Optical storage and charging energy scheduling method, system and device and medium

Abstract

The invention discloses an optical storage and charging energy scheduling method, system and device and a medium, relates to the technical field of optical storage and charging energy scheduling, is used for optimizing energy scheduling of an optical storage and charging system, and provides a day-ahead-day-real-time triple-time-scale multi-layer optical storage and charging energy scheduling method for solving the problem that the scheduling effect of a traditional scheme needs to be improved. According to the method, day-ahead optimization scheduling and intra-day optimization scheduling are combined, and an optimization scheduling task is split and is realized by two different models at two different times, so that the time granularity of optimization scheduling is shortened, and the length of an optimization time period is expanded. Besides, the method is also combined with a real-time scheduling strategy, and after the energy scheduling instruction obtained by day-ahead and intra-day prediction is obtained, the operation data of the optical storage and charging system at the current moment is also obtained in real time to correct the energy scheduling instruction, so that the problem that the operation safety of the optical storage and charging system which changes in real time cannot be adapted is solved; and the optical storage and charging system can be better enabled to be close to the optimal economic benefit all day long.

Description

Technical Field

[0001] The present application relates to the technical field of solar energy storage and charging scheduling, and in particular to a solar energy storage and charging scheduling method, system, device and medium. Background Art

[0002] As the number of electric vehicles increases year by year, the problem of electric vehicle charging cannot be ignored. If electric vehicles are directly connected to the power grid, it will cause a great impact on the power grid, bringing safety and economic risks to the power grid. Therefore, the photovoltaic storage and charging system is one of the best solutions to the problem of electric vehicle charging. It can not only solve a series of problems caused by electric vehicles to the power grid, but also help users save electricity costs and even gain benefits when dynamic electricity prices become more popular. Therefore, energy scheduling for photovoltaic storage and charging systems is becoming more and more critical.

[0003] In related technologies, research on solar-storage-charging energy scheduling mostly focuses on the day-ahead optimal scheduling of solar-storage-charging systems, and some solutions can also achieve intraday optimal scheduling. The above two optimization solutions are to predict the operating status of the solar-storage-charging system in a specific period of time, in order to obtain an energy scheduling strategy that can enable the solar-storage-charging system to achieve global economic optimization within the optimization period.

[0004] However, since computing resources are limited in practical applications, the energy scheduling optimization for each photovoltaic storage and charging system is a computationally intensive task. Therefore, due to hardware performance limitations, both day-ahead optimization scheduling and intraday optimization scheduling use a scheduling period of 15 minutes, 1 hour, etc. as a scheduling cycle, which is difficult to further reduce. Among them, the optimization period of intraday optimization scheduling (including multiple scheduling periods, that is, to ensure the global economic optimization of the system within the optimization period) is generally 4 hours, while the actual operation time unit of the photovoltaic storage and charging system is generally a day, and it is expected that the global economic optimization of the photovoltaic storage and charging system can be achieved within a day. In addition, the operating state of the photovoltaic storage and charging system changes in real time, and the scheduling instructions corresponding to a scheduling period are difficult to bring about the economic optimization at any time of the entire scheduling period. This goal can only be continuously approached by shortening the scheduling period. At the same time, since the operating state of the photovoltaic storage and charging system is constantly changing, a scheduling instruction is difficult to ensure the operating safety of the entire scheduling period, and problems such as violating necessary constraints or triggering battery state of charge (SOC) protection may occur at certain times.

[0005] Therefore, technicians in this field now urgently need an energy scheduling method for a photovoltaic storage and charging system to further reduce the time granularity of the scheduling instructions and make the actual operating state of the photovoltaic storage and charging system safer and closer to the economic optimum. Summary of the invention

[0006] The purpose of this application is to provide a method, system, device and medium for scheduling solar energy storage and charging, which are used to further reduce the time granularity of scheduling instructions, so that the actual operation state of the solar energy storage and charging system is safer and closer to the economic optimum.

[0007] In order to solve the above technical problems, the present application provides a method for scheduling solar energy storage and charging, including:

[0008] Predicting the energy storage charging and discharging state and the charging pile start and stop state through a pre-established day-ahead optimization scheduling model, and inputting the energy storage charging and discharging state and the charging pile start and stop state into a pre-established intraday optimization scheduling model;

[0009] Obtaining energy scheduling instructions through the intraday optimization scheduling model;

[0010] Obtain the operating status and constraint data of the solar-storage-charging system at the current moment;

[0011] modifying the energy dispatch command according to the operating status data;

[0012] The modified energy scheduling command is sent to the solar energy storage and charging system.

[0013] In a possible embodiment, the energy scheduling command includes: photovoltaic power generation power, energy storage charging and discharging power, and charging pile charging power;

[0014] The operating status and constraint data include: the real-time value of the load demand power corresponding to the scheduling period at the current moment;

[0015] Modifying the energy dispatch command according to the operating status data includes:

[0016] Determine the current buying and selling power according to the photovoltaic power generation power, the energy storage charging and discharging power, the charging pile charging power and the load demand power real-time value;

[0017] If the buying and selling power is greater than the preset selling power threshold, one or more of downwardly correcting the photovoltaic power generation power, downwardly correcting the energy storage charging and discharging power, and upwardly correcting the charging pile charging power are executed;

[0018] If the buying and selling power is less than the opposite of the preset buying power threshold, one or more of upward correction of the photovoltaic power generation power, upward correction of the energy storage charging and discharging power, and downward correction of the charging pile charging power are executed.

[0019] In a possible embodiment, the operating status and constraint data further include: an energy storage charging power threshold corresponding to the current scheduling period;

[0020] If the buying and selling power is greater than the preset selling power threshold, then performing one or more of downwardly correcting the photovoltaic power generation power, downwardly correcting the energy storage charging and discharging power, and upwardly correcting the charging pile charging power includes:

[0021] Determining the first target correction value according to the photovoltaic power generation power, the energy storage charging and discharging power, the real-time value of the load demand power and the power selling threshold;

[0022] Determining whether the first target correction value is greater than a preset charging pile charging power threshold;

[0023] If not, the charging power of the charging pile is corrected to the first target correction value, and the correction of the energy scheduling command is terminated;

[0024] If yes, the charging power of the charging pile is corrected to the charging power threshold of the charging pile;

[0025] Determining the second target correction value according to the photovoltaic power generation power, the charging pile charging power, the load demand power real-time value and the power selling threshold;

[0026] Determining whether the second target correction value is less than the inverse of the energy storage charging power threshold;

[0027] If not, the energy storage charging and discharging power is corrected to the second target correction value, and the correction of the energy scheduling command is terminated;

[0028] If yes, the energy storage charging and discharging power is corrected to the opposite of the energy storage charging power threshold;

[0029] Determining the third target correction value according to the energy storage charging and discharging power, the charging pile charging power, the load demand power real-time value and the power selling threshold;

[0030] The photovoltaic power generation power is corrected to the third target correction value.

[0031] In a possible embodiment, the operating status and constraint data further include: the maximum photovoltaic output value and energy storage discharge power threshold corresponding to the current scheduling period;

[0032] If the buying and selling power is less than the opposite of the preset buying power threshold, then performing one or more of upward correction of the photovoltaic power generation power, upward correction of the energy storage charging and discharging power, and downward correction of the charging pile charging power includes:

[0033] Determining the fourth target correction value according to the energy storage charging and discharging power, the charging pile charging power, the load demand power real-time value and the electricity purchase threshold;

[0034] Determining whether the fourth target correction value is greater than the photovoltaic maximum output value;

[0035] If not, the photovoltaic power generation power is corrected to the fourth target correction value, and the correction of the energy scheduling command is terminated;

[0036] If yes, the photovoltaic power generation power is corrected to the photovoltaic maximum output value;

[0037] Determining the fifth target correction value according to the photovoltaic power generation power, the charging pile charging power, the load demand power real-time value and the power purchase threshold;

[0038] Determining whether the fifth target correction value is greater than the energy storage discharge power threshold;

[0039] If not, the energy storage charging and discharging power is corrected to the fifth target correction value, and the correction of the energy scheduling command is terminated;

[0040] If yes, the energy storage charging and discharging power is corrected to the energy storage discharging power threshold;

[0041] Determining the sixth target correction value according to the photovoltaic power generation power, the energy storage charging and discharging power, the load demand power real-time value and the power purchase threshold;

[0042] Determining whether the sixth target correction value is less than 0;

[0043] If not, the charging power of the charging pile is corrected to the sixth target correction value, and the correction of the energy scheduling command is terminated;

[0044] If so, the charging power of the charging pile is corrected to 0.

[0045] In a possible embodiment, the operating status and constraint data further include: a maximum photovoltaic output value corresponding to the current scheduling period;

[0046] Before determining the current buying and selling power according to the photovoltaic power generation power, the energy storage charging and discharging power, the charging pile charging power and the load demand power real-time value, it also includes:

[0047] Determine whether the remaining energy storage capacity during the current scheduling period is within a preset SOC protection range;

[0048] If not, the energy storage charging and discharging power is corrected to 0;

[0049] Determine whether the remaining power of the electric vehicle during the current scheduling period is greater than a preset electric vehicle SOC protection upper limit value;

[0050] If yes, the charging power of the charging pile is corrected to 0;

[0051] Determine whether the current photovoltaic power generation power is greater than the photovoltaic maximum output value;

[0052] If so, the photovoltaic power generation power is corrected to the photovoltaic maximum output value.

[0053] In a possible embodiment, the operating status and constraint data further include: the energy storage backup power SOC corresponding to the current scheduling period;

[0054] The determining whether the remaining energy storage capacity in the current scheduling period is within a preset SOC protection range includes:

[0055] Determine whether the remaining energy storage capacity in the current scheduling period is greater than or equal to the lower limit of the energy storage backup power and less than or equal to the upper limit of the SOC protection range;

[0056] The energy storage backup power lower limit value is: the maximum value between the lower limit value of the SOC protection range and the energy storage backup power SOC.

[0057] In a possible embodiment, the day-ahead optimization scheduling model is a mixed integer linear programming model established with the goal of maximizing system benefits and with dynamic balance constraints, photovoltaic output constraints, energy storage operation constraints, energy storage backup constraints, charging pile constraints, and grid power constraints as constraints;

[0058] The intraday optimization scheduling model is a linear programming model established by a rolling optimization method with the goal of maximizing system benefits and with dynamic balance constraints, photovoltaic output constraints, energy storage operation constraints, energy storage backup constraints, charging pile constraints and grid power constraints as constraints.

[0059] In order to solve the above technical problems, the present application also provides a solar storage and charging energy scheduling system, including: a cloud platform and a coordination controller;

[0060] The cloud platform is in communication connection with the coordination controller, and the cloud platform is used to: predict the energy storage charging and discharging state and the charging pile start and stop state through a pre-established day-ahead optimization scheduling model, and input the energy storage charging and discharging state and the charging pile start and stop state into a pre-established day-ahead optimization scheduling model; obtain energy scheduling instructions through the day-ahead optimization scheduling model; and send the energy scheduling instructions to the coordination controller;

[0061] The coordination controller is communicatively connected to the photovoltaic storage and charging system, and is used to: obtain the operating status data of the photovoltaic storage and charging system at the current moment; modify the energy scheduling command according to the operating status data; and send the modified energy scheduling command to the photovoltaic storage and charging system.

[0062] In order to solve the above technical problems, the present application also provides a solar storage and charging energy scheduling device, including:

[0063] A day-ahead optimization module, used to predict the energy storage charging and discharging state and the charging pile start and stop state through a pre-established day-ahead optimization scheduling model, and input the energy storage charging and discharging state and the charging pile start and stop state into a pre-established intraday optimization scheduling model;

[0064] An intraday optimization module, used to obtain energy scheduling instructions through the intraday optimization scheduling model;

[0065] Operation monitoring module, used to obtain the operation status data of the solar-storage-charging system at the current moment;

[0066] A scheduling correction module, used for correcting the energy scheduling command according to the operating status data;

[0067] The scheduling execution module is used to send the modified energy scheduling command to the solar energy storage and charging system.

[0068] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned solar storage and charging energy scheduling method are implemented.

[0069] The energy scheduling method for the photovoltaic storage and charging system provided in the present application adopts a method that combines day-ahead optimization scheduling and intraday optimization scheduling, and splits the optimization scheduling task into two different models to be implemented at two different times. Among them, the day-ahead optimization scheduling model is mainly used to predict the energy storage charging and discharging status and the charging pile start and stop status of the next day. After the prediction results are transmitted to the intraday optimization scheduling model, the energy scheduling instructions for each scheduling period of the day (that is, the "next day" of the day-ahead optimization scheduling model) are predicted. Therefore, under the premise of limited hardware performance, it is beneficial for each model to shorten the length of the scheduling cycle, that is, to improve the time granularity of the optimization scheduling, and it is also beneficial for the intraday optimization scheduling model to extend the optimization period to one day, thereby realizing the energy scheduling with the maximum benefit of the photovoltaic storage and charging system throughout the day.

[0070] Furthermore, this method is also combined with a real-time scheduling strategy. After obtaining the energy scheduling instructions for the scheduling period at the current moment obtained through predictions before and during the day, the operating data of the photovoltaic storage and charging system at the current moment is also obtained in real time, and the energy scheduling instructions are corrected in real time to overcome the problem that the energy scheduling instructions predicted for a scheduling cycle cannot adapt to the operational safety of the photovoltaic storage and charging system that changes in real time, and it is also beneficial to make the energy scheduling of the photovoltaic storage and charging system closer to the global optimal economic benefit in a true sense.

[0071] In addition, although the real-time scheduling strategy is implemented on the basis of the day-ahead and day-intraday optimization scheduling, it does not rely on the prediction results of the model, but is independently implemented by obtaining the operating status data of the photovoltaic storage and charging system. On the one hand, it can be implemented independently of the day-ahead and day-intraday optimization scheduling, and can be completed by other processors or controllers; on the other hand, the optimization scheduling it implements focuses on the optimization scheduling at the current moment, and does not need to predict future scenarios, nor does it need to establish a complex prediction model like the day-ahead and day-intraday optimization scheduling. It is simple to implement, has low performance requirements for processors or controllers, and is easier to implement.

[0072] In summary, this method provides a multi-layer solar energy storage and charging scheduling method with three time scales: day-ahead, intraday, and real-time. Without imposing higher performance requirements on the hardware, it can shorten the time granularity of the optimized scheduling and extend the length of the optimized period, and can better make the solar energy storage and charging system close to the optimal economic benefits throughout the day.

[0073] The solar storage and charging energy scheduling system, device, and computer-readable storage medium provided in the present application correspond to the above method and have the same effect as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0075] Figure 1 A flow chart of a method for scheduling solar energy storage and charging provided by the present invention;

[0076] Figure 2 A flow chart of intraday rolling optimization provided by the present invention;

[0077] Figure 3 A flow chart of a method for scheduling solar energy storage and charging provided by the present invention;

[0078] Figure 4 A flow chart of a real-time scheduling control provided by the present invention;

[0079] Figure 5 A structural diagram of a solar energy storage and charging scheduling system provided by the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0081] The core of this application is to provide a method, system, device and medium for scheduling solar energy storage and charging.

[0082] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0083] The photovoltaic storage and charging system is a comprehensive system that incorporates the charging problem of electric vehicles into the photovoltaic power generation and energy storage system, including photovoltaic power generation, energy storage backup power and vehicle charging piles. Among the related technologies, there are currently two main solutions for achieving the optimal economic benefits of the photovoltaic storage and charging system: day-ahead optimization scheduling and intraday optimization scheduling.

[0084] Since dynamic electricity prices are usually released by power companies a day in advance, the day-ahead optimization dispatch can focus on this. Combined with the day-ahead photovoltaic power generation forecast data and load demand forecast data, a forecast model with the goal of maximizing the system revenue for the whole day is established, which is also the day-ahead optimization dispatch model. The model then outputs instructions that can be used for the energy dispatch of the photovoltaic storage and charging system for the next day. The energy dispatch instruction generally takes 15 minutes or 1 hour as a dispatch cycle, that is, the optimization dispatch method is to divide the operation of the photovoltaic storage and charging system in a day into multiple dispatch cycles of 15 minutes or 1 hour, and predict each dispatch cycle to obtain the corresponding dispatch instruction. Based on the dispatch instruction, the energy dispatch of the photovoltaic storage and charging system in the corresponding dispatch cycle is realized to achieve the optimal economic benefits of the photovoltaic storage and charging system throughout the day.

[0085] Compared with day-ahead optimization scheduling, intraday optimization scheduling is more timely, and the data that can be obtained and used as the basis for optimization scheduling is closer to the actual operating status of the photovoltaic storage and charging system. In addition, the accuracy of intraday optimization scheduling can be further improved through rolling optimization. However, the disadvantage of this solution is that the calculation is difficult and requires higher computing power of hardware. With the same scheduling cycle of 15 minutes or 1 hour, most of them only consider the maximum economy of ultra-short-term such as 4 hours, and do not consider the optimal economy of the system throughout the day.

[0086] It can be seen from this that no matter what kind of optimization scheduling scheme is currently used, it is far from the purpose of "real-time" optimization scheduling. It is impossible to ensure that the photovoltaic storage and charging system is always in a safe operating state, and the "optimal" effect of the global economic benefits of the photovoltaic storage and charging system achieved by it needs to be further improved.

[0087] Therefore, in order to solve the above problems, the present application provides a method for scheduling solar energy storage and charging, such as Figure 1 As shown, including:

[0088] S1: Predict the energy storage charging and discharging status and the charging pile start and stop status through the pre-established day-ahead optimization scheduling model, and input the energy storage charging and discharging status and the charging pile start and stop status into the pre-established intraday optimization scheduling model.

[0089] S2: Obtain energy scheduling instructions through the intraday optimization scheduling model.

[0090] S3: Obtain the operating status and constraint data of the solar-storage-charging system at the current moment.

[0091] S4: Correct the energy dispatch command according to the operating status data.

[0092] S5: Send the corrected energy scheduling command to the solar storage and charging system.

[0093] It should be noted that the solar energy storage and charging scheduling method provided in this application is an optimization scheduling method for three different time scales: day-ahead, intraday, and real-time. Among them, step S1 corresponds to the time scale of "day-ahead"; step S2 corresponds to the time scale of "intraday"; steps S3 and S4 correspond to the time scale of "real-time"; step S5 corresponds to the execution of the energy scheduling command of the smallest granularity, that is, corresponds to the time scale of "real-time".

[0094] Specifically, if the purpose of executing this method is to complete the global economic optimal scheduling of the photovoltaic storage and charging system on the "same day", then step S1 occurs on the day before the "same day", and the purpose can be achieved by executing it once; if the intraday optimization scheduling adopts a rolling optimization method, and assuming that the scheduling cycle is 15 minutes, then step S2 is performed once every 15 minutes within the "same day" to obtain the energy scheduling instructions of the photovoltaic storage and charging system for the next scheduling cycle at the current moment; and steps S3 and S4 are to correct the energy scheduling instructions obtained in step S2 in "real time".

[0095] Among them, due to the interval limit of data collection in step S3, the interval limit of instruction correction processing in step S4, and the interval limit of sending the corrected instruction to the photovoltaic storage and charging system in step S5, steps S3 to S5 cannot achieve "real time" in the true sense. However, based on the current hardware conditions, steps S3 to S5 can achieve scheduling optimization with a period of 500ms and 1s, which can be regarded as "real time" compared with the scheduling period of 15min and 1h and the optimization period of the photovoltaic storage and charging system in days.

[0096] It should also be noted that, since the intraday optimization scheduling corresponding to step S2 will generate new energy scheduling instructions every scheduling cycle within the "same day", the real-time scheduling corresponding to steps S3 and S4 will change the object of real-time correction every scheduling cycle.

[0097] Afterwards, this embodiment further explains the specific implementation of each step in the above method. First, for step S1, it can be seen from the above that step S1 corresponds to the optimized scheduling of "the day before". In the related technology, the day-ahead optimized scheduling can directly predict the energy scheduling instructions for each scheduling period in the "day". However, in this method, the day-ahead optimized scheduling mainly needs to obtain the energy storage charging and discharging status and the charging pile start and stop status of each scheduling period in the "day". That is, by predicting when the energy storage part of the solar storage and charging system will charge and discharge, and when the charging pile will start to charge the electric vehicle and when it will shut down to stop charging the electric vehicle, the global economic benefits of the solar storage and charging system on the "day" can be optimized.

[0098] As for step S2, it corresponds to the "intra-day" optimization scheduling. In the relevant technology, the intra-day optimization scheduling can realize the task of predicting the energy storage charging and discharging status and the charging pile start and stop status in each scheduling period of the "same day" as in the above step S1. However, in this method, the above prediction has been realized by the day-ahead optimization scheduling, and the intra-day optimization scheduling focuses on the prediction of specific energy scheduling instructions of the photovoltaic storage system, such as the power generation power of the photovoltaic power generation part, the charging and discharging power of the energy storage part (i.e., charging power or discharging power), and the charging power of the charging pile.

[0099] It should be noted that this embodiment does not limit the "energy scheduling instructions" in the above steps to only include: photovoltaic power generation power, energy storage charging and discharging power, and charging pile charging power. However, the above three energy scheduling instructions are the most basic scheduling instructions that can meet the energy scheduling needs of most photovoltaic storage and charging systems in practical applications. Depending on actual needs, more energy scheduling instructions than the above can also be included, and this embodiment does not limit this.

[0100] As can be seen from the above, step S1 and step S2 of this method provide a comprehensive optimization scheduling strategy of "day-ahead + day-intraday" to obtain energy scheduling instructions for the photovoltaic storage and charging system in each scheduling cycle of the day. Specifically, this method splits the complete energy scheduling optimization task; the energy storage charging and discharging state and the charging pile start and stop state that are relatively insensitive to real-time data changes are handed over to the day-ahead optimization scheduling, and the specific energy scheduling commands that are relatively more sensitive to real-time data changes are handed over to the day-ahead optimization scheduling to achieve; on the premise of ensuring the realization and completion of the optimization scheduling task, the energy optimization scheduling task is split into two tasks to be implemented by different optimization models at different times, thereby greatly reducing the computational difficulty of each optimization scheduling process, reducing the requirements for hardware computing power, and being conducive to shortening the time granularity of the scheduling cycle, and expanding the intraday optimization scheduling from the optimal prediction of short-term economic benefits of 4 hours to the optimal economic benefits of the whole day.

[0101] In addition, there are currently a variety of mature technical solutions for implementing the above-mentioned day-ahead optimization scheduling model and intraday optimization scheduling model, and this application does not limit this. However, this embodiment provides a possible implementation plan based on the purpose conditions, constraints, and model types of model establishment:

[0102] The day-ahead optimization dispatch model is a mixed integer linear programming model established with the goal of maximizing system revenue and with dynamic balance constraints, photovoltaic output constraints, energy storage operation constraints, energy storage backup constraints, charging pile constraints and grid power constraints as constraints.

[0103] The intraday optimization scheduling model is a linear programming model established by rolling optimization with the goal of maximizing system benefits and with dynamic balance constraints, photovoltaic output constraints, energy storage operation constraints, energy storage backup constraints, charging pile constraints and grid power constraints as constraints.

[0104] Specifically, the objective function with the goal of maximizing system benefits is shown in the following formula (1):

[0105]

[0106] Among them, maxf means the maximum system benefit; c sell，t 、c buy，t are the unit prices of electricity sold to and purchased from the grid during dispatch period t; P sell，t , P buy，t The power sold to the grid and purchased from the grid respectively; N T , Δt are the total number of scheduling periods and time intervals respectively.

[0107] The dynamic balance constraint is shown in equation (2):

[0108]

[0109] Among them, P pv，t is the photovoltaic power generation power in the scheduling period t; P dis，t , P cha，t are the energy storage discharge and charging power during the scheduling period t; P ev，t is the charging power of the charging pile during the scheduling period t (i.e., the charging power of the electric vehicle); is the load demand power forecast value in the scheduling period t.

[0110] The photovoltaic output constraint is shown in equation (3):

[0111]

[0112] in, is the predicted value of photovoltaic power generation in period t.

[0113] The energy storage operation constraints are shown in equations (4) to (9):

[0114] 0 <P dis，t ≤A dis，t P dis,max (4);

[0115] 0 <P cha，t ≤A cha，t P cha，max (5);

[0116] A dis，t +A cha，t ≤1 (6);

[0117] SOC min ≤SOC t ≤SOC max (7);

[0118]

[0119]

[0120] Among them, A dis，t , A cha，t are the discharge and charge states of energy storage during the t scheduling period, respectively, and are 0-1 variables; P dis，max , P cha，max are the maximum discharge and charging power of energy storage during the scheduling period t; SOC t is the remaining energy storage capacity during the scheduling period t; SOC min , SOC max are the lower and upper limits of the state of charge (SOC) protection range respectively; η cha , η disare the energy storage charging and discharging efficiencies respectively; E is the rated capacity of the energy storage; SOC0, They are the first and last moment SOC respectively.

[0121] Equations (4) to (6) and (7) to (9) are the constraints related to energy storage charging and discharging power and SOC, respectively.

[0122] The energy storage backup power constraint is shown in the following formula (10):

[0123] SOC t ≥SOC bac，t (10);

[0124] Among them, SOC bac，t is the energy storage backup power SOC during the scheduling period t.

[0125] The charging pile constraints are shown in equations (11) to (13):

[0126] A ev，t P ev，min ≤P ev，t ≤A ev，t P ev，max (11);

[0127]

[0128] SOC ev，t ≤SOC ev，max (13);

[0129] Among them, A ev，t The charging pile is turned on or off, which is also a 0-1 variable; P ev，min , P ev，max They are the minimum charging power and maximum charging power of the charging pile (or the minimum charging power and maximum charging power of the electric vehicle); SOC ev，t E is the remaining power of the electric vehicle during the dispatching period t; ev SOC is the rated capacity of electric vehicles; ev，max It is the upper limit of SOC protection for electric vehicles.

[0130] The grid power constraints are shown in equations (14) and (15):

[0131] 0≤P buy，t ≤P buy，max (14);

[0132] 0≤P sell，t ≤P sell,max (15);

[0133] Among them, P buy，max is the maximum power purchased from the power grid, that is, the maximum demand / capacity value, called the power purchase threshold; Psell,max The maximum power sold to the grid, that is, the reverse flow prevention target value, is called the power selling threshold.

[0134] Based on the above objective function and constraints, a mixed integer linear programming model is established to obtain the day-ahead optimization model in this embodiment. The model can be solved by calling a solver to obtain the energy storage charging and discharging state and the charging pile start and stop state.

[0135] Similarly, based on the above objective function and constraints, a linear programming model is established to obtain the intraday optimization model in this embodiment. However, it should be noted that the intraday optimization model in this embodiment adopts a rolling optimization method, that is, the time scale is the next scheduling period at the current time to the last scheduling period of the day. The specific rolling process is as follows Figure 2 As shown, assuming that the current time belongs to the scheduling period t, the next scheduling period is t+1; the scheduling period is 15 minutes, and there are 96 scheduling periods in the whole day; the optimization period corresponding to the intraday optimization scheduling at the current time is t+1~96, and the predicted scheduling instruction corresponds to the scheduling period t+1.

[0136] In addition, since the energy storage charging and discharging status and the charging pile start and stop status have been determined a few days ago, the decision variables of the intraday rolling optimization scheduling model are the energy storage charging and discharging power, photovoltaic power generation power and electric vehicle charging power. This model is a linear programming model and can also be directly called to solve the solver. The rolling optimization method provided in this embodiment can further make the energy scheduling of the photovoltaic storage and charging system close to the economic optimum throughout the day.

[0137] Afterwards, after obtaining the energy scheduling instruction corresponding to any scheduling cycle, steps S3 and S4 perform real-time correction on the energy scheduling instruction in combination with the operating status data of the photovoltaic storage and charging system at the current moment, so as to obtain the control instruction finally used for the energy scheduling of the photovoltaic storage and charging system. It should be noted that the operating status and constraint data of the photovoltaic storage and charging system obtained in step S3 are data that characterize the current operating status of the photovoltaic storage and charging system, or contain the constraint conditions that need to be followed when energy scheduling is performed on the photovoltaic storage and charging system. This embodiment does not limit the specific types of data contained in the operating status and constraint data, which should be determined according to the actual correction needs in step S4. Similarly, this embodiment also does not limit the correction strategy adopted for the energy scheduling instruction in step S4, which can be determined according to the different energy scheduling needs of the photovoltaic storage and charging system in the actual scenario.

[0138] Exemplarily, this embodiment provides a possible correction strategy and its corresponding operating state and constraint data implementation plan, and the energy scheduling instructions for correction in this embodiment include: photovoltaic power generation power P pv，opt , energy storage charging and discharging power P es，opt and charging pile charging power Pev，opt .

[0139] It is particularly important to note that, as can be seen from the above, the photovoltaic power generation power, energy storage charging and discharging power, and charging pile charging power obtained by the intraday optimization scheduling model are all prediction results for the next scheduling period (corresponding to the t+1 scheduling period) at the predicted time (assuming it is the t scheduling period). However, when it comes to the real-time correction of steps S3 to S5, that is, when the energy scheduling command needs to be executed, that is, the current moment when S3 to S5 is executed belongs to the t+1 scheduling period. Therefore, in steps S3 to S5, the t+1 scheduling period is used as the scheduling period corresponding to the current moment, and then the above P pv，opt , P es，opt and P ev，opt are all energy scheduling instructions for the t+1 scheduling period, that is, pv，t+1 =P pv，opt , P es，t+1 =P es，opt , P ev，t+1 =P ev，opt .

[0140] The operating status and constraint data include: the real-time value of the load demand power corresponding to the scheduling period at the current moment.

[0141] Step S4: Correcting the energy dispatch command according to the operation status data specifically includes:

[0142] S41: Determine the current buying and selling power according to the real-time values ​​of photovoltaic power generation, energy storage charging and discharging power, charging pile charging power and load demand power.

[0143] S42: If the buying and selling power is greater than the preset selling power threshold, one or more of downward correction of photovoltaic power generation power, downward correction of energy storage charging and discharging power, and upward correction of charging pile charging power is executed.

[0144] S43: If the buying and selling power is less than the opposite of the preset buying power threshold, one or more of upward correction of photovoltaic power generation power, upward correction of energy storage charging and discharging power, and downward correction of charging pile charging power is executed.

[0145] The purpose of step S41 is to check whether the power purchased from the power grid and sold to the power grid exceeds the limit.

[0146] -P buy,max ≤P pv，t+1 +P es,t+1 -P ev，t+1 -P load，t ≤P sell,max (16);

[0147] Among them, the buying and selling power is P pv，t+1+P es，t+1 -P ev，t+1 -Pl oad，t , where P load，t is the real-time value of load demand power. When the buy and sell power is positive, it means selling power, and when it is negative, it means buying power. The buying power threshold P buy,max The negative sign before is the dimension that unifies buying and selling electricity. If selling electricity is the positive direction, buying electricity corresponds to the negative direction. The buying threshold in equation (14) is a positive value, so the buying threshold P here is buy,max A negative sign is required before the formula. Similarly, the positive sign of each parameter before the formula indicates that the charging and discharging direction corresponding to the parameter is discharge, and the negative sign indicates that the charging and discharging direction corresponding to the parameter is charge.

[0148] When the buying and selling power is greater than the preset selling power threshold, it means that photovoltaic power generation and energy storage still have surplus while meeting the charging and load consumption of electric vehicles. At this time, the overall discharge power can be increased or the overall charging power can be reduced for optimized scheduling, which corresponds to step S42. Similarly, when the buying and selling power is less than the preset buying power threshold, it means that photovoltaic power generation and energy storage cannot meet the charging and load consumption of electric vehicles. At this time, the overall charging power can be increased or the overall discharge power can be reduced for optimized scheduling, which corresponds to step S43. That is, this embodiment provides an optimized scheduling correction control based on the demand of the light storage and charging system, so that the benefits of the light storage and charging system are closer to the optimal throughout the day.

[0149] Furthermore, for the situation in step S42, this embodiment also provides a possible specific correction scheme, and the operating status and constraint data also include: an energy storage charging power threshold corresponding to the current scheduling period.

[0150] The above step S42 specifically includes:

[0151] S42-1: Determine the first target correction value based on the photovoltaic power generation power, energy storage charging and discharging power, load demand power real-time value and power selling threshold.

[0152] The first target correction value, which is also the target value of the charging power of the charging pile for this correction, is:

[0153] P ev，t+1 =P pv，t+1 +P es，t+1 -P load，t -P sell,max (16);

[0154] The right side of the equal sign in formula (16) also represents the current corrected photovoltaic power generation power P ev，t+1 , which is the first target correction value mentioned above.

[0155] S42-2: Determine whether the first target correction value is greater than the preset charging power threshold of the charging pile; if not, go to step S42-3; if so, go to step S42-4.

[0156] That is:

[0157] P ev，t+1 ≤P ev，max (17);

[0158] If satisfied, proceed to step S42-3; otherwise, proceed to step S42-4.

[0159] S42-3: Correct the charging power of the charging pile to the first target correction value, and end the correction of the energy scheduling command.

[0160] S42-4: Correct the charging power of the charging pile to the discharge threshold of the charging pile.

[0161] That is:

[0162] P ev，t+1 =P ev，max (18);

[0163] S42-5: Determine the second target correction value based on the photovoltaic power generation power, the charging pile charging power, the real-time value of the load demand power and the power selling threshold.

[0164] The second target correction value, i.e. the target value of the charging power of the charging pile for this correction, is:

[0165] P es，t+1 =P ev，t+1 +P load，t +P sell,max -P pv，t+1 (19);

[0166] The right side of the equal sign in formula (19) represents the current correction of the charging power of the charging pile P es，t+1 , which is the second target correction value mentioned above.

[0167] S42-6: Determine whether the second target correction value is less than the inverse of the energy storage charging power threshold; if not, go to step S42-7; if yes, go to step S42-8.

[0168] That is:

[0169] P es，t+1 ≥-P cha，max (20);

[0170] If satisfied, proceed to step S42-7, otherwise, proceed to step S42-8.

[0171] S42-7: Correct the energy storage charging and discharging power to the second target correction value, and end the correction of the energy scheduling command.

[0172] S42-8: Correct the energy storage charging and discharging power to the opposite of the energy storage charging power threshold.

[0173] That is:

[0174] P es，t+1 =-P cha，max (twenty one);

[0175] S42-9: Determine a third target correction value based on the energy storage charging and discharging power, the charging pile charging power, the real-time value of the load demand power and the power selling threshold.

[0176] The third target correction value, which is also the target value of this correction of photovoltaic power generation, is:

[0177] P pv，t+1 =P ev，t+1 +P load，t +P sell,max -P es,t+1 (twenty two);

[0178] The right side of the equal sign in formula (22) represents the current corrected photovoltaic power generation power P pv，t+1 , which is the third target correction value mentioned above.

[0179] S42-10: Correct the photovoltaic power generation power to a third target correction value.

[0180] That is, in this embodiment, when entering the branch corresponding to step S42, the charging power of the charging pile is increased first. If the requirements of step S41 can be met, there is no need to correct the energy storage charging and discharging power and photovoltaic power generation power; if they cannot be met, the energy storage output is reduced first. If the reduced energy storage output can meet the requirements of step S41, there is no need to correct the photovoltaic power generation power; if it still cannot be met, the photovoltaic power generation power is finally corrected to meet the requirements of step S41. This embodiment corresponds to a real-time scheduling requirement for anti-backflow control, which can effectively avoid the backflow problem in the photovoltaic storage and charging system.

[0181] Furthermore, for the situation in step S43, this embodiment also provides a possible specific correction scheme, and the operating status and constraint data also include: the maximum photovoltaic output value and the energy storage discharge power threshold corresponding to the current scheduling period.

[0182] The above step S43 specifically includes:

[0183] S43-1: Determine a fourth target correction value based on the energy storage charging and discharging power, the charging pile charging power, the real-time value of the load demand power and the power purchase threshold.

[0184] The fourth target correction value, which is also the target value of the photovoltaic power generation power correction this time, is:

[0185] P pv，t+1 =P ev，t+1 +P load，t -P buy,max -P es，t+1 (twenty three);

[0186] The right side of the equal sign in formula (23) represents the current corrected photovoltaic power generation power P pv，t+1 , which is the fourth target correction value mentioned above.

[0187] S43-2: Determine whether the fourth target correction value is greater than the maximum photovoltaic output value; if not, go to step S43-3; if so, go to step S43-4.

[0188] That is:

[0189] P pv，t+1 ≤P mppt,t (twenty four);

[0190] Among them, P mppt，t The maximum output of the photovoltaic power generation part during the t scheduling period when the maximum power point tracking (MPPT) is used. If satisfied, proceed to step S43-3, otherwise proceed to step S43-4.

[0191] S43-3: Correct the photovoltaic power generation power to the fourth target correction value, and end the correction of the energy scheduling command.

[0192] S43-4: Correct the photovoltaic power generation power to the maximum photovoltaic output value.

[0193] That is:

[0194] P pv，t+1 =P mppt,t (25);

[0195] S43-5: Determine the fifth target correction value based on the photovoltaic power generation power, the charging pile charging power, the load demand power real-time value and the power purchase threshold.

[0196] The fifth target correction value, i.e. the target value of this correction of energy storage charging and discharging power, is:

[0197] P es，t+1 =P ev，t+1 +P load，t -P buy，max -P pv，t+1 (26);

[0198] The right side of the equal sign in formula (26) represents the current correction of the energy storage charging and discharging power P es，t+1 , which is the fifth target correction value mentioned above.

[0199] S43-6: Determine whether the fifth target correction value is greater than the energy storage discharge power threshold; if not, go to step S43-7; if yes, go to step S43-8.

[0200] That is:

[0201] P es，t+1 ≤P dis，max (27);

[0202] If satisfied, proceed to step S43-7, otherwise, proceed to step S43-8.

[0203] S43-7: Correct the energy storage charging and discharging power to the fifth target correction value, and end the correction of the energy scheduling command.

[0204] S43-8: Correct the energy storage charging and discharging power to the energy storage discharging power threshold.

[0205] That is:

[0206] P es，t+1 =P dis，max (28);

[0207] S43-9: Determine the sixth target correction value based on the photovoltaic power generation power, energy storage charging and discharging power, load demand power real-time value and power purchase threshold.

[0208] The sixth target correction value, i.e., the target value of the charging power of the charging pile for this correction, is:

[0209] P ev，t+1 =P pv，t+1 +P es，t+1 +P buy，max -P load，t (29);

[0210] The right side of the equal sign in formula (29) represents the current correction of the charging power of the charging pile P ev，t+1 , which is the sixth target correction value mentioned above.

[0211] S43-10: Determine whether the sixth target correction value is less than 0; if not, go to step S43-11; if so, go to step S43-12.

[0212] That is:

[0213] P ev，t+1 ≥0 (30);

[0214] If satisfied, then go to step S43-11, otherwise, it means P buy,max If the setting is unreasonable, go to step S43-12.

[0215] S43-11: Correct the charging power of the charging pile to the sixth target correction value, and end the correction of the energy scheduling command.

[0216] S43-12: Correct the charging power of the charging pile to 0.

[0217] That is:

[0218] P ev，t+1 =0 (31);

[0219] The purpose of implementing a scheduling strategy provided in this embodiment is to achieve dynamic capacity expansion when the photovoltaic and energy storage parts in the solar-storage-charging system cannot meet the charging and load loss of electric vehicles, so as to further optimize energy scheduling.

[0220] On the other hand, based on the demand control strategy provided in the above embodiments, and even further the anti-backflow control strategy and dynamic expansion strategy, this embodiment also provides a real-time scheduling strategy, and the operating status and constraint data also include: the maximum photovoltaic output value corresponding to the current scheduling period.

[0221] Then before step S41, the following steps are also included:

[0222] S40-1: Determine whether the remaining energy storage capacity in the current scheduling period is within the preset SOC protection range; if not, go to step S40-2; if so, go to step S40-3.

[0223] That is, this step corresponds to the above-mentioned SOC constraint, that is, corresponds to the judgment of formula (7).

[0224] S40-2: Correct the energy storage charging and discharging power to 0.

[0225] S40-3: Determine whether the remaining power of the electric vehicle in the current scheduling period is greater than the preset electric vehicle SOC protection upper limit value; if so, go to step S40-4; if not, go to step S40-5.

[0226] That is, there is a judgment as in the above formula (13).

[0227] S40-4: Correct the charging power of the charging pile to 0.

[0228] S40-5: Determine whether the current photovoltaic power generation power is greater than the maximum photovoltaic output value; if so, go to step S40-6.

[0229] That is, the judgment is as given in the above formula (24).

[0230] S40-6: Correct the photovoltaic power generation power to the maximum photovoltaic output value.

[0231] As can be seen from the above, the real-time scheduling strategy provided by this embodiment is determined based on the constraints that the above-mentioned photovoltaic storage and charging system must comply with during energy scheduling. It mainly corresponds to the SOC constraint conditions, that is, this embodiment provides an SOC protection strategy to ensure that the energy scheduling instructions can be modified in real time to adapt to the changing safe operation needs of the photovoltaic storage and charging system.

[0232] Furthermore, this embodiment also provides a real-time scheduling strategy based on the SOC protection strategy provided above, and the operating status and constraint data also include: the energy storage backup power SOC corresponding to the current scheduling period.

[0233] Then step S40-1 specifically includes:

[0234] Determine whether the remaining energy storage capacity in the current scheduling period is greater than or equal to the lower limit of the energy storage backup power and less than or equal to the upper limit of the SOC protection range.

[0235] The lower limit of the energy storage backup power is: the lower limit of the SOC protection range and the maximum value of the energy storage backup power SOC.

[0236] That is:

[0237] max(SOC min , SOC bac，t )≤SOC t ≤SOC max (32);

[0238] Among them, max(SOC min , SOC bac，t ) represents the maximum value between the lower limit value of the SOC protection range and the energy storage backup power SOC during the scheduling period t.

[0239] This embodiment provides a real-time scheduling strategy from the perspective of energy storage backup, which aims to ensure the backup power needs of the energy storage system on the basis of SOC protection, so that the energy scheduling needs of the solar storage and charging system can better meet actual requirements.

[0240] As can be seen from the above, this application provides a specific implementation scheme for day-ahead optimization scheduling and intraday optimization scheduling, and also provides possible implementation schemes for multiple real-time scheduling strategies. In one possible implementation scheme, the technical scheme including all the above implementation schemes is as follows: Figure 3 The optimization process architecture of solar energy storage and charging scheduling is shown in Figure 4 The real-time scheduling control flow chart shown.

[0241] In summary, the present application provides a multi-layered photovoltaic storage and charging energy scheduling method with triple time scales of day-ahead, intraday and real-time. Without imposing higher performance requirements on the hardware, it can shorten the time granularity of optimized scheduling and extend the length of the optimized period, and can better enable the photovoltaic storage and charging system to approach the optimal economic benefits throughout the day.

[0242] In the above embodiment, a method for scheduling a photovoltaic energy storage and charging is described in detail. The present application also provides an embodiment corresponding to a photovoltaic energy storage and charging scheduling system. Figure 5 As shown, a solar energy storage and charging scheduling system includes: a cloud platform 1 and a coordination controller 2;

[0243] The cloud platform 1 is communicatively connected to the coordination controller 2, and the cloud platform 1 is used to: predict the energy storage charging and discharging status and the charging pile start and stop status through a pre-established day-ahead optimization scheduling model, and input the energy storage charging and discharging status and the charging pile start and stop status into a pre-established intraday optimization scheduling model; obtain energy scheduling instructions through the intraday optimization scheduling model; and send the energy scheduling instructions to the coordination controller.

[0244] The coordination controller 2 is in communication connection with the photovoltaic storage and charging system 3, and the coordination controller 2 is used to: obtain the operating status data of the photovoltaic storage and charging system 3 at the current moment; modify the energy scheduling command according to the operating status data; and send the modified energy scheduling command to the photovoltaic storage and charging system 3.

[0245] That is, in the photovoltaic storage and charging energy scheduling system provided in this embodiment, there are three layers of architecture including the photovoltaic storage and charging system as the controlled object. The cloud platform layer is used to realize the optimal scheduling of the day before and within the day, obtain the energy scheduling instructions and transmit them to the coordination controller layer. The coordination controller layer is used to realize the real-time correction of the energy scheduling instructions, that is, to implement various real-time scheduling strategies provided in the above method embodiments. Finally, the corrected energy scheduling instructions are transmitted to the local photovoltaic storage and charging system for execution, completing the energy optimization scheduling of the photovoltaic storage and charging system.

[0246] It can be seen that in addition to the technical effects that the above methods can bring, in this system, the optimization scheduling of the day-ahead + day-intraday is separated from the real-time scheduling. The two are implemented independently to avoid interference, which also reduces the requirements for hardware performance. This modular design is easier to implement and easier to expand. For example, if the cloud platform can meet the computing requirements, it can be expanded to support the calculation of energy scheduling instructions for multiple sets of photovoltaic storage and charging systems at the same time, and the coordination controller generally corresponds one-to-one with the local photovoltaic storage and charging system.

[0247] In the above embodiments, a method and system for scheduling a photovoltaic energy storage and charging are described in detail, and the present application also provides an embodiment corresponding to a photovoltaic energy storage and charging scheduling device. It should be noted that the present application describes the embodiments of the device part from two perspectives, one is based on the perspective of functional modules, and the other is based on the perspective of hardware.

[0248] Based on the perspective of functional modules, this embodiment provides a solar energy storage and charging scheduling device, including:

[0249] The day-ahead optimization module is used to predict the energy storage charging and discharging status and the charging pile start and stop status through the pre-established day-ahead optimization scheduling model, and input the energy storage charging and discharging status and the charging pile start and stop status into the pre-established intraday optimization scheduling model;

[0250] Intraday optimization module, used to obtain energy scheduling instructions through intraday optimization scheduling model;

[0251] Operation monitoring module, used to obtain the operation status data of the solar-storage-charging system at the current moment;

[0252] A scheduling correction module, used to correct the energy scheduling command according to the operation status data;

[0253] The scheduling execution module is used to send the corrected energy scheduling command to the solar storage and charging system.

[0254] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, which will not be repeated here.

[0255] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps recorded in the above method embodiment are implemented.

[0256] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0257] The above is a detailed introduction to a method, system, device and medium for scheduling solar energy storage and charging provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the embodiments can refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can refer to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the present application.

[0258] It should also be noted that, in this specification, 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 "comprise", "include" 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, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

Claims

1. A method for scheduling solar energy storage and charging, characterized in that: include: Predicting the energy storage charging and discharging state and the charging pile start and stop state through a pre-established day-ahead optimization scheduling model, and inputting the energy storage charging and discharging state and the charging pile start and stop state into a pre-established intraday optimization scheduling model; Obtaining energy scheduling instructions through the intraday optimization scheduling model; Obtain the operating status and constraint data of the solar-storage-charging system at the current moment; modifying the energy dispatch command according to the operating status data; The modified energy scheduling command is sent to the solar energy storage and charging system.

2. The method for scheduling solar energy storage and charging according to claim 1, characterized in that: The energy scheduling command includes: photovoltaic power generation power, energy storage charging and discharging power, and charging pile charging power; The operating status and constraint data include: the real-time value of the load demand power corresponding to the scheduling period at the current moment; Modifying the energy dispatch command according to the operating status data includes: Determine the current buying and selling power according to the photovoltaic power generation power, the energy storage charging and discharging power, the charging pile charging power and the load demand power real-time value; If the buying and selling power is greater than the preset selling power threshold, one or more of downwardly correcting the photovoltaic power generation power, downwardly correcting the energy storage charging and discharging power, and upwardly correcting the charging pile charging power are executed; If the buying and selling power is less than the opposite of the preset buying power threshold, one or more of upward correction of the photovoltaic power generation power, upward correction of the energy storage charging and discharging power, and downward correction of the charging pile charging power are executed.

3. The method for scheduling solar energy storage and charging according to claim 2, characterized in that: The operating status and constraint data also include: the energy storage charging power threshold corresponding to the current scheduling period; If the buying and selling power is greater than the preset selling power threshold, then performing one or more of downwardly correcting the photovoltaic power generation power, downwardly correcting the energy storage charging and discharging power, and upwardly correcting the charging pile charging power includes: Determining the first target correction value according to the photovoltaic power generation power, the energy storage charging and discharging power, the real-time value of the load demand power and the power selling threshold; Determining whether the first target correction value is greater than a preset charging pile charging power threshold; If not, the charging power of the charging pile is corrected to the first target correction value, and the correction of the energy scheduling command is terminated; If yes, the charging power of the charging pile is corrected to the charging power threshold of the charging pile; Determining the second target correction value according to the photovoltaic power generation power, the charging pile charging power, the load demand power real-time value and the power selling threshold; Determining whether the second target correction value is less than the inverse of the energy storage charging power threshold; If not, the energy storage charging and discharging power is corrected to the second target correction value, and the correction of the energy scheduling command is terminated; If yes, the energy storage charging and discharging power is corrected to the opposite of the energy storage charging power threshold; Determining the third target correction value according to the energy storage charging and discharging power, the charging pile charging power, the load demand power real-time value and the power selling threshold; The photovoltaic power generation power is corrected to the third target correction value.

4. The method for scheduling solar energy storage and charging according to claim 2, characterized in that: The operating status and constraint data also include: the maximum photovoltaic output value and energy storage discharge power threshold corresponding to the current scheduling period; If the buying and selling power is less than the opposite of the preset buying power threshold, then one or more of upwardly correcting the photovoltaic power generation power, upwardly correcting the energy storage charging and discharging power, and downwardly correcting the charging pile charging power includes: Determining the fourth target correction value according to the energy storage charging and discharging power, the charging pile charging power, the load demand power real-time value and the electricity purchase threshold; Determining whether the fourth target correction value is greater than the photovoltaic maximum output value; If not, the photovoltaic power generation power is corrected to the fourth target correction value, and the correction of the energy scheduling command is terminated; If yes, the photovoltaic power generation power is corrected to the photovoltaic maximum output value; Determining the fifth target correction value according to the photovoltaic power generation power, the charging pile charging power, the load demand power real-time value and the power purchase threshold; Determining whether the fifth target correction value is greater than the energy storage discharge power threshold; If not, the energy storage charging and discharging power is corrected to the fifth target correction value, and the correction of the energy scheduling command is terminated; If yes, the energy storage charging and discharging power is corrected to the energy storage discharging power threshold; Determining the sixth target correction value according to the photovoltaic power generation power, the energy storage charging and discharging power, the load demand power real-time value and the power purchase threshold; Determining whether the sixth target correction value is less than 0; If not, the charging power of the charging pile is corrected to the sixth target correction value, and the correction of the energy scheduling command is terminated; If so, the charging power of the charging pile is corrected to 0.

5. The method for scheduling solar energy storage and charging according to claim 2, characterized in that: The operating status and constraint data also include: the maximum photovoltaic output value corresponding to the current scheduling period; Before determining the current buying and selling power according to the photovoltaic power generation power, the energy storage charging and discharging power, the charging pile charging power and the load demand power real-time value, it also includes: Determine whether the remaining energy storage capacity during the current scheduling period is within a preset SOC protection range; If not, the energy storage charging and discharging power is corrected to 0; Determine whether the remaining power of the electric vehicle during the current scheduling period is greater than a preset electric vehicle SOC protection upper limit value; If yes, the charging power of the charging pile is corrected to 0; Determine whether the current photovoltaic power generation power is greater than the photovoltaic maximum output value; If so, the photovoltaic power generation power is corrected to the photovoltaic maximum output value.

6. The method for scheduling solar energy storage and charging according to claim 5, characterized in that: The operating status and constraint data also include: the energy storage backup power SOC corresponding to the current scheduling period; The determining whether the remaining energy storage capacity in the current scheduling period is within a preset SOC protection range includes: Determine whether the remaining energy storage capacity in the current scheduling period is greater than or equal to the lower limit of the energy storage backup power and less than or equal to the upper limit of the SOC protection range; The energy storage backup power lower limit value is: the maximum value between the lower limit value of the SOC protection range and the energy storage backup power SOC.

7. The method for scheduling solar energy storage and charging according to any one of claims 1 to 6, characterized in that: The day-ahead optimization scheduling model is a mixed integer linear programming model established with the goal of maximizing system benefits and with dynamic balance constraints, photovoltaic output constraints, energy storage operation constraints, energy storage backup constraints, charging pile constraints and grid power constraints as constraints. The intraday optimization scheduling model is a linear programming model established by a rolling optimization method with the goal of maximizing system benefits and with dynamic balance constraints, photovoltaic output constraints, energy storage operation constraints, energy storage backup constraints, charging pile constraints and grid power constraints as constraints.

8. A solar energy storage and charging scheduling system, characterized in that: include: Cloud platform and orchestration controller; The cloud platform is in communication connection with the coordination controller, and the cloud platform is used to: predict the energy storage charging and discharging state and the charging pile start and stop state through a pre-established day-ahead optimization scheduling model, and input the energy storage charging and discharging state and the charging pile start and stop state into a pre-established day-ahead optimization scheduling model; obtain energy scheduling instructions through the day-ahead optimization scheduling model; and send the energy scheduling instructions to the coordination controller; The coordination controller is communicatively connected to the photovoltaic storage and charging system, and is used to: obtain the operating status data of the photovoltaic storage and charging system at the current moment; modify the energy scheduling command according to the operating status data; and send the modified energy scheduling command to the photovoltaic storage and charging system.

9. A light storage and charging energy scheduling device, characterized in that: include: A day-ahead optimization module, used to predict the energy storage charging and discharging state and the charging pile start and stop state through a pre-established day-ahead optimization scheduling model, and input the energy storage charging and discharging state and the charging pile start and stop state into a pre-established intraday optimization scheduling model; An intraday optimization module, used to obtain energy scheduling instructions through the intraday optimization scheduling model; Operation monitoring module, used to obtain the operation status data of the solar-storage-charging system at the current moment; A scheduling correction module, used for correcting the energy scheduling command according to the operating status data; The scheduling execution module is used to send the modified energy scheduling command to the solar energy storage and charging system.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the photovoltaic storage and charging energy scheduling method as described in any one of claims 1 to 7 are implemented.

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