Energy scheduling method of optical storage charging detection system and terminal

Abstract

The invention discloses an energy scheduling method of an optical storage charging detection system and a terminal, and the method comprises the steps: building a mathematical model based on a power conservation condition, DC side line loss and a relation between PCS input and output power by taking the minimization of PCS power consumption cost as a target; adding constraint conditions for the mathematical model to obtain a target model; the constraint conditions comprise SOC upper and lower limit constraints and voltage constraints; solving the target model, and performing energy scheduling based on a solving result; when modeling with the minimum PCS power consumption cost as the target is carried out, loss, voltage constraint and energy storage device constraint in the actual operation process are considered, energy scheduling can be considered more comprehensively and accurately, and the operation cost is reduced.

Description

[0001] This case is a divisional application based on the invention patent with application date of July 3, 2023, application number 202310802156.0, and name “A method, terminal and system for minimizing cost energy scheduling for photovoltaic storage, charging and inspection” as the parent case. Technical Field

[0002] The present invention relates to the technical field of energy scheduling and control, and in particular to an energy scheduling method and terminal for an optical storage charging and testing system. Background Art

[0003] As the popularity of electric vehicles increases, there are more and more charging stations as supporting facilities. One type of charging station is called a photovoltaic storage charging and inspection station, which has the functions of photovoltaic + energy storage + charging + detection. At present, the profit model of photovoltaic storage charging and inspection stations roughly includes two aspects. On the one hand, it is to increase the charging capacity of the charging and inspection station, and on the other hand, it is to reduce the operating costs of the re-inspection station. Minimizing the operating costs of the charging and inspection station through energy scheduling is an effective method, which is of great significance to improving the profitability of photovoltaic storage charging and inspection stations.

[0004] Currently, there is no mature method to solve the energy scheduling problem of photovoltaic storage charging and testing stations. Most solutions only perform simple energy scheduling according to the peak and valley electricity mechanism, which is not comprehensive and accurate enough. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an energy scheduling method and terminal for an optical storage charging and testing system, which can consider energy scheduling more comprehensively and accurately and reduce operating costs.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for minimizing cost of energy scheduling of photovoltaic storage charging and testing, comprising the steps of:

[0008] S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, the DC line loss and the relationship between the input and output power of PCS;

[0009] S2, adding constraints to the mathematical model to obtain a target model;

[0010] The constraints include SOC upper and lower limit constraints and voltage constraints;

[0011] S3. Solve the target model and perform energy scheduling based on the solution results.

[0012] An energy scheduling method for an optical storage charging and testing system, characterized by comprising the steps of:

[0013] S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, the DC line loss and the relationship between the input and output power of PCS;

[0014] S2. Adding constraints to the mathematical model to obtain a target model; the constraints include upper and lower limit constraints of SOC and voltage constraints;

[0015] The SOC upper and lower limit constraints are specifically:

[0016]

[0017] Among them, Cap represents energy, P represents power, △t represents time difference, and f() represents a function consisting of the parameters in brackets;

[0018] t_ess_nominal indicates the nominal value of the battery during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the output of the battery during period t, and t_ess_in indicates the input of the battery during period t;

[0019] The voltage constraint is specifically:

[0020]

[0021] Among them, Cap represents energy, η represents efficiency, P represents power, and △t represents time difference;

[0022] t_ess_actual indicates the actual battery value during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the battery output during period t, and t_ess_in indicates the battery input during period t;

[0023] The constraints include the following constraints in addition to the SOC upper and lower limit constraints and the voltage constraints:

[0024]

[0025] Wherein, P represents power;

[0026] t_pv_control_out represents the output of the photovoltaic controller during the t period, t_pv_control_out_limit represents the preset output limit of the photovoltaic controller during the t period, t_DC / DC_in represents the preset DC / DC device input during the t period, t_DC / DC_in_limit represents the preset DC / DC device input limit during the t period, t_ess_in represents the battery input during the t period, t_ess_in_limit represents the battery input limit during the t period, t_ess_out represents the battery output during the t period, t_ess_out_limit represents the battery output limit during the t period, t_pcs_out represents the PCS output during the t period, and t_pcs_out_limit represents the PCS output limit during the t period;

[0027] According to the power loss, we can get:

[0028]

[0029] S3. Solve the target model and perform energy scheduling based on the solution results.

[0030] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0031] A terminal for minimizing the cost of energy scheduling for optical storage, charging and testing, comprising a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above method for minimizing the cost of energy scheduling for optical storage, charging and testing when executing the computer program.

[0032] An energy dispatching terminal for an optical storage charging and testing system includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0033] S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, the DC line loss and the relationship between the input and output power of PCS;

[0034] S2. Adding constraints to the mathematical model to obtain a target model; the constraints include upper and lower limit constraints of SOC and voltage constraints;

[0035] The SOC upper and lower limit constraints are specifically:

[0036]

[0037] Among them, Cap represents energy, P represents power, △t represents time difference, and f() represents a function consisting of the parameters in brackets;

[0038] t_ess_nominal indicates the nominal value of the battery during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the output of the battery during period t, and t_ess_in indicates the input of the battery during period t;

[0039] The voltage constraint is specifically:

[0040]

[0041] Among them, Cap represents energy, η represents efficiency, P represents power, and △t represents time difference;

[0042] t_ess_actual indicates the actual battery value during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the battery output during period t, and t_ess_in indicates the battery input during period t;

[0043] The constraints include the following constraints in addition to the SOC upper and lower limit constraints and the voltage constraints:

[0044]

[0045] Wherein, P represents power;

[0046] t_pv_control_out represents the output of the photovoltaic controller during the t period, t_pv_control_out_limit represents the preset output limit of the photovoltaic controller during the t period, t_DC / DC_in represents the preset DC / DC device input during the t period, t_DC / DC_in_limit represents the preset DC / DC device input limit during the t period, t_ess_in represents the battery input during the t period, t_ess_in_limit represents the battery input limit during the t period, t_ess_out represents the battery output during the t period, t_ess_out_limit represents the battery output limit during the t period, t_pcs_out represents the PCS output during the t period, and t_pcs_out_limit represents the PCS output limit during the t period;

[0047] According to the power loss, we can get:

[0048]

[0049] S3. Solve the target model and perform energy scheduling based on the solution results.

[0050] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0051] A system for minimizing cost energy scheduling of photovoltaic storage, charging and inspection includes a storage, charging and inspection station and a control device. The storage, charging and inspection station includes a DC bus and photovoltaic panels, PCS, ESS and multiple charging piles connected to the DC bus. The control device controls the energy scheduling of the storage, charging and inspection station to implement the steps in the above method for minimizing cost energy scheduling of photovoltaic storage, charging and inspection.

[0052] The beneficial effects of the present invention are as follows: an energy scheduling method and terminal of a photovoltaic storage charging and testing system of the present invention, when modeling with the goal of minimizing PCS electricity costs, takes into account the losses, voltage constraints and constraints of energy storage devices in the actual operation process, and can consider energy scheduling more comprehensively and accurately, thereby reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for minimizing cost energy scheduling of photovoltaic storage charging and testing according to an embodiment of the present invention;

[0054] Figure 2 This is a structural diagram of a terminal for minimizing cost energy scheduling of optical storage charging and testing according to an embodiment of the present invention;

[0055] Figure 3 This is a structural example diagram of a storage and charging inspection station in a system for minimizing the cost of energy scheduling of optical storage and charging inspection according to an embodiment of the present invention;

[0056] Description of labels:

[0057] 1. A terminal for minimizing the cost of energy scheduling for optical storage, charging and testing; 2. Processor; 3. Memory. DETAILED DESCRIPTION

[0058] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0059] Please refer to Figure 1 , a method for minimizing the cost of energy scheduling of photovoltaic storage charging and testing, comprising the steps of:

[0060] S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, the DC line loss and the relationship between the input and output power of PCS;

[0061] S2, adding constraints to the mathematical model to obtain a target model;

[0062] The constraints include SOC upper and lower limit constraints and voltage constraints;

[0063] S3. Solve the target model and perform energy scheduling based on the solution results.

[0064] From the above description, it can be seen that the beneficial effects of the present invention are: a method, terminal and system for minimizing the cost of energy scheduling of photovoltaic storage charging and testing of the present invention, when modeling with the goal of minimizing PCS electricity costs, takes into account the losses, voltage constraints and constraints of energy storage devices in the actual operation process, and can consider energy scheduling more comprehensively and accurately, thereby reducing operating costs.

[0065] Furthermore, the establishment of the mathematical model in step S1 is specifically as follows:

[0066] With the goal of minimizing the PCS power cost, the following relationship can be obtained:

[0067]

[0068] Since the PCS input and output power have the following relationship:

[0069] η t_pcs_in P t_pcs_in =P t_pcs_out ;

[0070] Therefore, minimizing the PCS power cost is equivalent to:

[0071]

[0072] According to the power conservation condition, assuming that the photovoltaic device is on the DC side, the power balance relationship is:

[0073]

[0074] P t_线损 =f(P t_pcs_out ,P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in );

[0075] Then the PCS output power can be expressed as:

[0076] P t_pcs_out =f(P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in );

[0077] Among them, Cost represents cost, P represents power, △t represents time difference, price represents price, and η represents efficiency;

[0078] cs_in represents PCS input, t_pcs_in represents PCS input in period t, t_pcs_out represents PCS output in period t, t represents one of the 24 periods in a day, t_grid represents the grid in period t, t_pv_control_out represents the PV controller output in period t, t_ess_out represents the battery output in period t, t_ess_in represents the battery input in period t, t_line_loss represents the line loss in period t, t_DC / DC_in represents the DC / DC device input in period t, and t_charge_in represents the charging pile input in period t.

[0079] It can be seen from the above description that the above are the specific steps and contents for establishing the mathematical model.

[0080] Furthermore, the SOC upper and lower limit constraints are specifically:

[0081]

[0082] Among them, Cap represents energy, P represents power, △t represents time difference, and f() represents a function consisting of the parameters in brackets;

[0083] t_ess_nominal represents the battery nominal value during period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the battery initial value, t_ess_out represents the battery output during period t, and t_ess_in represents the battery input during period t.

[0084] From the above description, it can be seen that the SOC upper and lower limit constraints are specifically as shown above.

[0085] Furthermore, the voltage constraint is specifically:

[0086]

[0087] Among them, Cap represents energy, η represents efficiency, P represents power, and △t represents time difference;

[0088] t_ess_actual represents the actual battery value during period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the initial value of the battery, t_ess_out represents the battery output during period t, and t_ess_in represents the battery input during period t.

[0089] From the above description, it can be seen that the voltage constraint is specifically as shown above.

[0090] Furthermore, the constraints include the following constraints in addition to the SOC upper and lower limit constraints and the voltage constraints:

[0091]

[0092] Wherein, P represents power;

[0093] t_pv_control_out represents the output of the PV controller during period t, t_pv_control_out_limit represents the output limit of the PV controller during period t, t_DC / DC_in represents the input of the DC / DC device during period t, t_DC / DC_in_limit represents the input limit of the DC / DC device during period t, t_ess_in represents the battery input during period t, t_ess_in_limit represents the battery input limit during period t, t_ess_out represents the battery output during period t, t_ess_out_limit represents the battery output limit during period t, t_pcs_out represents the PCS output during period t, and t_pcs_out_limit represents the PCS output limit during period t.

[0094] From the above description, it can be seen that in addition to the SOC upper and lower limit constraints and voltage constraints, the present invention also takes into account the photovoltaic controller output limitation, DC / DC device input limitation, battery input limitation, battery output limitation and PCS output limitation as constraints on the mathematical model.

[0095] Furthermore, the solution to the target model in step S3 is specifically to seek a relatively optimal solution in the solution space formed by the target and constraint conditions based on a preset algorithm.

[0096] From the above description, it can be seen that the target model can form a solution space based on the target and constraints, and can seek one or more optimal solutions in the solution space through a specific algorithm for energy scheduling control.

[0097] Furthermore, the preset algorithm is a genetic algorithm or a particle swarm algorithm.

[0098] From the above description, it can be seen that the purpose of seeking the optimal solution can be achieved through genetic algorithm or particle swarm algorithm.

[0099] Please refer to Figure 2 A terminal for minimizing the cost of energy scheduling for optical storage charging and testing, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above method for minimizing the cost of energy scheduling for optical storage charging and testing when executing the computer program.

[0100] Please refer to Figure 3A system for minimizing the cost of energy scheduling of photovoltaic storage, charging and inspection includes a storage and charging inspection station and a control device. The storage and charging inspection station includes a DC bus and photovoltaic panels, PCS, ESS and multiple charging piles connected to the DC bus. The control device controls the energy scheduling of the storage and charging inspection station to implement the steps in the above method for minimizing the cost of energy scheduling of photovoltaic storage, charging and inspection.

[0101] The present invention provides a method, terminal and system for minimizing cost energy scheduling of a photovoltaic storage charging and testing station, which is suitable for energy scheduling control with minimizing cost as the goal.

[0102] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0103] A method for minimizing cost of energy scheduling of photovoltaic storage charging and testing, comprising the steps of:

[0104] S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, the DC line loss and the relationship between the input and output power of PCS;

[0105] The establishment of the mathematical model in step S1 is specifically as follows:

[0106] The photovoltaic storage charging and inspection station minimizes the cost of energy scheduling. Energy scheduling mainly uses energy storage devices (ESS) to transfer energy in time and space, thereby reducing the operating cost of the charging and inspection station. In essence, minimizing costs is equivalent to minimizing the cost of purchasing electricity from the power grid, which is equivalent to minimizing the cost of PCS electricity operation.

[0107] With the goal of minimizing the PCS power cost, the following relationship can be obtained:

[0108]

[0109] Since the PCS input and output power have the following relationship:

[0110] η t_pcs_in P t_pcs_in =P t_pcs_out ;

[0111] Therefore, minimizing the PCS power cost is equivalent to:

[0112]

[0113] According to the power conservation condition, assuming that the photovoltaic device is on the DC side, the power balance relationship is:

[0114]

[0115] Based on the research results of DC line loss, the line loss should have the following relationship:

[0116] P t_线损 =f(P t_pcs_out ,P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in );

[0117] Then the PCS output power can be expressed as:

[0118] P t_pcs_out =f(P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in );

[0119] Among them, Cost represents cost, P represents power, △t represents time difference, price represents price, and η represents efficiency;

[0120] cs_in represents PCS input, t_pcs_in represents PCS input in period t, t_pcs_out represents PCS output in period t, t represents one of the 24 periods in a day, t_grid represents the grid in period t, t_pv_control_out represents the PV controller output in period t, t_ess_out represents the battery output in period t, t_ess_in represents the battery input in period t, t_line_loss represents the line loss in period t, t_DC / DC_in represents the DC / DC device input in period t, and t_charge_in represents the charging pile input in period t.

[0121] S2, adding constraints to the mathematical model to obtain a target model;

[0122] The constraints include SOC upper and lower limit constraints and voltage constraints;

[0123] ESS has upper and lower limits of SOC and upper and lower limits of voltage. Since there is loss during the charging and discharging process of ESS, and SOC is calculated based on the external measurement value of the battery, this loss is not taken into account. Therefore, the upper and lower limits of SOC are called nominal capacity constraints here. The constraint is expressed as:

[0124]

[0125] Among them, Cap represents energy, P represents power, △t represents time difference, and f() represents a function consisting of the parameters in brackets;

[0126] t_ess_nominal represents the battery nominal value during period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the battery initial value, t_ess_out represents the battery output during period t, and t_ess_in represents the battery input during period t.

[0127] The voltage constraint is a direct physical limitation. The loss during the charging and discharging process will be directly reflected in the voltage, so the voltage constraint is called the actual capacity constraint here. The constraint is expressed as:

[0128]

[0129] Among them, Cap represents energy, η represents efficiency, P represents power, and △t represents time difference;

[0130] t_ess_actual represents the actual battery value during period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the initial value of the battery, t_ess_out represents the battery output during period t, and t_ess_in represents the battery input during period t.

[0131] Other constraints include the following:

[0132]

[0133] Wherein, P represents power;

[0134] t_pv_control_out represents the output of the PV controller during period t, t_pv_control_out_limit represents the output limit of the PV controller during period t, t_DC / DC_in represents the input of the DC / DC device during period t, t_DC / DC_in_limit represents the input limit of the DC / DC device during period t, t_ess_in represents the battery input during period t, t_ess_in_limit represents the battery input limit during period t, t_ess_out represents the battery output during period t, t_ess_out_limit represents the battery output limit during period t, t_pcs_out represents the PCS output during period t, and t_pcs_out_limit represents the PCS output limit during period t.

[0135] Among them, the data related to PV and DC / DC are given predicted values. If the predicted value exceeds the limit range, it should be corrected. The correction step should be performed when the predicted value is given. If the PCS value is negative, in the scenario where inversion is allowed, this value is the power of PV inverted to the AC side; if in the scenario where inversion is not allowed, this value is the power of PV abandoned light.

[0136] In addition, based on the power loss research of the above parts, it can be known that:

[0137]

[0138] In summary, by finding a set of suitable ESS charging and discharging strategies, the PCS cost can be minimized.

[0139] S3. Solve the target model and perform energy scheduling based on the solution results.

[0140] The solution of the target model in step S3 is specifically to seek a relatively optimal solution in the solution space formed by the target and the constraint conditions based on a preset algorithm;

[0141] The preset algorithm is a genetic algorithm or a particle swarm algorithm.

[0142] In this embodiment, currently commonly used planning model solution algorithms include genetic algorithms, particle swarm algorithms, etc. Such algorithms can find a relatively optimal solution in the solution space of objectives and constraints.

[0143] Take genetic algorithm as an example:

[0144] Under normal circumstances, the appropriate fitness function will be determined based on the specific relationship and constraints established and in accordance with the idea of ​​genetic algorithm. Through selection, crossover, mutation and other steps, a suitable solution program will be written to obtain a better solution.

[0145] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0146] A terminal 1 for minimizing the cost of energy scheduling for photovoltaic storage charging and testing comprises a processor 2, a memory 3 and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps in the above method for minimizing the cost of energy scheduling for photovoltaic storage charging and testing are implemented.

[0147] Please refer to Figure 3 , Embodiment 3 of the present invention is:

[0148] A system for minimizing the cost of energy scheduling of optical storage, charging and inspection, including a storage and charging inspection station and a control device, wherein the storage and charging inspection station can refer to Figure 3 As shown, it includes a DC bus and photovoltaic panels, PCS, ESS and multiple charging piles connected to the DC bus. The control device controls the energy scheduling of the storage, charging and inspection station to implement the steps in the above method of minimizing the cost of energy scheduling of photovoltaic storage, charging and inspection.

[0149] In other equivalent embodiments, the control device may be included in the storage and filling inspection station.

[0150] In summary, the method, terminal and system for minimizing the cost of energy scheduling for photovoltaic storage charging and testing provided by the present invention take into account the losses, voltage constraints and constraints of energy storage devices in the actual operation process when modeling with the goal of minimizing PCS electricity costs, and can consider energy scheduling more comprehensively and accurately and reduce operating costs.

[0151] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An energy scheduling method for an optical storage charging and testing system, characterized in that: Includes steps: S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, the DC line loss and the relationship between the input and output power of PCS; S2. Adding constraints to the mathematical model to obtain a target model; the constraints include upper and lower limit constraints of SOC and voltage constraints; The SOC upper and lower limit constraints are specifically: Among them, Cap represents energy, P represents power, △t represents time difference, and f() represents a function consisting of the parameters in brackets; t_ess_nominal indicates the nominal value of the battery during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the output of the battery during period t, and t_ess_in indicates the input of the battery during period t; The voltage constraint is specifically: Among them, Cap represents energy, η represents efficiency, P represents power, and △t represents time difference; t_ess_actual indicates the actual battery value during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the battery output during period t, and t_ess_in indicates the battery input during period t; The constraints include the following constraints in addition to the SOC upper and lower limit constraints and the voltage constraints: Wherein, P represents power; t_pv_control_out represents the output of the photovoltaic controller during the t period, t_pv_control_out_limit represents the preset output limit of the photovoltaic controller during the t period, t_DC / DC_in represents the preset DC / DC device input during the t period, t_DC / DC_in_limit represents the preset DC / DC device input limit during the t period, t_ess_in represents the battery input during the t period, t_ess_in_limit represents the battery input limit during the t period, t_ess_out represents the battery output during the t period, t_ess_out_limit represents the battery output limit during the t period, t_pcs_out represents the PCS output during the t period, and t_pcs_out_limit represents the PCS output limit during the t period; According to the power loss, we can get: S3. Solve the target model and perform energy scheduling based on the solution results.

2. The energy scheduling method of an optical storage charging and testing system according to claim 1 is characterized in that: The establishment of the mathematical model in step S1 is specifically as follows: With the goal of minimizing the PCS power cost, the following relationship can be obtained: Since the PCS input and output power have the following relationship: η t_pcs_in P.S t_pcs_in JP t_pcs_out 100. Therefore, minimizing the PCS power cost is equivalent to: According to the power conservation condition, assuming that the photovoltaic device is on the DC side, the power balance relationship is: Combined line loss: P t_线损 =f(P t_pcs_out ,P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in ); Then the PCS output power can be expressed as: P t_pcs_out =f(P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in ); Among them, Cost represents cost, P represents power, △t represents time difference, price represents price, and η represents efficiency; cs_in represents PCS input, t_pcs_in represents PCS input in period t, t_pcs_out represents PCS output in period t, t represents one of the 24 periods in a day, t_grid represents the grid in period t, t_pv_control_out represents the PV controller output in period t, t_ess_out represents the battery output in period t, t_ess_in represents the battery input in period t, t_line_loss represents the line loss in period t, t_DC / DC_in represents the DC / DC device input in period t, and t_charge_in represents the charging pile input in period t.

3. The energy scheduling method of an optical storage charging and testing system according to claim 1 is characterized in that: The solution to the target model in step S3 is specifically to seek a relatively optimal solution in the solution space formed by the target and constraint conditions based on a preset algorithm.

4. The energy scheduling method of the optical storage charging and testing system according to claim 3 is characterized in that: The preset algorithm is a genetic algorithm; Step S3 includes: According to the established relationship and constraint conditions, the fitness function is determined based on the genetic algorithm, and the target model is solved through the steps including selection, crossover and mutation.

5. The energy scheduling method of an optical storage charging and testing system according to claim 3 is characterized in that: The preset algorithm is a particle swarm algorithm.

6. An energy dispatching terminal of an optical storage charging and testing system, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, the DC line loss and the relationship between the input and output power of PCS; S2. Adding constraints to the mathematical model to obtain a target model; the constraints include upper and lower limit constraints of SOC and voltage constraints; The SOC upper and lower limit constraints are specifically: Among them, Cap represents energy, P represents power, △t represents time difference, and f() represents a function consisting of the parameters in brackets; t_ess_nominal indicates the nominal value of the battery during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the output of the battery during period t, and t_ess_in indicates the input of the battery during period t; The voltage constraint is specifically: Among them, Cap represents energy, η represents efficiency, P represents power, and △t represents time difference; t_ess_actual indicates the actual battery value during period t, lower_limit indicates the lower limit, up_limit indicates the upper limit, ess_init indicates the initial value of the battery, t_ess_out indicates the battery output during period t, and t_ess_in indicates the battery input during period t; The constraints include the following constraints in addition to the SOC upper and lower limit constraints and the voltage constraints: Wherein, P represents power; t_pv_control_out represents the output of the photovoltaic controller during the t period, t_pv_control_out_limit represents the preset output limit of the photovoltaic controller during the t period, t_DC / DC_in represents the preset DC / DC device input during the t period, t_DC / DC_in_limit represents the preset DC / DC device input limit during the t period, t_ess_in represents the battery input during the t period, t_ess_in_limit represents the battery input limit during the t period, t_ess_out represents the battery output during the t period, t_ess_out_limit represents the battery output limit during the t period, t_pcs_out represents the PCS output during the t period, and t_pcs_out_limit represents the PCS output limit during the t period; According to the power loss, we can get: S3. Solve the target model and perform energy scheduling based on the solution results.

7. The energy dispatching terminal of the optical storage charging and testing system according to claim 6, characterized in that: The establishment of the mathematical model in step S1 is specifically as follows: With the goal of minimizing the PCS power cost, the following relationship can be obtained: Since the PCS input and output power have the following relationship: η t_pcs_in P.S t_pcs_in JP t_pcs_out 100. Therefore, minimizing the PCS power cost is equivalent to: According to the power conservation condition, assuming that the photovoltaic device is on the DC side, the power balance relationship is: Combined line loss: P t_线损 =f(P t_pcs_out ,P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in ); Then the PCS output power can be expressed as: P t_pcs_out =f(P t_pv_control_out ,P t_ess_out ,P t_ess_in ,P t_DC / DC_in ); Among them, Cost represents cost, P represents power, △t represents time difference, price represents price, and η represents efficiency; cs_in represents PCS input, t_pcs_in represents PCS input in period t, t_pcs_out represents PCS output in period t, t represents one of the 24 periods in a day, t_grid represents the grid in period t, t_pv_control_out represents the PV controller output in period t, t_ess_out represents the battery output in period t, t_ess_in represents the battery input in period t, t_line_loss represents the line loss in period t, t_DC / DC_in represents the DC / DC device input in period t, and t_charge_in represents the charging pile input in period t.

8. The energy dispatching terminal of the optical storage charging and testing system according to claim 6, characterized in that: The solution to the target model in step S3 is specifically to seek a relatively optimal solution in the solution space formed by the target and constraint conditions based on a preset algorithm.

9. The energy dispatching terminal of the optical storage charging and testing system according to claim 8, characterized in that: The preset algorithm is a genetic algorithm; Step S3 includes: According to the established relationship and constraint conditions, the fitness function is determined based on the genetic algorithm, and the target model is solved through steps including selection, crossover and mutation.

10. The energy dispatching terminal of the optical storage charging and testing system according to claim 8, characterized in that: The preset algorithm is a particle swarm algorithm.

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