Considering the flexibility of resource scheduling methods, systems, and media that involve state-temporal coupling

Abstract

This invention discloses a method, system, and medium for scheduling flexible resources considering state-temporal coupling, comprising: acquiring operating parameters of each flexible resource and data on the coordination relationship between the electricity market and the carbon market; inputting the operating parameters into a preset upper-level optimization model, optimizing to minimize the total cost of flexible resources, and obtaining the target bid amount for each flexible resource within a preset time period; constructing a middle-level optimization model based on the target bid amount and state-temporal coupling constraints, optimizing to minimize the total penalty of the desired power trajectory, and obtaining the desired power trajectory with a time step; constructing a lower-level optimization model based on the desired power trajectory and power control smoothness constraints, optimizing to minimize the total penalty of actual power deviation, and obtaining the control power input for each flexible resource for resource scheduling. This application enables precise tracking and real-time control of flexible resources.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling, and more particularly to a flexible resource scheduling method, system, and medium that considers state-temporal coupling. Background Technology

[0002] The large-scale integration of renewable energy sources (such as wind and solar power) poses a greater challenge to the supply and demand balance of the power system, as the intermittency and uncertainty of these energy sources increase the complexity of grid operation. Meanwhile, the scale of flexible resources such as electric vehicles, 5G base stations, data centers, and distributed energy storage is continuously expanding. These resources have rapid response capabilities and can effectively mitigate the volatility of renewable energy, improving the stability and reliability of the power system. Furthermore, the rational scheduling of flexible resources can optimize participation strategies in the electricity and carbon markets, reduce operating costs, improve resource utilization efficiency, and achieve low-carbon operation. Therefore, the scheduling of flexible resources is of great significance for improving the flexibility, reliability, and economy of the power system.

[0003] Currently, the scheduling of flexible resources mainly focuses on optimization at a single time scale, typically based on the premise of separating resource states for independent optimization. While this approach can improve resource utilization efficiency to some extent, it has significant limitations in practical applications. First, existing methods often neglect the carbon responsibility that flexible resources bear in actual operation, failing to collaboratively model the carbon emission costs of different resources, resulting in a failure to fully consider low-carbon goals in resource operation. Second, existing technologies only consider the impact of flexible resource scheduling on short-term time scales, ignoring resource failures and functional degradation that may result from frequent scheduling, and lacking awareness of optimizing resource operation benefits from a life-cycle perspective. Furthermore, existing methods fail to fully consider the temporal coupling characteristics of resources in modeling collaboratively differentiated flexible resources, leading to scheduling schemes that do not match actual state changes, affecting the accuracy and reliability of scheduling. These shortcomings limit the efficient utilization of flexible resources in power systems, urgently requiring an optimization scheduling method that can comprehensively consider multiple time scales, carbon constraints, and state coupling. Summary of the Invention

[0004] This invention provides a flexible resource scheduling method, system, and medium that considers state-time coupling to respond in real time to rapid changes in the power system and achieve precise tracking and real-time control of flexible resources.

[0005] An embodiment of the present invention provides a flexible resource scheduling method that considers state-temporal coupling, comprising:

[0006] The system acquires operational parameters of various flexible resources and data on the synergistic relationship between the electricity market and the carbon market, wherein the operational parameters include charging power, discharging power, and carbon emission intensity.

[0007] The operating parameters are input into a preset upper-level optimization model, and the optimization solution is performed with the goal of minimizing the total cost of the flexible resources within a preset time period, so as to obtain the target bid amount of each flexible resource with a time interval within the preset time period. The upper-level optimization model is constructed based on the collaborative relationship data of the electricity market and the carbon market.

[0008] Based on the target bid amount and state-time coupling constraints, a mid-level optimization model is constructed. The optimization solution is performed with the goal of minimizing the total penalty of the expected power trajectory of the flexibility resource, so as to obtain the expected power trajectory of the flexibility resource with time step.

[0009] Based on the desired power trajectory and power control smoothness constraints, a lower-level optimization model is constructed. The optimization solution is performed with the objective of minimizing the total penalty for the actual power deviation of the flexible resources, and the control power input of each flexible resource is obtained.

[0010] Resource scheduling is performed on each of the aforementioned flexibility resources based on the aforementioned control power inputs.

[0011] This application's embodiments obtain operating parameters of various flexible resources and relationship data between the electricity market and the carbon market, providing real-time input information for subsequent optimization models. This ensures that scheduling decisions are based on the latest resource status and market conditions, laying the foundation for real-time response to rapid changes in the power system. The upper-level optimization model solves for the target bid quantity, enabling resource planning at the market level and providing strategic guidance for real-time response to rapid changes in the power system, ensuring reasonable resource allocation under economic and low-carbon objectives. The middle-level optimization model generates the desired power trajectory, refining the target bid quantity into a more specific power trajectory within a time step, providing an intermediate target for real-time control of flexible resources and ensuring that resources can accurately track real-time demand changes in the power system according to a predetermined trajectory. The lower-level optimization model determines the control power input, directly determining the specific control commands for flexible resources on a second-level time scale, ensuring that resources can respond to rapid changes in the power system in real time, achieving precise tracking and control. Through resource scheduling execution, optimization decisions are transformed into specific resource operation commands, achieving real-time control and precise tracking of flexible resources, ensuring that resources can quickly and accurately respond to real-time demand changes in the power system, maintaining stable power system operation. Compared with existing technologies, this application can respond to rapid changes in the power system in real time, and achieve precise tracking and real-time control of flexible resources.

[0012] Furthermore, the flexibility resources include one or more combinations of the following: electric vehicles, energy storage systems, distributed energy resources, 5G base stations, data centers, and battery swapping stations.

[0013] Furthermore, the step of inputting the operating parameters into a preset upper-level optimization model, and optimizing the solution with the objective of minimizing the total cost of the flexible resources within a preset time period, yields the target bid amount for each flexible resource within a time interval during the preset time period, specifically as follows:

[0014] A higher-level optimization model is constructed based on the data on the synergistic relationship between the electricity market and the carbon market.

[0015] Based on the operating parameters and the collaborative relationship data, the objective function is to minimize the difference between the sum of the charging and discharging costs and carbon quota purchase costs of flexible resources and the sum of the electricity sales revenue, frequency regulation capacity revenue, and frequency regulation mileage revenue of flexible resources. Under the conditions of satisfying the upper and lower limits of power, upper and lower limits of bidding quantity, and the definition constraints of bidding quantity of flexible resources, the upper-level optimization model is solved to obtain the target bidding quantity of each flexible resource with a time interval within a preset time period.

[0016] By solving for the target bidding volume through an upper-level optimization model, resources can be planned in advance at the market level, providing strategic guidance for real-time response to rapid changes in the power system and ensuring that resources are rationally allocated under economic and low-carbon objectives.

[0017] Furthermore, the constraints include power constraints, bid quantity constraints, and bid quantity definition constraints:

[0018] The power constraint means that the charging power and discharging power of the flexibility resource must meet the corresponding lower power limit and upper power limit, respectively.

[0019] The bidding volume constraint requires that the bidding volume of the flexible resources jointly participating in the electricity market and carbon market must be between a preset lower limit and a preset upper limit.

[0020] The constraint on the bid quantity definition is that the target bid quantity is a weighted combination of the baseline power, frequency modulation capacity, and frequency modulation mileage of the flexibility resources.

[0021] Furthermore, the intermediate-level optimization model constructed based on the target bid amount and state-time coupling constraints is optimized with the objective of minimizing the total penalty of the expected power trajectory of the flexibility resource, thereby obtaining the expected power trajectory of the flexibility resource with a time step, specifically as follows:

[0022] Obtain the state parameters and state transition relationships of each flexibility resource;

[0023] Based on the target bid amount, the state parameters, and the state transition relationship, a mid-level optimization model containing state-temporal coupling constraints is constructed. The sum of the penalty for deviation of the expected power trajectory of the flexible resource from the bid amount, the penalty for power change, and the penalty for state deviation is used as the objective function. Under the condition of satisfying the state-temporal coupling constraints and the ramp-up constraints, the mid-level optimization model is solved with the time step as the rolling window to obtain the expected power trajectory of the flexible resource covering each time interval.

[0024] By generating the desired power trajectory through the intermediate-level optimization model, the target bid amount can be refined into a power trajectory within a more specific time step, providing an intermediate target for the real-time control of flexible resources and ensuring that resources can accurately track the real-time demand changes of the power system according to the predetermined trajectory.

[0025] Furthermore, the state-time coupling constraint is configured to be related to the state variables of the flexibility resource in the next time step, the state variables in the current time step, and the charging power, discharging power, and corresponding charging and discharging efficiency of other associated flexibility resources in the current time step; the ramp constraint is configured to limit the rate of change of the desired power trajectory between adjacent time steps to not exceed the maximum allowable rate of change of the flexibility resource.

[0026] Furthermore, the lower-level optimization model constructed based on the desired power trajectory and power control smoothness constraints, with the objective of minimizing the total penalty for the actual power deviation of the flexibility resources, is optimized to obtain the control power input of each flexibility resource, specifically as follows:

[0027] The upper and lower limits of power regulation rate, the upper and lower limits of power output, and the actual disturbance parameters for acquiring flexibility resources;

[0028] Based on the desired power trajectory, the upper and lower limits of the power adjustment rate, the upper and lower limits of the power output, and the actual disturbance parameters, an objective function is established using the sum of the actual power deviation penalty of the flexibility resource, the smoothness penalty of the control power input, and the smoothness penalty of the actual output power. Under the condition of satisfying the control rate constraint and the actual output power constraint, the lower-level optimization model is solved with a second-level time scale as the rolling window to obtain the control power input of each flexibility resource covering each time step.

[0029] By determining the control power input through the lower-level optimization model, the specific control commands for the flexibility resources on a second-level time scale can be directly determined, ensuring that the resources can respond in real time to the rapid changes in the power system and achieve precise tracking and control.

[0030] Furthermore, the objective function also includes: the actual power deviation penalty, the smoothness penalty of the control power input, and the smoothness penalty of the actual output power are all equipped with adjustable weight coefficients. The weight coefficients are used to dynamically adjust the importance ratio of each penalty item in the objective function according to the type of different flexibility resources and real-time operation requirements.

[0031] Another embodiment of the present invention provides a flexible resource scheduling system that considers state-temporal coupling, including: an acquisition module, a first solution module, a second solution module, a third solution module, and a scheduling module;

[0032] The acquisition module is used to acquire the operating parameters of various flexible resources and the data on the synergistic relationship between the electricity market and the carbon market, wherein the operating parameters include charging power, discharging power and carbon emission intensity;

[0033] The first solution module is used to input the operating parameters into a preset upper-level optimization model, and perform optimization solution with the goal of minimizing the total cost of the flexible resources in a preset time period, to obtain the target bid amount of each flexible resource with a time interval in the preset time period, wherein the upper-level optimization model is constructed based on the collaborative relationship data of the electricity market and the carbon market;

[0034] The second solution module is used to construct a mid-level optimization model based on the target bid amount and state-time coupling constraints, and to perform optimization with the goal of minimizing the total penalty of the expected power trajectory of the flexibility resource, so as to obtain the expected power trajectory of the flexibility resource with time steps.

[0035] The third solution module is used to construct a lower-level optimization model based on the desired power trajectory and power control smoothness constraints, and to perform optimization with the objective of minimizing the total penalty for the actual power deviation of the flexibility resources, so as to obtain the control power input of each flexibility resource.

[0036] The scheduling module is used to schedule the flexibility resources based on the control power inputs.

[0037] This application's embodiments obtain operating parameters of various flexible resources and relationship data between the electricity market and the carbon market, providing real-time input information for subsequent optimization models. This ensures that scheduling decisions are based on the latest resource status and market conditions, laying the foundation for real-time response to rapid changes in the power system. The upper-level optimization model solves for the target bid quantity, enabling resource planning at the market level and providing strategic guidance for real-time response to rapid changes in the power system, ensuring reasonable resource allocation under economic and low-carbon objectives. The middle-level optimization model generates the desired power trajectory, refining the target bid quantity into a more specific power trajectory within a time step, providing an intermediate target for real-time control of flexible resources and ensuring that resources can accurately track real-time demand changes in the power system according to a predetermined trajectory. The lower-level optimization model determines the control power input, directly determining the specific control commands for flexible resources on a second-level time scale, ensuring that resources can respond to rapid changes in the power system in real time, achieving precise tracking and control. Through resource scheduling execution, optimization decisions are transformed into specific resource operation commands, achieving real-time control and precise tracking of flexible resources, ensuring that resources can quickly and accurately respond to real-time demand changes in the power system, maintaining stable power system operation. Compared with existing technologies, this application can respond to rapid changes in the power system in real time, and achieve precise tracking and real-time control of flexible resources.

[0038] Furthermore, the first solution module includes a construction unit and a solution unit:

[0039] The construction unit is used to construct an upper-level optimization model based on the collaborative relationship data of the electricity market and the carbon market.

[0040] The solution unit is used to minimize the difference between the sum of the charging and discharging costs and carbon quota purchase costs of flexible resources and the sum of the electricity sales revenue, frequency regulation capacity revenue and frequency regulation mileage revenue of flexible resources, based on the operating parameters and the collaborative relationship data. Under the condition of satisfying the upper and lower limits of power, upper and lower limits of bidding quantity, and definition constraints of bidding quantity of flexible resources, the upper and lower limit of power, bidding quantity, and target bidding quantity of each flexible resource within a preset time period is obtained.

[0041] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform steps of the flexible resource scheduling method of the present invention that takes into account state-temporal coupling. Attached Figure Description

[0042] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating an embodiment of the flexible resource scheduling method considering state-temporal coupling provided in this application;

[0044] Figure 2 This is a flowchart illustrating an embodiment of the flexible resource scheduling system that considers state-temporal coupling provided in this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0047] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0048] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0049] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0050] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0051] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0052] The integration of renewable energy sources presents greater challenges to the power system, while flexibility resources can mitigate volatility and improve stability and reliability. Proper scheduling of these resources can also optimize market participation strategies, reduce operating costs, and achieve low-carbon operation. Existing scheduling methods focus on optimization at a single time scale, which has limitations. They neglect carbon responsibility, fail to collaboratively model carbon emission costs, lack a full life-cycle perspective, and do not fully consider temporal coupling characteristics. These shortcomings limit the efficient utilization of flexibility resources.

[0053] See Figure 1 To achieve accurate tracking and real-time control of flexible resources, an embodiment of the present invention provides a flexible resource scheduling method that considers state-temporal coupling, including steps S101 to S105.

[0054] Step S101: Obtain the operating parameters of each flexible resource and the data on the synergistic relationship between the electricity market and the carbon market, wherein the operating parameters include charging power, discharging power and carbon emission intensity;

[0055] In some embodiments, the flexibility resources include one or more combinations of the following: electric vehicles, energy storage systems, distributed energy resources, 5G base stations, data centers, and battery swapping stations.

[0056] In some embodiments, operating parameters of each flexibility resource are acquired, including charging power, discharging power, state variables (such as the SOC of energy storage devices, the charging state of electric vehicles, etc.), and carbon emission intensity. This can be achieved by installing smart sensors on the flexibility resource devices to monitor charging power, discharging power, and state variables in real time, or by using a SCADA system to collect and transmit these operating parameters to a central dispatch system.

[0057] In some embodiments, data on the synergistic relationship between the electricity market and the carbon market is acquired. This synergistic data includes electricity market prices, capacity prices, mileage prices, carbon emission allowance prices, and carbon emission intensity. Specifically, market price and allowance information are acquired in real time through data interfaces with the electricity market and the carbon market, and carbon emission intensity data for flexible resources is obtained using a carbon emission monitoring system.

[0058] It should be noted that carbon emission intensity This refers to the carbon emission intensity of resources such as electric vehicles and distributed energy storage stations. It is calculated by combining power generation on the power generation side with carbon emissions. It relates to how much of the electricity used by these resources is from renewable energy sources and how much is from thermal power. For example, if an electric vehicle is charging at a charging station, the electricity used at the charging station comes partly from renewable energy sources and partly from thermal power. If the renewable energy output is high, the carbon emission intensity at the charging station is low, meaning the carbon emission intensity of the electricity used by the electric vehicle is low. Conversely, if the thermal power output is high, the carbon emission intensity at the charging station is high, meaning the carbon emission intensity of the electricity used by the electric vehicle is high.

[0059] It should be noted that the carbon emission allowances obtained free of charge... The carbon emission allowances obtained free of charge from resources such as electric vehicles and distributed energy storage stations are calculated using the baseline method, and the amount of free carbon emission allowances obtained by different industries varies. In reality, this coefficient is a constant for a given industry. In the model, this coefficient is also a time-invariant constant.

[0060] Step S102: Input the operating parameters into a preset upper-level optimization model, and perform optimization solution with the goal of minimizing the total cost of the flexible resources in a preset time period to obtain the target bid amount of each flexible resource with a time interval in the preset time period. The upper-level optimization model is constructed based on the collaborative relationship data of the electricity market and the carbon market.

[0061] In some embodiments, the step of inputting the operating parameters into a preset upper-level optimization model and optimizing the solution with the objective of minimizing the total cost of the flexible resources within a preset time period to obtain the target bidding amount of each flexible resource with a time interval within the preset time period specifically involves: constructing an upper-level optimization model based on the collaborative relationship data of the electricity market and the carbon market; using the difference between the sum of the charging and discharging costs and carbon quota purchase costs of the flexible resources and the sum of the electricity sales revenue, frequency regulation capacity revenue, and frequency regulation mileage revenue of the flexible resources as the objective function, and solving the upper-level optimization model under the conditions of satisfying the upper and lower limits of power, upper and lower limits of bidding amount, and definition constraints of the bidding amount of the flexible resources to obtain the target bidding amount of each flexible resource with a time interval within the preset time period.

[0062] It should be noted that the upper-level optimization model is a model for flexible resources to participate in bidding in the electricity market and carbon market. The electricity market in which flexible resources participate includes the spot market and the frequency regulation ancillary service market. The calculation cost of flexible resources in the frequency regulation market includes the frequency regulation capacity cost and the frequency regulation mileage cost. The target of flexible resources participating in the carbon market is carbon emission allowances.

[0063] In some embodiments, the constraints include power constraints, bid quantity constraints, and bid quantity definition constraints: the power constraint requires that the charging power and discharging power of the flexibility resource meet the corresponding lower and upper power limits, respectively; the bid quantity constraint requires that the bid quantity of the flexibility resource jointly participating in the electricity market and carbon market be between a preset lower and upper bid quantity limit; and the bid quantity definition constraint requires that the target bid quantity be a weighted combination of the flexibility resource's base power, frequency regulation capacity, and frequency regulation mileage.

[0064] In some embodiments, the expression of the upper-level optimization model is specifically:

[0065] The objective function is:

[0066] ;

[0067] In the formula, It is the revenue from electricity sales based on the benchmark power of flexible resources; For electricity market prices; Let i be the base power of the flexibility resource i at time t. The power injected into the grid by the flexibility resource is positive and negative. The time interval can be in hours, such as 1 hour. , These are capacity revenue and mileage revenue, respectively, for flexibility resources; Price based on capacity; The frequency modulation capacity reported by flexibility resource i at time t; Price based on mileage; The frequency modulation mileage reported by resource i at time t for flexibility; , These are the costs of charging and discharging flexible resources; The discharge price for flexible resource i; The discharge power of flexible resource i at time t; The charging price for flexible resource i; The charging power of the flexibility resource i at time t; It is the cost of flexible resources purchasing carbon allowances in the carbon market; The carbon emission allowance price of flexible resource i at time t; The carbon emission intensity of flexible resource i at time t; The amount of carbon emission allowance that flexible resource i can obtain free of charge per unit power at time t, as obtained by the baseline method;

[0068] The power upper and lower limit constraints are:

[0069] ;

[0070] In the formula, The lower limit of the charging power of flexibility resource i at time t; The upper limit of the charging power of the flexibility resource i at time t; The lower limit of the discharge power of flexibility resource i at time t; The upper limit of the discharge power of the flexibility resource i at time t;

[0071] The upper and lower limits of the bid quantity are constrained as follows:

[0072] ;

[0073] In the formula, This represents the lower limit of the bid volume for joint participation in the electricity market and carbon market for the i-th resource at time t. This represents the upper limit of the bid volume for joint participation in the electricity market and carbon market for the i-th resource at time t.

[0074] The constraints defined for the bid quantity are as follows:

[0075] ;

[0076] In the formula, The target bid amount for flexibility resource i at time t is derived from the upper-level optimization model.

[0077] By solving for the target bidding volume through an upper-level optimization model, resources can be planned in advance at the market level, providing strategic guidance for real-time response to rapid changes in the power system and ensuring that resources are rationally allocated under economic and low-carbon objectives.

[0078] Step S103: Based on the target bid amount and state-time coupling constraints, construct a mid-level optimization model, and optimize the solution with the goal of minimizing the total penalty of the expected power trajectory of the flexibility resource to obtain the expected power trajectory of the flexibility resource with time steps.

[0079] In some embodiments, the step of constructing a mid-level optimization model based on the target bid amount and state-temporal coupling constraints, and optimizing the solution with the objective of minimizing the total penalty of the expected power trajectory of the flexible resources to obtain the expected power trajectory of the flexible resources with time steps, specifically involves: obtaining the state parameters and state transition relationships of each flexible resource; constructing a mid-level optimization model containing state-temporal coupling constraints based on the target bid amount, the state parameters, and the state transition relationships; using the sum of the penalty for deviation of the expected power trajectory of the flexible resources from the bid amount, the penalty for power change, and the penalty for state deviation as the objective function; and solving the mid-level optimization model with the time step as the rolling window under the condition of satisfying the state-temporal coupling constraints and the ramp-up constraints to obtain the expected power trajectory of the flexible resources covering each time interval.

[0080] It should be noted that state parameters are key variables describing the current operating state of flexibility resources, typically including: the State of Charge (SOC) of energy storage devices, representing the remaining power of the energy storage device; the charging status of electric vehicles, representing the current power level and charging demand of the electric vehicle; the operating status of heat pumps or air conditioning systems, representing the current operating mode (cooling, heating) and power demand of the equipment; and state variables of other flexibility resources, such as the generation status of distributed energy sources and the backup power status of 5G base stations.

[0081] It should be noted that state transition relationships describe the state changes of flexible resources across different time steps. These relationships are typically determined by the following factors: the physical characteristics of the resource, such as the charging and discharging efficiency of energy storage devices and the charging efficiency of electric vehicles; user behavior, such as the usage patterns of electric vehicles and user settings of air conditioning systems; and external conditions, such as the impact of weather conditions on distributed energy generation. State transition relationships can be modeled and predicted using historical data, physical models, or machine learning methods. However, how to obtain these relationships is not the focus of this application and will not be discussed further here.

[0082] In some embodiments, the state-time coupling constraint is configured to be related to the state variables of the flexibility resource in the next time step, the state variables in the current time step, and the charging power, discharging power, and corresponding charging and discharging efficiency of other associated flexibility resources in the current time step; the ramp constraint is configured to limit the rate of change of the desired power trajectory between adjacent time steps to not exceed the maximum allowable rate of change of the flexibility resource.

[0083] In some embodiments, the expression of the mid-level optimization model is specifically:

[0084] The objective function is:

[0085] ;

[0086] In the formula, It is a penalty for deviation of the expected power trajectory of flexible resources from the bid amount. The purpose is to make the expected power trajectory of the middle layer as consistent as possible with the bid amount of the upper layer, and reduce the penalty cost of the market for the inconsistency between the actual power of flexible resources and the reported power curve. The expected power trajectory of flexibility resource i at time step h is the main decision variable of this layer model. It is the optimized trajectory control target and serves as the reference input for predictive control (MPC) of the lower-level optimization model. The value of h can be on the order of minutes, such as 15 minutes. This is a dimensionless weighting parameter for smoothing the desired power trajectory; its function is to adjust the degree of penalty for the rate of change of the desired power. The larger the value, the more it prefers scheduling curves with stable trajectories and slow changes; The penalty for expected power changes in flexibility resources reflects the power changes over adjacent time steps; the larger the value, the greater the power change. The expected power trajectory of flexibility resource i at time step h-1; It is a penalty for deviation of the state of flexibility resources, reflecting the change of the state of flexibility resources in adjacent time steps. The larger the value, the greater the state change. The purpose is to keep the state of flexibility resources stable and avoid extreme operation. This is a dimensionless weighting parameter for state deviations, used to characterize the degree to which scheduling behavior affects the state objectives of flexibility resources. The larger the value, the higher the priority is given to maintaining the flexibility resource status near the reference range; For flexible resource i at time step h, it is used to characterize the impact of scheduling actions on the resource state, such as the SOC of energy storage and the charging state of electric vehicles. This serves as a reference value for the state variables of the flexibility resource i at time step h, preventing the state from deviating excessively from the normal range due to scheduling or maintaining the equipment operating within the preset state curve.

[0087] The state-time coupling constraint is:

[0088] ;

[0089] In the formula, For the flexibility resource i, the state variable at time step h+1; For the flexibility of resource j, the charging power in time step h; For the flexibility of resource j, the discharge power at time step h; The charging efficiency of flexible resource j to flexible resource i; The discharge efficiency of flexible resource j on flexible resource i; this constraint reflects the temporal coupling of the state of the flexible resource itself and the temporal coupling between resources;

[0090] The climbing constraint is:

[0091] ;

[0092] In the formula, Represents the desired power trajectory The change between adjacent time steps cannot exceed ; The maximum expected power change rate allowed for flexibility resource i is defined to avoid abrupt scheduling and ensure continuity. The purpose of this constraint is to limit jumps in the expected power trajectory and prevent lower-level optimization models from being unable to track it.

[0093] By generating the desired power trajectory through the intermediate-level optimization model, the target bid amount can be refined into a power trajectory within a more specific time step, providing an intermediate target for the real-time control of flexible resources and ensuring that resources can accurately track the real-time demand changes of the power system according to the predetermined trajectory.

[0094] Step S104: Based on the desired power trajectory and power control smoothness constraints, construct a lower-level optimization model, and perform optimization solution with the objective of minimizing the total penalty for the actual power deviation of the flexibility resources to obtain the control power input of each flexibility resource;

[0095] In some embodiments, the lower-level optimization model is constructed based on the desired power trajectory and power control smoothness constraints, and the optimization solution is performed with the objective of minimizing the total penalty for actual power deviation of the flexible resources to obtain the control power input of each flexible resource. Specifically, this involves: obtaining the upper and lower limits of the power adjustment rate, the upper and lower limits of the power output, and the actual disturbance parameters of the flexible resources; establishing an objective function based on the sum of the penalty for actual power deviation of the flexible resources, the smoothness penalty of the control power input, and the smoothness penalty of the actual output power, under the condition of satisfying the control rate constraint and the actual output power constraint, and solving the lower-level optimization model with a second-level time scale as the rolling window to obtain the control power input of each flexible resource covering each time step.

[0096] It should be noted that the upper and lower limits of the power regulation rate are determined by the physical characteristics of the flexibility resource. For example, for a battery energy storage system, the upper limit of its power regulation rate may be determined by the battery's charging and discharging capacity, while the lower limit may be close to zero. The upper and lower limits of power output refer to the maximum and minimum power that the flexibility resource can output during operation. For example, for an electric vehicle, its maximum output power may be determined by the battery capacity and motor efficiency, while the minimum output power may be zero (i.e., neither charging nor discharging). The actual disturbance parameters reflect the external disturbances that the flexibility resource may be subjected to during actual operation, such as voltage fluctuations and load changes. The specific methods for obtaining these parameters are not the focus of this application and will not be discussed further here.

[0097] In some embodiments, the objective function further includes: the actual power deviation penalty, the smoothness penalty of the control power input, and the smoothness penalty of the actual output power are all equipped with adjustable weight coefficients, which are used to dynamically adjust the importance ratio of each penalty item in the objective function according to the type of different flexibility resources and real-time operation requirements.

[0098] In some embodiments, the expression of the lower-level optimization model is specifically:

[0099] The objective function is:

[0100] ;

[0101] In the formula, The actual power deviation penalty for flexible resources reflects the degree to which the actual power output of flexible resources deviates from the expected power trajectory. The larger the value, the greater the deviation. The purpose is to make the actual output of flexible resources as close as possible to the expected power trajectory given by the middle layer. For flexibility resources i in Expected power trajectory over time scale; For flexibility resources i in Actual output power over time, such as the actual power consumption of air conditioning and the actual power consumption of ice storage; This is a penalty for the smoothness of the control power input of the flexibility resource. It reflects the smoothness of the control power input of the flexibility resource. The larger the value, the worse the smoothness. The purpose is to suppress the change amplitude of each control command, prevent excessive control jumps that could lead to excessive degradation of the equipment's function, and improve the stability of the control strategy. For flexibility resources i in Time-scale control power input, such as the power setting of the air conditioner compressor, the loading or unloading control power of ice storage, and the charging and discharging control power of battery energy storage; For flexibility resources i in Time-scale control power input; The purpose of penalizing the smoothness of the actual output power of the flexible resources is to suppress the fluctuation of the actual output of the flexible resources over time, so as to make the system response curve smoother and more continuous. For flexibility resources i in Actual output power over time;

[0102] The control rate constraint is:

[0103] ;

[0104] In the formula, the control rate constraint is the flexibility resource i in... Time-scale control power input With Time-scale control power input Change Cannot exceed ; The maximum value of the control power input variation for flexibility resource i;

[0105] The actual output power constraint is:

[0106] ,

[0107] In the formula, For flexibility resources i in Actual output power over time; It is a flexible resource i in The time-scale control power input and actual output power perturbation term is caused by unknown external interference, voltage limitations, execution errors, and other factors.

[0108] By determining the control power input through the lower-level optimization model, the specific control commands for the flexibility resources on a second-level time scale can be directly determined, ensuring that the resources can respond in real time to the rapid changes in the power system and achieve precise tracking and control.

[0109] For example, this application includes a three-layer model optimization framework, in which the overall model prediction and control mechanism is embedded in the lower-layer optimization model and the middle-layer optimization model to achieve flexible coordination and rolling optimization at different time scales, including: (1) The upper-layer optimization model, based on the relationship data of flexible resources participating in the electricity market and carbon market for a predicted future period, derives the optimal bidding amount of flexible resources with time intervals for a future period. For example, the upper-layer optimization model uses hours as the time interval to predict the electricity market price for the next 24 hours. Frequency modulation capacity FM mileage Charging price Discharge price Carbon emission allowance prices Carbon emission intensity Carbon emission quotas Based on the relational data, the optimal hourly bid volume / target bid power plan is optimized, including... , and ,Right now If there are changes in relational data, the target bid volume plan for each hour can be updated in a timely manner. (2) The intermediate optimization model selects the best bid volume for flexible resources with time intervals in the future in sequence. Using the time step as the time window, the expected power trajectory of flexible resources with time steps is generated sequentially using the rolling optimization method until the complete time interval is covered. For example, the intermediate optimization model uses 15 minutes as the time step. Based on the target bid volume plan of the upper optimization model, and based on the time sequence, the best bid volume of the first flexible resource with time interval is selected first, and so on. The expected power trajectory for the next 15 minutes is generated using the rolling optimization method. Each rolling window is 15 minutes, advancing 15 minutes until the entire 1-hour time period is covered. The output of the intermediate optimization model is used as the target trajectory input of the lower optimization model. (3) The lower optimization model sequentially selects the expected power trajectory of the flexible resources with time steps, using a second-level time scale as the time window, and uses a rolling optimization method to sequentially generate the control power input of the flexible resources with a second-level time scale until the entire time step is covered; for example, the lower optimization model uses a second-level (5 seconds) time scale, based on the 15-minute expected power trajectory provided by the intermediate optimization model ( ), combined with future disturbance predictions ( ), and perform rolling optimization. Each rolling optimization has a 5-second time window, and the optimization objective is to generate a control power input strategy for flexibility resources within the next 5 seconds, while satisfying system constraints. Then, the control command for the first 5 seconds can be executed in real time to deal with actual system disturbances. Then, the system status is updated and the next round of rolling optimization is entered until the complete 15-minute period is covered. Step (3) is repeated until the complete time interval (e.g., 1 hour) is covered. Steps (1)-(3) are repeated until the complete future period (e.g., 24 hours) is covered, and the control power input can be obtained.

[0110] Step S105: Perform resource scheduling on each of the flexibility resources based on each of the control power inputs.

[0111] In some embodiments, after obtaining the control power input for each flexibility resource on a second-level timescale, the scheduling system sends real-time control commands to each flexibility resource based on the control power input generated by the lower-level optimization model. Upon receiving the control commands, the flexibility resources adjust their operating state to achieve the specified power output. For example: energy storage devices adjust their charging and discharging power according to the control commands; electric vehicles adjust their charging power or discharge to the grid according to the control commands; air conditioning systems adjust their cooling or heating power according to the control commands; and distributed energy resources adjust their power generation power according to the control commands.

[0112] It should be noted that during resource scheduling, the actual operating status of flexible resources needs to be acquired in real time through sensors and monitoring equipment, including actual output power and equipment status. This actual operating status is then fed back to the scheduling system, which evaluates the control effect based on the feedback information. If there is a deviation between the actual output power and the expected power trajectory, the scheduling system will recalculate the control power input and issue new control commands to achieve closed-loop control.

[0113] The beneficial effects of this invention are as follows:

[0114] 1. The model of this invention adopts a three-layer decoupled structure of upper-layer bidding strategy, middle-layer trajectory decomposition, and lower-layer trinomial MPC control, which makes market behavior, resource coordination and dynamic control form a closed loop, and the functional division is clearer.

[0115] 2. This invention supports market participation and dynamic control at the functional level. The upper-level optimization model considers bidding feasibility, the middle-level optimization model considers state-time coupling, and the lower-level optimization model adopts a three-term MPC to support the smooth operation of flexible resources. Compared with existing methods that only optimize the power of flexible resources while ignoring the physical execution and state evolution of flexible resources, this invention improves the robustness of the control system by introducing prediction error and output slope constraints, and is suitable for state-driven resources such as electric vehicles and heat pumps.

[0116] 3. The middle and lower layer optimization models of this invention adopt rolling time window scheduling and combine three-term MPC for continuous closed-loop control. Compared with the existing time-sharing optimization schemes, this invention enables the scheduling system to have self-correction capabilities.

[0117] 4. In terms of cost-effectiveness, this invention enhances equipment protection and extends lifespan. This invention introduces output change penalty in the three-term MPC, which effectively controls frequent start-stop and drastic fluctuations of resources. For comfort-sensitive resources such as air conditioning load, smooth output control can reduce user interference. In energy storage systems, avoiding rapid SOC fluctuations can extend battery life.

[0118] This invention constructs a flexible resource expected power trajectory based on state-temporal coupling constraints, ensuring that the characterization of its expected power accurately reflects the state evolution coupling relationship of the resource. This solves the problem of scheduling schemes not matching actual state changes due to neglecting state coupling. In the lower-level model, the actual output deviation penalty introduced into the objective function effectively suppresses jumps in the output power of flexible resources, improves control accuracy, and reduces resource performance degradation caused by excessive power fluctuations. This solves the problem of difficulty in smoothly and accurately controlling resource output in practical applications. This invention considers the collaborative participation of multiple flexible resources in the electricity-carbon market, integrating the heterogeneity and time-varying nature of carbon emission intensity and carbon price in different regions to achieve low-carbon optimized operation of multiple types of resources.

[0119] This application's embodiments obtain operating parameters of various flexible resources and relationship data between the electricity market and the carbon market, providing real-time input information for subsequent optimization models. This ensures that scheduling decisions are based on the latest resource status and market conditions, laying the foundation for real-time response to rapid changes in the power system. The upper-level optimization model solves for the target bid quantity, enabling resource planning at the market level and providing strategic guidance for real-time response to rapid changes in the power system, ensuring reasonable resource allocation under economic and low-carbon objectives. The middle-level optimization model generates the desired power trajectory, refining the target bid quantity into a more specific power trajectory within a time step, providing an intermediate target for real-time control of flexible resources and ensuring that resources can accurately track real-time demand changes in the power system according to a predetermined trajectory. The lower-level optimization model determines the control power input, directly determining the specific control commands for flexible resources on a second-level time scale, ensuring that resources can respond to rapid changes in the power system in real time, achieving precise tracking and control. Through resource scheduling execution, optimization decisions are transformed into specific resource operation commands, achieving real-time control and precise tracking of flexible resources, ensuring that resources can quickly and accurately respond to real-time demand changes in the power system, maintaining stable power system operation. Compared with existing technologies, this application can respond to rapid changes in the power system in real time, and achieve precise tracking and real-time control of flexible resources.

[0120] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0121] An embodiment of the present invention provides a flexible resource scheduling system that considers state-temporal coupling, including: an acquisition module 100, a first solution module 200, a second solution module 300, a third solution module 400, and a scheduling module 500;

[0122] The acquisition module 100 is used to acquire the operating parameters of each flexible resource and the data on the synergistic relationship between the electricity market and the carbon market, wherein the operating parameters include charging power, discharging power and carbon emission intensity;

[0123] The first solution module 200 is used to input the operating parameters into a preset upper-level optimization model, and perform optimization solution with the goal of minimizing the total cost of the flexible resources in a preset time period, to obtain the target bid amount of each flexible resource with a time interval in the preset time period, wherein the upper-level optimization model is constructed based on the collaborative relationship data of the electricity market and the carbon market;

[0124] The second solution module 300 is used to construct a mid-level optimization model based on the target bid amount and state-time coupling constraints, and to perform optimization with the goal of minimizing the total penalty of the expected power trajectory of the flexibility resource, so as to obtain the expected power trajectory of the flexibility resource with time steps.

[0125] The third solution module 400 is used to construct a lower-level optimization model based on the desired power trajectory and power control smoothness constraints, and to perform optimization with the objective of minimizing the total penalty for the actual power deviation of the flexibility resources, so as to obtain the control power input of each flexibility resource.

[0126] The scheduling module 500 is used to perform resource scheduling on each of the flexibility resources based on each of the control power inputs.

[0127] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the flexible resource scheduling method that takes into account state-temporal coupling provided by any of the above-described method embodiments of the present invention.

[0128] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0129] Based on the above embodiments of the flexible resource scheduling method considering state-temporal coupling, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the flexible resource scheduling method considering state-temporal coupling of any embodiment of the present invention.

[0130] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0131] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0132] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0133] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the flexible resource scheduling method considering state-temporal coupling described in any of the above-described method embodiments of the present invention.

[0134] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0135] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A flexibility resource scheduling method considering state time sequence coupling, characterized in that, The method comprises the following steps: acquiring operation parameters of each flexibility resource and coordination relationship data of the electricity market and the carbon market, wherein the operation parameters comprise charging power, discharging power and carbon emission intensity, and the coordination relationship data comprises electricity market price, capacity price, mileage price, carbon emission quota price and carbon emission intensity; inputting the operation parameters into a preset upper-level optimization model, and performing optimization solving with the minimum total cost of the flexibility resource in a preset time period as the target, to obtain target bidding quantities of each flexibility resource with time intervals in the preset time period, wherein the upper-level optimization model is constructed based on the coordination relationship data of the electricity market and the carbon market; constructing a middle-level optimization model comprising state time sequence coupling constraints according to the target bidding quantities, state parameters and state transition relationship, and performing optimization solving with the minimum total penalty of the expected power trajectory of the flexibility resource as the target, to obtain the expected power trajectory of the flexibility resource with time steps; acquiring upper and lower limits of power regulation rate, upper and lower limits of power output and actual disturbance parameters of the flexibility resource; and establishing a target function with the sum of actual power deviation penalty of the flexibility resource, smoothness penalty of control power input and smoothness penalty of actual output power as the target function, under the conditions of satisfying control rate constraints and actual output power constraints, and performing solving on a lower-level optimization model with a rolling window of a second-level time scale, to obtain control power input of each flexibility resource; scheduling the flexibility resource based on the control power input. 2.The flexible resource scheduling method considering state-aware timing coupling according to claim 1, wherein, The flexibility resource comprises one or more combinations of the following: an electric vehicle, an energy storage system, a distributed energy source, a 5G base station, a data center and a battery swap station. 3.The flexible resource scheduling method considering state-aware timing coupling according to claim 1, wherein, The operation parameters are input into a preset upper-level optimization model, and optimization solving is performed with the minimum total cost of the flexibility resource in a preset time period as the target, to obtain target bidding quantities of each flexibility resource with time intervals in the preset time period, specifically: constructing an upper-level optimization model based on the coordination relationship data of the electricity market and the carbon market; minimizing the difference between the sum of charging and discharging costs and carbon quota purchase costs of the flexibility resource and the sum of electricity sales income, frequency modulation capacity income and frequency modulation mileage income of the flexibility resource as a target function, under the conditions of satisfying constraints of the flexibility resource, to perform solving on the upper-level optimization model, to obtain target bidding quantities of each flexibility resource with time intervals in the preset time period. 4.The flexible resource scheduling method considering state-aware timing coupling according to claim 3, wherein, The constraints comprise power constraints, bidding quantity constraints and bidding quantity definition constraints: the power constraints are that the charging power and the discharging power of the flexibility resource need to meet corresponding power lower limits and power upper limits respectively; the bidding quantity constraints are that the bidding quantity of the flexibility resource participating in the electricity market and the carbon market jointly needs to be between a preset bidding quantity lower limit and a bidding quantity upper limit; The target bid quantity defines a constraint that the target bid quantity is a weighted combination of the benchmark power, frequency modulation capacity and frequency modulation mileage of the flexible resource. 5.The flexible resource scheduling method considering state-aware time-coupled coupling according to claim 1, characterized in that, The target bid quantity, state parameters and state transition relationship are used to construct a middle-layer optimization model containing state time sequence coupling constraints, and the expected power trajectory of the flexible resource is obtained by optimization solving with the minimum total penalty of the expected power trajectory of the flexible resource as the target. The state parameters and state transition relationship of each flexible resource are obtained. The target bid quantity, state parameters and state transition relationship are used to construct a middle-layer optimization model containing state time sequence coupling constraints, and the expected power trajectory of the flexible resource is obtained by optimization solving with the minimum total penalty of the expected power trajectory of the flexible resource as the target. 6.The flexible resource scheduling method of considering state timing coupling according to claim 5, wherein, The state time sequence coupling constraint is configured to relate the state variable of the flexible resource at the next time step, the state variable at the current time step, and the charging power, discharging power and corresponding charging and discharging efficiency of other associated flexible resources at the current time step; and the ramping constraint is configured to limit the change rate of the expected power trajectory between adjacent time steps to be less than the maximum change rate allowed by the flexible resource. 7.The flexible resource scheduling method of considering state timing coupling according to claim 1, characterized in that, The target function further includes that the actual power deviation penalty, the smoothing degree penalty of the control power input and the smoothing degree penalty of the actual output power are each provided with an adjustable weight coefficient, and the weight coefficient is used to dynamically adjust the importance proportion of each penalty item in the target function according to the types of different flexible resources and real-time operation requirements. 8.A flexibility resource scheduling system considering state time coupling, characterized in that, The method comprises: The acquisition module, the first solving module, the second solving module, the third solving module and the scheduling module; The acquisition module is configured to acquire operation parameters of each flexible resource and coordination relationship data of an electricity market and a carbon market, wherein the operation parameters include charging power, discharging power and carbon emission intensity, and the coordination relationship data includes electricity market price, capacity price, mileage price, carbon emission quota price and carbon emission intensity; The first solving module is configured to input the operation parameters into a preset upper-layer optimization model, and perform optimization solving with the minimum total cost of the flexible resource in a preset time period as the target, to obtain a target bid quantity of each flexible resource having a time interval in the preset time period, wherein the upper-layer optimization model is constructed based on the coordination relationship data of the electricity market and the carbon market; The second solving module is configured to construct a middle-layer optimization model containing state time sequence coupling constraints according to the target bid quantity, state parameters and state transition relationship, and perform optimization solving with the minimum total penalty of the expected power trajectory of the flexible resource as the target, to obtain an expected power trajectory of the flexible resource having a time step. The second solving module is configured to construct a middle-layer optimization model containing state time sequence coupling constraints according to the target bid quantity, state parameters and state transition relationship, and perform optimization solving with the minimum total penalty of the expected power trajectory of the flexible resource as the target, to obtain an expected power trajectory of the flexible resource having a time step. The third solving module is configured to acquire a power adjustment rate upper limit and a power adjustment rate lower limit, a power output upper limit and a power output lower limit and an actual disturbance parameter of the flexibility resource; establish a target function taking a sum of an actual power deviation penalty of the flexibility resource, a smoothness penalty of the control power input and a smoothness penalty of the actual output power as the target function according to the expected power trajectory, the power adjustment rate upper limit and the power adjustment rate lower limit, the power output upper limit and the power output lower limit and the actual disturbance parameter; solve a lower-level optimization model to obtain the control power input of each flexibility resource under the condition of satisfying a control rate constraint and an actual output power constraint and taking a second-level time scale as a rolling window. The scheduling module is configured to perform resource scheduling on each flexibility resource based on the control power input of each flexibility resource.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is configured to implement the flexibility resource scheduling method considering state time sequence coupling according to any one of claims 1-7 when executed by the processor.

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