(q) is the position of the red-tailed hawk at the qth iteration; X best is the currently known global optimal position; X mean is the average position of the current population; Levy(d) is a random step size matrix that follows the Levy distribution, d is the variable dimension; Q max is the maximum number of iterations; TF(q) is the dynamic scaling factor.

[0159] b. Low-altitude hovering: Use a smaller step size to accurately search, and continuously perform fine-tuning and local search around the optimal solution to more accurately approach the optimal solution, as shown in the following formula:

[0160] X(q)=X best +[x(q)+y(q)]·[X(q)-Xmean ] (25)

[0161] Where x and y represent random direction and length factor respectively. The random direction represents a random direction in two-dimensional or higher-dimensional space and the distance scaling ratio along that direction. The length factor uses the deviation of the individual position relative to the average position of the population to perform local search.

[0162] cc diving predation: Based on the population position mean and the global optimal position, combined with the dynamic scaling factor TF, random direction, length factor, and ω and θ, the candidate direction of the individual's new position is calculated.

[0163]

[0164]

[0165]

[0166] Where ω and θ are the acceleration and gravity factor of the red-tailed hawk, respectively, which are used to control the degree of dependence on the global optimal solution and the contraction or expansion of the search range.

[0167] In the above three stages, if the cost of the new position is less than the cost of the original position, the new position is accepted and the position and cost of the corresponding individual in the population are updated. At the same time, it is checked whether it is better than the current global optimal solution. If so, the global optimal solution is updated. In addition, all new solutions are restricted to the search boundary, as shown in the following formula:

[0168]

[0169] In the formula They represent the upper and lower bounds of the search space respectively.

[0170] Step S3: Send the charging station energy scheduling strategy to the charging pile for execution. When the previous scheduling cycle is completed, the vehicle-pile status is updated synchronously, and then return to S1 to collect the interaction data between the charging pile and the electric vehicle, and then perform the charging station energy scheduling for the next scheduling cycle.

[0171] S3-1. The dispatch center sends the optimal energy dispatch strategy to each charging pile. After receiving the instruction, the charging pile charges or discharges the electric vehicle according to the requirements of the dispatch strategy.

[0172] S3-2. When a scheduling cycle ends, the charging station management system collects vehicle and charging pile information, including data such as the current state of charge of the electric vehicle, the health status of the battery, and the working status of the charging pile;

[0173] S3-3. The updated status information is synchronized to the charging station management system. The system restarts data collection and analysis and enters the next scheduling cycle based on the latest data.

[0174] It should be noted that the above embodiments are not intended to limit the protection scope of the present invention, and equivalent changes or substitutions made on the basis of the above technical solutions all fall within the protection scope of the claims of the present invention.

Claims

1. A charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation, characterized in that: The following steps are involved: S1. The dispatch center sets the charging station dispatch cycle. Before each dispatch cycle starts, the charging station management system collects the interaction data between each charging pile and the electric vehicle. According to the battery state of charge when the electric vehicle arrives and the expected battery state of charge when it leaves, the electric vehicle battery state of charge operation constraint model is constructed. S2. Taking the minimization of the total cost of charging stations and the minimization of the degradation cost of electric vehicle batteries as the optimization goals, and taking the battery capacity, charge and discharge rate, and battery health factors as constraints, a charging station energy scheduling model is constructed, and the optimal energy scheduling strategy is formulated using the Red-tailed Hawk optimization algorithm. S3: Send the energy scheduling strategy of the charging station to the charging pile for execution. When the previous scheduling cycle is completed, the vehicle-pile status is updated synchronously, and then return to S1 to collect the interaction data between the charging pile and the electric vehicle, and then perform the energy scheduling of the charging station in the next scheduling cycle.

2. A charging station energy scheduling method taking into account both charging station revenue and battery attenuation compensation according to claim 1, characterized in that: In S1, the battery state of charge operation constraint model of the electric vehicle is constructed according to the battery state of charge when the electric vehicle arrives and the expected battery state of charge when it leaves, which is as follows: During the charging of electric vehicles, the scheduling system obtains the charging start time t of the electric vehicle from the charging pile arr,i , Battery state of charge SOC before charging arr,i and the owner's expected departure time t dep,i Compared with the expected departure state of charge SOC dep,i , calculate the charging demand of electric vehicle i: The charge state of the i-th electric vehicle at time t is shown in formula (1): Where η i is the charging and discharging efficiency of electric vehicle i, is the battery capacity of electric vehicle i, Δt is the scheduling period, is the charging power of electric vehicle i at time t, specifically: Where η cha,i With η dis,i are the charging efficiency and discharging efficiency of electric vehicle i, and are the charging and discharging power of the charging station corresponding to electric vehicle i at time t, Indicates the charge and discharge status, 0 means discharging, 1 means charging, At any time, electric vehicles have three working modes: charging, idling and discharging, and the battery state of charge of electric vehicles must always be within its feasible domain: In the formula, and They are the upper and lower limits of the state of charge of the electric vehicle battery, specifically: Where P i max,cha With P i max,dis are the maximum charging and discharging power of electric vehicle i, In addition, equations (6) and (7) must be satisfied to ensure that the charging pile can complete charging within the user's expected charging cycle and achieve the user's expected SOC dep,i The battery state of charge of the i-th electric vehicle at time t is:

3. The method for energy scheduling of a charging station taking into account both charging station revenue and battery attenuation compensation according to claim 1, characterized in that: In S2, the optimization objectives are to minimize the total cost of charging stations and the degradation cost of electric vehicle batteries, specifically: By minimizing the total cost of the charging station and the degradation cost of the electric vehicle battery, the objective function is constructed: In the formula, is the total cost, normalized by the factor Scale the total cost of the charging station TCC and the electric vehicle battery degradation cost BDC values ​​to have the same order of magnitude; τ is the Pareto weighting factor, and τ∈(0,1), the weights of the total cost of the charging station and the electric vehicle battery degradation cost can be adjusted according to actual needs.

4. According to the method for energy scheduling of charging stations taking into account both charging station revenue and battery attenuation compensation according to claim 3, in S2, the energy scheduling model of the charging station and the total cost of the charging station are specifically: In the formula, is the system's dispatch demand for electric vehicle i at time t, N is the total dispatch period, is the charging power of electric vehicle i at time t, is the discharge power of electric vehicle i at time t; π t is the price signal based on the real-time electricity price at time t, C EG,NF The cost of purchasing electricity from the grid is defined as: In the formula, is the peak power purchased from the grid each year, is the peak price. Since taxes are added during the electricity purchase process, the electricity purchase price is higher than the price sold from the grid. Therefore, the price signal π needs to be adjusted. t Make adjustments: Where P t EG,consumpt is the total power consumption of the charging station, P t EG,consumpt ≥0 means that the charging station purchases electricity from the power grid, otherwise it sells electricity; c EG,addon To fix energy prices, For dynamic real-time electricity prices, The electricity purchased and sold from the grid is not allowed to exceed the maximum installed capacity P EG,MIC ,Right now:

5. The method for energy scheduling of a charging station taking into account both charging station revenue and battery attenuation compensation according to claim 3, characterized in that: In S2, the battery degradation cost is: In the formula, The battery degradation cost generated when charging and discharging 1KWh, is the net amount of electricity exchanged by the i-th electric vehicle at time t, Introducing a Boolean variable to indicate the available state of each electric vehicle and ensuring that the equation is applicable to the time period during which the vehicle is available, the battery degradation of each electric vehicle at time t is calculated as follows: Where C Bat is the battery cost, the denominator is the total charge and discharge capacity of the battery of electric vehicle i before it reaches the waste standard under certain charge and discharge conditions; DOD is the depth of discharge, is the battery degradation coefficient, and this coefficient is affected by external factors: Where, T Bat is the battery temperature of the electric vehicle, is the charging rate of the electric vehicle charger, β1~β8 are fitting parameters; is the average state of charge of the battery of the i-th electric vehicle during use.

6. A charging station energy scheduling method taking into account both charging station revenue and battery attenuation compensation according to claim 1, characterized in that: In S2, battery capacity, charge and discharge rate, and battery health factors are used as constraints, specifically: a. Energy management constraints: Electric vehicle batteries have limited capacity, and it is necessary to accurately track the battery energy state at each point in time, while also considering natural discharge and energy conversion efficiency: In the formula, Indicates the maximum available battery capacity of an electric vehicle, is the energy state of electric vehicle i when it arrives at the charging station, b. Charge and discharge power constraints The charging and discharging power of electric vehicles is limited by the physical characteristics of the charging pile and the electric vehicle battery. It is necessary to appropriately limit the charging and discharging power of electric vehicles to ensure that the charging and discharging power of electric vehicles is within the tolerance range of the charging pile and the power grid: In the formula, are the charging and discharging decision variables, is the discharge rate of the electric vehicle charger, c. Minimum state of charge constraint When an electric vehicle leaves a charging station, it must have enough power to complete the subsequent journey, ensuring that the vehicle reaches a certain minimum power requirement when leaving the charging station: d. Battery health constraints The battery of an electric vehicle will gradually age during use. Therefore, when controlling the charging and discharging of the electric vehicle, a certain amount of spare capacity needs to be reserved to extend the battery life and avoid overcharging or over-discharging: In the formula, λ min , max is the reserve capacity factor; e. Mutually exclusive operation constraints, In the process of controlling the charging and discharging of electric vehicles, only one operation is performed at the same time:

7. A charging station energy scheduling method taking into account both charging station revenue and battery attenuation compensation according to claim 1, characterized in that: In S2, the optimal energy scheduling strategy is formulated using the Red-tailed Hawk optimization algorithm, specifically: The red-tailed hawk optimization algorithm consists of three stages: high-altitude hovering, low-altitude hovering, and diving for prey. It gradually transitions from global search to local optimization, with an iteration and feedback mechanism between them: a. High-altitude hovering: Randomly distribute in the entire search space, and use global search to get a preliminary understanding of the general outline of the problem space. New candidate solutions are generated by the following formula: X(q)=X best +Levy(d)·[X mean -X(q-1)]·TF(q) (23) Where X(q) is the position of the red-tailed hawk at the qth iteration; X best is the currently known global optimal position; X mean is the average position of the current population; Levy(d) is a random step size matrix that follows the Levy distribution, d is the variable dimension; Q max is the maximum number of iterations; TF(q) is the dynamic scaling factor, b. Low-altitude hovering: Use a smaller step size to accurately search, and continuously perform fine-tuning and local search around the optimal solution to more accurately approach the optimal solution, as shown in the following formula: X(q)=X best +[x(q)+y(q)]·[X(q)-X mean ] (25) In the formula, x and y represent random direction and length factor respectively. The random direction represents the random direction in two-dimensional or higher-dimensional space and the distance scaling ratio along the direction; the length factor uses the deviation of the individual position relative to the average position of the population to perform local search. c. Diving predation: Based on the population position mean and the global optimal position, combined with the dynamic scaling factor TF, random direction, length factor, and ω and θ, the candidate direction of the individual's new position is calculated. Where ω and θ are the acceleration and gravity factor of the red-tailed hawk, respectively, which are used to control the degree of dependence on the global optimal solution and the contraction or expansion of the search range. In the above three stages, if the cost of the new position is less than the cost of the original position, the new position is accepted and the position and cost of the corresponding individual in the population are updated. At the same time, it is checked whether it is better than the current global optimal solution. If so, the global optimal solution is updated. In addition, all new solutions are restricted to the search boundary, as shown in the following formula: In the formula X represents the upper and lower bounds of the search space.

8. The method for energy scheduling of a charging station taking into account both charging station revenue and battery attenuation compensation according to claim 1, characterized in that: Step S3 is specifically as follows: S3-1. The dispatch center sends the optimal energy dispatch strategy to each charging pile. After receiving the instruction, the charging pile charges or discharges the electric vehicle according to the requirements of the dispatch strategy. S3-2. When a scheduling cycle ends, the charging station management system collects vehicle and charging pile information, including data such as the current state of charge of the electric vehicle, the health status of the battery, and the working status of the charging pile; S3-3. The updated status information is synchronized to the charging station management system. The system restarts data collection and analysis and enters the next scheduling cycle based on the latest data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the charging station energy scheduling method that takes into account both charging station revenue and battery attenuation compensation as described in any one of claims 1 to 8 above is implemented.

10. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, a charging station energy scheduling method taking into account both charging station revenue and battery attenuation compensation as described in any one of claims 1 to 8 is implemented.

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