Resource scheduling method and device applied to optical storage and charging system and electronic equipment

Abstract

This application provides a resource scheduling method, apparatus, and electronic device for photovoltaic-storage-charging systems, relating to the field of power dispatching technology. The method includes: responding to a resource scheduling instruction issued by the power grid and determining a preset future time period indicated by the resource scheduling instruction; then obtaining a preset load baseline for the photovoltaic-storage-charging system within the preset future time period and inputting the preset load baseline into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model; further, using a preset algorithm to solve the multi-objective optimization model to obtain the resource scheduling result of the photovoltaic-storage-charging system within the preset future time period. The method of this application can improve the effectiveness of resource scheduling in photovoltaic-storage-charging systems.

Description

Technical Field

[0001] This application relates to the field of power dispatching technology, and in particular to a resource dispatching method, apparatus and electronic equipment applied to a photovoltaic-storage-charging system. Background Technology

[0002] Driven by the strategic goals of "carbon peaking" and "carbon neutrality," the large-scale application of distributed energy and electric vehicles has become a key direction. Among them, how to rationally schedule the resources in the photovoltaic-storage-charging system to participate in grid demand response, as a coupling carrier between new energy and load, has become a major research issue for those skilled in the art.

[0003] In existing technologies, resource scheduling for photovoltaic-storage-charging systems mainly relies on optimization strategies for single resources or simple combinations based on empirical rules. For example, some existing technologies achieve local optimization by fixing the photovoltaic output ratio, energy storage charging and discharging strategies, or charging pile load allocation rules. Furthermore, existing technologies typically employ a single objective function, such as minimizing cost or maximizing revenue, to achieve resource scheduling for photovoltaic-storage systems.

[0004] However, existing technologies neglect the coupling relationship and dynamic response characteristics among the resources of photovoltaic, energy storage and charging systems, and lack a quantitative model of the overall participation of photovoltaic, energy storage and charging systems in grid demand response capabilities. This results in a lack of scientific basis for resource scheduling of photovoltaic, energy storage and charging systems, which in turn affects the effectiveness of resource scheduling of photovoltaic, energy storage and charging systems. Summary of the Invention

[0005] This application provides a resource scheduling method, apparatus, and electronic device for photovoltaic storage and charging systems, in order to solve the technical problem of low efficiency in resource scheduling of existing photovoltaic storage and charging systems.

[0006] Firstly, this application provides a resource scheduling method for a photovoltaic energy storage and charging system, comprising:

[0007] In response to a resource scheduling instruction issued by the power grid, a preset future time period indicated by the resource scheduling instruction is determined; wherein, the resource scheduling instruction represents the power grid's demand for adjusting the discharge behavior of the photovoltaic-storage-charging system within the preset future time period;

[0008] Obtain a preset load baseline for the photovoltaic storage and charging system within the preset future time period; wherein, the preset load baseline represents the predicted discharge behavior of the photovoltaic storage and charging system within the preset future time period without responding to the resource scheduling instruction;

[0009] The preset load baseline is input into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model; a preset algorithm is used to solve the multi-objective optimization model to obtain the resource scheduling result of the photovoltaic storage and charging system within the preset future time period; wherein, the demand response model represents the resource scheduling decision made by the photovoltaic storage and charging system in response to the resource scheduling instruction.

[0010] In one possible design, the photovoltaic-energy storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit;

[0011] The preset load baseline is input into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model, including:

[0012] Obtain a pre-built demand response model for the photovoltaic storage and charging system, and obtain a pre-built system revenue model for the photovoltaic storage and charging system;

[0013] By integrating the preset demand response model and the preset system revenue model, and setting optimization objectives, a multi-objective optimization model for the photovoltaic storage and charging system is constructed; the preset load baseline is input into the multi-objective optimization model.

[0014] In one possible design, obtaining a pre-built demand response model for the optical storage and charging system includes:

[0015] Determine the first discharge power of the photovoltaic unit, the second discharge power of the energy storage unit, and the third discharge power of the charging pile unit within the preset future time period;

[0016] Based on the first discharge power, the second discharge power, and the third discharge power, a preset demand response model for the photovoltaic energy storage and charging system is generated.

[0017] In one possible design, obtaining a pre-built system benefit model for the optical storage and charging system includes:

[0018] Determine the first discharge power adjustment value of the photovoltaic unit and the second discharge power adjustment value of the energy storage unit within the preset future time period;

[0019] The net revenue from charging services of the charging pile unit and the power compensation unit price of the power grid are obtained within the preset future time period; wherein, the power compensation unit price represents the service unit price paid by the power grid for regulating the discharge behavior of the photovoltaic-storage-charging system.

[0020] Based on the first discharge power adjustment value, the second discharge power adjustment value, the net revenue of the charging service, and the unit price of the power compensation, a preset system revenue model for the photovoltaic energy storage and charging system is generated.

[0021] In one possible design, after generating a multi-objective optimization model for the optical storage and charging system, the method further includes:

[0022] Set working constraints for the photovoltaic unit, energy storage unit, and charging pile unit for the multi-objective optimization model.

[0023] In one possible design, a preset algorithm is used to solve the multi-objective optimization model, including:

[0024] The multi-objective optimization model is solved using a pre-defined multi-objective optimization algorithm;

[0025] Alternatively, a pre-defined multi-objective ant colony optimization algorithm can be used to solve the multi-objective optimization model.

[0026] In one possible design, the photovoltaic-energy storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit;

[0027] The resource scheduling results include the following:

[0028] Within the preset future time period, the first adjusted discharge power of the photovoltaic unit, the second adjusted discharge power of the energy storage unit, the third adjusted discharge power of the charging pile unit, the charging unit price of the charging pile unit, and the revenue of the photovoltaic-energy storage-charging system.

[0029] Secondly, this application provides a resource scheduling device for a photovoltaic energy storage and charging system, comprising:

[0030] The determination module is used to determine the preset future time period indicated by the resource scheduling instruction issued by the power grid in response to the resource scheduling instruction; wherein the resource scheduling instruction represents the power grid's demand for adjusting the discharge behavior of the photovoltaic-storage-charging system within the preset future time period.

[0031] The acquisition module is used to acquire the preset load baseline of the photovoltaic storage and charging system within the preset future time period; wherein, the preset load baseline represents the predicted value of the discharge behavior of the photovoltaic storage and charging system within the preset future time period without responding to the resource scheduling instruction;

[0032] The input module is used to input the preset load baseline into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model;

[0033] The processing module is used to solve the multi-objective optimization model using a preset algorithm to obtain the resource scheduling result of the optical storage and charging system within the preset future time period; wherein, the demand response model represents the resource scheduling decision made by the optical storage and charging system in response to the resource scheduling instruction.

[0034] In one possible design, the photovoltaic-energy storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit;

[0035] The input module includes:

[0036] The acquisition submodule is used to acquire a preset demand response model pre-built for the optical storage and charging system, and to acquire a preset system revenue model pre-built for the optical storage and charging system.

[0037] The processing submodule is used to construct a multi-objective optimization model for the optical storage and charging system by integrating the preset demand response model and the preset system benefit model and setting optimization objectives.

[0038] The input submodule is used to input the preset load baseline into the multi-objective optimization model.

[0039] In one possible design, the acquisition submodule includes:

[0040] A determining component is used to determine the first discharge power of the photovoltaic unit, the second discharge power of the energy storage unit, and the third discharge power of the charging pile unit within the preset future time period.

[0041] A generation component is used to generate a preset demand response model for the photovoltaic energy storage and charging system based on the first discharge power, the second discharge power, and the third discharge power.

[0042] In one possible design, the determining component is further configured to determine a first discharge power adjustment value of the photovoltaic unit and a second discharge power adjustment value of the energy storage unit within the preset future time period.

[0043] The acquisition submodule further includes: an acquisition component, used to acquire the net revenue of the charging business of the charging pile unit and the power compensation unit price of the power grid within the preset future time period; wherein, the power compensation unit price represents the service unit price paid by the power grid for the adjustment of the discharge behavior of the photovoltaic-storage-charging system;

[0044] The generation component is also used to generate a preset system revenue model for the photovoltaic-storage-charging system based on the first discharge power adjustment value, the second discharge power adjustment value, the net revenue of the charging service, and the unit price of the power compensation.

[0045] In one possible design, the input module further includes:

[0046] The configuration submodule is used to set working constraints for the photovoltaic unit, energy storage unit, and charging pile unit in the multi-objective optimization model.

[0047] In one possible design, the processing module includes: a solving submodule, used for:

[0048] The multi-objective optimization model is solved using a pre-defined multi-objective optimization algorithm;

[0049] Alternatively, a pre-defined multi-objective ant colony optimization algorithm can be used to solve the multi-objective optimization model.

[0050] In one possible design, the photovoltaic-energy storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit;

[0051] The resource scheduling results include the following:

[0052] Within the preset future time period, the first adjusted discharge power of the photovoltaic unit, the second adjusted discharge power of the energy storage unit, the third adjusted discharge power of the charging pile unit, the charging unit price of the charging pile unit, and the revenue of the photovoltaic-energy storage-charging system.

[0053] Thirdly, this application provides an electronic device comprising: at least one processor and a memory; the memory storing computer-executable instructions; the at least one processor executing the computer-executable instructions stored in the memory, causing the at least one processor to perform the method described in the first aspect above and various possible designs.

[0054] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the methods described in the first aspect above and various possible designs.

[0055] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the methods described in the first aspect above and various possible designs.

[0056] The resource scheduling method, apparatus, and electronic equipment provided in this application for photovoltaic-storage-charging systems respond to resource scheduling instructions issued by the power grid and determine a preset future time period indicated by the resource scheduling instructions. The resource scheduling instructions represent the power grid's demand for regulating the discharge behavior of the photovoltaic-storage-charging system within the preset future time period. Then, a preset load baseline for the photovoltaic-storage-charging system within the preset future time period is obtained, whereby the preset load baseline represents the predicted value of the discharge behavior of the photovoltaic-storage-charging system within the preset future time period without responding to the resource scheduling instructions. Further, the preset load baseline is input into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model. The demand response model represents the resource scheduling decisions made by the photovoltaic-storage-charging system in response to the resource scheduling instructions. Finally, a preset algorithm is used to solve the multi-objective optimization model to obtain the resource scheduling results of the photovoltaic-storage-charging system within the preset future time period. This application fully considers the coupling relationships and dynamic response characteristics between the resources of the photovoltaic-storage-charging system by integrating the operational characteristics of the resources included in the photovoltaic-storage-charging system into a demand response model. Simultaneously, a system benefit model for the photovoltaic-storage-charging system is introduced, and a dynamic optimization algorithm is designed to achieve a balance between resource scheduling, system benefits, and grid stability, thereby improving the effectiveness of resource scheduling for the photovoltaic-storage-charging system. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0058] Figure 1 A flowchart illustrating a resource scheduling method for a photovoltaic storage and charging system provided in an embodiment of this application;

[0059] Figure 2 This illustration shows the solution of a multi-objective optimization model for a resource scheduling method applied to a photovoltaic-storage-charging system provided in this application embodiment. Figure 1 ;

[0060] Figure 3 This illustration shows the solution of a multi-objective optimization model for a resource scheduling method applied to a photovoltaic-storage-charging system provided in this application embodiment. Figure 2 ;

[0061] Figure 4 This is a schematic diagram of the structure of a resource scheduling device applied to a photovoltaic storage and charging system provided in an embodiment of this application;

[0062] Figure 5 This is a hardware structure diagram of the electronic device provided in the embodiments of this application.

[0063] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0064] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0065] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0066] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0067] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0068] First, the terms used in this application will be explained.

[0069] Photovoltaic-storage-charging system: This is an integrated energy system that combines photovoltaics, energy storage, and charging. It typically consists of three parts: photovoltaic power generation equipment, energy storage batteries, and charging piles, forming a clean energy microgrid that can operate independently or work in conjunction with the power grid.

[0070] Driven by the strategic goals of "carbon peaking" and "carbon neutrality," a new power system based on new energy sources is being rapidly constructed, with the large-scale application of distributed energy and electric vehicles becoming key directions. Currently, with the increase in the number of electric vehicles, the scale of fast-charging and supercharging piles connected to the power grid is gradually increasing.

[0071] To address the step impact of charging piles on the power grid, charging networks are often equipped with distributed resource devices such as energy storage and photovoltaics. Therefore, the photovoltaic-energy storage-charging system, composed of photovoltaics, energy storage, and charging piles, has become an important coupling carrier between new energy sources and power loads.

[0072] Against this backdrop, how to rationally allocate resources in photovoltaic-storage-charging systems to participate in grid demand response, so as to reduce grid pressure, improve the utilization rate of new energy sources, and maximize the benefits of photovoltaic-storage-charging systems, has become a major research issue for those skilled in the art.

[0073] In existing technologies, resource scheduling for photovoltaic-storage-charging systems mainly relies on optimization strategies for single resources or simple combinations based on empirical rules. For example, some existing technologies achieve local optimization by fixing the photovoltaic output ratio, energy storage charging and discharging strategies, or charging pile load allocation rules. Furthermore, existing technologies typically employ a single objective function, such as minimizing cost or maximizing revenue, to achieve resource scheduling for photovoltaic-storage systems.

[0074] However, existing technologies do not fully consider the coupling relationship and operational differences between photovoltaics, energy storage and charging piles, such as the weather dependence of photovoltaics, the charging and discharging power limitations of energy storage, and the load response characteristics of charging piles, resulting in rigid resource scheduling and difficulty in dynamically adapting to the real-time requirements of grid demand response.

[0075] Furthermore, existing technologies achieve resource scheduling through optimization strategies for single resources, lacking a quantitative model of the overall participation of photovoltaic, energy storage, and charging systems in grid demand response capabilities, resulting in a lack of scientific basis for resource scheduling of photovoltaic, energy storage, and charging systems.

[0076] Furthermore, the single objective function used in existing technologies is difficult to handle high-dimensional nonlinear constraints and multi-objective problems, resulting in insufficient global optimality of resource scheduling results and difficulty in meeting the real-time requirements of day-ahead scheduling.

[0077] Therefore, existing technologies suffer from the technical problem of low efficiency in resource scheduling of photovoltaic energy storage and charging systems.

[0078] To address the aforementioned technical problems, the inventors, starting from existing technologies, first analyzed the operational characteristics of photovoltaic-storage-charging systems and discovered that resource coupling modeling is key to solving the lack of flexibility in resource scheduling. Subsequently, they proposed a coupling modeling approach that integrates the operational characteristics of photovoltaics, energy storage, and charging piles into a demand response model. Simultaneously, for multi-objective problems, the inventors also proposed a multi-objective optimization model integrating a pre-set demand response model and a pre-set system revenue model, and designed an optimization solution framework for this multi-objective optimization model to improve the effectiveness of resource scheduling in photovoltaic-storage-charging systems.

[0079] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] This application provides a resource scheduling method for photovoltaic storage and charging systems. Figure 1 This is a flowchart illustrating a resource scheduling method applied to a photovoltaic storage and charging system, as provided in an embodiment of this application. Figure 1 As shown, the resource scheduling method applied to the photovoltaic energy storage and charging system includes:

[0081] S101. In response to the resource dispatching instruction issued by the power grid, determine the preset future time period indicated by the resource dispatching instruction.

[0082] S102. Obtain the preset load baseline of the photovoltaic storage and charging system within a preset future time period.

[0083] Explanatoryly, resource scheduling instructions refer to the grid's demand for adjusting the discharge behavior of a photovoltaic-storage-charging system within a preset future time period. In this embodiment, the photovoltaic-storage-charging system includes photovoltaic units, energy storage units, and charging pile units.

[0084] For example, the power grid requires the photovoltaic-storage-charging system to obtain 100 kWh less net electricity from the grid between 10:00 and 11:00 tomorrow than originally planned (preset load baseline). In the example, "10:00-11:00 tomorrow" refers to the preset future time period, and "the net electricity obtained from the grid must be 100 kWh less than originally planned" is the resource scheduling instruction issued by the power grid to the photovoltaic-storage-charging system.

[0085] It should be noted that the preset load baseline refers to the predicted discharge behavior of the photovoltaic-storage-charging system within a preset future time period in the absence of resource dispatch instructions. In other words, it represents the net electricity that the photovoltaic-storage-charging system is expected to obtain from the grid within the preset future time period during normal operation.

[0086] Specifically, the load baseline representing the daily discharge behavior characteristics (the variation of discharge behavior over time) of the photovoltaic-storage-charging system is:

[0087] (1)

[0088] in,

[0089] (2)

[0090] (3)

[0091] In the formula, This indicates the load baseline of the photovoltaic energy storage and charging system. This indicates the number of typical days selected when calculating the preset load baseline. Indicates that the photovoltaic energy storage and charging system is in the first A typical day Discharge behavior during specific time periods. This indicates the performance of the photovoltaic-storage-charging system across all typical days. Average discharge behavior over a given period.

[0092] This indicates that after typical day screening based on the constraints in formula (2), the photovoltaic storage and charging system will be in the selected day. A typical day Discharge behavior during a specific period of time, The number of , This indicates the number of typical days after filtering.

[0093] Understandably, when obtaining the preset load baseline of the photovoltaic storage and charging system within a preset future time period, the preset future time period is used instead of the values ​​in formulas (1)-(3). This allows us to determine the required preset load baseline.

[0094] S103. Input the preset load baseline into the multi-objective optimization model constructed based on the preset demand response model and the preset system revenue model; use the preset algorithm to solve the multi-objective optimization model to obtain the resource scheduling results of the photovoltaic storage and charging system in the preset future time period.

[0095] It should be noted that, in order to address the uncertainty and unpredictability of resource dispatch instructions issued by the power grid, this embodiment pre-constructs a demand response model for the photovoltaic-storage-charging system. The demand response model characterizes the resource dispatch decisions made by the photovoltaic-storage-charging system in response to resource dispatch instructions issued by the power grid.

[0096] Specifically, the demand response model for the photovoltaic-storage-charging system is as follows:

[0097] (4)

[0098] In the formula, This is a demand response model for a photovoltaic-storage-charging system, representing the system's performance in [various applications / conditions]. The discharge power that participates in demand response during a given time period. Indicates that the photovoltaic unit is in Discharge power participating in demand response during the specified time period Indicates that the energy storage unit is in Discharge power participating in demand response during the specified time period Indicates that the charging pile unit is in The discharge power participating in demand response during a given time period. For illustrative purposes, "participating in demand response" here refers to responding to resource dispatch instructions issued by the power grid. Subsequent occurrences of "participating in demand response" will have the same meaning and will not be elaborated further.

[0099] in,

[0100] (5)

[0101] (6)

[0102] (7)

[0103] (8)

[0104] (9)

[0105] In the formula, The given value represents the proportion of photovoltaic units participating in demand response. Indicates that the photovoltaic unit is in The upper limit of discharge power during a given time period. The given value represents the proportion of energy storage units participating in demand response. Indicates that the energy storage unit is in The upper limit of discharge power during a given time period. Indicates that the energy storage unit is in The charging power during a given period should be understood as follows: the energy storage unit is charged from two sources, one from the photovoltaic unit and the other from the power grid. Indicates that the energy storage unit is in The discharge power of the photovoltaic units consumed during the time period, This represents a step signal used to determine whether an energy storage unit needs to purchase electricity from the grid. Indicates that the energy storage unit is in Electricity purchase capacity during a given time period. Indicates that the charging pile unit is in The charging power during a given period should be understood as the charging source for the charging pile unit being the energy storage unit.

[0106] It should be noted that in this embodiment, the time scale for the power grid to issue resource dispatch instructions is 1 hour. Therefore, when formulating the day-ahead dispatch plan for the photovoltaic-storage-charging system, whole hours (such as 00:00, 01:00, 02:00, etc.) can be used as the time interval, that is, a total of 24 measurement time intervals are set in a day. Assuming that the discharge power of the photovoltaic units, energy storage units and charging pile units included in the photovoltaic-storage-charging system participating in demand response remains constant in each time interval, the demand response model of the photovoltaic-storage-charging system shown in formula (4) can be written as:

[0107] (10)

[0108] In the formula, Indicates in Any point in time selected within the time period, This indicates a preset duration.

[0109] Understandably, the preset future time periods are used to replace the values ​​in formulas (5), (6), and (9), respectively. The first discharge power of the photovoltaic unit in the preset future time period can be obtained by formula (5), the second discharge power of the energy storage unit in the preset future time period can be obtained by formula (6), and the third discharge power of the charging pile unit in the preset future time period can be obtained by formula (9).

[0110] Furthermore, by substituting the first discharge power, the second discharge power, and the third discharge power into formula (4), a preset demand response model for the photovoltaic energy storage and charging system within a preset future time period can be generated.

[0111] Since the photovoltaic-energy storage-charging system is mainly used for charging electric vehicles, the profitability of the system should also be considered when adjusting the first, second, and third discharge powers. Therefore, this embodiment also pre-constructs a system profitability model for the photovoltaic-energy storage-charging system.

[0112] Specifically, the system benefit model for the photovoltaic-storage-charging system is as follows:

[0113] (11)

[0114] In the formula, This is the system revenue model for the photovoltaic-storage-charging system, representing the scheduling revenue of the photovoltaic-storage-charging system. This indicates the duration during which the optical storage and charging system participates in demand response within a scheduling cycle (usually one day). In formula (11), the term "photovoltaic unit," "energy storage unit," or "charging pile unit" is used to indicate that the photovoltaic unit, energy storage unit, or charging pile unit is used in the formula. This refers only to photovoltaic units or energy storage units. Indicates the first Each unit in The time period refers to the discharge power adjusted to participate in demand response. Indicates that the photovoltaic energy storage and charging system is in The revenue price for participating in demand response during a specific time period is the fee paid by the grid to the photovoltaic-storage-charging system for every watt of discharge power it adjusts. This represents the net income of the charging station unit.

[0115] Explained, in a photovoltaic-storage-charging system, the internal charging and discharging costs are zero. This means the revenue from the photovoltaic unit does not include the revenue from charging the energy storage unit, and vice versa. Therefore, the revenue of both the photovoltaic unit and the energy storage unit comes entirely from participating in demand response.

[0116] For a charging station unit, its revenue consists of two parts: electricity sales revenue and charging service fees. Therefore, the net revenue E of the charging station unit is... CS The calculation is as follows:

[0117] (12)

[0118] In the formula, This indicates the revenue of the charging station unit. This indicates the cost of the photovoltaic energy storage and charging system. This represents the set of electricity sales periods for each charging station unit. This represents the set of electricity purchase periods for each charging station unit. express The electricity price for electric vehicles charging at charging stations during specific time periods. express The service fee for electric vehicles charging at charging stations during certain time periods. express The electricity purchase price of the power grid during the specified time period.

[0119] Explaining the cost, the cost of a photovoltaic-storage-charging system comprises the assembly cost of the equipment (photovoltaic units, energy storage units, and charging pile units) and the grid electricity purchase cost. Since the charging pile units derive all their power from the energy storage units, and the energy storage units themselves derive their power from the photovoltaic units and the grid, the grid electricity purchase cost only needs to consider the electricity purchased from the energy storage units. Specifically, the cost of a photovoltaic-storage-charging system... for:

[0120] (13)

[0121] In the formula, This represents the set of time periods during which the photovoltaic, energy storage, and charging system purchases electricity from the grid. Indicates that the photovoltaic energy storage and charging system is in Electricity purchase capacity during a given time period. Indicates the first The assembly cost of each unit is divided into three categories: photovoltaic unit, energy storage unit, and charging pile unit.

[0122] Combining formula (11), assuming the photovoltaic unit corresponds to The value is 1, corresponding to the energy storage unit The value is 2. Based on this, if the first discharge power adjustment value of the photovoltaic unit within the preset future time period is determined, that is... The second discharge power adjustment value of the energy storage unit The net revenue from charging services of the charging pile unit is... and the electricity compensation unit price of the power grid, i.e. Substituting the first discharge power adjustment value, the second discharge power adjustment value, the net revenue of the charging business, and the unit price of power compensation into formula (11) will generate the preset system revenue model of the photovoltaic-storage-charging system within a preset future time period.

[0123] Explained, the grid's electricity compensation unit price refers to the service price paid by the grid for regulating the discharge behavior of a photovoltaic-storage-charging system. In other words, it's the fee paid by the grid to the photovoltaic-storage-charging system for every watt of discharge power regulated. .

[0124] It should be noted that after obtaining the preset demand response model and preset system revenue model of the photovoltaic storage and charging system within a preset future time period, this embodiment generates a multi-objective optimization model for the photovoltaic storage and charging system by integrating the preset demand response model and preset system revenue model and setting optimization objectives.

[0125] It should be understood that the optimization objectives need to fully consider the benefits of the photovoltaic-storage-charging system participating in demand response; that is, the scheduling optimization of photovoltaic units, energy storage units, and charging pile units should be aimed at maximizing overall benefits. Simultaneously, the optimization objectives should also include: maximizing the discharge power of the photovoltaic-storage-charging system participating in demand response, and minimizing the curtailed photovoltaic power of the photovoltaic-storage-charging system.

[0126] Therefore, the generated multi-objective optimization model is as follows:

[0127] (14)

[0128] (15)

[0129] In the formula, The weight representing the returns, Indicates the weight of costs. and The constraints of formula (15) need to be satisfied. It represents all time periods within a scheduling cycle (usually one day). Right now This indicates that the charging station unit is in The discharge power that participates in demand response during a given time period. Indicates that the charging pile unit is in Available discharge power during the time period. Describing photovoltaic units in The actual discharge power during the time period. Indicates that the photovoltaic energy storage and charging system is in The discharge power of the photovoltaic units consumed during the time period.

[0130] Thus, a multi-objective optimization model for a photovoltaic-storage-charging system is obtained. Furthermore, operational constraints are set for the photovoltaic unit, the energy storage unit, and the charging pile unit in this multi-objective optimization model.

[0131] Specifically, for photovoltaic (PV) units, if a PV unit only participates in demand response, its participating power must not exceed its total discharge power; if a PV unit participates in demand response while also supplying power to energy storage units and charging pile units, the sum of its participating power and supply power must not exceed its current power generation. Therefore, the operating constraints for PV units are:

[0132] (16)

[0133] (17)

[0134] In the formula, This indicates the upper limit of the discharge power of a photovoltaic unit within a day. This indicates the lower limit of the photovoltaic unit's discharge power within a day. The upper limit of the discharge power generally occurs during the period of maximum sunlight intensity at noon.

[0135] For energy storage units, the discharge power during the demand response period must not exceed the remaining discharge power at that moment, and the remaining discharge power of the energy storage unit at any given time period must not exceed its total discharge power. Therefore, the operating constraints for energy storage units are:

[0136] (18)

[0137] (19)

[0138] (20)

[0139] In the formula, Indicates that the energy storage unit is in Remaining discharge power during the period This indicates the total discharge power of the energy storage unit.

[0140] It should be understood that the charging and discharging power of energy storage units also has upper and lower limits:

[0141] (twenty one)

[0142] (twenty two)

[0143] In the formula, This indicates the upper limit of the charging power of the energy storage unit. This indicates the lower limit of the charging power of the energy storage unit. Indicates that the energy storage unit is in Discharge power during the period This indicates the upper limit of the discharge power of the energy storage unit. This indicates the lower limit of the discharge power of the energy storage unit.

[0144] For the charging pile unit, it is only necessary to ensure that the discharge power of the charging pile is non-negative. Therefore, the operating constraints of the charging pile unit are:

[0145] (twenty three)

[0146] Next, the preset load baseline of the photovoltaic storage and charging system within a preset future time period is input into the multi-objective optimization model, and then the multi-objective optimization algorithm or the multi-objective ant colony optimization algorithm is used to solve the multi-objective optimization model.

[0147] The multi-objective optimization algorithm used in this embodiment is the Pareto front algorithm, and the multi-objective ant colony optimization algorithm used is the Pareto front algorithm considering ant colony optimization. Furthermore, the solution processes of the Pareto front algorithm and the Pareto front algorithm considering ant colony optimization will be explained separately.

[0148] For Pareto front algorithms, the Pareto front refers to a set of solutions that achieve the optimal trade-off among multiple objectives, where no single solution improves any objective without compromising the others. In other words, given a multi-objective function... its optimal solution Defined as:

[0149] (twenty four)

[0150] (25)

[0151] In the formula, Ω represents a feasible solution. Specifically,

[0152] (26)

[0153] In the formula, and Indicates constraints. This includes the operating constraints of the photovoltaic unit, the energy storage unit, and the charging pile unit in this embodiment. This includes the preset load baseline and its constraints.

[0154] Interpretive, multi-objective function Let Ω∈R n Mapping to set Π∈R r Π represents the objective function space. If And there are no other [others]. , making If at least one of them is a strict inequality, then This is the Pareto optimal solution.

[0155] It should be noted that the Pareto front algorithm focuses on maximizing individual gains in multi-objective problems, ensuring that a single objective reaches its optimal state within a certain region, thereby solving the multi-objective problem. Specifically, Figure 2 This illustration shows the solution of a multi-objective optimization model for a resource scheduling method applied to a photovoltaic-storage-charging system provided in this application embodiment. Figure 1 ,like Figure 2 As shown, under the premise of using the Pareto front algorithm, the fitness function of the multi-objective optimization model is first calculated.

[0156] It should be understood that the calculated fitness function consists of two parts: one part is the benefit of the photovoltaic-storage-charging system participating in demand response. The other part is the discharge power regulation value of the photovoltaic-storage-charging system participating in demand response. :

[0157] (27)

[0158] (28)

[0159] In the formula, This indicates the discharge power of the photovoltaic-storage-charging system participating in demand response. This indicates the preset load baseline.

[0160] Furthermore, determine the solution set that satisfies the fitness function. If in the solution set In, there exists a solution , making To obtain the maximum value, and If the minimum value is obtained, then To obtain the Pareto front solution that satisfies the conditions, the fitness function and determination are then repeated. These two steps continue until a termination condition is met, such as reaching a preset number of iterations, ultimately outputting the first adjusted discharge power of the photovoltaic unit within a preset future time period. The second regulated discharge power of the energy storage unit The third adjustment of the discharge power of the charging pile unit The unit price of charging at charging piles and the revenue from photovoltaic storage and charging systems .

[0161] For Pareto front algorithms considering ant colony optimization, in ant colony optimization, the walking paths of ants represent feasible solutions to a multi-objective problem, and the total walking paths of the entire ant colony constitute the solution space of the multi-objective problem. Specifically, the basic principle of ant colony optimization is as follows:

[0162] (1) Ants release pheromones along the path that are related to the path length. The path is constructed as follows:

[0163] (29)

[0164] In the formula, Indicates the path to be constructed. Factors indicating the importance of pheromones This represents the importance factor of the heuristic function. and Used to regulate pheromones and heuristic functions The interaction between them. Indicates: if path to path The higher the pheromone concentration, the better. The larger the path and path The greater the probability of being selected; if the path to path The shorter the length, the better. The larger the path and path The greater the probability of being selected.

[0165] (2) For shorter paths, ants release more pheromones. As time progresses, the amount of pheromones accumulated on shorter paths gradually increases, and the number of ants choosing shorter paths also increases. The pheromone updates are as follows:

[0166] (30)

[0167] In the formula, This represents the volatility coefficient of the information system. This indicates the number of ants. This indicates the degree of influence of the ant colony on pheromones. Indicates the first ant in the colony The degree of influence of an ant on pheromones.

[0168] (3) The pheromone on the optimal path becomes larger and larger.

[0169] (4) The entire ant colony will converge on the optimal path under the effect of positive feedback, and eventually the ant colony will find the optimal walking path.

[0170] Based on the basic principles of the ant colony algorithm described above, Figure 3 This illustration shows the solution of a multi-objective optimization model for a resource scheduling method applied to a photovoltaic-storage-charging system provided in this application embodiment. Figure 2 ,like Figure 3 As shown, under the premise of using the Pareto front algorithm considering ant colony optimization, the process of solving the multi-objective optimization model in this embodiment is as follows:

[0171] (1) Initialize the population architecture, which includes 24 time periods. and 24 time periods At the same time, pheromones are initialized.

[0172] (2) Calculate the fitness function of individuals in the population. ,in, .

[0173] (3) Determine the solution set that satisfies the fitness function, and find the Pareto front solution from the solution set.

[0174] (4) Determine the Pareto front solution found. Is it less than ,and Is it less than If it is greater than or equal to, return to step (2); if it is less than, continue to step (5).

[0175] (5) Update pheromones.

[0176] (6) Number of iterations Add 1.

[0177] (7) Determine the number of iterations Is it greater than the preset maximum number of iterations? If the result is less than or equal to, return to step (2); if the result is greater than, output the result within the preset future time period. , , , and .

[0178] The resource scheduling method for photovoltaic-storage-charging systems provided in this application responds to resource scheduling instructions issued by the power grid and determines a preset future time period indicated by the resource scheduling instructions. The resource scheduling instructions represent the power grid's demand for adjusting the discharge behavior of the photovoltaic-storage-charging system within the preset future time period. Then, a preset load baseline for the photovoltaic-storage-charging system within the preset future time period is obtained, where the preset load baseline represents the predicted discharge behavior of the photovoltaic-storage-charging system within the preset future time period without responding to the resource scheduling instructions. Further, a preset demand response model and a preset system revenue model pre-constructed for the photovoltaic-storage-charging system are obtained. By integrating the preset demand response model and the preset system revenue model, a multi-objective optimization model for the photovoltaic-storage-charging system is generated. Simultaneously, working constraints for photovoltaic units, energy storage units, and charging pile units are set for the multi-objective optimization model. The demand response model represents the resource scheduling decisions made by the photovoltaic-storage-charging system in response to the resource scheduling instructions. The preset load baseline is input into the multi-objective optimization model, and a multi-objective optimization algorithm or a preset multi-objective ant colony optimization algorithm is used to solve the multi-objective optimization model to obtain the resource scheduling results for the photovoltaic-storage-charging system within the preset future time period. This application integrates the operational characteristics of the resources included in the photovoltaic-storage-charging system into a demand response model, fully considering the coupling relationships and dynamic response characteristics among the resources of the photovoltaic-storage-charging system. Simultaneously, it introduces a system benefit model for the photovoltaic-storage-charging system and designs a dynamic optimization algorithm to achieve a balance between resource scheduling, system benefits, and grid stability, thereby improving the effectiveness of resource scheduling in the photovoltaic-storage-charging system.

[0179] Figure 4 This is a schematic diagram of the structure of a resource scheduling device applied to a photovoltaic storage and charging system provided in an embodiment of this application, as shown below. Figure 4 As shown, the resource scheduling device 400 applied to the optical storage and charging system includes: a determination module 401, an acquisition module 402, an input module 403, and a processing module 404;

[0180] The determining module 401 is used to respond to the resource scheduling instruction issued by the power grid and determine the preset future time period indicated by the resource scheduling instruction; wherein the resource scheduling instruction represents the power grid's demand for adjusting the discharge behavior of the photovoltaic-storage-charging system within the preset future time period.

[0181] The acquisition module 402 is used to acquire the preset load baseline of the photovoltaic storage and charging system within a preset future time period; wherein, the preset load baseline represents the predicted value of the discharge behavior of the photovoltaic storage and charging system within the preset future time period without responding to resource scheduling instructions.

[0182] Input module 403 is used to input the preset load baseline into the multi-objective optimization model constructed based on the preset demand response model and the preset system revenue model;

[0183] The processing module 404 is used to solve the multi-objective optimization model using a preset algorithm to obtain the resource scheduling results of the photovoltaic storage and charging system within a preset future time period; wherein, the demand response model represents the resource scheduling decision made by the photovoltaic storage and charging system in response to the resource scheduling command.

[0184] In one possible design, the photovoltaic-storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit;

[0185] Input module 403 includes:

[0186] The acquisition submodule 4031 is used to acquire a pre-built demand response model for the photovoltaic storage and charging system, and a pre-built system revenue model for the photovoltaic storage and charging system.

[0187] The processing submodule 4032 is used to construct a multi-objective optimization model for the photovoltaic storage and charging system by integrating a preset demand response model and a preset system benefit model and setting optimization objectives.

[0188] Input submodule 4033 is used to input a preset load baseline into the multi-objective optimization model.

[0189] In one possible design, submodule 4031 is obtained, including:

[0190] The components are determined to determine the first discharge power of the photovoltaic unit, the second discharge power of the energy storage unit, and the third discharge power of the charging pile unit within a preset future time period.

[0191] A generation component is used to generate a preset demand response model for the photovoltaic energy storage and charging system based on the first discharge power, the second discharge power, and the third discharge power.

[0192] In one possible design, the components are determined, and the first discharge power regulation value of the photovoltaic unit and the second discharge power regulation value of the energy storage unit are also determined within a preset future time period.

[0193] The acquisition submodule 4031 also includes: an acquisition component, used to acquire the net revenue of the charging business of the charging pile unit and the power compensation unit price of the power grid within a preset future time period; wherein, the power compensation unit price represents the service unit price paid by the power grid for regulating the discharge behavior of the photovoltaic-storage-charging system;

[0194] The generation component is also used to generate a preset system revenue model for the photovoltaic-storage-charging system based on the first discharge power adjustment value, the second discharge power adjustment value, the net revenue of the charging business, and the unit price of power compensation.

[0195] In one possible design, input module 403 also includes:

[0196] Submodule 4034 is configured to set working constraints for photovoltaic units, energy storage units, and charging pile units in the multi-objective optimization model.

[0197] In one possible design, the processing module 404 includes: a solver submodule 4041, used for:

[0198] A pre-defined multi-objective optimization algorithm is used to solve the multi-objective optimization model;

[0199] Alternatively, a pre-defined multi-objective ant colony optimization algorithm can be used to solve the multi-objective optimization model.

[0200] In one possible design, the photovoltaic-storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit;

[0201] The resource scheduling results include the following:

[0202] Within a predetermined future time period, the first regulated discharge power of the photovoltaic unit, the second regulated discharge power of the energy storage unit, the third regulated discharge power of the charging pile unit, the charging unit price of the charging pile unit, and the revenue of the photovoltaic-energy storage-charging system.

[0203] The resource scheduling device for photovoltaic storage and charging systems provided in this application embodiment can be used to execute the resource scheduling method for photovoltaic storage and charging systems in any of the above embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0204] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented in software via processing element calls, while others are implemented in hardware. Additionally, these modules can be fully or partially integrated together, or implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0205] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 5 As shown, the electronic device may include: transceiver 51, processor 52, and memory 53.

[0206] Processor 52 executes computer execution instructions stored in memory, causing processor 52 to perform the scheme in the above embodiments. Processor 52 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0207] The memory 53 is connected to the processor 52 via the system bus and completes communication between them. The memory 53 is used to store computer program instructions.

[0208] Transceiver 51 can be used to communicate and interact with other devices.

[0209] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0210] The electronic device provided in this application embodiment can be used to execute the method provided in any of the above embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0211] This application also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the methods provided in any of the above embodiments.

[0212] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium, and when the at least one processor executes the computer program, it can implement the method provided in any of the above embodiments.

[0213] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0214] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0215] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0216] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0217] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0218] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0219] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0220] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0221] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0222] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0223] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A resource scheduling method applied to a photovoltaic-storage-charging system, characterized in that, include: In response to a resource scheduling instruction issued by the power grid, a preset future time period indicated by the resource scheduling instruction is determined; wherein, the resource scheduling instruction represents the power grid's demand for adjusting the discharge behavior of the photovoltaic-storage-charging system within the preset future time period; Obtain a preset load baseline for the photovoltaic storage and charging system within the preset future time period; wherein, the preset load baseline represents the predicted discharge behavior of the photovoltaic storage and charging system within the preset future time period without responding to the resource scheduling instruction; The preset load baseline is input into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model; the multi-objective optimization model is solved using a preset algorithm to obtain the resource scheduling results of the photovoltaic storage and charging system within the preset future time period; wherein, the demand response model represents the resource scheduling decision made by the photovoltaic storage and charging system in response to the resource scheduling instruction.

2. The method according to claim 1, characterized in that, The photovoltaic-storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit; The preset load baseline is input into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model, including: Obtain a pre-built demand response model for the photovoltaic storage and charging system, and obtain a pre-built system revenue model for the photovoltaic storage and charging system; By integrating the preset demand response model and the preset system revenue model, and setting optimization objectives, a multi-objective optimization model for the photovoltaic storage and charging system is constructed; the preset load baseline is input into the multi-objective optimization model.

3. The method according to claim 2, characterized in that, Obtaining a pre-constructed demand response model for the optical storage and charging system includes: Determine the first discharge power of the photovoltaic unit, the second discharge power of the energy storage unit, and the third discharge power of the charging pile unit within the preset future time period; Based on the first discharge power, the second discharge power, and the third discharge power, a preset demand response model for the photovoltaic energy storage and charging system is generated.

4. The method according to claim 2, characterized in that, Obtaining a pre-constructed system benefit model for the photovoltaic storage and charging system includes: Determine the first discharge power adjustment value of the photovoltaic unit and the second discharge power adjustment value of the energy storage unit within the preset future time period; The net revenue from charging services of the charging pile unit and the power compensation unit price of the power grid are obtained within the preset future time period; wherein, the power compensation unit price represents the service unit price paid by the power grid for regulating the discharge behavior of the photovoltaic-storage-charging system. Based on the first discharge power adjustment value, the second discharge power adjustment value, the net revenue of the charging service, and the unit price of the power compensation, a preset system revenue model for the photovoltaic energy storage and charging system is generated.

5. The method according to claim 2, characterized in that, After generating a multi-objective optimization model for the optical storage and charging system, the method further includes: Set working constraints for the photovoltaic unit, energy storage unit, and charging pile unit for the multi-objective optimization model.

6. The method according to claim 1, characterized in that, The multi-objective optimization model is solved using a preset algorithm, including: The multi-objective optimization model is solved using a pre-defined multi-objective optimization algorithm; Alternatively, a pre-defined multi-objective ant colony optimization algorithm can be used to solve the multi-objective optimization model.

7. The method according to any one of claims 1 to 5, characterized in that, The photovoltaic-storage-charging system includes a photovoltaic unit, an energy storage unit, and a charging pile unit; The resource scheduling results include the following: Within the preset future time period, the first adjusted discharge power of the photovoltaic unit, the second adjusted discharge power of the energy storage unit, the third adjusted discharge power of the charging pile unit, the charging unit price of the charging pile unit, and the revenue of the photovoltaic-energy storage-charging system.

8. A resource scheduling device applied to a photovoltaic-storage-charging system, characterized in that, include: The determination module is used to determine the preset future time period indicated by the resource scheduling instruction issued by the power grid in response to the resource scheduling instruction; wherein the resource scheduling instruction represents the power grid's demand for adjusting the discharge behavior of the photovoltaic-storage-charging system within the preset future time period. The acquisition module is used to acquire the preset load baseline of the photovoltaic storage and charging system within the preset future time period; wherein, the preset load baseline represents the predicted value of the discharge behavior of the photovoltaic storage and charging system within the preset future time period without responding to the resource scheduling instruction; The input module is used to input the preset load baseline into a multi-objective optimization model constructed based on a preset demand response model and a preset system revenue model; The processing module is used to solve the multi-objective optimization model using a preset algorithm to obtain the resource scheduling result of the optical storage and charging system within the preset future time period; wherein, the demand response model represents the resource scheduling decision made by the optical storage and charging system in response to the resource scheduling instruction.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the resource scheduling method for an optical storage and charging system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the resource scheduling method for an optical storage and charging system as described in any one of claims 1 to 7.

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