Load scheduling parameter determination method and apparatus, and electronic device

Abstract

The invention discloses a load scheduling parameter determination method and apparatus, and an electronic device. The method comprises the following steps: acquiring load demand parameters of a plurality of shiftable loads in a power system; according to the load demand parameters, determining first total demand power values corresponding to a plurality of translational loads at a plurality of moments; calling a target function and a corresponding constraint condition; according to the first total demand power values corresponding to the multiple moments respectively, solving an objective function under a constraint condition by taking a first objective as an objective, and obtaining initial scheduling parameters corresponding to the multiple shiftable loads respectively; and according to the initial scheduling parameters corresponding to the plurality of shiftable loads, determining target scheduling parameters corresponding to the plurality of shiftable loads. According to the invention, the technical problem that the calculation complexity and the result precision are difficult to balance when the scheduling parameters corresponding to the plurality of shiftable loads are determined in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more specifically, to a method, apparatus, and electronic device for determining load dispatching parameters. Background Technology

[0002] In power system operation, the dispatching of movable loads is of great significance for optimizing resource allocation and improving grid stability. However, determining the dispatching parameters for multiple movable loads often faces the challenge of balancing computational complexity with result accuracy. Traditional methods often directly solve for the start time and power of each movable load as discrete decision variables. Due to the large number of loads and complex combinations, the computational load increases exponentially, resulting in extremely low efficiency and failing to meet real-time dispatching requirements. Even when using heuristic algorithms to simplify the calculation, it is easy to get trapped in local optima, compromising result accuracy. Furthermore, existing solutions often ignore the dynamic changes in grid operation constraints and actual user needs, making the dispatching parameters impractical in real-world applications and failing to effectively coordinate the interests of the grid and users. Therefore, a new movable load dispatching method is urgently needed that can reduce computational complexity, improve solution efficiency, and ensure result accuracy, effectively combining theoretical optimization with practical application.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for determining load scheduling parameters, which at least solves the technical problem in the related art of balancing computational complexity and result accuracy when determining the scheduling parameters corresponding to multiple transferable loads.

[0005] According to one aspect of the present invention, a method for determining load dispatching parameters is provided, comprising: acquiring load demand parameters of multiple movable loads in a power system; determining, based on the load demand parameters, first total demand power values ​​corresponding to the multiple movable loads at multiple times; retrieving an objective function and corresponding constraints, wherein the objective function includes a deviation term, the deviation term representing the deviation between the total demand power term and the total restored power term corresponding to the multiple times, wherein the total restored power term is determined based on computational dispatching parameter terms corresponding to the multiple movable loads, the computational dispatching parameter terms representing computational dispatching parameters generated during the calculation of the objective function; solving the objective function under the constraints based on the first total demand power values ​​corresponding to the multiple times, with a first objective as the objective, to obtain initial dispatching parameters corresponding to the multiple movable loads, wherein the first objective includes the objective of minimizing the objective function value; and determining target dispatching parameters corresponding to the multiple movable loads based on the initial dispatching parameters corresponding to the multiple movable loads.

[0006] Optionally, determining target scheduling parameters for the plurality of movable loads based on their respective initial scheduling parameters includes: when there are multiple total demand power values, determining first total restoration power values ​​for each of the plurality of time periods based on the initial scheduling parameters for each of the plurality of movable loads; when the deviation between the first total demand power values ​​and the first total restoration power values ​​for each of the plurality of time periods is greater than or equal to a predetermined threshold, determining a second total demand power value with a priority lower than the first total demand power value from the plurality of total demand power values; determining the deviation between the second total demand power values ​​and the second total restoration power values ​​for each of the plurality of time periods, until it is determined that the deviation between the corresponding total demand power value and the corresponding total restoration power value is less than the predetermined threshold, thereby obtaining the target scheduling parameters.

[0007] Optionally, obtaining load demand parameters for multiple portable loads in a power system includes: when the load demand parameters include power setting intervals corresponding to multiple portable loads, determining random variables and simulation counts in a simulation function, wherein the random variables are the start times corresponding to the multiple portable loads respectively, and the simulation counts are greater than a second threshold; based on the simulation function, simulating the start times of the multiple portable loads for the specified number of simulation counts to obtain simulated total power values ​​corresponding to the multiple times; and determining the power setting intervals corresponding to the multiple portable loads based on the simulated total power values ​​corresponding to the multiple times.

[0008] Optionally, determining the first total demand power value corresponding to the plurality of movable loads at multiple times based on the load demand parameters includes: retrieving a solution function and corresponding constraints, wherein the functional properties of the solution function include the property of taking the total demand power corresponding to the multiple times as a continuous decision variable; and determining the first total demand power value corresponding to the multiple times based on the load demand parameters and with a second objective as the objective, by solving the solution function under the corresponding constraints, wherein the second objective includes the load demand satisfaction objective.

[0009] Optionally, based on the first total demand power values ​​corresponding to the plurality of times, and with the first objective as the objective, the objective function under the constraints is solved to obtain the initial scheduling parameters corresponding to the plurality of movable loads, including: determining an objective optimization algorithm based on the function properties of the objective function and the power system scenario, wherein the function properties include properties with the start time of operation as a discrete decision variable, and the objective optimization algorithm includes a particle swarm optimization algorithm; and based on the first total demand power values ​​corresponding to the plurality of times, and with the first objective as the objective, the objective optimization algorithm is selected to solve the objective function under the constraints to obtain the initial scheduling parameters corresponding to the plurality of movable loads.

[0010] Optionally, before solving the objective function under the constraints based on the first total demand power values ​​corresponding to the plurality of times, with the first objective as the objective, and obtaining the initial scheduling parameters corresponding to the plurality of movable loads, the method further includes: determining a deviation term representing the deviation between the total demand power term and the total restoration power term corresponding to the plurality of times, and a priority adjustment term representing the scheduling priority of the movable loads; and constructing the objective function based on the deviation term and the priority adjustment term.

[0011] Optionally, before solving the objective function under the constraints based on the first total demand power values ​​corresponding to the multiple times, with the first objective as the objective, the method further includes: when the load demand parameters include the dispatchable time corresponding to multiple movable loads, the power setting interval corresponding to the multiple movable loads and the total power demand, determining the power setting interval constraint based on the power setting interval corresponding to the multiple movable loads, determining the dispatchable time constraint based on the dispatchable time corresponding to the multiple movable loads, and determining the total power demand constraint based on the total power demand corresponding to the multiple movable loads.

[0012] According to one aspect of the present invention, a load dispatching parameter determination apparatus is provided, comprising: an acquisition module for acquiring load demand parameters of multiple movable loads in a power system; a first determination module for determining, based on the load demand parameters, first total demand power values ​​corresponding to the multiple movable loads at multiple times; a retrieval module for retrieving an objective function and corresponding constraints, wherein the objective function includes a deviation term, the deviation term representing the deviation between the total demand power term and the total restored power term corresponding to the multiple times, wherein the total restored power term is determined based on the operational dispatching parameter terms corresponding to the multiple movable loads; a second determination module for solving the objective function under the constraints based on the first total demand power values ​​corresponding to the multiple times, with a first objective as the objective, to obtain initial dispatching parameters corresponding to the multiple movable loads, wherein the first objective includes the objective of minimizing the objective function value; and a third determination module for determining target dispatching parameters corresponding to the multiple movable loads based on the initial dispatching parameters corresponding to the multiple movable loads.

[0013] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the load scheduling parameter determination method as described in any of the preceding claims.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the load scheduling parameter determination method as described in any of the preceding claims.

[0015] In this embodiment of the invention, load demand parameters of multiple movable loads in a power system are obtained; based on the load demand parameters, the first total demand power values ​​corresponding to the multiple movable loads at multiple times are determined; an objective function and corresponding constraints are retrieved, wherein the objective function includes a deviation term, which represents the deviation between the total demand power term and the total restored power term at multiple times, wherein the total restored power term is determined based on the operational scheduling parameter terms corresponding to the multiple movable loads; based on the first total demand power values ​​at multiple times, with a first objective as the objective, the objective function under the constraints is solved to obtain the initial scheduling parameters corresponding to the multiple movable loads, wherein the first objective includes the objective of minimizing the objective function value; based on the initial scheduling parameters corresponding to the multiple movable loads, the target scheduling parameters corresponding to the multiple movable loads are determined. The continuous stage transforms the dispersed load scheduling into global power optimization, simplifying the calculation and obtaining the theoretically optimal benchmark. The discrete stage restores specific times based on the benchmark, ensuring the optimization accuracy of the target scheduling parameters. Ultimately, by adjusting the initial scheduling parameters, the target scheduling parameters are obtained to adapt them to the real-world scenario, achieving a balance between computational speed and result accuracy. This significantly improves the efficiency and accuracy of shiftable load scheduling, thereby solving the technical problem in related technologies where it is difficult to balance computational complexity and result accuracy when determining the scheduling parameters corresponding to multiple shiftable loads. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a load scheduling parameter determination method according to an embodiment of the present invention;

[0018] Figure 2 This is a flowchart illustrating the load scheduling parameter determination method provided by an optional embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram showing the results of the load scheduling parameter determination method provided by an optional embodiment of the present invention;

[0020] Figure 4 This is a structural block diagram of a load scheduling parameter determination device according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1

[0024] According to an embodiment of the present invention, an embodiment of a load scheduling parameter determination method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart of a load scheduling parameter determination method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S102: Obtain load demand parameters for multiple shiftable loads in the power system;

[0027] In step S102 of this application, load demand parameters of multiple shiftable loads in the power system are obtained.

[0028] This involves a power system, which is a comprehensive network for the generation, transmission, distribution, and use of electricity, including power plants, substations, transmission and distribution networks, and end users. The power system in this application includes shiftable loads, and the power system can be dispatched by scheduling these shiftable loads.

[0029] This involves several shiftable loads, which are loads whose electricity consumption periods can be flexibly adjusted within permissible limits. In other words, these are loads whose electricity consumption periods can be flexibly adjusted within a certain time frame without affecting normal user operation; their electricity consumption time is dispatchable, but their total electricity demand is fixed, such as electric vehicle charging and washing machine operation.

[0030] This includes load demand parameters, which describe the specific values ​​of the load characteristics that can be shifted, including but not limited to the power curve of a single device (power changes during operation), continuous working duration (the time required to run for a single power consumption), total power demand, allowable scheduling time interval (e.g., electric vehicles can be charged from 18:00 to 8:00 the next day), and the number of devices.

[0031] Step S104: Based on the load demand parameters, determine the first total demand power values ​​corresponding to multiple shiftable loads at multiple times.

[0032] In step S104 of this application, multiple movable loads are determined, and their corresponding first total power demand values ​​are determined at multiple times.

[0033] This involves the first total power demand value, which is the total power demand value of multiple movable loads at various times, obtained through calculation or simulation. For example, the total power demand of all movable loads within the range of 0-1.

[0034] In this step, the discrete power demand of individual loads is transformed into a continuous total power demand value, simplifying the complexity of the subsequent optimization problem. Within the continuous variable optimization framework, the total power of all movable loads at multiple time points is obtained through simple calculation.

[0035] Step S106: Retrieve the objective function and the corresponding constraints. The objective function includes a deviation term, which represents the deviation between the total demand power term and the total restored power term at multiple times. The total restored power term is determined based on the operation and scheduling parameter terms corresponding to multiple transferable loads. The operation and scheduling parameter terms are used to represent the operation and scheduling parameters generated during the operation of the objective function.

[0036] In step S106 of this application, the objective function and the corresponding constraints are retrieved.

[0037] This involves an objective function, which measures the quality of a scheduling scheme. In this application, the objective can be minimizing the deviation, i.e., the difference between the total demand power term obtained through continuous solution and the total restored power term obtained through discrete reconstruction. In other words, given the total demand power, the objective function calculates the scheduling of each movable load while satisfying this demand as much as possible. This optimization process considers the deviation between the demand power and the restored power (the power obtained by reconstructing the simulated real situation based on the scheduling results).

[0038] This involves constraints, which are restrictions on decision variables, such as the range of power values, energy conservation, and equipment operating time limits.

[0039] This involves a deviation term, which is an indicator reflecting the difference between the total demand power term and the total restoration power term at multiple times. The difference can be reflected in various ways, such as by calculating the sum of the squares of the differences between the two corresponding values.

[0040] This involves the total restored power term, which is the total power obtained after restoring the load to its starting time based on the corresponding scheduling parameters. In other words, it represents the actual power supply at multiple times according to the corresponding scheduling parameters. Specifically, it calculates the total power value at each time point based on the discrete scheduling parameters (such as the starting time) of the shiftable load, and then determines the deviation between this total power value and the corresponding value of the total demand power term.

[0041] This involves computational scheduling parameters, which are parameters generated during the optimization process and used to describe the scheduling strategy for movable loads. These intermediate scheduling parameters, such as the start time of movable loads, are generated during the iterative process of solving the objective function, so that the optimal target scheduling parameters can be determined at the end of the iterative calculation.

[0042] Retrieve the objective function and its corresponding constraints, clarifying the calculation method of the deviation term and the correlation between relevant parameters. By clearly defining the objective and constraints, we ensure that subsequent optimization solutions have a clear direction and boundaries, avoiding results that exceed the practically feasible range.

[0043] Step S108: Based on the first total demand power values ​​corresponding to multiple times, and with the first objective as the objective, solve the objective function under the constraints to obtain the initial scheduling parameters corresponding to multiple movable loads, wherein the first objective includes the objective with the minimum objective function value;

[0044] In step S108 of this application, initial scheduling parameters corresponding to multiple transferable loads are obtained.

[0045] This involves initial scheduling parameters, which are preliminary scheduling schemes for the transferable loads obtained by solving the objective function, such as the start time and duration of operation for each transferable load.

[0046] Using the initial total demand power as input, and employing the corresponding algorithm, the objective function is solved under the constraint conditions to obtain the initial scheduling parameters of the movable load that minimize the objective function value. Since the function properties include those with the start time of operation as a discrete decision variable, it can achieve the conversion from continuous theoretical solutions to discrete practical solutions, yielding an initial scheme that can be directly used for scheduling, balancing optimization accuracy and computational speed.

[0047] Step S110: Based on the initial scheduling parameters corresponding to the multiple transferable loads, determine the target scheduling parameters corresponding to the multiple transferable loads.

[0048] In step S110 of this application, target scheduling parameters corresponding to multiple transferable loads are determined.

[0049] This involves target scheduling parameters, which are the final adjustment results of the initial scheduling parameters and can be an executable solution that takes into account the details of the actual scenario.

[0050] In this step, the target scheduling parameters are obtained by adjusting the initial scheduling parameters, making the determined scheduling parameters more accurate.

[0051] Through steps S102-S110 above, load demand parameters of multiple movable loads in the power system are obtained; based on the load demand parameters, the first total demand power values ​​corresponding to the multiple movable loads at multiple times are determined; the objective function and corresponding constraints are retrieved, wherein the objective function includes a deviation term, which represents the deviation between the total demand power term and the total restored power term corresponding to the multiple times, wherein the total restored power term is determined based on the operational scheduling parameter terms corresponding to the multiple movable loads; based on the first total demand power values ​​corresponding to the multiple times, with the first objective as the objective, the objective function under the constraints is solved to obtain the initial scheduling parameters corresponding to the multiple movable loads, wherein the first objective includes the objective of minimizing the objective function value; based on the initial scheduling parameters corresponding to the multiple movable loads, the target scheduling parameters corresponding to the multiple movable loads are determined. The continuous stage transforms the decentralized load scheduling into global power optimization, simplifying the calculation and obtaining the theoretical optimal benchmark. The discrete stage restores specific times based on the benchmark, ensuring the optimization accuracy of the target scheduling parameters. Ultimately, by adjusting the initial scheduling parameters, the target scheduling parameters are obtained to adapt them to the real-world scenario, achieving a balance between computational speed and result accuracy. This significantly improves the efficiency and accuracy of shiftable load scheduling, thereby solving the technical problem in related technologies where it is difficult to balance computational complexity and result accuracy when determining the scheduling parameters corresponding to multiple shiftable loads.

[0052] As an optional embodiment, the target scheduling parameters corresponding to the multiple transferable loads are determined based on the initial scheduling parameters corresponding to the multiple transferable loads, including: when there are multiple total demand power values, determining the first total restoration power values ​​corresponding to multiple time periods based on the initial scheduling parameters corresponding to the multiple transferable loads; when the deviation between the first total demand power values ​​corresponding to the multiple time periods and the first total restoration power values ​​is greater than or equal to a predetermined threshold, determining a second total demand power value with a priority lower than the first total demand power value from the multiple total demand power values; determining the deviation between the second total demand power values ​​corresponding to the multiple time periods and the second total restoration power values, until it is determined that the deviation between the corresponding total demand power value and the corresponding total restoration power value is less than the predetermined threshold, thereby obtaining the target scheduling parameters.

[0053] In this embodiment, the process of determining the target scheduling parameters is described.

[0054] This involves multiple total power demand values, which refer to the total power reference values ​​obtained at multiple moments during the continuous solution phase, including multiple schemes with different priorities (such as the first and second total power demand values).

[0055] This involves a predetermined threshold, which represents a preset allowable deviation range (such as 5kW or 5% of the total power) used to determine whether the restoration result is acceptable.

[0056] This involves priorities, which represent the order of importance of total power demand values. Priorities are usually determined based on factors such as grid stability, user experience, and operating costs (the first priority is the best, and the second priority is the second best alternative).

[0057] This involves a second total demand power value, which represents a continuous benchmark value with a priority lower than the first total demand power value, and serves as an alternative when the deviation exceeds the limit.

[0058] This involves a first total restored power value, which is the total power restored based on the determined target scheduling parameters, in order to determine the difference between the power obtained by restoring the simulated real situation based on the scheduling results and the required power.

[0059] This involves the second total restored power value, which is a discrete restored power value recalculated based on the second total demand power value, that is, the total power corresponding to the load dispatch parameters adjusted based on the new benchmark.

[0060] In this step, based on initial scheduling parameters (such as the start time of each load), the actual total power of all transferable loads at each time point (i.e., the first total restored power value) is calculated as the basis for comparison with the first total demand power value (benchmark value). For example, if the initial scheduling parameters are that 20 devices are operating at time t1, each with a power of 5kW, then the first total restored power value is 100kW. The deviation between the first total demand power value and the first total restored power value is compared. If the deviation exceeds a predetermined threshold (e.g., 10kW > 5kW), then a second total demand power value with the next lower priority is selected from a set of preset total demand power values ​​to provide a new benchmark for the next round of restoration. For example, the first priority benchmark emphasizes load smoothing, while the second priority benchmark can appropriately relax the smoothing requirements, prioritizing the flexibility of user electricity use. The second total restored power value is recalculated based on the second total demand power value, and the deviation is evaluated. If it still exceeds the threshold, then a lower priority total demand power value is selected and the above process is repeated until the deviation between the total demand power value of a certain priority and the corresponding total restored power value is less than the threshold. The scheduling parameter at this point is the target scheduling parameter. For example, if the restoration deviation under the second priority benchmark is (3kW < 5kW), then the scheduling parameters corresponding to this benchmark are determined as the target scheme.

[0061] By defining predetermined thresholds to define the allowable difference between the restored results and the theoretical benchmark, the scheduling scheme is ensured to remain within the optimization objective. A priority mechanism provides alternative benchmarks, enhancing the fault tolerance and adaptability of the scheduling system. Through iterative verification and priority advancement, the final scheduling scheme is ensured to meet both deviation requirements (theoretical optimal closeness) and practical feasibility (adapting to constraints of equipment, users, and the power grid), balancing optimization accuracy and practical feasibility.

[0062] It should be noted that this optional implementation, based on the determination of the minimum value of the objective function (which is already the optimal calculation result under the first total demand power), further determines whether it meets the accuracy requirements through restoration and threshold verification. That is, it can determine whether the scheduling parameters determined by the continuous optimization variable method are inaccurate through verification. If inaccurate, it further optimizes to determine the optimal calculation result under the second total demand power until the accuracy requirements are met, thus ensuring the accuracy of the determined scheduling parameters.

[0063] The system is scalable. When calculating the first total restored power value, the actual power fluctuation of the equipment needs to be considered. For example, the power of a certain type of equipment may vary within ±5%. A power correction coefficient can be introduced to make the restoration result closer to the actual operating state. A dynamically adjustable threshold and priority mechanism can also be implemented to adapt to different scenarios and improve the versatility of the solution.

[0064] As an optional embodiment, obtaining load demand parameters for multiple transferable loads in a power system includes: when the load demand parameters include power setting intervals corresponding to multiple transferable loads, determining the random variable and the number of simulations in the simulation function, wherein the random variable is the start time corresponding to each of the multiple transferable loads, and the number of simulations is greater than a second threshold; based on the simulation function, simulating the start time of the multiple transferable loads to obtain the simulated total power value corresponding to each of the multiple times; and determining the power setting interval corresponding to the multiple transferable loads based on the simulated total power value corresponding to each of the multiple times.

[0065] In this embodiment, power setting ranges corresponding to multiple shiftable loads are determined.

[0066] This involves the power setting range, which refers to the allowable power range of a single movable load and is the power boundary for load operation.

[0067] This involves simulation functions, which are mathematical models used to simulate the operating state of a load. The inputs are variables such as the start time of the load, and the outputs are the corresponding power values ​​or total power.

[0068] This involves random variables, which are quantities whose values ​​are uncertain in the simulation and need to be randomly generated. In this case, it refers to the start time of the load that can be transferred.

[0069] This includes the start time, which refers to the specific time when the load can be transferred to start operation, such as when an electric vehicle starts charging at 18:30.

[0070] This involves the number of simulations, which represents the total number of times the simulation function is executed (e.g., 1000 times). The more simulations, the closer the simulation results are to the actual distribution.

[0071] This involves a second threshold, which represents the minimum number of preset simulations (e.g., 500 times) to ensure that there are enough simulations to guarantee the reliability of the results.

[0072] This involves the simulated total power value, which represents the sum of the power of all movable loads at a certain moment during the simulation. For example, if there are 3 devices operating at time t1 with power of 2kW, 3kW and 2kW respectively, the simulated total power value is 7kW.

[0073] When the load demand parameters include a power setting range, the start time of each movable load is used as a random variable, and the number of simulations must exceed a second threshold to provide a basic framework for subsequent simulations. The number of simulations for the start time of each movable load is randomly generated. In each simulation, the total power at each moment is calculated based on the start time and power setting range of each load, ultimately obtaining 1000 sets of simulated total power values ​​for multiple moments. The simulated total power values ​​at multiple moments are statistically analyzed; for example, the total power range of 1000 simulations at moment t1 is 5-15kW, and at moment t2 it is 8-20kW. Combined with constraints such as grid carrying capacity and load operation safety, the power setting range of each movable load is deduced and optimized. For example, if the simulated total power repeatedly exceeds the grid upper limit of 20kW at a certain moment, the power setting range of some loads can be narrowed to ensure that the total power remains within a safe range.

[0074] By clearly defining the boundaries of random variables and the number of simulations, the simulation process is ensured to focus on the variables, and the number of simulations is sufficient to cover different scenario combinations, avoiding result deviations due to insufficient simulation. Through extensive simulations covering different start-time combinations, the obtained simulated total power value reflects the distribution characteristics of the total load power, thereby accurately determining the power setting range corresponding to multiple shiftable loads. The power setting range determined based on a large amount of simulation data more closely matches the actual operating scenario, meeting the load's power demand while avoiding exceeding the total power limit, thus balancing flexibility and safety.

[0075] As an optional embodiment, based on load demand parameters, the first total demand power value corresponding to multiple shiftable loads at multiple times is determined, including: retrieving the solution function and the corresponding constraints, wherein the functional properties of the solution function include the property of taking the total demand power corresponding to the multiple times as a continuous decision variable; based on the load demand parameters, with a second objective as the objective, solving the solution function under the corresponding constraints to determine the first total demand power value corresponding to the multiple times, wherein the second objective includes the load demand satisfaction objective.

[0076] In this embodiment, the solution function is invoked to determine the first total power demand value corresponding to multiple time points.

[0077] This involves a solution function, which is a mathematical function used to solve optimization problems in order to obtain the optimal solution for the target continuous variable through calculation.

[0078] This involves constraints, which are restrictions on variables in the solution function, such as the total electricity consumption needing to match the load demand.

[0079] This involves the concept of function properties, which represent the mathematical characteristics of the solution function. By using the total power demand at multiple time points as a continuous decision variable, the discrete-variable optimization problem is transformed into a continuous-variable optimization problem. Specifically, the focus shifts from considering the power demand of each movable load at each time point to determining the power demand of all movable loads at each time point, thereby determining the final scheduling parameters. Continuous-variable optimization simplifies and improves the solution process for optimization problems with movable loads as decision variables.

[0080] Among them, the second objective is the goal that needs to be achieved during the optimization process, including the load demand satisfaction objective. The load demand satisfaction objective can ensure that the core requirements such as the total power demand and running time of the load that can be transferred are met.

[0081] In this step, a preset solution function and corresponding constraints are retrieved from the system. The solution function uses the total demand power at multiple time points as continuous decision variables for subsequent solutions. For example, a least-squares function with hourly total demand power as a continuous variable is retrieved, along with constraints such as hourly total power ≤ 150kW and total daily electricity consumption equal to 2000kWh. Based on load demand parameters, such as total electricity consumption and operating time, the solution function is solved under the constraints with the goal of meeting load demand. Finally, the first total demand power values ​​at multiple time points are obtained.

[0082] In this step, the discrete problem is transformed into a continuous problem by solving functions and constraints based on continuous variables, reducing the solution complexity. By focusing on meeting load demand, the optimization results are ensured to remain consistent with core user electricity needs. The initial total demand power value obtained by solving the function provides a continuous benchmark for subsequent discrete scheduling, balancing feasibility and optimization.

[0083] Optionally, the choice of solution function can be dynamically adapted according to the scenario. For example, a robust optimization function with strong anti-interference ability can be selected when the load fluctuates greatly. Constraints can be divided into hard constraints and soft constraints. Hard constraints, such as grid capacity, cannot be violated, while soft constraints, such as power smoothness, can be appropriately relaxed to improve the flexibility of the solution. Multi-objective fusion and dynamic adjustment are supported, and different optimization focuses can be adapted according to grid conditions, user types, etc., to improve the universality of the solution.

[0084] As an optional embodiment, based on the first total demand power values ​​corresponding to multiple times, and with the first objective as the objective, the objective function under constraints is solved to obtain the initial scheduling parameters corresponding to multiple transferable loads. This includes: determining the objective optimization algorithm based on the function properties of the objective function and the power system scenario, wherein the function properties include properties with the start time of operation as a discrete decision variable, and the objective optimization algorithm includes particle swarm optimization algorithm; and selecting the objective optimization algorithm based on the first total demand power values ​​corresponding to multiple times, with the first objective as the objective, to solve the objective function under constraints and obtain the initial scheduling parameters corresponding to multiple transferable loads.

[0085] In this embodiment, the steps for determining the initial scheduling parameters corresponding to the multiple movable loads are described.

[0086] This involves objective optimization algorithms, which are computational methods used to solve objective functions to obtain the optimal solution. The selection of these algorithms needs to match the variable types and function characteristics of the problem. Their core function is to efficiently find the optimal solution that satisfies the constraints.

[0087] This includes the start time of operation, which indicates the specific time when the load can be moved and started, such as when a piece of equipment starts at 9:00.

[0088] This involves the Particle Swarm Optimization (PSO) algorithm, which simulates the cooperative behavior of flocks of birds foraging. Through the movement, information sharing, and iterative updates of individual particles in the solution space, it seeks the optimal solution. It is suitable for discrete optimization problems, especially adept at handling multi-constraint scenarios. It should be noted that, in addition to PSO, other discrete optimization algorithms, such as genetic algorithms and simulated annealing, can be flexibly selected depending on the scenario. For example, when extremely high accuracy is required but longer computation time is acceptable, a genetic algorithm can be used. When the number of computational tasks is small, precise algorithms such as branch and bound can be used.

[0089] This step involves selecting a suitable objective optimization algorithm based on the objective function with the start time of operation as a discrete decision variable, combined with the actual scenario of the power system. Since the start time is a discrete variable, the particle swarm optimization algorithm is chosen due to its efficient handling of discrete variables and strong global search capability. The particle swarm optimization algorithm finds a set of start times that minimizes the deviation between the corresponding total restored power and the baseline value.

[0090] This step ensures that the selected optimization algorithm is highly compatible with the discrete problem, avoiding low solution efficiency or getting trapped in local optima due to algorithm mismatch. Combining the algorithm with the power system scenario enhances the applicability of the solution in practical engineering. Through the adapted target optimization algorithm, near-optimal initial scheduling parameters are efficiently solved in discrete decision variable scenarios, ensuring that the results closely match the first total demand power in the continuous phase while satisfying all constraints. Guided by the primary objective, the optimization direction ensures that it focuses on the core requirements. Through constraint restrictions and the algorithm's global search capability, the obtained initial scheduling parameters satisfy both engineering feasibility and are close to theoretical optimality, providing a high-quality foundation for subsequently determining the target scheduling parameters.

[0091] As an optional embodiment, before solving the objective function under constraints based on the first total demand power values ​​corresponding to multiple times and taking the first objective as the objective, and obtaining the initial scheduling parameters corresponding to multiple transferable loads, the method further includes: determining a deviation term representing the deviation between the total demand power term and the total restored power term corresponding to multiple times, and a priority adjustment term representing the scheduling priority of the transferable loads; and constructing the objective function based on the deviation term and the priority adjustment term.

[0092] This embodiment illustrates the process of constructing the objective function.

[0093] This involves a priority adjustment term, which is a mathematical term used to reflect the scheduling priority of different transferable loads. By adjusting the weights or coefficients, the objective function is tilted towards higher priority loads during optimization. For example, when the priority of industrial production load is higher than that of residential load, the corresponding adjustment term has a larger weight.

[0094] In this step, an appropriate deviation quantification method can be selected based on the scheduling accuracy requirements. For example, absolute deviation can be chosen to pursue robustness, while sum-of-squares deviation can be chosen to avoid the influence of outliers, ensuring that the difference between the continuous benchmark and the discrete results can be accurately reflected. Secondly, considering the power system scenario, such as prioritizing important loads during peak hours, user type (e.g., hospital loads have higher priority than ordinary residents), or equipment characteristics (e.g., high-power loads have higher priority than low-power loads), different adjustment term coefficients or weights are assigned to different loads. For example, the priority coefficient for industrial loads is set to 1.2, and for residential loads to 0.8. Finally, the determined deviation terms and priority adjustment terms are integrated through weighted summation or superposition to form a complete objective function.

[0095] The introduction of the priority adjustment term allows the objective function to take into account both technical accuracy and actual scheduling needs, avoiding the neglect of load priority differences due to the pursuit of minimizing deviation, and avoiding the situation where only deviation is considered while ignoring priority, or where only priority is considered, resulting in excessively low accuracy.

[0096] As an optional embodiment, before solving the objective function under constraints based on the first total demand power values ​​corresponding to multiple times and with the first objective as the objective, the method further includes: when the load demand parameters include the dispatchable time corresponding to multiple movable loads, the power setting interval corresponding to multiple movable loads, and the total power demand, determining the power setting interval constraint based on the power setting interval corresponding to multiple movable loads, determining the dispatchable time constraint based on the dispatchable time corresponding to each of the multiple movable loads, and determining the total power demand constraint based on the total power demand corresponding to multiple movable loads.

[0097] This embodiment illustrates the process of constructing target constraints.

[0098] This involves the schedulable time, which refers to the time range during which a single transferable load is allowed to start and run. For example, an electric vehicle can be charged from 20:00 to 7:00 the next day. It includes attributes such as the upper and lower limits of the start time and the duration of continuous operation, and is the time boundary for load scheduling.

[0099] This involves power setting range constraints, which are constraints established based on power setting ranges. These constraints require that the power of a single load during operation must be within its set range, such as a device power not being lower than 5kW or higher than 10kW.

[0100] This involves schedulable time constraints, which are constraints based on schedulable time. These constraints require that the start time of the load must be within the allowed time interval and that the running time must not exceed the duration. For example, electric vehicle charging must start between 20:00 and 7:00 the next day and last for 4 hours.

[0101] This involves total power demand constraints, which are constraints established based on total power demand, requiring that the total electricity consumption of all loads within the scheduling cycle equals the total power demand.

[0102] By constructing three types of constraints, problems such as power overruns, time conflicts, or power mismatches in the scheduling scheme are avoided. This provides explicit constraints for solving the subsequent objective function. Explicit constraints reduce the ineffective search space of the optimization algorithm; for example, in particle swarm optimization, there is no need to explore start times outside the time window, thus improving solution speed and accuracy. The parameterized design of the constraints allows the scheme to directly align with actual load characteristics, avoiding a disconnect between theory and practice, and laying the foundation for generating executable scheduling parameters.

[0103] Optionally, user preferences can be superimposed on the schedulable time constraints, such as residential users tending to start the washing machine after 22:00. By using priority weights, a combination of hard time boundaries and flexible user habits can be achieved to enhance the customer experience.

[0104] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0105] An optional embodiment of the present invention provides a method for determining load scheduling parameters. Figure 2 This is a flowchart illustrating the load scheduling parameter determination method provided by an optional embodiment of the present invention. Figure 3 This is a schematic diagram comparing the results of the load scheduling parameter determination method provided by an optional embodiment of the present invention, as shown in the figure. Figure 2 As shown in Figure 3, Figure 3 The application effect of the method of the optional embodiment of the present invention in a certain test system can be demonstrated. It can be seen that the optimization result and the restoration result are quite close, and it has the conditions for practical application. The method of the optional embodiment of the present invention transforms the decentralized load scheduling into global power optimization through the continuous stage, simplifying the calculation and obtaining the theoretical optimal benchmark. The discrete stage restores the specific time based on the benchmark, ensuring the optimization accuracy of the target scheduling parameters. Finally, the target scheduling parameters are obtained by adjusting the initial scheduling parameters to adapt them to the real scenario, achieving a balance between calculation speed and result accuracy, significantly improving the efficiency and accuracy of movable load scheduling, and thus solving the technical problem in related technologies where it is difficult to balance computational complexity and result accuracy when determining the scheduling parameters corresponding to multiple movable loads.

[0106] S1, obtain the load demand parameters of multiple shiftable loads in the power system, wherein the load demand parameters include the dispatchable time corresponding to the multiple shiftable loads, the power setting range corresponding to the multiple shiftable loads, and the total power demand;

[0107] It should be noted that power systems include both stationary loads and movable loads. The optional embodiments of this invention consider movable loads.

[0108] Shiftable load (SL) refers to a load whose usage time can be adjusted by electricity users based on electricity pricing information, ensuring continuous supply of electricity to the load within a certain time range. A shiftable load model reflects information such as load size, duration, and selectable time range for load usage. The set of shiftable loads at node i within a specific time period is:

[0109] ;

[0110] Assume the number of portable load devices included in the i-th user is . (i.e., user i owns) (A set of controllable electrical equipment, referred to as controllable electrical equipment). The load that user i can transfer during the h-th time period is the sum of the transferable load values ​​of all controllable electrical equipment, which can also be called the transferable load model. ,in, Let h be the transferable load amplitude of the kth controllable electrical device of user i at time h (i.e., the power demand of the kth controllable electrical device).

[0111] The constraints are as follows:

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] in, This indicates that the k-th movable load of user i starts working from the k-th time. The power value at each moment (assuming that each controllable electrical device maintains an inherent load curve once it starts working, that is, the power demand at each moment is a fixed value). The starting time of the transferable load. The duration of the transferable load (given in advance); Selectable time interval for shiftable load, duration The starting time of the load transferable should be taken within this time interval; The total power of the shiftable load in each time period should equal the power demand.

[0117] S2, retrieve the solution function and the corresponding constraints, wherein the functional properties of the solution function include the property of taking the total demand power corresponding to multiple time points as continuous decision variables;

[0118] According to the shiftable load model, the total shiftable load for a user at any given time is determined by the starting power consumption time, selectable time interval, and power consumption range of each controllable electrical device. In the optimization model with shiftable load as the decision variable, the number of variables to be solved is:

[0119] ;

[0120] in, Let N represent the number of selectable times for the start time of the k-th load transferable user i, where N represents the total number of users.

[0121] If the optimization problem uses movable loads as decision variables, then it is necessary to solve for discrete variables. Generally speaking, solving discrete variable problems is more difficult than solving continuous variable problems.

[0122] This is because the decision space of discrete variables is discontinuous and non-convex, and optimization algorithms used to solve continuous problems cannot be directly applied to solving optimization problems with discrete variables.

[0123] Therefore, an optional embodiment of the present invention proposes a practical processing method for discrete transferable load models. The above-mentioned transferable load model is subjected to continuous equivalence to obtain an equivalent model.

[0124] The total movable load of user i at each time step is modeled as a continuous variable:

[0125] For the i-th producer-consumer, the set of transferable loads within a specified time period is:

[0126] ;

[0127] Among them, the transferable load at each moment It should meet the following requirements:

[0128] (Same as the schedulable time constraints mentioned above)

[0129] (Same as the total power demand constraint mentioned above)

[0130] in, for The range of values ​​for; It is an optional time interval for shiftable loads; It is the total demand for user i's transferable load, and it is the sum of transferable loads at all times.

[0131] S3, based on the load demand parameters and with the second objective as the goal, solve the solution function under the corresponding constraints to determine the first total demand power value corresponding to multiple time points, such as obtaining the h-th time value of user i. The first total demand power value (optimized value for shiftable load) at time ), wherein the second objective includes the load demand satisfaction objective;

[0132] S4, retrieve the objective function and corresponding constraints. The objective function includes a deviation term, which represents the deviation between the total demand power term and the total restored power term at multiple times. The total restored power term is determined based on the operation and scheduling parameter terms corresponding to multiple movable loads. The operation and scheduling parameter terms are used to represent the operation and scheduling parameters generated during the operation of the objective function. The objective constraints include power setting interval constraints, schedulable time constraints, and total power demand constraints.

[0133] It should be noted that the practical equivalent method for the movable load model transforms a discrete variable optimization problem into a continuous variable optimization problem, making the solution process for optimization problems with movable loads as decision variables simpler and more efficient. However, in practical engineering, simply providing users with the sum of movable loads at each moment is clearly insufficient; the effective information ultimately guiding users' movable load arrangements remains the start time of the movable loads. This necessitates restoring the total movable load result obtained from the practical equivalent method to the start time of each user's individual movable loads to determine if the error is sufficiently small and if the calculated results are valid. Therefore, it is necessary to restore the equivalent model solution results, i.e., to solve for the start time of each user's individual movable loads, obtaining the total restored power to ensure the accuracy of the results.

[0134] The objective function can be as follows:

[0135] ;

[0136] in, Let h be the movable load amplitude of the k-th controllable electrical device of user i. Let H be the restored load amplitude of the k-th controllable electrical equipment of user i at time h, where H represents all times.

[0137] S5. Based on the properties of the objective function and the power system scenario, determine the objective optimization algorithm. The properties of the function include those with the start time of operation as a discrete decision variable, and the objective optimization algorithm includes the particle swarm optimization algorithm.

[0138] S6. Based on the first total demand power values ​​corresponding to multiple times, with the first objective as the objective, select the objective optimization algorithm to solve the objective function under the constraints, and obtain the initial scheduling parameters corresponding to multiple movable loads respectively. Among them, the first objective includes the objective with the minimum objective function value.

[0139] S7, when there are multiple total demand power values, determine the first total restoration power values ​​corresponding to multiple time periods based on the initial scheduling parameters corresponding to the multiple transferable loads respectively;

[0140] S8, if the deviation between the first total demand power value and the first total restoration power value at multiple times is greater than or equal to a predetermined threshold, determine the second total demand power value with a priority lower than the first total demand power value from the multiple total demand power values.

[0141] like End the process; otherwise, return to the above steps and use the next step after... The optimization results replace Recalculate.

[0142] S9, determine the deviation between the second total demand power value and the second total restoration power value corresponding to multiple time points, until it is determined that the deviation between the corresponding total demand power value and the corresponding total restoration power value is less than a predetermined threshold, and obtain the target scheduling parameters.

[0143] This includes obtaining load demand parameters for multiple shiftable loads in the power system, including:

[0144] When the load demand parameters include power setting ranges corresponding to multiple movable loads, determine the random variables and simulation number in the simulation function, where the random variables are the start times corresponding to the multiple movable loads respectively, and the simulation number is greater than the second threshold.

[0145] Based on the simulation function, the simulation is performed a number of times at the start time of multiple movable loads to obtain the simulated total power values ​​corresponding to multiple times.

[0146] Based on the simulated total power values ​​at multiple times, the power setting ranges corresponding to multiple movable loads are determined.

[0147] For example:

[0148] In the equivalent model The range of values Obtained using Monte Carlo simulation method.

[0149] As can be seen from the implementation principle of the Monte Carlo method, regardless of whether the objective function is linear or not, or whether the random variable is normally distributed, as long as the number of simulations is sufficient, the results obtained will be relatively accurate. In the Monte Carlo simulation, the random variable is the start time of the k-th movable load for user i, and the number of simulations is... It should be much larger than a certain threshold (same as the second threshold mentioned above), namely, the number of all combinations at the start time of the K movable loads. The set of movable loads obtained through x simulations is: ,but It can be obtained from the following formula: , .

[0150] Before obtaining the initial scheduling parameters corresponding to multiple movable loads by solving the objective function under constraints based on the first total demand power values ​​corresponding to multiple time points and taking the first objective as the objective, the process also includes:

[0151] Determine the deviation term, which represents the difference between the total demand power term and the total restoration power term at multiple times, and the priority adjustment term, which represents the priority of the shiftable load scheduling.

[0152] Based on the deviation term and the priority adjustment term, construct the objective function.

[0153] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0155] Example 2

[0156] According to embodiments of the present invention, an apparatus for implementing the above-described load scheduling parameter determination method is also provided. Figure 4This is a structural block diagram of a load dispatching parameter determination device according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes: an acquisition module 402, a first determination module 404, a retrieval module 406, a second determination module 408, and a third determination module 410. The device will be described in detail below.

[0157] The system comprises: an acquisition module 402 for acquiring load demand parameters of multiple portable loads in a power system; a first determination module 404 connected to the acquisition module 402 for determining the first total demand power value corresponding to the multiple portable loads at multiple times based on the load demand parameters; a retrieval module 406 connected to the first determination module 404 for retrieving the objective function and corresponding constraints, wherein the objective function includes a deviation term, which represents the deviation between the total demand power term and the total restored power term corresponding to the multiple times, wherein the total restored power term is determined based on the operational scheduling parameter terms corresponding to the multiple portable loads; a second determination module 408 connected to the retrieval module 406 for solving the objective function under constraints based on the first total demand power value corresponding to the multiple times, with the first objective as the objective, to obtain the initial scheduling parameters corresponding to the multiple portable loads, wherein the first objective includes the objective function value minimizing; and a third determination module 410 connected to the second determination module 408 for determining the target scheduling parameters corresponding to the multiple portable loads based on the initial scheduling parameters corresponding to the multiple portable loads.

[0158] It should be noted here that the above-mentioned acquisition module 402, first determination module 404, retrieval module 406, second determination module 408 and third determination module 410 correspond to steps S102 to S110 in the method for determining load scheduling parameters. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0159] Example 3

[0160] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the load scheduling parameter determination method of any of the above embodiments.

[0161] Example 4

[0162] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the load scheduling parameter determination method described above.

[0163] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0164] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0168] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0169] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining load dispatching parameters, characterized in that, include: Obtain load demand parameters for multiple shiftable loads in the power system; Based on the load demand parameters, determine the first total demand power value corresponding to the plurality of movable loads at multiple times; The objective function and corresponding constraints are retrieved. The objective function includes a deviation term, which represents the deviation between the total demand power term and the total restoration power term corresponding to the plurality of time points. The total restoration power term is determined based on the operation and scheduling parameter terms corresponding to the plurality of movable loads. The operation and scheduling parameter terms are used to represent the operation and scheduling parameters generated during the operation of the objective function. Based on the first total demand power values ​​corresponding to the multiple time points, and with the first objective as the objective, the objective function under the constraints is solved to obtain the initial scheduling parameters corresponding to the multiple movable loads, wherein the first objective includes the objective with the minimum objective function value; Based on the initial scheduling parameters corresponding to the multiple movable loads, the target scheduling parameters corresponding to the multiple movable loads are determined.

2. The method according to claim 1, characterized in that, Based on the initial scheduling parameters corresponding to the plurality of movable loads, the target scheduling parameters corresponding to the plurality of movable loads are determined, including: When there are multiple total demand power values, the first total restoration power values ​​corresponding to the multiple time points are determined based on the initial scheduling parameters corresponding to the multiple transferable loads. If the deviation between the first total demand power value and the first total restoration power value corresponding to the plurality of times is greater than or equal to a predetermined threshold, a second total demand power value with a priority lower than the first total demand power value is determined from the plurality of total demand power values. The deviations between the second total demand power value and the second total restoration power value corresponding to the plurality of time points are determined until the deviation between the corresponding total demand power value and the corresponding total restoration power value is less than the predetermined threshold, thereby obtaining the target scheduling parameters.

3. The method according to claim 1, characterized in that, Obtain load demand parameters for multiple shiftable loads in the power system, including: When the load demand parameters include power setting ranges corresponding to multiple movable loads, the random variables and simulation times in the simulation function are determined, wherein the random variables are the start times corresponding to the multiple movable loads respectively, and the simulation times are greater than a second threshold. Based on the simulation function, the simulation is performed a certain number of times at the start time of the multiple movable loads to obtain the simulated total power values ​​corresponding to the multiple times. Based on the simulated total power values ​​corresponding to the multiple time points, the power setting range corresponding to the multiple movable loads is determined.

4. The method according to claim 1, characterized in that, Based on the load demand parameters, determine the first total demand power values ​​corresponding to the plurality of movable loads at multiple times, including: The solution function and its corresponding constraints are retrieved, wherein the functional properties of the solution function include properties that take the total demand power corresponding to multiple time points as continuous decision variables; Based on the load demand parameters, with the second objective as the objective, the solution function under the corresponding constraints is solved to determine the first total demand power value corresponding to the multiple time points, wherein the second objective includes the load demand satisfaction objective.

5. The method according to claim 1, characterized in that, Based on the first total demand power values ​​corresponding to the plurality of times, and with the first objective as the objective, the objective function under the constraints is solved to obtain the initial scheduling parameters corresponding to the plurality of movable loads, including: Based on the properties of the objective function and the power system scenario, an objective optimization algorithm is determined, wherein the properties of the objective function include those with the start time of operation as a discrete decision variable, and the objective optimization algorithm includes a particle swarm optimization algorithm; Based on the first total demand power values ​​corresponding to the multiple time points, and taking the first objective as the objective, the objective optimization algorithm is selected to solve the objective function under the constraints, thereby obtaining the initial scheduling parameters corresponding to the multiple movable loads.

6. The method according to claim 1, characterized in that, Based on the first total demand power values ​​corresponding to the multiple time points, and taking the first objective as the objective, before solving the objective function under the constraints to obtain the initial scheduling parameters corresponding to the multiple movable loads, the process further includes: Determine the deviation term, which represents the difference between the total demand power term and the total restoration power term at multiple times, and the priority adjustment term, which represents the priority of the shiftable load scheduling. The objective function is constructed based on the deviation term and the priority adjustment term.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the first total demand power values ​​corresponding to the multiple time points, and taking the first objective as the objective, before solving the objective function under the constraints, the process further includes: When the load demand parameters include the dispatchable time corresponding to multiple movable loads, the power setting interval corresponding to the multiple movable loads, and the total power demand, the power setting interval constraint is determined based on the power setting interval corresponding to the multiple movable loads, the dispatchable time constraint is determined based on the dispatchable time corresponding to each of the multiple movable loads, and the total power demand constraint is determined based on the total power demand corresponding to the multiple movable loads.

8. A load dispatching parameter determination device, characterized in that, include: The acquisition module is used to acquire load demand parameters of multiple shiftable loads in the power system. The first determining module is used to determine the first total demand power value corresponding to the plurality of movable loads at multiple times based on the load demand parameters; The retrieval module is used to retrieve the objective function and the corresponding constraints. The objective function includes a deviation term, which represents the deviation between the total demand power term and the total restoration power term corresponding to the plurality of time points. The total restoration power term is determined based on the operation and scheduling parameter terms corresponding to the plurality of movable loads. The second determining module is used to solve the objective function under the constraints based on the first total demand power values ​​corresponding to the multiple times, with the first objective as the objective, to obtain the initial scheduling parameters corresponding to the multiple movable loads, wherein the first objective includes the objective function value that is minimized; The third determining module is used to determine the target scheduling parameters corresponding to the multiple movable loads based on the initial scheduling parameters corresponding to the multiple movable loads respectively.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the load scheduling parameter determination method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the load scheduling parameter determination method as described in any one of claims 1 to 7.

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