Energy storage scheduling method and system based on integrated photovoltaic storage and charging system

Abstract

The present invention provides an energy storage scheduling method and system based on an integrated photovoltaic storage and charging system, and relates to the technical field of energy system operation scheduling. The present invention predicts the output and charging load of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station in the next time period to obtain the prediction result for the next time period; based on the prediction result for the next time period, an optimization model for the next time period is constructed with the goal of maximizing the operating profit of the integrated photovoltaic storage and charging power station in the time period; the optimization model is solved to obtain a scheduling plan. Based on the existing operation mode of the integrated photovoltaic storage and charging power station, the present invention plans a scheduling plan for the energy storage system within a period of time to improve the overall profit of the integrated photovoltaic storage and charging power station.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system operation scheduling, and specifically to an energy storage scheduling method and system based on a photovoltaic storage and charging integrated system. Background Art

[0002] The integrated photovoltaic, storage and charging power station covers the coordinated control of photovoltaic power generation, energy storage system and electric vehicle charging piles. According to the peak, flat and valley electricity prices and photovoltaic grid-connected electricity prices, the energy storage system is scheduled to charge and discharge, and the photovoltaic surplus power is connected to the grid at the same time, realizing the economical and efficient operation of the integrated photovoltaic, storage and charging power station.

[0003] The operating mode of the existing integrated photovoltaic, storage and charging power station is to dispatch the energy storage system in real time according to the real-time photovoltaic output and charging pile power.

[0004] However, real-time scheduling does not plan the charging and discharging of energy storage over the entire time range, making it difficult to maximize overall benefits, resulting in low overall benefits. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the deficiencies in the prior art, the present invention provides an energy storage scheduling method and system based on a photovoltaic storage and charging integrated system, which solves the technical problem of low overall efficiency of the existing scheduling methods.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] In a first aspect, the present invention provides an energy storage scheduling method based on a photovoltaic storage and charging integrated system, comprising:

[0010] At present, the output of the photovoltaic power generation system and the charging load of the photovoltaic storage and charging integrated power station in the next time period are predicted to obtain the prediction result for the next time period;

[0011] Based on the prediction results for the next time period, an optimization model for the next time period is constructed with the goal of maximizing the operating profit of the integrated photovoltaic power station within the time period;

[0012] Solve the optimization model and obtain the scheduling solution.

[0013] Preferably, the optimization model includes optimization objectives and constraints, wherein the optimization objectives include:

[0014]

[0015] Where T is the total time period of the next time period; I ev (t) is the electricity consumption income of the charging pile of the photovoltaic storage and charging integrated power station during period t; Ipv (t) is the income from the surplus power of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station during period t; C total (t) is the total cost of the integrated photovoltaic power station during period t, t = 0, 1, 2, 3…T;

[0016] I ev (t)=(P ev (t)+P service (t))×P ev_load ×Δt

[0017] I pv (t) = P pv ×P pv_grid (t)×Δt

[0018] C total (t) = P grid (t)×P grid_power (t)×Δt

[0019] P grid_power (t) = P grid_cs (t)+P grid_es (t)

[0020] Among them, P ev (t) is the electricity price of the charging pile during period t, P service (t) is the charging pile service fee during period t, P ev_load (t) is the power consumption of the charging pile in period t; Δt is the scheduling time interval; P pv The photovoltaic grid-connected electricity price for the integrated photovoltaic storage and charging power station is the same at every moment. pv_grid (t) is the photovoltaic power grid-connected power of the integrated photovoltaic storage and charging power station during period t; P grid (t) is the electricity purchase price of the integrated photovoltaic storage and charging power station during period t, P grid_cs (t) is the power of the integrated photovoltaic power station for charging piles from the grid during period t, P grid_es (t) is the power of the integrated photovoltaic power station for charging the energy storage during the period t, P grid_power (t) is the total power of the integrated photovoltaic storage and charging power station connected to the grid during period t.

[0021] Preferably, the constraint condition includes that the energy storage system charges and discharges for at least half an hour, specifically:

[0022]

[0023] Among them, b ch (t) is a 0-1 variable, indicating the charging state of the energy storage system during period t;

[0024]

[0025] Among them, b dch (t) is a 0-1 variable, indicating the discharge state of the energy storage system during period t.

[0026] Preferably, the constraint condition includes that the energy storage regulation rate is limited to a power change within a time period t that cannot exceed 30MW;

[0027]

[0028] Among them, P charge (t) is the charging power of the energy storage system during period t, P dis (t) is the discharge power of the energy storage system during period t.

[0029] Preferably, the constraints include operation constraints of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station, power demand constraints of the charging piles, and operation constraints of the energy storage system;

[0030] Among them, the operation constraints of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station are as follows:

[0031] P pv_power (t) = P pv_cs (t)+P pv_es (t)+P pv_grid (t)

[0032] Among them, P pv_power (t) is the predicted output of the photovoltaic power generation system during period t; P pv_cs (t) is the power of photovoltaic power output to the charging pile during period t; P pv_es (t) is the power of photovoltaic output to charge energy storage during period t;

[0033] The photovoltaic grid connection and the grid power disconnection cannot occur at the same time. The constraints are as follows:

[0034] 0≤P pv_grid (t)≤M×b pv (t)

[0035] 0≤P grid_power (t)≤M×b grid (t)

[0036] b grid (t)+b pv (t)≤1

[0037] Among them, M is a large number specified in advance, b pv (t) is a 0-1 variable, indicating the photovoltaic surplus grid-connected status during period t, 1 indicates photovoltaic grid-connected, and 0 indicates photovoltaic grid-off; b grid (t) is a 0-1 variable, indicating the power-off status of the power grid during period t, 1 indicates that the power grid is off, and 0 indicates that the power grid is not off;

[0038] The power demand constraints of charging piles are as follows:

[0039] P ev_load (t) = P pv_cs (t)+P grid_cs (t)+P des_cs (t)×η

[0040] Among them, P des_cs (t) is the power discharged from the energy storage system to the charging pile during period t, and η is the discharge efficiency of the energy storage system;

[0041] The operation constraints of the energy storage system include output constraints, energy storage system power constraints and energy storage battery sustainable operation constraints, as follows:

[0042] P charge (t) = P grid_es (t)+P pv_es (t)

[0043] P dis (t) = P des_cs (t)

[0044] 0≤P charge (t)≤P max_charge ×b ch (t)

[0045] 0≤P dis (t)≤P max_dis ×b dch (t)

[0046] b ch (t)+b dch (t)≤1

[0047] E(t+1)=E(t)+(P charge (t)-P dis (t))×Δt

[0048] E min ≤E(t)≤E max

[0049] Among them, P charge (t) is the charging power of the energy storage system during period t, P dis (t) is the discharge power of the energy storage system during period t, b ch (t) and b dch (t) is a 0-1 variable, which represents the charging and discharging state of the energy storage system during period t; E(t) is the remaining capacity of the energy storage system during period t, and E min and E max are the minimum and maximum capacities of the energy storage system respectively.

[0050] Preferably, the scheduling time interval is 15 minutes.

[0051] Preferably, solving the optimization model to obtain a scheduling solution includes:

[0052] In the photovoltaic power generation system, the photovoltaic output is used to power the charging piles, the photovoltaic output is used to charge the energy storage, and the photovoltaic surplus is connected to the grid; the power is used to power the charging piles when the grid is down, and the power is used to charge the energy storage when the grid is down; the charging and discharging power of the energy storage system and the amount of the energy storage system are used as decision variables, pyomo is used to establish the model, and the cplex solver is used to solve the optimization model.

[0053] In a second aspect, the present invention provides an energy storage dispatching system based on a photovoltaic storage and charging integrated system, comprising:

[0054] The prediction module is used to predict the output of the photovoltaic power generation system and the charging load of the photovoltaic storage and charging integrated power station in the next time period, and obtain the prediction result for the next time period;

[0055] The optimization module constructs a model to build an optimization model for the next time period based on the prediction results of the next time period with the goal of maximizing the operating profit of the integrated photovoltaic storage and charging power station within the time period;

[0056] The solution module is used to solve the optimization model and obtain the scheduling solution.

[0057] In a third aspect, the present invention provides a storage medium storing a computer program for energy storage scheduling based on an integrated photovoltaic storage and charging system, wherein the computer program enables a computer to execute the energy storage scheduling method based on an integrated photovoltaic storage and charging system as described above.

[0058] In a third aspect, the present invention provides an electronic device, comprising:

[0059] one or more processors;

[0060] Memory; and

[0061] One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the energy storage scheduling method based on the integrated photovoltaic storage and charging system as described above.

[0062] (III) Beneficial effects

[0063] The present invention provides a method and system for energy storage scheduling based on an integrated photovoltaic storage and charging system. Compared with the prior art, it has the following beneficial effects:

[0064] The present invention predicts the output and charging load of the photovoltaic power generation system of the photovoltaic storage and charging integrated power station in the next time period to obtain the prediction result of the next time period; based on the prediction result of the next time period, an optimization model for the next time period is constructed with the goal of maximizing the operating profit of the photovoltaic storage and charging integrated power station in the time period; the optimization model is solved to obtain a scheduling plan. Based on the existing photovoltaic storage and charging integrated power station operation mode, the present invention plans the energy storage system scheduling plan within a period of time to improve the overall profit of the photovoltaic storage and charging integrated power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 The block diagram is a method for energy storage scheduling based on an integrated photovoltaic storage and charging system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] The embodiments of the present application solve the technical problem of low overall efficiency of existing scheduling methods by providing an energy storage scheduling method and system based on an integrated photovoltaic storage and charging system, and plan a storage system scheduling plan within a period of time based on the existing photovoltaic storage and charging integrated power station operation mode, thereby improving the overall benefits of the photovoltaic storage and charging integrated power station.

[0069] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0070] The operating mode of the existing integrated photovoltaic, storage and charging power station is to dispatch the energy storage system in real time according to the real-time photovoltaic output and charging pile power, without planning the charging and discharging plan of the energy storage over the entire time range to achieve the goal of maximizing overall benefits.

[0071] At the same time, in the prior art, the optimization model of integrated photovoltaic storage and charging is usually solved using heuristic algorithms such as particle swarm algorithm. The calculation process of particle swarm algorithm is: initialize the particle swarm; continuously update the new speed and new position of the particles according to the current position and speed until the convergence condition is reached, and then the optimization ends. However, the convergence accuracy and stability of the heuristic algorithm are affected by the initial distribution of the population; it is very challenging to select appropriate parameters to obtain the optimal solution.

[0072] At the same time, the existing scheduling methods do not consider improving the sustainable working performance of energy storage batteries and extending the service life of energy storage systems. If this problem is taken into consideration, because there is currently no mathematical expression to express this process for these two constraints, the solution will be slow if it is solved through heuristic algorithms.

[0073] To overcome the above problems, the embodiment of the present invention plans a scheduling plan for the energy storage system within a period of time based on the existing photovoltaic storage and charging integrated power station operation mode to improve the overall benefits of the photovoltaic storage and charging integrated power station. At the same time, through mathematical modeling, all constraints are fully considered, and the consideration of improving the sustainable working performance of the energy storage battery is also taken into account. It is expressed through a statement, and then the solver is used to solve the model, which can effectively reduce the difficulty and calculation of the algorithm, thereby reducing the processing time of the algorithm and having strong robustness.

[0074] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0075] The embodiment of the present invention provides a method for energy storage scheduling based on an integrated photovoltaic storage and charging system. Figure 1 As shown, including:

[0076] S1. Predict the output of the photovoltaic power generation system and the charging load of the photovoltaic storage and charging integrated power station in the next time period, and obtain the prediction result for the next time period;

[0077] S2. Based on the prediction results for the next time period, an optimization model for the next time period is constructed with the goal of maximizing the operating profit of the integrated photovoltaic power station within the time period;

[0078] S3. Solve the optimization model and obtain the scheduling plan.

[0079] Based on the existing operation mode of the photovoltaic storage and charging integrated power station, the embodiment of the present invention plans a storage system scheduling plan within a period of time to improve the overall revenue of the photovoltaic storage and charging integrated power station.

[0080] The following is a detailed description of each step:

[0081] In step S1, the output of the photovoltaic power generation system and the charging load of the photovoltaic storage and charging integrated power station in the next time period are predicted to obtain the prediction result for the next time period. The specific implementation process is as follows:

[0082] In the embodiment of the present invention, the next time period is selected as the second day, and the photovoltaic power generation system output of the photovoltaic storage and charging integrated power station on the second day and the charging load on the second day are predicted at present. The prediction of the photovoltaic power generation system output of the photovoltaic storage and charging integrated power station on the second day and the charging load on the second day is based on historical data and meteorological data of the second day. This process is a prior art and will not be described in detail here.

[0083] In step S2, based on the prediction results of the next time period, an optimization model for the next time period is constructed with the goal of maximizing the operating profit of the integrated photovoltaic storage and charging power station within the time period. The specific implementation process is as follows:

[0084] The optimization model includes optimization objectives and constraints, where the optimization objective is:

[0085] The goal is to maximize the operating income of the integrated photovoltaic power station during the time period (in the embodiment of the present invention, it refers to the second day, that is, within one day). The operating income during the time period includes the income from the electricity used by the charging pile, plus the income from the surplus power of the photovoltaic power generation system of the integrated photovoltaic power station, minus the total cost. The formula is as follows:

[0086]

[0087] Where T is the total time period of the next time period; I ev (t) is the electricity consumption income of the charging pile of the photovoltaic storage and charging integrated power station during period t; I pv (t) is the income from the surplus power of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station during period t; C total (t) is the total cost of the photovoltaic storage and charging integrated power station in time period t, t = 0, 1, 2, 3 ... T. In the embodiment of the present invention, the next time period is the next day, each time period is 15 minutes, T = 95, t = 0, 1, 2, 3 ... 95.

[0088] The electricity revenue of charging piles in the integrated photovoltaic power station during period t I ev (t) is calculated from the power consumption of the charging pile at each moment, the electricity price of the charging pile, and the service fee of the charging pile. The formula is as follows:

[0089] I ev (t)=(P ev (t)+P service (t))×P ev_load ×Δt

[0090] Among them, P ev (t) is the electricity price of the charging pile during period t, Pservice (t) is the charging pile service fee during period t, P ev_load (t) is the power consumption of the charging pile in period t; Δt is the scheduling time interval;

[0091] The income of the photovoltaic power generation system surplus power connected to the grid by the integrated photovoltaic power station during period t I pv (t) is calculated from the photovoltaic grid-connected power and photovoltaic grid-connected electricity price at each moment, and the formula is as follows:

[0092] I pv (t) = P pv ×P pv_grid (t)×Δt

[0093] Among them, P pv The photovoltaic grid-connected electricity price for the integrated photovoltaic storage and charging power station is the same at every moment. pv_grid (t) is the photovoltaic grid-connected power of the integrated photovoltaic storage and charging power station during period t;

[0094] The total cost of the integrated photovoltaic storage and charging power station during period t C total (t) is calculated from the power grid power consumption and power grid price at each moment, and the formula is as follows:

[0095] C total (t) = P grid (t)×P grid_power (t)×Δt

[0096] P grid_power (t) = P grid_cs (t)+P grid_es (t)

[0097] Among them, P grid (t) is the electricity purchase price of the integrated photovoltaic storage and charging power station during period t, P grid_cs (t) is the power of the integrated photovoltaic power station for charging piles from the grid during period t, P grid_es (t) is the power of the integrated photovoltaic power station charging the energy storage from the grid during period t, P grid_power (t) is the total power of the integrated photovoltaic storage and charging power station connected to the grid during period t.

[0098] The constraints include:

[0099] The photovoltaic power generation system operation constraints of the integrated photovoltaic storage and charging power station, the charging pile power demand constraints, and the energy storage system operation constraints.

[0100] The operation constraints of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station are as follows:

[0101] P pv_power (t) = P pv_cs (t)+P pv_es(t)+P pv_grid (t)

[0102] Among them, P pv_power (t) is the predicted output of the photovoltaic power generation system during period t; P pv_cs (t) is the power of photovoltaic power output to the charging pile during period t; P pv_es (t) is the power of photovoltaic output to charge energy storage during period t;

[0103] The photovoltaic grid connection and the grid power disconnection cannot occur at the same time. The constraints are as follows:

[0104] 0≤P pv_grid (t)≤M×b pv (t)

[0105] 0≤P grid_power (t)≤M×b grid (t)

[0106] b grid (t)+b pv (t)≤1

[0107] Among them, M is a large number specified in advance, b pv (t) is a 0-1 variable, indicating the photovoltaic surplus grid-connected status during period t, 1 indicates photovoltaic grid-connected, and 0 indicates photovoltaic grid-off; b grid (t) is a 0-1 variable, indicating the power-off status of the power grid during period t, 1 indicates that the power grid is off, and 0 indicates that the power grid is not off;

[0108] The power demand constraints of charging piles are as follows:

[0109] P ev_load (t) = P pv_cs (t)+P grid_cs (t)+P des_cs (t)×η

[0110] Among them, P des_cs (t) is the power discharged from the energy storage system to the charging pile during period t, and η is the discharge efficiency of the energy storage system;

[0111] The operation constraints of the energy storage system include output constraints, energy storage system power constraints and energy storage battery sustainable operation constraints, as follows:

[0112] P charge (t) = P grid_es (t)+P pv_es (t)

[0113] P dis (t) = P des_cs (t)

[0114] 0≤Pcharge (t)≤P max_charge ×b ch (t)

[0115] 0≤P dis (t)≤P max_dis ×b dch (t)

[0116] b ch (t)+b dch (t)≤1

[0117] E(t+1)=E(t)+(P charge (t)-P dis (t))×Δt

[0118] E min ≤E(t)≤E max

[0119] Among them, P charge (t) is the charging power of the energy storage system during period t, P dis (t) is the discharge power of the energy storage system during period t, b ch (t) and b dch (t) is a 0-1 variable, which represents the charging and discharging state of the energy storage system during period t; E(t) is the remaining capacity of the energy storage system during period t, and E min and E max are the minimum and maximum capacities of the energy storage system respectively;

[0120] In order to improve the sustainable working performance of energy storage batteries and extend the service life of energy storage systems, there are two constraints:

[0121] Constraint 1: The energy storage system charges and discharges for at least half an hour:

[0122]

[0123]

[0124] Among them, b ch (t) is a 0-1 variable, indicating the charging state of the energy storage system during period t; b dch (t) is a 0-1 variable, indicating the discharge state of the energy storage system during period t.

[0125] The above is for charging the energy storage system for at least half an hour, and similar for discharging the energy storage system for at least half an hour;

[0126] Constraint 2: Energy storage regulation rate limit: 30MW / 15min, that is, the energy storage power change value cannot exceed 30MW every 15 minutes;

[0127]

[0128] Among the two constraints mentioned above, the second constraint is the same, which can give energy storage a climbing slope.

[0129] In step S3, the optimization model is solved to obtain a scheduling solution. The specific implementation process is as follows:

[0130] In the photovoltaic power generation system, the photovoltaic output is used for charging piles, the photovoltaic output is used to charge energy storage, and the photovoltaic surplus is connected to the grid; the power is used for charging piles when the grid is down, and the power is used to charge energy storage when the grid is down; the charging and discharging power of the energy storage system and the amount of energy of the energy storage system are used as decision variables, pyomo is used to establish the model, and the cplex solver is used to solve the above optimization model. The steps are as follows:

[0131] (1) Install the required software packages, specifically the Python library Pyomo and the solver CPLEX, and set environment variables to ensure that CPLEX can be found by Pyomo;

[0132] (2) Use Pyomo to define the optimization model by creating a Pyomo model object and then adding variables, objective functions, and constraints.

[0133] (3) Select and configure the CPLEX solver. If the CPLEX installation path is not automatically recognized, you can manually specify the path.

[0134] (4) Solve the model and analyze the results, specifically to obtain the optimal solution of the decision variables and the value of the objective function.

[0135] The embodiment of the present invention further provides an energy storage scheduling system based on a photovoltaic storage and charging integrated system, comprising:

[0136] The prediction module is used to predict the output of the photovoltaic power generation system and the charging load of the photovoltaic storage and charging integrated power station in the next time period, and obtain the prediction result for the next time period;

[0137] The optimization module constructs a model to build an optimization model for the next time period based on the prediction results of the next time period with the goal of maximizing the operating profit of the integrated photovoltaic storage and charging power station within the time period;

[0138] The solution module is used to solve the optimization model and obtain the scheduling solution.

[0139] It can be understood that the energy storage scheduling system based on the integrated photovoltaic storage and charging system provided in the embodiment of the present invention corresponds to the energy storage scheduling method based on the integrated photovoltaic storage and charging system mentioned above. The explanations, examples, beneficial effects and other parts of the relevant contents can refer to the corresponding contents in the energy storage scheduling method based on the integrated photovoltaic storage and charging system, and will not be repeated here.

[0140] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program for energy storage scheduling based on an integrated photovoltaic storage and charging system, wherein the computer program enables a computer to execute the energy storage scheduling method based on an integrated photovoltaic storage and charging system as described above.

[0141] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the energy storage scheduling method based on the integrated photovoltaic storage and charging system as described above.

[0142] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0143] 1. Based on the existing operation mode of the photovoltaic, storage and charging integrated power station, the embodiment of the present invention plans a storage system scheduling plan within a period of time to improve the overall revenue of the photovoltaic, storage and charging integrated power station.

[0144] 2. The embodiment of the present invention uses mathematical modeling to fully consider various constraints, and also takes into account the consideration of improving the sustainable working performance of the energy storage battery. It is expressed through a statement, and then the solver is used to solve the model, which can effectively reduce the difficulty and amount of calculation of the algorithm, thereby reducing the processing time of the algorithm and having strong robustness.

[0145] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0146] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy storage scheduling based on a photovoltaic storage and charging integrated system, characterized in that: include: At present, the output of the photovoltaic power generation system and the charging load of the photovoltaic storage and charging integrated power station in the next time period are predicted to obtain the prediction result for the next time period; Based on the prediction results for the next time period, an optimization model for the next time period is constructed with the goal of maximizing the operating profit of the integrated photovoltaic power station within the time period; Solve the optimization model and obtain the scheduling solution.

2. The energy storage scheduling method based on the photovoltaic storage and charging integrated system according to claim 1 is characterized in that: The optimization model includes optimization objectives and constraints, wherein the optimization objectives include: Where T is the total time period of the next time period; I ev (t) is the electricity consumption income of the charging pile of the photovoltaic storage and charging integrated power station during period t; I pv (t) is the income from the surplus power of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station during period t; C total (t) is the total cost of the integrated photovoltaic power station during period t, t = 0, 1, 2, 3…T; I ev (t)=(P ev (t)+P service (t))×P ev_load ×Δt I pv (t)=P pv ×P pv_grid (t)×Δt C total (t)=P grid (t)×P grid_power (t)×Δt P grid_power (t)=P grid_cs (t)+P grid_es (t) Among them, P ev (t) is the electricity price of the charging pile during period t, P service (t) is the charging pile service fee during period t, P ev_load (t) is the power consumption of the charging pile in period t; Δt is the scheduling time interval; P pv The photovoltaic grid-connected electricity price for the integrated photovoltaic storage and charging power station is the same at every moment. pv_grid (t) is the photovoltaic power grid-connected power of the integrated photovoltaic storage and charging power station during period t; P grid (t) is the electricity purchase price of the integrated photovoltaic storage and charging power station during period t, P grid_cs (t) is the power of the integrated photovoltaic power station for charging piles from the grid during period t, P grid_es (t) is the power of the integrated photovoltaic power station charging the energy storage from the grid during period t, P grid_power (t) is the total power of the integrated photovoltaic storage and charging power station connected to the grid during period t.

3. The energy storage scheduling method based on the integrated photovoltaic storage and charging system according to claim 2 is characterized in that: The constraint condition includes that the energy storage system charges and discharges for at least half an hour, specifically: Among them, b ch (t) is a 0-1 variable, indicating the charging state of the energy storage system during period t; Among them, b dch (t) is a 0-1 variable, indicating the discharge state of the energy storage system during period t.

4. The energy storage scheduling method based on the photovoltaic storage and charging integrated system according to claim 2 is characterized in that: The constraints include that the energy storage regulation rate is limited to a power change of no more than 30MW within a period t; Among them, P charge (t) is the charging power of the energy storage system during period t, P dis (t) is the discharge power of the energy storage system during period t.

5. The energy storage scheduling method based on the photovoltaic storage and charging integrated system according to claim 2 is characterized in that: The constraints mentioned are the operation constraints of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station, the power demand constraints of the charging piles and the operation constraints of the energy storage system; Among them, the operation constraints of the photovoltaic power generation system of the integrated photovoltaic storage and charging power station are as follows: P pv_power (t)=P pv_cs (t)+P pv_es (t)+P pv_grid (t) Among them, P pv_power (t) is the predicted output of the photovoltaic power generation system during period t; P pv_cs (t) is the power of photovoltaic power output to the charging pile during period t; P pv_es (t) is the power of photovoltaic output to charge energy storage during period t; The photovoltaic grid connection and the grid power disconnection cannot occur at the same time. The constraints are as follows: 0≤P pv_grid (t)≤M×b pv (t) 0≤P grid_power (t)≤M×b grid (t) b grid (t)+b pv (t)≤1 Among them, M is a large number specified in advance, b pv (t) is a 0-1 variable, indicating the photovoltaic surplus grid-connected status during period t, 1 indicates photovoltaic grid-connected, and 0 indicates photovoltaic grid-off; b grid (t) is a 0-1 variable, indicating the power-off status of the power grid during period t, 1 indicates that the power grid is off, and 0 indicates that the power grid is not off; The power demand constraints of charging piles are as follows: P ev_load (t)=P pv_cs (t)+P grid_cs (t)+P des_cs (t)×η Among them, P des_cs (t) is the power discharged from the energy storage system to the charging pile during period t, and η is the discharge efficiency of the energy storage system; The operation constraints of the energy storage system include output constraints, energy storage system power constraints and energy storage battery sustainable operation constraints, as follows: P charge (t)=P grid_es (t)+P pv_es (t) P dis (t)=P des_cs (t) 0≤P charge (t)≤P max_charge ×b ch (t) 0≤P dis (t)≤P max_dis ×b dch (t) b ch (t)+b dch (t)≤1 E(t+1)=E(t)+(P charge (t)-P dis (t))×Δt E min ≤E(t)≤E max Among them, P charge (t) is the charging power of the energy storage system during period t, P dis (t) is the discharge power of the energy storage system during period t, b ch (t) and b dch (t) is a 0-1 variable, which represents the charging and discharging state of the energy storage system during period t; E(t) is the remaining capacity of the energy storage system during period t, and E min and E max are the minimum and maximum capacities of the energy storage system respectively.

6. The energy storage scheduling method based on the photovoltaic storage and charging integrated system according to claim 2 is characterized in that: The scheduling time interval is 15 minutes.

7. The energy storage scheduling method based on the photovoltaic storage and charging integrated system according to any one of claims 1 to 6, characterized in that: Solving the optimization model to obtain a scheduling solution includes: In the photovoltaic power generation system, the photovoltaic output is used to power the charging piles, the photovoltaic output is used to charge the energy storage, and the photovoltaic surplus is connected to the grid; the power is used to power the charging piles when the grid is down, and the power is used to charge the energy storage when the grid is down; the charging and discharging power of the energy storage system and the amount of the energy storage system are used as decision variables, pyomo is used to establish the model, and the cplex solver is used to solve the optimization model.

8. An energy storage dispatching system based on an integrated photovoltaic storage and charging system, characterized in that: include: The prediction module is used to predict the output of the photovoltaic power generation system and the charging load of the photovoltaic storage and charging integrated power station in the next time period, and obtain the prediction result for the next time period; The optimization module constructs a model to build an optimization model for the next time period based on the prediction results of the next time period with the goal of maximizing the operating profit of the integrated photovoltaic storage and charging power station within the time period; The solution module is used to solve the optimization model and obtain the scheduling solution.

9. A storage medium, characterized in that: It stores a computer program for energy storage scheduling based on an integrated photovoltaic storage and charging system, wherein the computer program enables the computer to execute the energy storage scheduling method based on an integrated photovoltaic storage and charging system as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: one or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include programs for executing the energy storage scheduling method based on the integrated photovoltaic storage and charging system as described in any one of claims 1 to 7.

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