An energy scheduling method and terminal for a photovoltaic energy storage charging and testing system

Abstract

This invention discloses an energy dispatching method and terminal for a photovoltaic energy storage charging and testing system. With the goal of minimizing the power cost of the PCS (Power Conservation System), a mathematical model is established based on power conservation conditions, DC-side line losses, and the relationship between PCS input and output power. Constraints are added to the mathematical model to obtain the target model. These constraints include upper and lower limits of State of Charge (SOC) and voltage constraints. The target model is solved, and energy dispatching is performed based on the solution results. By considering losses, voltage constraints, and energy storage device constraints during actual operation when modeling with the goal of minimizing PCS power costs, energy dispatching can be considered more comprehensively and accurately, thereby reducing operating costs.

Description

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

[0002] This invention relates to the field of energy scheduling and control technology, and in particular to an energy scheduling method and terminal for a photovoltaic energy storage charging and testing system. Background Technology

[0003] As the popularity of electric vehicles increases, the number of supporting charging stations is also growing. One type of charging station is called a photovoltaic-energy storage charging and testing station, which has the functions of photovoltaic + energy storage + charging + testing. Currently, the profit model of photovoltaic-energy storage charging and testing stations mainly includes two aspects: one is to increase the charging capacity of the charging and testing station, and the other is to reduce the operating cost of the charging and testing station. Minimizing the operating cost of the charging and testing station through energy dispatch is an effective method, which is of great significance to improving the profitability of photovoltaic-energy storage charging and testing stations.

[0004] Currently, there is no mature method to solve the energy scheduling problem of photovoltaic, energy storage, charging and testing stations. Most solutions only take simple energy scheduling based on the peak-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 a photovoltaic energy storage charging and testing system, which can more comprehensively and accurately consider energy scheduling and reduce operating costs.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

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

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

[0009] S2. Add constraints to the mathematical model to obtain the 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 a photovoltaic energy storage charging and testing system, characterized by comprising the following steps:

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

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

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

[0016]

[0017] Where Cap represents energy, P represents power, Δt represents time difference, and f() represents a function consisting of the parameters in parentheses;

[0018] t_ess_nominal represents the nominal battery value during time 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 time period t, and t_ess_in represents the battery input during time period t.

[0019] The voltage constraint is specifically as follows:

[0020]

[0021] Where Cap represents energy, η represents efficiency, P represents power, and Δt represents time difference;

[0022] t_ess_actual represents the actual battery performance during time period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the battery initial state, t_ess_out represents the battery output during time period t, and t_ess_in represents the battery input during time period t.

[0023] In addition to the SOC upper and lower limit constraints and voltage constraints, the constraints also include the following:

[0024]

[0025] Where P represents power;

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

[0027] Based on power loss, we can obtain:

[0028]

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

[0030] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0031] A terminal for minimizing the cost of energy scheduling for photovoltaic storage, charging, and testing includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-mentioned method for minimizing the cost of energy scheduling for photovoltaic storage, charging, and testing.

[0032] An energy scheduling terminal for a photovoltaic energy storage charging and testing system includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

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

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

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

[0036]

[0037] Where Cap represents energy, P represents power, Δt represents time difference, and f() represents a function consisting of the parameters in parentheses;

[0038] t_ess_nominal represents the nominal battery value during time 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 time period t, and t_ess_in represents the battery input during time period t.

[0039] The voltage constraint is specifically as follows:

[0040]

[0041] Where Cap represents energy, η represents efficiency, P represents power, and Δt represents time difference;

[0042] t_ess_actual represents the actual battery performance during time period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the battery initial state, t_ess_out represents the battery output during time period t, and t_ess_in represents the battery input during time period t.

[0043] In addition to the SOC upper and lower limit constraints and voltage constraints, the constraints also include the following:

[0044]

[0045] Where P represents power;

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

[0047] Based on power loss, we can obtain:

[0048]

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

[0050] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0051] A system for minimizing the cost of energy scheduling for photovoltaic, energy storage, charging, and inspection includes an energy storage, charging, and inspection station and a control device. The energy 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 energy storage, charging, and inspection station to realize the steps in the above-mentioned method for minimizing the cost of energy scheduling for photovoltaic, energy storage, charging, and inspection.

[0052] The beneficial effects of the present invention are as follows: When modeling a photovoltaic energy storage charging and testing system with the goal of minimizing the power cost of the PCS, the present invention considers the losses, voltage constraints and energy storage device constraints in the actual operation process, and can more comprehensively and accurately consider energy scheduling, thereby reducing operating costs. Attached Figure Description

[0053] Figure 1 This is a flowchart of a method for minimizing the cost of energy scheduling for 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 the cost of energy scheduling for optical storage, charging, and testing, according to an embodiment of the present invention.

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

[0056] Label Explanation:

[0057] 1. A terminal for energy scheduling with minimized cost for optical storage, charging, and testing; 2. A processor; 3. A memory. Detailed Implementation

[0058] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

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

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

[0061] S2. Add constraints to the mathematical model to obtain the 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] As can be seen from the above description, the beneficial effects of the present invention are as follows: the method, terminal and system for minimizing the cost of energy scheduling of photovoltaic storage charging and testing according to the present invention, when modeling with the goal of minimizing the power cost of PCS, takes into account the losses, voltage constraints and energy storage device constraints in the actual operation process, and can more comprehensively and accurately consider energy scheduling and reduce operating costs.

[0065] Furthermore, the establishment of the mathematical model in step S1 specifically involves:

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

[0067]

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

[0069] η t_pcs_in P t_pcs_in =P t_pcs_out ;

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

[0071]

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

[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] The output power of the PCS can then 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] Where Cost represents cost, P represents power, Δt represents time difference, price represents price, and η represents efficiency;

[0078] cs_in represents PCS input, t_pcs_in represents PCS input during time period t, t_pcs_out represents PCS output during time period t, t represents a time period among the 24 time periods of a day, t_grid represents the power grid during time period t, t_pv_control_out represents the photovoltaic controller output during time period t, t_ess_out represents the battery output during time period t, t_ess_in represents the battery input during time period t, t_line loss represents the line loss during time period t, t_DC / DC_in represents the DC / DC device input during time period t, and t_charge_in represents the charging pile input during time period t.

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

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

[0081]

[0082] Where Cap represents energy, P represents power, Δt represents time difference, and f() represents a function consisting of the parameters in parentheses;

[0083] t_ess_nominal represents the nominal battery value during time 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 time period t, and t_ess_in represents the battery input during time period t.

[0084] As described above, the upper and lower limits of SOC are specifically as shown above.

[0085] Furthermore, the voltage constraint specifically refers to:

[0086]

[0087] Where Cap represents energy, η represents efficiency, P represents power, and Δt represents time difference;

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

[0089] As described above, the voltage constraint is as shown in the figure.

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

[0091]

[0092] Where P represents power;

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

[0094] As described above, in addition to the SOC upper and lower limits and voltage constraints, this invention also considers photovoltaic controller output limits, DC / DC device input limits, battery input limits, battery output limits, and PCS output limits as constraints on the mathematical model.

[0095] Furthermore, in step S3, solving the target model specifically involves seeking a relatively optimal solution in the solution space formed by the target and constraints based on a preset algorithm.

[0096] As described above, the target model, based on the objective and constraints, can form a solution space. It can use a specific algorithm to find one or more optimal solutions in the solution space for energy scheduling control.

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

[0098] As can be seen from the above description, the goal of finding the optimal solution can be achieved through genetic algorithms or particle swarm optimization algorithms.

[0099] Please refer to Figure 2 A terminal for minimizing the cost of energy scheduling for optical storage, charging, and testing includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the above-mentioned method for minimizing the cost of energy scheduling for optical storage, charging, and testing.

[0100] Please refer to Figure 3A system for minimizing the cost of energy scheduling for photovoltaic, energy storage, charging, and inspection includes an energy storage, charging, and inspection station and a control device. The energy 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 energy storage, charging, and inspection station to realize the steps in the above-mentioned method for minimizing the cost of energy scheduling for photovoltaic, energy storage, charging, and inspection.

[0101] The present invention provides a method, terminal, and system for minimizing the cost of energy scheduling for photovoltaic energy storage charging and testing stations, applicable to energy scheduling control with the goal of minimizing costs.

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

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

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

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

[0106] The energy dispatching strategy for photovoltaic-storage-charging-and-testing stations minimizes energy costs. Energy dispatching primarily utilizes energy storage devices (ESS) to transfer energy across time and space, thereby reducing the operating costs of the charging-and-testing stations. Essentially, minimizing costs is equivalent to minimizing grid power purchase costs and minimizing PCS (Power Consumption System) operating costs.

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

[0108]

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

[0110] η t_pcs_in P t_pcs_in =P t_pcs_out ;

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

[0112]

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

[0114]

[0115] Based on the research results on DC-side line losses, 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] The output power of the PCS can then 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] Where Cost represents cost, P represents power, Δt represents time difference, price represents price, and η represents efficiency;

[0120] cs_in represents PCS input, t_pcs_in represents PCS input during time period t, t_pcs_out represents PCS output during time period t, t represents a time period among the 24 time periods of a day, t_grid represents the power grid during time period t, t_pv_control_out represents the photovoltaic controller output during time period t, t_ess_out represents the battery output during time period t, t_ess_in represents the battery input during time period t, t_line loss represents the line loss during time period t, t_DC / DC_in represents the DC / DC device input during time period t, and t_charge_in represents the charging pile input during time period t.

[0121] S2. Add constraints to the mathematical model to obtain the target model;

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

[0123] The ESS (Effective Storage Function) has upper and lower limits for State of Charge (SOC) and voltage. Since there are losses during the charging and discharging process of the ESS, and the SOC is calculated based on external measurements of the battery without considering these losses, the SOC upper and lower limits are referred to here as nominal capacity constraints. These constraints are expressed as follows:

[0124]

[0125] Where Cap represents energy, P represents power, Δt represents time difference, and f() represents a function consisting of the parameters in parentheses;

[0126] t_ess_nominal represents the nominal battery value during time 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 time period t, and t_ess_in represents the battery input during time period t.

[0127] Voltage constraint is a direct physical limitation; losses during charging and discharging are directly reflected in the voltage. Therefore, voltage constraint is referred to here as actual capacity constraint, which is expressed as follows:

[0128]

[0129] Where Cap represents energy, η represents efficiency, P represents power, and Δt represents time difference;

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

[0131] Other constraints also include the following:

[0132]

[0133] Where P represents power;

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

[0135] The PV and DC / DC related data are given predicted values. If the predicted values ​​exceed the limits, they should be corrected. The correction steps should be performed when the predicted values ​​are given. If the PCS value is negative, in scenarios where inversion is allowed, this value is the power of photovoltaic power inverted to the AC side; in scenarios where inversion is not allowed, this value is the power of photovoltaic curtailment.

[0136] Furthermore, based on the power loss studies in the aforementioned sections, it can be concluded that:

[0137]

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

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

[0140] In step S3, solving the target model specifically involves seeking a relatively optimal solution in the solution space formed by the target and constraints based on a preset algorithm.

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

[0142] In this embodiment, commonly used algorithms for solving planning models include genetic algorithms and particle swarm optimization. These algorithms can find a relatively optimal solution in the solution space of the objective and constraints.

[0143] Taking genetic algorithms as an example:

[0144] Under normal circumstances, based on the established relational formula and constraints, and following the principles of genetic algorithms, a suitable fitness function is determined. Through steps such as selection, crossover, and mutation, a suitable solution program is written to obtain a better solution.

[0145] Please refer to Figure 2 Embodiment two of the present invention is as follows:

[0146] A terminal 1 for minimizing the cost of energy scheduling for photovoltaic storage, charging, and testing includes 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, it implements the steps in the above-mentioned method for minimizing the cost of energy scheduling for photovoltaic storage, charging, and testing.

[0147] For reference Figure 3 Embodiment 3 of the present invention is as follows:

[0148] A system for minimizing the cost of energy dispatching through photovoltaic energy storage, charging, and testing includes an energy storage, charging, and testing station and a control device. The energy storage, charging, and testing station can be referenced from... Figure 3 As shown, the system 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 energy storage, charging, and inspection station, realizing the steps in the above-mentioned method for minimizing the cost of energy scheduling for photovoltaic, energy storage, charging, and inspection.

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

[0150] In summary, the method, terminal, and system for minimizing the cost of photovoltaic, energy storage, charging, and testing provided by this invention, when modeling with the goal of minimizing the power cost of the PCS, considers losses, voltage constraints, and constraints of the energy storage device during actual operation, and can more comprehensively and accurately consider energy scheduling, thereby reducing operating costs.

[0151] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An energy scheduling method for a photovoltaic energy storage charging and testing system, characterized in that, Including the following steps: S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, DC-side line loss, and the relationship between PCS input and output power. S2. Add constraints to the mathematical model to obtain the target model; the constraints include SOC upper and lower limit constraints and voltage constraints. The SOC upper and lower limit constraints are specifically as follows: ； Where Cap represents energy, P represents power, Δt represents time difference, and f() represents a function consisting of the parameters in parentheses; t_ess_nominal represents the nominal battery value during time 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 time period t, and t_ess_in represents the battery input during time period t. The voltage constraint is specifically as follows: ； Where Cap represents energy, η represents efficiency, P represents power, and Δt represents time difference; t_ess_actual represents the actual battery performance during time period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the battery initial state, t_ess_out represents the battery output during time period t, and t_ess_in represents the battery input during time period t. In addition to the SOC upper and lower limit constraints and voltage constraints, the constraints also include the following: ； Where P represents power; t_pv_control_out represents the output of the photovoltaic controller during time period t, t_pv_control_out_limit represents the preset output limit of the photovoltaic controller during time period t, t_DC / DC_in represents the preset input of the DC / DC device during time period t, t_DC / DC_in_limit represents the preset input limit of the DC / DC device during time period t, t_ess_in represents the battery input during time period t, t_ess_in_limit represents the battery input limit during time period t, t_ess_out represents the battery output during time period t, t_ess_out_limit represents the battery output limit during time period t, t_pcs_out represents the PCS output during time period t, t_pcs_out_limit represents the PCS output limit during time period t; Based on power loss, we can obtain: ； S3. Solve the target model and perform energy scheduling based on the solution results; The establishment of the mathematical model in step S1 is specifically as follows: With the goal of minimizing the electricity cost of the PCS, the following relationship can be expressed: ； Because the input and output power of the PCS have the following relationship: ； Therefore, minimizing the power cost of the PCS is equivalent to: ； According to the power conservation condition, assuming the photovoltaic device is on the DC side, the power balance relationship is as follows: ； Considering line losses: ； The output power of the PCS can then be expressed as: ； Where Cost represents cost, P represents power, Δt represents time difference, price represents price, and η represents efficiency; cs_in represents PCS input, t_pcs_in represents PCS input during time period t, t_pcs_out represents PCS output during time period t, t represents a time period among the 24 time periods of a day, t_grid represents the power grid during time period t, t_pv_control_out represents the photovoltaic controller output during time period t, t_ess_out represents the battery output during time period t, t_ess_in represents the battery input during time period t, t_line loss represents the line loss during time period t, t_DC / DC_in represents the DC / DC device input during time period t, and t_charge_in represents the charging pile input during time period t.

2. The energy scheduling method for a photovoltaic energy storage charging and testing system according to claim 1, characterized in that, In step S3, solving the target model specifically involves seeking a relatively optimal solution in the solution space formed by the target and constraints based on a preset algorithm.

3. The energy scheduling method for a photovoltaic energy storage charging and testing system according to claim 2, characterized in that, The preset algorithm is a genetic algorithm; Step S3 includes: Based on the established relationships and constraints, the fitness function is determined using a genetic algorithm, and the target model is solved through steps including selection, crossover, and mutation.

4. The energy scheduling method for a photovoltaic energy storage charging and testing system according to claim 2, characterized in that, The preset algorithm is the particle swarm optimization algorithm.

5. An energy dispatch terminal for a photovoltaic energy 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, it performs the following steps: S1. To minimize the power cost of PCS, a mathematical model is established based on the power conservation condition, DC-side line loss, and the relationship between PCS input and output power. S2. Add constraints to the mathematical model to obtain the target model; the constraints include SOC upper and lower limit constraints and voltage constraints. The SOC upper and lower limit constraints are specifically as follows: ； Where Cap represents energy, P represents power, Δt represents time difference, and f() represents a function consisting of the parameters in parentheses; t_ess_nominal represents the nominal battery value during time 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 time period t, and t_ess_in represents the battery input during time period t. The voltage constraint is specifically as follows: ； Where Cap represents energy, η represents efficiency, P represents power, and Δt represents time difference; t_ess_actual represents the actual battery performance during time period t, lower_limit represents the lower limit, up_limit represents the upper limit, ess_init represents the battery initial state, t_ess_out represents the battery output during time period t, and t_ess_in represents the battery input during time period t. In addition to the SOC upper and lower limit constraints and voltage constraints, the constraints also include the following: ； Where P represents power; t_pv_control_out represents the output of the photovoltaic controller during time period t, t_pv_control_out_limit represents the preset output limit of the photovoltaic controller during time period t, t_DC / DC_in represents the preset input of the DC / DC device during time period t, t_DC / DC_in_limit represents the preset input limit of the DC / DC device during time period t, t_ess_in represents the battery input during time period t, t_ess_in_limit represents the battery input limit during time period t, t_ess_out represents the battery output during time period t, t_ess_out_limit represents the battery output limit during time period t, t_pcs_out represents the PCS output during time period t, t_pcs_out_limit represents the PCS output limit during time period t; Based on power loss, we can obtain: ； S3. Solve the target model and perform energy scheduling based on the solution results; The establishment of the mathematical model in step S1 is specifically as follows: With the goal of minimizing the electricity cost of the PCS, the following relationship can be expressed: ； Because the input and output power of the PCS have the following relationship: ； Therefore, minimizing the power cost of the PCS is equivalent to: ； According to the power conservation condition, assuming the photovoltaic device is on the DC side, the power balance relationship is as follows: ； Considering line losses: ； The output power of the PCS can then be expressed as: ； Where Cost represents cost, P represents power, Δt represents time difference, price represents price, and η represents efficiency; cs_in represents PCS input, t_pcs_in represents PCS input during time period t, t_pcs_out represents PCS output during time period t, t represents a time period among the 24 time periods of a day, t_grid represents the power grid during time period t, t_pv_control_out represents the photovoltaic controller output during time period t, t_ess_out represents the battery output during time period t, t_ess_in represents the battery input during time period t, t_line loss represents the line loss during time period t, t_DC / DC_in represents the DC / DC device input during time period t, and t_charge_in represents the charging pile input during time period t.

6. The energy dispatching terminal of the photovoltaic energy storage charging and testing system according to claim 5, characterized in that, In step S3, solving the target model specifically involves seeking a relatively optimal solution in the solution space formed by the target and constraints based on a preset algorithm.

7. The energy dispatching terminal of a photovoltaic energy storage charging and testing system according to claim 6, characterized in that, The preset algorithm is a genetic algorithm; Step S3 includes: Based on the established relationships and constraints, the fitness function is determined using a genetic algorithm, and the target model is solved through steps including selection, crossover, and mutation.

8. The energy dispatching terminal of the photovoltaic energy storage charging and testing system according to claim 6, characterized in that, The preset algorithm is the particle swarm optimization algorithm.

Images