A scheduling method, system, medium and device for flexible resource load tasks

Abstract

The application discloses a kind of flexible resource load task scheduling method, system, medium and equipment, belong to electric power system scheduling field, the method is: acquisition demand side flexibility and non-flexibility load task data and electricity price data;Task scheduling model is constructed with the minimum operation cost as target, including task completion time and power upper and lower limit constraint;Corresponding Lagrange function is established, and the dual variable of each constraint is introduced;KKT condition is used to solve Lagrange function, and the value of dual variable is obtained;Combined with electricity price data, the differential pricing of each flexibility load task is calculated;According to the pricing result, generate control strategy, implement task scheduling.Therefore, by implementing the present application, the problem of lack of accurate cost basis in the scheduling decision of the flexibility resource of the prior art electric power system can be solved.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching and relates to a method, system, medium, and equipment for scheduling flexible resource load tasks. Background Technology

[0002] With the rapid development of high-proportion renewable energy grid integration, electric vehicle charging and swapping, and integrated electricity-gas energy systems, the demand for flexible resources in the power system is increasing. Demand-side flexible resources (such as interruptible loads, energy storage, and electric vehicle aggregators) have become key means to ensure the safe and economical operation of the power grid. These resources can not only adjust power output but also possess "time-domain flexibility" characteristics, allowing them to provide services forward, backward, or continuously over time. In the modern electricity market environment, how to effectively dispatch these time-domain flexible resources to meet user demand while optimizing system operating costs has become an important issue in power system operation.

[0003] Existing technologies primarily achieve flexible resource scheduling by constructing optimization models, but these methods typically have significant limitations. For example, CN109559035A iteratively optimizes flexible resource allocation through upper-level planning and lower-level scheduling, but its cost indicators mainly focus on annual investment and operating costs, failing to refine the dynamic flexibility costs for each time period and resource. CN112651177A incorporates "flexibility service fees" into the distribution network resource allocation target, but still uses total annualized costs to represent them, failing to map shadow prices to specific time-domain adjustment actions. CN109840808A improves the Shapley value to distribute profits within load aggregators, but this method emphasizes profit allocation and does not construct a Lagrange dual framework to analyze the marginal costs of flexible actions. These existing scheduling methods typically treat flexible resources as adjustable capacity, representing costs with annualized investment or average operating costs, without characterizing the dynamic marginal costs incurred when resources provide services at different time periods and durations. Furthermore, stochastic factors such as electricity price fluctuations and load forecasting errors are not explicitly incorporated into the model, making it difficult for the generated scheduling schemes to reflect the real adjustment environment. Moreover, since the system's dual variable (shadow price) is not mapped to specific time-series adjustment actions, existing scheduling schemes usually cannot distinguish the cost contribution of various flexible resources at different times, and it is also difficult to assess the risk exposure of uncertainty to adjustment decisions. This may lead to unreasonable scheduling decisions, with insufficient compensation for some resources on the one hand, and the system still facing the risk of high costs or adjustment failure on the other hand. Summary of the Invention

[0004] This application provides a method, system, medium, and device for scheduling flexible resource load tasks, which can solve the problem that the scheduling decision of flexible resources in power systems lacks accurate cost basis in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a method for scheduling flexible resource load tasks, comprising:

[0006] Collect resource load task data and electricity price data from the demand side of the power system; wherein, the resource load task data includes data on flexible resource load tasks and non-flexible resource load tasks;

[0007] Based on the resource load task data and electricity price data, an objective function for the task scheduling model is constructed with the goal of minimizing operating costs. The task scheduling model is configured with corresponding model constraints, including completion time constraints and upper and lower power limits for flexible resource load tasks.

[0008] Based on the objective function and model constraints, a corresponding Lagrangian function is constructed; wherein, the Lagrangian function contains each dual variable corresponding to the model constraints;

[0009] Based on the preset KKT conditions, the Lagrangian function is solved, each of the dual variables is extracted, and combined with the electricity price data, the differentiated pricing results for each flexible resource load task are calculated.

[0010] Based on the differentiated pricing results, output the control strategy for each flexible resource load task, and schedule the operation of each flexible resource load task.

[0011] Compared with existing technologies, the embodiments of this application have the following beneficial effects: By collecting resource load task data and electricity price data from the demand side of the power system, comprehensive information on the operation status of the power grid is obtained, providing a complete data foundation for subsequent scheduling decisions; furthermore, by constructing a task scheduling model with the goal of minimizing operating costs, the economic operating efficiency of the power system is optimized, and the overall operating costs are reduced; simultaneously, by constructing a Lagrangian function containing each dual variable based on the objective function and model constraints, the system constraints are integrated into the objective function, providing a mathematical basis for subsequently extracting the dual variable (i.e., shadow price) corresponding to the completion time constraint; then, based on the KKT conditions, the Lagrangian function is solved and the dual variable is extracted, and the differentiated pricing result is calculated in combination with the electricity price data, realizing the accurate quantification of the marginal cost of different types of flexible resources in different time periods, overcoming the defect of the existing technology where the shadow price is disconnected from the specific adjustment action; finally, the control strategy is output and scheduling is carried out based on the differentiated pricing result, ensuring that the scheduling decision has an accurate cost basis. The synergistic effect of the aforementioned technical features constructs a mapping mechanism of "dual variable (shadow price) - adjustment action," integrating the dual variable corresponding to the system completion time constraint into the adjustment command. This enables precise calculation of the marginal cost of each type of flexibility resource in a single time period, solving the core problem that existing technologies cannot accurately quantify the dynamic marginal cost of flexibility resources providing services at different times and durations. It ensures a positive surplus condition where the total user payment is not less than the electricity purchase expenditure, and can provide reliable cost signals for the construction of a tiered spot market and flexibility trading platform for the power system.

[0012] In some embodiments of the first aspect of this application, the objective function for constructing a task scheduling model based on the resource load task data and electricity price data, with the goal of minimizing operating costs, includes:

[0013] Based on electricity price data, flexible resource load tasks, and non-flexible resource load tasks, an objective function is constructed as follows:

[0014] Where T represents the total time step after discretization, and J represents the total number of flexible resource load tasks. This represents the price of electricity in the electricity market at time t. This represents the power resources allocated to the j-th flexible resource load task at time t. This represents the load power of the non-flexible resource load task at time t.

[0015] Compared with existing technologies, the above embodiments have the following beneficial effects: An objective function is constructed based on electricity price data and flexible and non-flexible resource load task data. The total time step T in the formula is used to discretize the time dimension to calculate costs in different time periods. The total number J of flexible resource load tasks clarifies the number of controllable objects. The electricity price at time t is used to quantify the electricity cost at that time. The electricity resources allocated to the j-th flexible resource load task at time t and the load power of the non-flexible resource load task respectively characterize the electricity consumption of adjustable and fixed loads. By summing the costs of each time period and each task, the accurate quantification of operating costs is achieved, providing a mathematical basis for subsequent optimization.

[0016] In some embodiments of the first aspect of this application, the model constraints include: completion time constraints and power upper and lower limit constraints for flexible resource load tasks;

[0017] The completion time constraint for the flexible resource load task is expressed as follows:

[0018] ;in, and Let represent the deadline and arrival time of the j-th flexibility resource load task, respectively. This represents the total electrical energy required for the j-th flexible resource load task. Let represent the dual variable corresponding to the completion time constraint of the j-th flexible resource load task;

[0019] The power upper and lower limit constraints of the flexible resource load task are expressed as follows:

[0020] ;

[0021] ;

[0022] in, This represents the maximum operating power of the flexible resource load task at time t. Let represent the dual variable corresponding to the power lower bound constraint of the j-th flexible resource load task at time t. Let represent the dual variable corresponding to the power upper limit constraint at time t.

[0023] Compared to existing technologies, the above embodiments have the following beneficial effects: The task completion time constraint of the flexibility resource load in the model constraints ensures that the task is completed within the allowed time window and meets energy requirements by limiting the interval between arrival and deadline times and the total power resource demand. The task arrival and deadline parameters are used to precisely define the schedulable time range of the task, and the total power resources required by the task are used to guarantee the total energy needed to complete the task. The dual variable (i.e., shadow price) corresponding to the completion time constraint is used to subsequently calculate the time-domain flexibility value of the task. The lower limit constraint in the power upper and lower limit constraints prevents negative power allocation to ensure scheduling feasibility. The dual variable corresponding to the power lower limit constraint reflects the degree of relaxation of the power lower limit constraint. The upper limit constraint, by limiting the total load power to not exceed the maximum operating power, avoids system overload and ensures stable operation of the power system. The dual variable corresponding to the power upper limit constraint is used to characterize the degree of system capacity strain. The comprehensive application of these constraints achieves accurate modeling of the time-domain characteristics of flexibility resources, providing a theoretical basis for differentiated pricing.

[0024] In some embodiments of the first aspect of this application, constructing the corresponding Lagrangian function based on the objective function and model constraints includes:

[0025] Based on the objective function and the dual variables in the model constraints, the Lagrangian function is constructed as follows:

[0026] .

[0027] Compared with existing technologies, the above embodiments have the following beneficial effects: a Lagrangian function is constructed based on the objective function and model constraints, and the dual variables of the lower resource limit constraint, the task completion constraint, and the upper resource limit constraint are incorporated into the function. Through mathematical transformation, the constraints are integrated with the objective function, providing a unified mathematical framework for solving the optimal solution using KKT conditions, and achieving cost minimization optimization under constraints.

[0028] In some embodiments of the first aspect of this application, the step of solving the Lagrangian function based on preset KKT conditions, extracting each of the dual variables, and calculating the differentiated pricing results for each flexible resource load task in conjunction with the electricity price data includes:

[0029] Based on the dual variables and the electricity price data, the KKT conditions are expressed as follows:

[0030] ;

[0031] The KKT conditions are transformed to represent the dual variable corresponding to the completion time constraint of the flexible resource load task as the differentiated pricing result of the flexible resource load task, as follows:

[0032] .

[0033] Compared to existing technologies, the above embodiments have the following advantages: The Lagrangian function is solved based on preset KKT conditions, ensuring optimality of the solution by ensuring the partial derivative of power resource allocation is zero; the dual variable (i.e., shadow price) for completing time constraints is extracted by combining electricity price data, and defined as the differentiated pricing result for flexible resource load tasks. This pricing result achieves the organic integration of electricity price, resource occupancy, and time-domain value, reflecting the marginal benefits of regulation services. Simultaneously, this pricing mechanism ensures that the total user payment is not less than the electricity expenditure, satisfying the positive surplus condition and ensuring the economic sustainability of flexible resource operators.

[0034] Secondly, the present invention also provides a scheduling system for flexible resource load tasks, comprising: a data acquisition module, an objective function construction module, a Lagrange function construction module, a solution calculation module, and an output scheduling module;

[0035] The data acquisition module is used to collect resource load task data and electricity price data on the demand side of the power system; wherein the resource load task data includes data on flexible resource load tasks and non-flexible resource load tasks.

[0036] The objective function construction module is used to construct an objective function for the task scheduling model based on the resource load task data and electricity price data, with the goal of minimizing operating costs; wherein, the task scheduling model is configured with corresponding model constraints; the model constraints include the completion time constraint and power upper and lower limit constraint for flexible resource load tasks;

[0037] The Lagrangian function construction module is used to construct a corresponding Lagrangian function based on the objective function and model constraints; wherein, the Lagrangian function includes each dual variable corresponding to the model constraints;

[0038] The solution and calculation module is used to solve the Lagrangian function based on the preset KKT conditions, extract each of the dual variables, and calculate the differentiated pricing results for each flexible resource load task in combination with the electricity price data.

[0039] The output scheduling module is used to output the control strategy of each flexible resource load task according to the differentiated pricing results, and to schedule the operation of each flexible resource load task.

[0040] Compared with existing technologies, the above embodiments of this application have the following beneficial effects: By collecting resource load task data and electricity price data from the demand side of the power system, comprehensive information on the operation status of the power grid is obtained, providing a complete data foundation for subsequent dispatch decisions; furthermore, by constructing a task dispatch model with the goal of minimizing operating costs, the economic operating efficiency of the power system is optimized, and the overall operating cost is reduced; simultaneously, by constructing a Lagrangian function containing each dual variable based on the objective function and model constraints, the system constraints are integrated into the objective function, providing a mathematical basis for subsequently extracting the dual variable (i.e., shadow price) corresponding to the completion time constraint; then, based on the KKT conditions, the Lagrangian function is solved and the dual variable is extracted, and the differentiated pricing result is calculated in combination with the electricity price data, realizing the accurate quantification of the marginal cost of different time periods and different types of flexible resources, overcoming the defect of the existing technology where the shadow price is disconnected from the specific adjustment action; finally, the control strategy is output and dispatch is carried out based on the differentiated pricing result, ensuring that the dispatch decision has an accurate cost basis. The synergistic effect of the aforementioned technical features constructs a mapping mechanism of "dual variable (shadow price) - adjustment action," integrating the dual variable corresponding to the system completion time constraint into the adjustment command. This enables precise calculation of the marginal cost of each type of flexibility resource in a single time period, solving the core problem that existing technologies cannot accurately quantify the dynamic marginal cost of flexibility resources providing services at different times and durations. It ensures a positive surplus condition where the total user payment is not less than the electricity purchase expenditure, and can provide reliable cost signals for the construction of a tiered spot market and flexibility trading platform for the power system.

[0041] In some embodiments of the second aspect of this application, the objective function construction module includes: an objective function construction unit;

[0042] The objective function construction unit is used to construct an objective function based on electricity price data, flexible resource load tasks, and non-flexible resource load task data, as shown below:

[0043] Where T represents the total time step after discretization, and J represents the total number of flexible resource load tasks. This represents the price of electricity in the electricity market at time t. This represents the power resources allocated to the j-th flexible resource load task at time t. This represents the load power of the non-flexible resource load task at time t.

[0044] Compared with existing technologies, the above embodiments have the following beneficial effects: An objective function is constructed based on electricity price data and flexible and non-flexible resource load task data. The total time step T in the formula is used to discretize the time dimension to calculate costs in different time periods. The total number J of flexible resource load tasks clarifies the number of controllable objects. The electricity price at time t is used to quantify the electricity cost at that time. The electricity resources allocated to the j-th flexible resource load task at time t and the load power of the non-flexible resource load task respectively characterize the electricity consumption of adjustable and fixed loads. By summing the costs of each time period and each task, the accurate quantification of operating costs is achieved, providing a mathematical basis for subsequent optimization.

[0045] In some embodiments of the second aspect of this application, the Lagrange function construction module includes: a Lagrange function construction unit;

[0046] The Lagrangian function construction unit is used to construct a Lagrangian function based on the objective function and each dual variable in the model constraints, as shown below:

[0047] ;in, This represents the arrival time of the j-th flexibility resource load task. This represents the total electrical energy required for the j-th flexible resource load task. Let represent the dual variable corresponding to the completion time constraint of the j-th flexibility resource load task. This represents the maximum operating power of the flexible resource load task at time t. Let represent the dual variable corresponding to the power lower bound constraint of the j-th flexible resource load task at time t. Let represent the dual variable corresponding to the power upper limit constraint at time t.

[0048] Compared with existing technologies, the above embodiments have the following beneficial effects: a Lagrangian function is constructed based on the objective function and model constraints, and the dual variables of the lower resource limit constraint, the task completion constraint, and the upper resource limit constraint are incorporated into the function. Through mathematical transformation, the constraints are integrated with the objective function, providing a unified mathematical framework for solving the optimal solution using KKT conditions, and achieving cost minimization optimization under constraints.

[0049] Thirdly, the present invention also provides a scheduling device for flexible resource load tasks, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded onto the processor, implements the steps of any one of the flexible resource load task scheduling methods of the present invention.

[0050] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the flexible resource load task scheduling methods of the present invention. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a flexible resource load scheduling method provided in some embodiments of the present invention.

[0052] Figure 2 This is a schematic diagram of the structure of a flexible resource load task scheduling system provided in some embodiments of the present invention.

[0053] Figure 3 : This is a structural diagram of a flexible resource load task scheduling device provided in some embodiments of the present invention.

[0054] Figure 4 This is a schematic diagram illustrating the temporal flexibility of a flexible resource provided in some embodiments of the present invention.

[0055] Figure 5 This is a schematic diagram of a pricing mechanism provided in some embodiments of the present invention.

[0056] Figure 6 This is a pricing curve diagram of various computing power tasks at different start times, provided in some embodiments of the present invention.

[0057] Figure 7 This is a pricing curve for various computing power tasks at different deadlines, provided in some embodiments of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1:

[0060] Please refer to Figure 1 To address the problem of insufficient precise cost basis for scheduling decisions of flexible resources in power systems in existing technologies, an embodiment of the present invention provides a scheduling method for flexible resource load tasks, comprising steps S1 to S6:

[0061] Step S1: Collect resource load task data and electricity price data from the demand side of the power system; wherein, the resource load task data includes data on flexible resource load tasks and non-flexible resource load tasks.

[0062] Step S2: Based on the resource load task data and electricity price data, construct the objective function of the task scheduling model with the goal of minimizing operating costs; wherein, the task scheduling model is configured with corresponding model constraints; the model constraints include the completion time constraint and power upper and lower limit constraint of the flexible resource load task.

[0063] Specifically, when building the model, it is necessary to consider the flexibility of differentiated time domains, such as... Figure 4 The diagram illustrates the time-domain flexibility of flexible resources. Time-domain flexibility of demand-side flexible resources refers to the ability of certain loads in a power system to complete within a specific timeframe. If these loads possess a certain degree of time flexibility—that is, a time margin for processing—then adjustments can be made to their timing without affecting power supply quality. This ability to flexibly adjust over time is called time-domain flexibility. The operating cost of flexible resources is calculated by multiplying the temporarily adjustable workload by the inflexible fixed workload by the real-time market electricity price.

[0064] Furthermore, the construction of the objective function in step S2 can be implemented through the following preferred embodiment, including step S21, as follows:

[0065] S21: Based on electricity price data, flexible resource load tasks, and non-flexible resource load task data, construct the objective function as follows:

[0066] Where T represents the total time step after discretization, and J represents the total number of flexible resource load tasks. This represents the price of electricity in the electricity market at time t. This represents the power resources allocated to the j-th flexible resource load task at time t. This represents the load power of the non-flexible resource load task at time t.

[0067] In this preferred embodiment, an objective function is constructed based on electricity price data and flexible and non-flexible resource load task data. The total time step T in the formula is used to discretize the time dimension to calculate costs in different time periods. The total number J of flexible resource load tasks specifies the number of controllable objects. The electricity price at time t is used to quantify the electricity cost at that time. The electricity resources allocated to the j-th flexible resource load task and the load power of the non-flexible resource load task at time t respectively represent the electricity consumption of adjustable and fixed loads. By summing the costs of each time period and each task, the operating cost is accurately quantified, providing a mathematical basis for subsequent optimization.

[0068] After building the model, you also need to configure the corresponding constraints, specifically:

[0069] Furthermore, the model constraints include: completion time constraints and power upper and lower limit constraints for flexible resource load tasks;

[0070] Specifically, the completion time constraint for the flexible resource load task is expressed as follows:

[0071] ;in, and Let represent the deadline and arrival time of the j-th flexibility resource load task, respectively. This represents the total electrical energy required for the j-th flexible resource load task. Let represent the dual variable corresponding to the completion time constraint of the j-th flexible resource load task;

[0072] The power upper and lower limit constraints of the flexible resource load task are expressed as follows:

[0073] ;

[0074] ;

[0075] in, This represents the maximum operating power of the flexible resource load task at time t. Let represent the dual variable corresponding to the power lower bound constraint of the j-th flexible resource load task at time t. Let represent the dual variable corresponding to the power upper limit constraint at time t.

[0076] In this preferred embodiment, the task completion time constraint of the flexibility resource load in the model constraints ensures that the task is completed within the allowed time window and meets the energy requirements by limiting the interval between the arrival time and the deadline time and the total power resource demand. The task arrival time and deadline time parameters are used to accurately define the time range of the task that can be scheduled, and the total power resource required by the task is used to guarantee the total energy required for the task to be completed. The dual variable (i.e., shadow price) corresponding to the completion time constraint is used to calculate the time-domain flexibility value of the task in the subsequent calculation. The lower limit constraint of the power upper and lower limit constraints prevents negative power allocation to ensure scheduling feasibility. The dual variable corresponding to the power lower limit constraint reflects the relaxation degree of the power lower limit constraint. The upper limit constraint avoids system overload and ensures the stable operation of the power system by limiting the total load power to not exceed the maximum operating power. The dual variable corresponding to the power upper limit constraint is used to characterize the system capacity tension. The comprehensive application of these constraints realizes the accurate modeling of the time-domain characteristics of flexibility resources and provides a theoretical basis for differentiated pricing.

[0077] Step S3: Based on the objective function and model constraints, construct the corresponding Lagrangian function; wherein the Lagrangian function contains each dual variable corresponding to the model constraints.

[0078] Furthermore, step S3 can be implemented through the following preferred embodiment, including step S31, as follows:

[0079] S31: Based on the objective function and the dual variables in the model constraints, construct the Lagrangian function, expressed as follows:

[0080] .

[0081] In this preferred embodiment, a Lagrangian function is constructed based on the objective function and model constraints. The dual variables of the lower resource limit constraint, the dual variables of the task completion constraint, and the dual variables of the upper resource limit constraint are incorporated into the function. Through mathematical transformation, the constraints are integrated with the objective function, providing a unified mathematical framework for solving the optimal solution using KKT conditions, thereby achieving cost minimization optimization under constraints.

[0082] Step S4: Based on the preset KKT conditions, solve the Lagrangian function, extract each of the dual variables, and combine the electricity price data to calculate the differentiated pricing results for each flexible resource load task.

[0083] Furthermore, step S4 can be implemented through the following preferred embodiments, including steps S41-S42, as follows:

[0084] S41: Based on each dual variable and the electricity price data, the KKT conditions are expressed as follows:

[0085] ;

[0086] Here, The physical meaning of this is the cost change caused by an increase of 1 unit in resource demand for work, reflecting the marginal benefit of adjusting services. Furthermore, it will... As a result of pricing for flexible resource load tasks, the method is as follows:

[0087] S42: Transform the KKT conditions to use the dual variable representing the completion time constraint of the flexible resource load task as the differentiated pricing result of the flexible resource load task, as follows: .

[0088] Thus, this scheme establishes a mathematical model to characterize the resource requirements and temporal flexibility of different flexible resource load tasks. Based on this, linear constraints required for the optimization problem are given, and different shadow prices are derived using these constraints. Finally, differentiated pricing results for flexible resource load tasks are derived. Specific results can be found in [reference needed]. Figure 5 The diagram shows a pricing mechanism.

[0089] In this preferred embodiment, the Lagrangian function is solved based on preset KKT conditions. The optimality of the solution is ensured by ensuring that the partial derivative with respect to power resource allocation is zero. The dual variable (i.e., shadow price) that completes the time constraint is extracted by combining electricity price data. It is defined as the differentiated pricing result of the flexible resource load task. This pricing result realizes the organic integration of electricity price, resource occupation and time domain value, reflects the marginal benefit of regulation service, and ensures that the total user payment is not less than the electricity expenditure, meets the positive surplus condition, and ensures the economic sustainability of the flexible resource operator.

[0090] Step S5: Based on the differentiated pricing results, output the control strategy for each flexible resource load task, and schedule the operation of each flexible resource load task.

[0091] In summary, compared with the prior art, the above embodiments of this application have the following beneficial effects: By collecting resource load task data and electricity price data from the demand side of the power system, comprehensive information on the power grid operation status is obtained, providing a complete data foundation for subsequent dispatch decisions; furthermore, by constructing a task dispatch model with the goal of minimizing operating costs, the economic operating efficiency of the power system is optimized, and the overall operating cost is reduced; simultaneously, by constructing a Lagrangian function containing each dual variable based on the objective function and model constraints, the system constraints are integrated into the objective function, providing a mathematical basis for subsequently extracting the dual variable (i.e., shadow price) corresponding to the completion time constraint; then, based on the KKT conditions, the Lagrangian function is solved and the dual variable is extracted, and the differentiated pricing result is calculated in combination with the electricity price data, realizing the accurate quantification of the marginal cost of different time periods and different types of flexible resources, overcoming the defect of the existing technology where the shadow price is disconnected from the specific adjustment action; finally, the control strategy is output and dispatch is carried out based on the differentiated pricing result, ensuring that the dispatch decision has an accurate cost basis. The synergistic effect of the aforementioned technical features constructs a mapping mechanism of "dual variable (shadow price) - adjustment action," integrating the dual variable corresponding to the system completion time constraint into the adjustment command. This enables precise calculation of the marginal cost of each type of flexibility resource in a single time period, solving the core problem that existing technologies cannot accurately quantify the dynamic marginal cost of flexibility resources providing services at different times and durations. It ensures a positive surplus condition where the total user payment is not less than the electricity purchase expenditure, and can provide reliable cost signals for the construction of a tiered spot market and flexibility trading platform for the power system.

[0092] As a supplement, this plan also verifies the above-mentioned differentiated pricing results to ensure that the final pricing control plan meets the positive surplus condition:

[0093] First, the total revenue from all flexible resource load tasks is represented by the pricing result. The product of the total resource requirements of each flexible resource load task and the electricity charges that the flexible resource load task needs to pay to the power system based on the real-time market electricity price π constitutes the operating cost; due to the constraints of arrival time and deadline, outside the interval between the arrival time and deadline, Since the values ​​of all values ​​are zero, the expression for the total sales surplus of the flexibility resource operator is:

[0094] .

[0095] According to the KKT conditions The expression is mainly affected by real-time electricity prices. The impact is that when flexible resources are scarce, an additional charge is added to the original electricity price. , For non-negative numbers, when t is within the interval between arrival and deadline times, the dual variable is... The corresponding lower bound constraint fails; therefore, the dual variable... If the real-time electricity price is zero, add a non-negative number to it, then subtract zero. The resulting value is greater than the real-time electricity price itself.

[0096] ;

[0097] Substituting the above expression into the expression for total sales surplus, we obtain the final pricing scheme:

[0098] .

[0099] Therefore, the final pricing scheme can guarantee the positive surplus condition, that is, the total user payment is greater than or equal to the electricity expenditure, which mathematically proves the rationality of this application. Specific Implementation Example 1:

[0101] Taking data centers as an example, the pricing for various computing tasks is shown in Table 1 below:

[0102] Table 1

[0103]

[0104] After calculating differentiated pricing using this scheme, each task is scheduled in a way that minimizes operating costs. The scheduling results for various computing power tasks are shown in Table 2 below:

[0105] Table 2

[0106]

[0107] As can be seen from Table 2, each task was scheduled to be executed during periods of lower electricity prices. Compared to Task 2, Task 1 had a more pressing deadline and less time flexibility, and therefore was priced higher. Compared to Task 7, although Task 6 had a longer deadline, its time flexibility was not valuable because its scheduling window was during a period of rising electricity prices. Therefore, the pricing results for these two types of tasks were the same.

[0108] The sensitivity analysis of the task arrival time is as follows:

[0109] The deadline parameter for flexible resource load tasks is determined by combining parameters such as task arrival time and deadline that affect pricing results. A sensitivity analysis was conducted to determine the pricing outcome when the deadline changed from being extended by 4 hours to being brought forward by 4 hours. The pricing outcome was as follows: Figure 6 The chart shown illustrates pricing curves for various computing tasks at different start times, from... Figure 6As can be seen, the pricing of flexible resources decreases when the deadline is delayed. The pricing results indicate that tasks with greater time flexibility have lower fees. Tasks 1 to 5 arrive in the range of decreasing electricity prices and are actually executed close to the deadline; therefore, arriving earlier does not reduce costs, and the pricing remains unchanged. Tasks 6 to 8 have already reached the earliest arrival limit and cannot be advanced further; therefore, the sensitivity results for earlier arrival are not shown in the figure. Task 6 has no feasible solution under certain arrival time conditions, so only one point is shown in the figure. When the arrival time of Task 7 is delayed by three to four hours, it will be scheduled to a higher electricity price period, causing its pricing to increase. Although the arrival time of Task 8 is in the range of increasing electricity prices, the electricity price is actually lower when it approaches the deadline; therefore, delayed arrival does not affect costs, and the pricing remains unchanged.

[0110] The sensitivity analysis of the task deadline is as follows:

[0111] Arrival time parameters for flexible resource load tasks Sensitivity analysis was conducted to determine the price outcome when the arrival time changed from 4 hours late to 4 hours early. The pricing results are as follows: Figure 7 The chart shown illustrates pricing curves for various computing tasks at different deadlines. Figure 7 As can be seen, when the deadline is postponed, the pricing of the task decreases, indicating that tasks with greater time flexibility have lower fees. Task 1 occurs during a period of declining electricity prices; postponing it by one unit of time avoids the high-price zone, resulting in a lower price. Tasks 2, 6, and 7 have deadlines during periods of rising electricity prices; postponing them does not provide additional flexibility, so the pricing remains unchanged. Tasks 3 to 5 have deadlines during peak electricity price periods, when resources are scarce; postponing the deadline does not provide scheduling flexibility, so the pricing remains unchanged. If the deadline for Task 7 is postponed by more than 3 hours, it may enter a period of lower electricity prices, thus lowering the price. If the deadline for Task 8 is advanced by more than 3 hours, it will have to be executed during a period of higher electricity prices, leading to an increased price.

[0112] In summary, the scheduling method proposed in this invention can achieve differentiated pricing and schedule various flexible resource load tasks according to the pricing results, so that the total user payment is greater than or equal to the electricity expenditure, generating a positive surplus, thereby ensuring economic benefits in scheduling operations.

[0113] Example 2:

[0114] Please refer to Figure 2 Based on the same inventive concept, the present invention discloses a scheduling system for flexible resource load tasks, comprising: a data acquisition module M1, an objective function construction module M2, a Lagrange function construction module M3, a solution calculation module M4, and an output scheduling module M5;

[0115] The data acquisition module M1 is used to collect resource load task data and electricity price data on the demand side of the power system; wherein the resource load task data includes data on flexible resource load tasks and non-flexible resource load tasks.

[0116] The objective function construction module M2 is used to construct an objective function for the task scheduling model based on the resource load task data and electricity price data, with the goal of minimizing operating costs; wherein, the task scheduling model is configured with corresponding model constraints; the model constraints include the completion time constraint and power upper and lower limit constraint of the flexible resource load task.

[0117] Furthermore, the objective function construction module M2 includes: an objective function construction unit;

[0118] The objective function construction unit is used to construct an objective function based on electricity price data, flexible resource load tasks, and non-flexible resource load task data, as shown below:

[0119] Where T represents the total time step after discretization, and J represents the total number of flexible resource load tasks. This represents the price of electricity in the electricity market at time t. This represents the power resources allocated to the j-th flexible resource load task at time t. This represents the load power of the non-flexible resource load task at time t.

[0120] In this preferred embodiment, an objective function is constructed based on electricity price data and flexible and non-flexible resource load task data. The total time step T in the formula is used to discretize the time dimension to calculate costs in different time periods. The total number J of flexible resource load tasks specifies the number of controllable objects. The electricity price at time t is used to quantify the electricity cost at that time. The electricity resources allocated to the j-th flexible resource load task and the load power of the non-flexible resource load task at time t respectively represent the electricity consumption of adjustable and fixed loads. By summing the costs of each time period and each task, the operating cost is accurately quantified, providing a mathematical basis for subsequent optimization.

[0121] Furthermore, the model constraints include: completion time constraints and power upper and lower limit constraints for flexible resource load tasks;

[0122] The completion time constraint for the flexible resource load task is expressed as follows:

[0123] ;in, and Let represent the deadline and arrival time of the j-th flexibility resource load task, respectively. This represents the total electrical energy required for the j-th flexible resource load task. Let represent the dual variable corresponding to the completion time constraint of the j-th flexible resource load task;

[0124] The power upper and lower limit constraints of the flexible resource load task are expressed as follows:

[0125] ;

[0126] ;

[0127] in, This represents the maximum operating power of the flexible resource load task at time t. Let represent the dual variable corresponding to the power lower bound constraint of the j-th flexible resource load task at time t. Let represent the dual variable corresponding to the power upper limit constraint at time t.

[0128] In this preferred embodiment, the task completion time constraint of the flexibility resource load in the model constraints ensures that the task is completed within the allowed time window and meets the energy requirements by limiting the interval between the arrival time and the deadline time and the total power resource demand. The task arrival time and deadline time parameters are used to accurately define the time range of the task that can be scheduled, and the total power resource required by the task is used to guarantee the total energy required for the task to be completed. The dual variable (i.e., shadow price) corresponding to the completion time constraint is used to calculate the time-domain flexibility value of the task in the subsequent calculation. The lower limit constraint of the power upper and lower limit constraints prevents negative power allocation to ensure scheduling feasibility. The dual variable corresponding to the power lower limit constraint reflects the relaxation degree of the power lower limit constraint. The upper limit constraint avoids system overload and ensures the stable operation of the power system by limiting the total load power to not exceed the maximum operating power. The dual variable corresponding to the power upper limit constraint is used to characterize the system capacity tension. The comprehensive application of these constraints realizes the accurate modeling of the time-domain characteristics of flexibility resources and provides a theoretical basis for differentiated pricing.

[0129] The Lagrangian function construction module M3 is used to construct the corresponding Lagrangian function based on the objective function and model constraints; wherein, the Lagrangian function includes each dual variable corresponding to the model constraints.

[0130] Furthermore, the Lagrange function construction module M3 includes: a Lagrange function construction unit;

[0131] The Lagrangian function construction unit is used to construct a Lagrangian function based on the objective function and each dual variable in the model constraints, as shown below:

[0132] ;in, This represents the arrival time of the j-th flexibility resource load task. This represents the total electrical energy required for the j-th flexible resource load task. Let represent the dual variable corresponding to the completion time constraint of the j-th flexibility resource load task. This represents the maximum operating power of the flexible resource load task at time t. Let represent the dual variable corresponding to the power lower bound constraint of the j-th flexible resource load task at time t. Let represent the dual variable corresponding to the power upper limit constraint at time t.

[0133] In this preferred embodiment, a Lagrangian function is constructed based on the objective function and model constraints. The dual variables of the lower resource limit constraint, the dual variables of the task completion constraint, and the dual variables of the upper resource limit constraint are incorporated into the function. Through mathematical transformation, the constraints are integrated with the objective function, providing a unified mathematical framework for solving the optimal solution using KKT conditions, thereby achieving cost minimization optimization under constraints.

[0134] The solution calculation module M4 is used to solve the Lagrangian function based on the preset KKT conditions, extract each of the dual variables, and calculate the differentiated pricing results for each flexible resource load task in combination with the electricity price data.

[0135] Furthermore, the solution calculation module M4 includes: a representation unit and a transformation unit;

[0136] The expression unit is used to express the KKT conditions based on each dual variable and the electricity price data as follows: ;

[0137] The transformation unit is used to transform the expression of the KKT conditions, and take the dual variable corresponding to the completion time constraint of the flexible resource load task as the differentiated pricing result of the flexible resource load task, as follows: .

[0138] In this preferred embodiment, the Lagrangian function is solved based on preset KKT conditions. The optimality of the solution is ensured by ensuring that the partial derivative with respect to power resource allocation is zero. The dual variable (i.e., shadow price) that completes the time constraint is extracted by combining electricity price data. It is defined as the differentiated pricing result of the flexible resource load task. This pricing result realizes the organic integration of electricity price, resource occupation and time domain value, reflects the marginal benefit of regulation service, and ensures that the total user payment is not less than the electricity expenditure, meets the positive surplus condition, and ensures the economic sustainability of the flexible resource operator.

[0139] The output scheduling module M5 is used to output the control strategy of each flexible resource load task according to the differentiated pricing results, and to schedule the operation of each flexible resource load task.

[0140] In summary, compared with existing technologies, the embodiments of this application have the following beneficial effects: By collecting resource load task data and electricity price data from the demand side of the power system, comprehensive information on the power grid operation status is obtained, providing a complete data foundation for subsequent scheduling decisions; furthermore, by constructing a task scheduling model with the goal of minimizing operating costs, the economic operating efficiency of the power system is optimized, and the overall operating cost is reduced; simultaneously, by constructing a Lagrangian function containing each dual variable based on the objective function and model constraints, the system constraints are integrated into the objective function, providing a mathematical basis for subsequently extracting the dual variable (i.e., shadow price) corresponding to the completion time constraint; then, based on KKT conditions, the Lagrangian function is solved and the dual variable is extracted, and combined with electricity price data, a differentiated pricing result is calculated, realizing the accurate quantification of the marginal cost of different time periods and different types of flexible resources, overcoming the defect of the existing technology where shadow price is disconnected from specific adjustment actions; finally, the control strategy is output and scheduling is carried out based on the differentiated pricing result, ensuring that the scheduling decision has an accurate cost basis. The synergistic effect of the aforementioned technical features constructs a mapping mechanism of "dual variable (shadow price) - adjustment action," integrating the dual variable corresponding to the system completion time constraint into the adjustment command. This enables precise calculation of the marginal cost of each type of flexibility resource in a single time period, solving the core problem that existing technologies cannot accurately quantify the dynamic marginal cost of flexibility resources providing services at different times and durations. It ensures a positive surplus condition where the total user payment is not less than the electricity purchase expenditure, and can provide reliable cost signals for the construction of a tiered spot market and flexibility trading platform for the power system.

[0141] Example 3:

[0142] Figure 3 A structural diagram of a flexible resource load scheduling device according to this application is presented. For example... Figure 3 As shown, the scheduling device for this flexible resource load task may include: processor N1, memory N2, data interface N3, and communication bus N4.

[0143] Wherein: processor N1, memory N2, and data interface N3 communicate with each other through communication bus N4; data interface N3 is used for data communication with other devices such as input devices or output devices; processor N1 is used to execute program N5, specifically it can execute the relevant steps in any of the above-mentioned flexible resource load task scheduling method embodiments.

[0144] Specifically, program N5 may include program code, which includes computer-executable instructions.

[0145] The processor N1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The scheduling device for flexible resource workload tasks includes one or more processors, which may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0146] Memory N2 is used to store program N5. Memory N2 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage.

[0147] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Furthermore, the embodiments in this application are not directed to any particular programming language.

[0148] Example 4:

[0149] This invention also provides a computer-readable storage medium storing at least one executable instruction that, when executed on a flexible resource load task scheduling device / system, causes the flexible resource load task scheduling device / system to perform a flexible resource load task scheduling method from any of the above method embodiments.

[0150] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. Similarly, for the purpose of simplification and aiding understanding of one or more aspects of the invention, in the above description of exemplary embodiments of this application, various features of the embodiments are sometimes grouped together in a single embodiment, figure, or description thereof. The claims, which follow the detailed description, are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0151] Those skilled in the art will understand that the modules in the device of the embodiment can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiment can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components, except that at least some of such features and / or processes or units are mutually exclusive.

Claims

1. A method for scheduling a flexible resource load task, characterized in that, The method comprises the following steps: Collecting resource load task data and electricity price data on the demand side of a power system; wherein the resource load task data comprises data of flexible resource load tasks and non-flexible resource load tasks; Based on the resource load task data and the electricity price data, a target function of a task scheduling model is constructed with the objective of minimizing operating costs; wherein the task scheduling model is configured with corresponding model constraints; the model constraints comprise completion time constraints and power upper and lower limit constraints of the flexible resource load tasks; Based on the target function and the model constraints, a corresponding Lagrange function is constructed; wherein the Lagrange function contains each dual variable corresponding to the model constraints; Based on a preset KKT condition, the Lagrange function is solved, each dual variable is extracted, and the differentiated pricing results of each flexible resource load task are calculated in combination with the electricity price data; According to the differentiated pricing results, the regulation and control strategies of each flexible resource load task are output, and the operation of each flexible resource load task is scheduled; Wherein, based on the target function and the model constraints, the corresponding Lagrange function is constructed, which comprises: Based on the target function and each dual variable in the model constraints, the Lagrange function is constructed, which is expressed as follows: ； wherein, denotes the electricity energy price at time t in the electricity market, denotes the power resource allocated to the jth flexible resource load task at time t, denotes the load power of the non-flexible resource load task at time t, denotes the total electricity energy resource required by the jth flexible resource load task, denotes the dual variable corresponding to the completion time constraint of the jth flexible resource load task, and denote the deadline and arrival time of the jth flexible resource load task, respectively, denotes the maximum operating power of the flexible resource load task at time t, denotes the dual variable corresponding to the lower power limit constraint of the jth flexible resource load task at time t, denotes the dual variable corresponding to the upper power limit constraint at time t, T denotes the total time steps after discretization, and J denotes the total number of flexible resource load tasks.

2. The method of claim 1, wherein, Based on the resource load task data and the electricity price data, the target function of the task scheduling model is constructed with the objective of minimizing operating costs, which comprises: Based on the data of the electricity price, the flexible resource load tasks and the non-flexible resource load tasks, the target function is constructed, which is expressed as follows: ; where T denotes the total time steps after discretization, J denotes the total number of flexibility resource load tasks, denotes the electricity energy price at time t in the electricity market, denotes the power resource allocated to the jth flexibility resource load task at time t, denotes the load power of non-flexibility resource load tasks at time t.

3. The method of claim 2, wherein the step of scheduling the tasks on the flexible resource is further characterized by, The model constraints comprise the completion time constraints and the power upper and lower limit constraints of the flexible resource load tasks; The completion time constraints of the flexible resource load tasks are expressed as follows: ; where, and denote the deadline and arrival time of the jth flexible resource load task, respectively, denote the total electrical energy resource required by the jth flexible resource load task, denote the dual variable corresponding to the completion time constraint of the jth flexible resource load task; The power upper and lower limit constraints of the flexible resource load tasks are expressed as follows: ； ； wherein, denotes the maximum running power of the flexible resource load task at time t, denotes the dual variable corresponding to the power lower bound constraint of the jth flexible resource load task at time t, denotes the dual variable corresponding to the power upper bound constraint at time t.

4. The method of claim 3, wherein, Based on the preset KKT condition, the Lagrange function is solved, each dual variable is extracted, and the differentiated pricing results of each flexible resource load task are calculated in combination with the electricity price data, which comprises: Based on each dual variable and the electricity price data, the KKT condition is expressed as follows: ； The KKT condition is transformed in expression, and the dual variable corresponding to the completion time constraints of the flexible resource load tasks is taken as the differentiated pricing result of the flexible resource load tasks, which is expressed as follows: 。 5. A system for scheduling of tasks on flexible resources, characterized in that, The method comprises the following steps: A data acquisition module, a target function construction module, a Lagrange function construction module, a solution calculation module and an output scheduling module are included; The data acquisition module is used to collect resource load task data and electricity price data on the demand side of a power system; wherein the resource load task data comprises data of flexible resource load tasks and non-flexible resource load tasks; The target function construction module is used to construct a target function of a task scheduling model with the objective of minimizing operating costs based on the resource load task data and the electricity price data; wherein the task scheduling model is configured with corresponding model constraints; the model constraints comprise completion time constraints and power upper and lower limit constraints of the flexible resource load tasks; The Lagrange function construction module is configured to construct a corresponding Lagrange function based on the objective function and the model constraints; wherein the Lagrange function contains each dual variable corresponding to the model constraints; The solving calculation module is configured to solve the Lagrange function based on a preset KKT condition, extract each dual variable, and calculate a differentiated pricing result of each flexible resource load task in combination with the electricity price data; The output scheduling module is configured to output a regulation and control strategy of each flexible resource load task according to each differentiated pricing result, and schedule the operation of each flexible resource load task; The Lagrange function construction unit is configured to construct a Lagrange function based on the objective function and each dual variable in the model constraints, and the Lagrange function is expressed as follows: ； wherein, denotes the electricity energy price at time t in the electricity market, denotes the power resource allocated to the jth flexible resource load task at time t, denotes the load power of the non-flexible resource load task at time t, denotes the total electricity energy resource required by the jth flexible resource load task, denotes the dual variable corresponding to the completion time constraint of the jth flexible resource load task, and denote the deadline and arrival time of the jth flexible resource load task, respectively, denotes the maximum operating power of the flexible resource load task at time t, denotes the dual variable corresponding to the lower power limit constraint of the jth flexible resource load task at time t, denotes the dual variable corresponding to the upper power limit constraint at time t, T denotes the total time steps after discretization, and J denotes the total number of flexible resource load tasks.

6. The flexible resource load task scheduling system of claim 5, wherein, The objective function construction module includes an objective function construction unit. The objective function construction unit is configured to construct an objective function based on the electricity price data, the data of the flexible resource load task, and the data of the non-flexible resource load task, and the objective function is expressed as follows: ; where T denotes the total time steps after discretization, J denotes the total number of flexibility resource load tasks, denotes the electricity energy price at time t in the electricity market, denotes the power resource allocated to the jth flexibility resource load task at time t, denotes the load power of non-flexibility resource load tasks at time t.

7. A device for scheduling tasks on flexible resource loadings, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is loaded into the processor to implement the steps of the flexible resource load task scheduling method according to any one of claims 1-4.

8. A computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the flexible resource load task scheduling method according to any one of claims 1-4.

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