Power distribution area flexible resource regulation and control method and system based on cloud edge collaboration

Abstract

The invention discloses a flexible resource regulation and control method and system for a power distribution area based on cloud edge collaboration, and the method comprises the steps: carrying out the cost function modeling of a photovoltaic power generation device, a wind power generation device and a distributed energy storage device, and constructing an optimal scheduling problem of resource regulation and control according to a cost function obtained through modeling; iteratively solving an optimization scheduling problem in the edge computing equipment, and uploading a solving result to the cloud computing platform; and a control strategy issued by the cloud computing platform based on the solving result is received in the edge computing equipment, and the photovoltaic inverter, the wind power converter and the distributed energy storage device are controlled by using the received control strategy. The invention aims to reduce a large amount of computing work in a centralized cloud computing platform, transfer the computing work to the edge computing equipment close to the flexible resource terminal so as to reduce the burden of the cloud computing platform and reduce the operation cost of the power distribution area, and realize the minimization of the flexible resource operation cost of the power distribution area and the maximization of the power utilization rate.

Description

Technical Field

[0001] The present invention belongs to the technology of flexible resource cost optimization and energy scheduling in the distribution substation area of the power system field, and particularly relates to a flexible resource regulation method and system for a distribution substation area based on cloud-edge collaboration. Background Art

[0002] With the continuous increase of new flexible resources and demand loads in the existing distribution substation area, the amount of data generated by the system has expanded sharply, thus increasing the complexity of the scheduling work. The existing resource regulation method for the distribution substation area is to use a centralized cloud computing platform for centralized regulation. However, the method of using a centralized cloud computing platform for centralized regulation will cause the optimal scheduling problem of the distribution substation area to occupy a large amount of computing resources, resulting in an overburdened existing centralized cloud computing platform. In addition, it also reduces the response speed of real-time computing, making the regulation decision slow to react. Summary of the Invention

[0003] The technical problem to be solved by the present invention: In view of the above problems of the prior art, a flexible resource regulation method and system for a distribution substation area based on cloud-edge collaboration are provided. The present invention aims to reduce a large amount of computing work in the centralized cloud computing platform, transfer it to the edge computing device close to the flexible resource terminal to reduce the burden on the cloud computing platform and reduce the operation cost of the distribution substation area, and realize the minimization of the operation cost of the flexible resources in the distribution substation area and the maximization of the power utilization rate.

[0004] To solve the above technical problems, the technical solution adopted by the present invention is as follows: A flexible resource regulation method for a distribution substation area based on cloud-edge collaboration includes the following steps: S1, perform cost function modeling on photovoltaic power generation, wind power generation, and distributed energy storage devices; S2, construct an optimal scheduling problem for resource regulation according to the cost function obtained by modeling; S3, iteratively solve the optimal scheduling problem in the edge computing device, and upload the obtained optimal output power to the cloud computing platform after each iterative solution and obtain the judgment result of whether the cloud computing platform converges to the optimal output power. If the received judgment result is that the optimal output power converges, jump to step S4; otherwise, continue to iteratively solve the optimal scheduling problem; S4, receive the globally optimal output power sent by the cloud computing platform in the edge computing device, and use the received globally optimal output power to control the photovoltaic inverter, wind power converter, and distributed energy storage device respectively.

[0005] Optionally, when performing cost function modeling in step S1, the function expression of the cost function of photovoltaic power generation obtained by modeling is: , Where, is the cost of photovoltaic power generation, is the number of photovoltaic inverters, is the maintenance and operation cost function of the photovoltaic inverter, is the i th photovoltaic inverter at the t actual output power at time is the unit power income of photovoltaic power generation, is the th iteration of the penalty coefficient for curtailment of photovoltaic power generation, is the expected power demand of the distribution substation area for the i th photovoltaic power generation inverter; the functional expression of the cost function of wind power generation obtained by modeling is: , wherein, is the cost of wind power generation, is the number of wind power converters, is the maintenance and operation cost function of wind power generation, is the i th wind power converter at the t actual output power at time is the unit power income of wind power generation, is the th iteration of the penalty coefficient for curtailment of wind power generation, is the expected power demand of the distribution substation area for the i th wind power generation converter; the functional expression of the cost function of the centralized energy storage device obtained by modeling is: , wherein, is the cost of the centralized energy storage device, is the number of centralized energy storage devices, is the cost function of the maintenance and operation of the centralized energy storage device, is the i th centralized energy storage device at the t actual output or input power at time is the unit power income of the output electric energy of the centralized energy storage device or the unit power cost of the input electric energy, is the th iteration of the penalty coefficient for the idle capacity of the centralized energy storage device, is the expected output or input power of the centralized energy storage device.

[0006] Optionally, the functional expression of the maintenance and operation cost function of the photovoltaic inverter is: , Among them, , and are all cost coefficients of the photovoltaic inverter; The functional expression of the maintenance and operation cost function of the wind power generation is: , Among them, , and are respectively the cost coefficients of the wind power generation; The functional expression of the maintenance and operation cost function of the decentralized energy storage device is: , Among them, , and are respectively the cost coefficients of the decentralized energy storage device.

[0007] Optionally, when constructing the optimal scheduling problem of resource regulation according to the cost function obtained by modeling in step S2, the optimal scheduling problem of resource regulation includes the objective function and constraint conditions of the total operation cost of flexible resources in the distribution substation area. The functional expression of the objective function of the total operation cost of flexible resources in the distribution substation area is: , Among them, is the total operation cost of flexible resources in the distribution substation area, is the cost of photovoltaic power generation, is the cost of wind power generation, is the cost of the decentralized energy storage device; The constraint conditions include: , , , , Among them, is the actual output power of the i th photovoltaic inverter at the t th moment, is the actual output power of the i th wind power converter at the t th moment, is the actual output or input power of the i th decentralized energy storage device at the t th moment, represents t the demand power of the load in the distribution substation area system at the and Respectively represent the upper and lower limits of the photovoltaic inverter output power, and They represent the upper and lower limits of the wind power converter output power respectively. Indicates the upper and lower limits of the absolute value of the charging and discharging power of the distributed energy storage device.

[0008] Optionally, in step S3, iteratively solving the optimization scheduling problem in the edge computing device includes: S3.1, construct a cascade system model for target cascade analysis based on the optimization scheduling problem, the cascade system model includes a system-level distribution area cost model, and three subsystem-level cost models of photovoltaic power generation, wind power generation, and distributed energy storage devices, wherein the function expression of the system-level distribution area cost model is: , in, The actual output power of the subsystem-level model uploaded to the distribution area system-level cost model, The expected power of the subsystem-level model uploaded to the substation area system-level cost model, It is the deviation between the actual output power and the expected output power of the distribution area system; The functional expression of the subsystem-level cost model of photovoltaic power generation is: , in, The expected output power of the photovoltaic inverter from the system level to the subsystem level, It is the deviation between the actual output power of the photovoltaic inverter and the expected power; The functional expression of the subsystem-level cost model of wind power generation is: , in, The expected output power of the wind power converter from the system level to the subsystem level, is the deviation between the actual output power of the wind power converter and the expected power; The functional expression of the subsystem-level cost model of distributed energy storage devices is: , in, The expected charging and discharging power of the distributed energy storage device from the system level to the subsystem level, It is the deviation between the actual charging and discharging power of the distributed energy storage device and the expected power; S3.2, use the target cascade analysis method to solve the cascade system model to obtain the solution, including any i PV inverters in t The actual output power at the moment , the actual output power of any i th wind power converter at t time, and the actual output or input power of any i th decentralized energy storage device at t time. .

[0009] Optionally, step S3.2 includes: S3.2.1, solving the distribution substation cost model at the system level to obtain the target variables, where the target variables refer to the expected output power of the photovoltaic inverter at the subsystem level, the expected output power of the wind power converter, and the expected charge and discharge power of the decentralized energy storage device that minimize the total operating cost F of the flexible resources in the distribution substation cost model at the system level; S3.2.2, updating and transmitting the target variables to the cost models at the three subsystem levels of photovoltaic power generation, wind power generation, and decentralized energy storage devices, and solving the cost models at the subsystem level under the determined target variable values to obtain the solution results, including the actual output power of any i th photovoltaic inverter at t time, , the actual output power of any i th wind power converter at t time, and the actual output or input power of any i th decentralized energy storage device at t time. ; S3.2.3, determining whether the preset convergence condition is satisfied, where the preset convergence condition refers to or , where is the actual output power uploaded from the subsystem-level model to the distribution substation system-level cost model, is the expected power uploaded from the subsystem-level model to the distribution substation system-level cost model, is the deviation between the actual output power and the expected output power of the distribution substation system; is the number of iterations, is the preset maximum number of iterations; if the preset convergence condition is not satisfied, then update the penalty coefficients in the cost functions of photovoltaic power generation, wind power generation, and decentralized energy storage devices according to the following formula: , where is the penalty coefficient for curtailment of photovoltaic power generation at the th iteration, is the penalty coefficient for curtailment of wind power at the th iteration, is the The penalty coefficient for the idle capacity of the distributed energy storage device in the first iteration is: is the current iteration number, is a constant term and satisfies ; The expected output power of the photovoltaic inverter from the system level to the subsystem level, The expected output power of the wind power converter from the system level to the subsystem level, The expected charging and discharging power of the distributed energy storage device from the system level to the subsystem level, For the i The actual output power of a photovoltaic inverter, For the i The actual output power of a wind turbine converter is For the i The actual output or input power of a distributed energy storage device; jump to step S3.2.1; otherwise, output the final solution result.

[0010] Optionally, when uploading the optimal output power obtained by the solution to the cloud computing platform after each iterative solution in step S3, the total operating cost of the flexible resources in the distribution station area is also uploaded to the cloud computing platform. After receiving the optimal output power, the cloud computing platform compares the total operating cost of the flexible resources in the distribution station area of ​​the optimal output power received this time with the total operating cost of the flexible resources in the distribution station area of ​​the optimal output power received last time. If the total operating cost of the flexible resources in the distribution station area of ​​the optimal output power received this time is lower, a judgment result that the optimal output power has not yet converged is generated and sent to the edge computing device; otherwise, a judgment result that the optimal output power has converged is generated, and the optimal output power received last time is used as the global optimal output power and the judgment result is sent to the edge computing device.

[0011] In addition, the present invention also provides a flexible resource control system for distribution station areas based on cloud-edge collaboration, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the flexible resource control method for distribution station areas based on cloud-edge collaboration.

[0012] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the flexible resource control method of distribution station area based on cloud-edge collaboration through a processor.

[0013] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the flexible resource control method for distribution station areas based on cloud-edge collaboration through a processor.

[0014] Compared with the prior art, the present invention can mainly achieve the following beneficial effects: The flexible resource regulation method for a distribution transformer area based on cloud-edge collaboration in the present invention includes cost function modeling for photovoltaic power generation, wind power generation, and distributed energy storage devices, constructing an optimal scheduling problem for resource regulation according to the cost functions obtained from the modeling; iteratively solving the optimal scheduling problem in an edge computing device and uploading the solution results to a cloud computing platform; receiving, in the edge computing device, a control strategy issued by the cloud computing platform based on the solution results, and using the received control strategy to control photovoltaic inverters, wind power converters, and distributed energy storage devices. The flexible resource regulation method for a distribution transformer area based on cloud-edge collaboration in the present invention can reduce a large amount of computing work in a centralized cloud computing platform, transfer it to an edge computing device close to the flexible resource terminal to reduce the burden on the cloud computing platform and lower the operation cost of the distribution transformer area, and achieve the minimization of the operation cost of flexible resources in the distribution transformer area and the maximization of power utilization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the basic process of the method in an embodiment of the present invention.

[0016] Figure 2 It is a schematic diagram of the topological structure of a distribution transformer area in an embodiment of the present invention.

[0017] Figure 3 It is a flow chart of an optimization algorithm based on the objective cascade analysis method in an embodiment of the present invention.

[0018] Figure 4 It is a schematic diagram of a cascade system model constructed for objective cascade analysis in an embodiment of the present invention.

[0019] Figure 5 It is a framework diagram of flexible resource regulation based on a cloud-edge collaboration architecture in an embodiment of the present invention.

[0020] Figure 6 It is an energy scheduling diagram of photovoltaic power generation in an embodiment of the present invention.

[0021] Figure 7 It is an energy scheduling diagram of wind power generation in an embodiment of the present invention.

[0022] Figure 8 It is an energy scheduling diagram of a distributed energy storage device in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] As Figure 1As shown in the figure, the flexible resource regulation method for the distribution substation area based on cloud-edge collaboration includes the following steps: S1. Build cost functions for photovoltaic power generation, wind power generation, and distributed energy storage devices; S2. Construct an optimal scheduling problem for resource regulation according to the cost functions obtained from the modeling; S3. Iteratively solve the optimal scheduling problem in the edge computing device, and after each iterative solution, upload the obtained optimal output power to the cloud computing platform and obtain the judgment result of the cloud computing platform on whether the optimal output power converges. If the received judgment result is that the optimal output power converges, jump to step S4; otherwise, continue to iteratively solve the optimal scheduling problem; S4. Receive the globally optimal output power sent by the cloud computing platform in the edge computing device, and use the received globally optimal output power to control the photovoltaic inverter, wind power converter, and distributed energy storage device respectively.

[0025] Figure 2 This is a schematic diagram of the topological structure of the distribution substation area. The distribution substation area includes multiple photovoltaic power generations, wind power generations, distributed energy storage devices, and electrical loads, which are respectively connected to the transmission bus of the distribution substation area through power electronic conversion devices. The transmission bus of the distribution substation area is connected to the large power grid through a transformer.

[0026] When building the cost function in step S1 of this embodiment, the function expression of the cost function of photovoltaic power generation obtained from the modeling is: , where, is the cost of photovoltaic power generation, is the number of photovoltaic inverters, is the maintenance and operation cost function of the photovoltaic inverter, is the i th t actual output power of the photovoltaic inverter at time is the unit power income of photovoltaic power generation, is the penalty coefficient for curtailment of photovoltaic power generation during the th iteration, i is the expected power demand of the distribution substation area for the , where, is the cost of wind power generation, is the number of wind power converters, is the maintenance and operation cost function of wind power generation, is the i tht The actual output power at a moment is the unit power income of wind power generation is the penalty coefficient for wind curtailment in wind power generation at the th iteration, i and is the expected power demand of the distribution substation area for the th wind power converter; The functional expression of the cost function of the decentralized energy storage device obtained by modeling is: Among them, is the cost of the decentralized energy storage device, is the number of decentralized energy storage devices, is the cost function of the maintenance and operation of the decentralized energy storage device, is the i th decentralized energy storage device at the t moment's actual output or input power, is the unit power income of the output electric energy of the decentralized energy storage device or the unit power cost of the input electric energy, is the th iteration's penalty coefficient for the idle capacity of the decentralized energy storage device, and is the expected output or input power of the decentralized energy storage device.

[0027] In this embodiment, the functional expression of the maintenance and operation cost function of the photovoltaic inverter is: , Among them, 、 and are all cost coefficients of the photovoltaic inverter; The functional expression of the maintenance and operation cost function of the wind power generation is: , Among them, 、 and are respectively the cost coefficients of the wind power generation; The functional expression of the maintenance and operation cost function of the decentralized energy storage device is: , Among them, 、 and are respectively the cost coefficients of the decentralized energy storage device.

[0028] When constructing the optimal scheduling problem of resource regulation according to the cost function obtained by modeling in step S2 of this embodiment, the optimal scheduling problem of resource regulation includes the objective function and constraint conditions of the total operating cost of flexible resources in the distribution substation area. The function expression of the objective function of the total operating cost of flexible resources in the distribution substation area is: , wherein, is the total operating cost of flexible resources in the distribution substation area, is the cost of photovoltaic power generation, is the cost of wind power generation, is the cost of the decentralized energy storage device; The constraint conditions include: , , , , wherein, is the i th t actual output power of the photovoltaic inverter at time is the i th t actual output power of the wind power converter at time is the i th t actual output or input power of the decentralized energy storage device at time represents t demand power of the load in the distribution substation area system at time and respectively represent the upper and lower limits of the output power of the photovoltaic inverter, and respectively represent the upper and lower limits of the output power of the wind power converter, represents the upper and lower limits of the absolute value of the charge and discharge power of the decentralized energy storage device.

[0029] As Figure 3 shown, in this embodiment, in step S3, iteratively solving the optimal scheduling problem in the edge computing device includes: S3.1, constructing a cascade system model for target cascade analysis according to the optimal scheduling problem. As Figure 4 shown, the cascade system model includes a distribution substation area cost model at the system level and cost models at the subsystem levels of photovoltaic power generation, wind power generation, and decentralized energy storage devices. The function expression of the distribution substation area cost model at the system level is: , wherein, The actual output power of the subsystem-level model uploaded to the distribution area system-level cost model, The expected power of the subsystem-level model uploaded to the substation area system-level cost model, It is the deviation between the actual output power and the expected output power of the distribution area system; The functional expression of the subsystem-level cost model of photovoltaic power generation is: , in, The expected output power of the photovoltaic inverter from the system level to the subsystem level, It is the deviation between the actual output power of the photovoltaic inverter and the expected power; The functional expression of the subsystem-level cost model of wind power generation is: , in, The expected output power of the wind power converter from the system level to the subsystem level, is the deviation between the actual output power of the wind power converter and the expected power; The functional expression of the subsystem-level cost model of distributed energy storage devices is: , in, The expected charging and discharging power of the distributed energy storage device from the system level to the subsystem level, It is the deviation between the actual charging and discharging power of the distributed energy storage device and the expected power; S3.2, use the target cascade analysis method to solve the cascade system model to obtain the solution, including any i PV inverters in t The actual output power at the moment , any i Wind turbine converters in t The actual output power at the moment and any i Distributed energy storage devices t The actual output or input power at the moment .

[0030] In this embodiment, a simplified objective cascade analysis algorithm is used to solve the optimal scheduling problem. By designing a fast iterative method based on the traditional objective cascade analysis method, the optimization accuracy and speed of the algorithm are improved, enabling the simplified algorithm to quickly obtain an approximate optimal solution within a finite number of iterations, reducing the operating cost of edge computing devices and increasing the utilization rate of flexible resources, thereby reducing the operating cost of flexible resources in the distribution substation area. At the same time, the use of a solution method based on the cloud-edge collaborative architecture reduces the computing burden on the cloud computing platform. The simplified objective cascade analysis method adopted in this embodiment realizes the minimization of the total system cost through a cyclic mechanism of decomposing objectives, independent optimization, feedback coordination, and updating penalties. First, solve the system-level problem, then update and transfer the objective variables to the subsystem-level problems, then solve the subsystem-level problems under the determined objective variable values, and finally judge the convergence condition. If the total cost meets the convergence condition, the loop ends. Otherwise, update the penalty coefficient according to the following formula and solve the loop until the convergence condition is met or the maximum number of iterations is reached N . Specifically, step S3.2 of this embodiment includes: S3.2.1, Solve the cost model of the distribution substation area at the system level to obtain the objective variables, where the objective variables refer to the expected output power of the photovoltaic inverter at the subsystem level, the expected output power of the wind power converter, and the expected charge and discharge power of the distributed energy storage device that minimize the total operating cost F of the flexible resources in the distribution substation area of the system-level cost model S3.2.2, Update and transfer the objective variables to the cost models at the three subsystem levels of photovoltaic power generation, wind power generation, and distributed energy storage devices. Solve the subsystem-level cost models under the determined objective variable values to obtain the solution results, including the actual output power of any i th photovoltaic inverter at t time , the actual output power of any i th wind power converter at t time , and the actual output or input power of any i th distributed energy storage device at t time ; S3.2.3, Judge whether the preset convergence condition is satisfied. The preset convergence condition refers to or , where is the actual output power uploaded from the subsystem-level model to the distribution substation area system-level cost model, is the expected power uploaded from the subsystem-level model to the distribution substation area system-level cost model, is the deviation between the actual output power and the expected output power of the distribution substation area system; is the number of iterations, is the preset maximum number of iterations; if the preset convergence condition is not met, the penalty coefficient in the cost function of photovoltaic power generation, wind power generation, and distributed energy storage device is updated according to the following formula: , in, For the The penalty coefficient for photovoltaic power abandonment in the first iteration is: For the The penalty coefficient for wind power abandonment in the iteration, For the The penalty coefficient for the idle capacity of the distributed energy storage device in the first iteration is: is the current iteration number, is a constant term and satisfies ; The expected output power of the photovoltaic inverter from the system level to the subsystem level, The expected output power of the wind power converter from the system level to the subsystem level, The expected charging and discharging power of the distributed energy storage device from the system level to the subsystem level, For the i The actual output power of a photovoltaic inverter, For the i The actual output power of a wind turbine converter is For the i The actual output or input power of a distributed energy storage device; jump to step S3.2.1; otherwise, output the final solution result.

[0031] like Figure 5As shown, in this embodiment, the solution iteration of the flexible resource cost function optimization problem is carried out in the edge computing device. After each iteration, the result is uploaded to the cloud computing platform for comparison of the optimal result, and it is judged whether the optimization result converges. Then, the flexible resources are scheduled according to the optimal result to minimize the operating cost and maximize the power utilization rate. It should be noted that steps S1 and S2 of this embodiment are executed in the edge computing device. In addition, they can also be executed on the cloud computing platform or other devices according to needs. When uploading the obtained optimal output power to the cloud computing platform after each iteration solution in step S3 of this embodiment, it also includes uploading the total operating cost of the flexible resources in the distribution transformer area to the cloud computing platform. After receiving the optimal output power, the cloud computing platform compares the total operating cost of the flexible resources in the distribution transformer area of the optimal output power received this time with the total operating cost of the flexible resources in the distribution transformer area of the optimal output power received last time. If the total operating cost of the flexible resources in the distribution transformer area of the optimal output power received this time is lower, a judgment result that the optimal output power has not converged is generated and sent to the edge computing device; otherwise, a judgment result that the optimal output power has converged is generated, and the optimal output power received last time is used as the global optimal output power and this judgment result is sent to the edge computing device.

[0032] To verify the method for regulating flexible resources in the distribution transformer area based on cloud-edge collaboration in this embodiment, an experiment was conducted in this embodiment for a distribution transformer area including 1 photovoltaic inverter, 1 wind power converter, and 1 decentralized energy storage device. The parameters of the photovoltaic inverter are set as follows: Photovoltaic inverter: The maximum output power is 30 kW, and the maximum output power of the wind power converter is 40 kW. The parameters of the decentralized energy storage device are set as follows: The maximum charging power is 25 kW, the maximum discharging power is 25 kW, and the maximum energy storage capacity is 60 kWh. The method of local iteration operation of the edge computing device and comparison of the results by the cloud computing platform to determine whether they converge is used for optimal dispatching of the distribution transformer area system. Four iterative optimization calculations are performed, and the energy dispatching diagrams of the three subsystems obtained are respectively as Figure 6 , Figure 7 and Figure 8 shown, and the iterative results are shown in Table 1. Among them, Figure 6 is the energy dispatching diagram of photovoltaic power generation in the embodiment of the present invention, Figure 7 is the energy dispatching diagram of wind power generation in the embodiment of the present invention, Figure 8 is the energy dispatching diagram of the decentralized energy storage device in the embodiment of the present invention.

[0033] Table 1: Optimal dispatching results of flexible resources in the distribution transformer area

[0034] From Table 1, Figure 6 , Figure 7 and Figure 8It can be seen that as the number of iterations increases, the output powers of the photovoltaic inverter and the wind power converter gradually increase, and the charging power of the distributed energy storage device also keeps increasing, indicating that the flexible resource scheduling direction of the distribution substation area is continuously optimized. After the 4th iteration ends, the scheduling result meets the convergence condition. At this time, the output powers of the photovoltaic inverter and the wind power converter are very close to the maximum power, and the power utilization rate is very high. At the same time, the distributed energy storage device also has a high power utilization rate. Therefore, it can be proved that the flexible resource regulation method for the distribution substation area based on cloud-edge collaboration in this embodiment effectively realizes the collaborative optimization of various flexible resources in the distribution substation area; compared with the result of non-optimized scheduling, the flexible resource regulation method for the distribution substation area based on cloud-edge collaboration in this embodiment improves the utilization rates of photovoltaic power generation, wind power generation, and distributed energy storage in the distribution substation area, thereby reducing the operation cost of the distribution substation area system.

[0035] In addition, this embodiment also provides a flexible resource regulation system for a distribution substation area based on cloud-edge collaboration, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the flexible resource regulation method for the distribution substation area based on cloud-edge collaboration.

[0036] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored, and the computer program or instruction is programmed or configured to execute the flexible resource regulation method for the distribution substation area based on cloud-edge collaboration through a processor.

[0037] In addition, this embodiment also provides a computer program product, including a computer program or instruction, and the computer program or instruction is programmed or configured to execute the flexible resource regulation method for the distribution substation area based on cloud-edge collaboration through a processor.

[0038] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present invention can be in the form of a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be in the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code. The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means that implements the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0039] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A flexible resource control method for distribution area based on cloud-edge collaboration, characterized in that: The steps include: S1, cost function modeling for photovoltaic power generation, wind power generation, and distributed energy storage devices; S2, construct the optimal scheduling problem of resource control based on the cost function obtained by modeling; S3, iteratively solving the optimization scheduling problem in the edge computing device, and uploading the optimal output power obtained after each iterative solution to the cloud computing platform and obtaining the judgment result of the cloud computing platform on whether the optimal output power converges. If the judgment result received is that the optimal output power converges, jump to step S4; otherwise, continue to iteratively solve the optimization scheduling problem; S4, receiving the global optimal output power issued by the cloud computing platform in the edge computing device, and using the received global optimal output power to control the photovoltaic inverter, wind power converter and distributed energy storage device respectively.

2. According to the method for flexible resource control of distribution area based on cloud-edge collaboration according to claim 1, it is characterized in that: When cost function modeling is performed in step S1, the function expression of the cost function of photovoltaic power generation obtained by modeling is: ， in, is the cost of photovoltaic power generation, is the number of PV inverters, is the maintenance and operation cost function of the PV inverter, For the i PV inverters in t The actual output power at the moment, is the unit power benefit of photovoltaic power generation, For the The penalty coefficient for photovoltaic power abandonment in the first iteration is: For the distribution station area i The expected power demand of a photovoltaic inverter; the functional expression of the cost function of wind power generation obtained by modeling is: ， in, The cost of wind power generation, is the number of wind turbine converters, is the maintenance and operation cost function of wind power generation, For the i Wind turbine converters in t The actual output power at the moment, is the unit power benefit of wind power generation, For the The penalty coefficient for wind power abandonment in the iteration, For the distribution station area i The expected power demand of a wind turbine converter; the function expression of the cost function of the distributed energy storage device obtained by modeling is: ， in, is the cost of distributed energy storage device, is the number of distributed energy storage devices, is the cost function for the maintenance and operation of distributed energy storage devices, For the i Distributed energy storage devices t The actual output or input power at the moment, The unit power benefit of the distributed energy storage device outputting electric energy or the unit power cost of the input electric energy, For the The penalty coefficient for the idle capacity of the distributed energy storage device in the first iteration is: It is the expected output or input power of the distributed energy storage device.

3. According to claim 2, the flexible resource control method for distribution area based on cloud-edge collaboration is characterized in that: The functional expression of the maintenance and operation cost function of the photovoltaic inverter is: ， in, , and All are cost coefficients of photovoltaic inverters; The functional expression of the maintenance and operation cost function of wind power generation is: ， in, , and are the cost coefficients of wind power generation respectively; The functional expression of the cost function of the maintenance and operation of the distributed energy storage device is: ， in, , and are the cost coefficients of distributed energy storage devices respectively.

4. The method for flexible resource control of distribution area based on cloud-edge collaboration according to claim 2 is characterized in that: When constructing the optimal scheduling problem of resource regulation according to the cost function obtained by modeling in step S2, the optimal scheduling problem of resource regulation includes the objective function and constraint conditions of the total cost of flexible resource operation in the distribution station area. The functional expression of the objective function of the total cost of flexible resource operation in the distribution station area is: ， in, The total cost of flexible resource operation in the distribution area. is the cost of photovoltaic power generation, The cost of wind power generation, is the cost of the distributed energy storage device; the constraints include: ， ， ， ， in, For the i PV inverters in t The actual output power at the moment, For the i Wind turbine converters in t The actual output power at the moment, For the i Distributed energy storage devices t The actual output or input power at the moment, express t The power demand of the load in the distribution area system at any moment, and Respectively represent the upper and lower limits of the photovoltaic inverter output power, and They represent the upper and lower limits of the wind power converter output power respectively. Indicates the upper and lower limits of the absolute value of the charging and discharging power of the distributed energy storage device.

5. The method for flexible resource control of distribution area based on cloud-edge collaboration according to claim 2 is characterized in that: In step S3, iteratively solving the optimization scheduling problem in the edge computing device includes: S3.1, construct a cascade system model for target cascade analysis based on the optimization scheduling problem, the cascade system model includes a system-level distribution area cost model, and three subsystem-level cost models of photovoltaic power generation, wind power generation, and distributed energy storage devices, wherein the function expression of the system-level distribution area cost model is: ， in, The actual output power of the subsystem-level model uploaded to the distribution area system-level cost model, The expected power of the subsystem-level model uploaded to the substation area system-level cost model, It is the deviation between the actual output power and the expected output power of the distribution area system; The functional expression of the subsystem-level cost model of photovoltaic power generation is: ， in, The expected output power of the photovoltaic inverter from the system level to the subsystem level, It is the deviation between the actual output power of the photovoltaic inverter and the expected power; The functional expression of the subsystem-level cost model of wind power generation is: ， in, The expected output power of the wind power converter from the system level to the subsystem level, is the deviation between the actual output power of the wind power converter and the expected power; The functional expression of the subsystem-level cost model of distributed energy storage devices is: ， in, The expected charging and discharging power of the distributed energy storage device from the system level to the subsystem level, It is the deviation between the actual charging and discharging power of the distributed energy storage device and the expected power; S3.2, use the target cascade analysis method to solve the cascade system model to obtain the solution, including any i PV inverters in t The actual output power at the moment , any i Wind turbine converters in t The actual output power at the moment and any i Distributed energy storage devices t The actual output or input power at the moment .

6. The method for flexible resource control of distribution area based on cloud-edge collaboration according to claim 5 is characterized in that: Step S3.2 includes: S3.2.1, solving the system-level distribution area cost model to obtain the target variable, wherein the target variable refers to the expected output power of the subsystem-level photovoltaic inverter, the expected output power of the wind power converter, and the expected charging and discharging power of the distributed energy storage device that minimizes the total cost F of the distribution area flexible resource operation of the system-level distribution area cost model; S3.2.2, update the target variable and pass it to the three subsystem-level cost models of photovoltaic power generation, wind power generation, and distributed energy storage devices, and solve the subsystem-level cost model under the determined target variable value to obtain the solution result, including any i PV inverters in t The actual output power at the moment , any i Wind turbine converters in t The actual output power at the moment and any i Distributed energy storage devices t The actual output or input power at the moment ; S3.2.3, determine whether the preset convergence condition is met, the preset convergence condition refers to or ,in The actual output power of the subsystem-level model uploaded to the distribution area system-level cost model, The expected power of the subsystem-level model uploaded to the substation area system-level cost model, It is the deviation between the actual output power and the expected output power of the distribution area system; is the number of iterations, is the preset maximum number of iterations; if the preset convergence condition is not met, the penalty coefficient in the cost function of photovoltaic power generation, wind power generation, and distributed energy storage device is updated according to the following formula: ， in, For the The penalty coefficient for photovoltaic power abandonment in the first iteration is: For the The penalty coefficient for wind power abandonment in the iteration, For the The penalty coefficient for the idle capacity of the distributed energy storage device in the first iteration is: is the current iteration number, is a constant term and satisfies ; The expected output power of the photovoltaic inverter from the system level to the subsystem level, The expected output power of the wind power converter from the system level to the subsystem level, The expected charging and discharging power of the distributed energy storage device from the system level to the subsystem level, For the i The actual output power of a photovoltaic inverter, For the i The actual output power of a wind turbine converter is For the i The actual output or input power of a distributed energy storage device; jump to step S3.2.1; otherwise, output the final solution result.

7. The method for flexible resource control of distribution area based on cloud-edge collaboration according to claim 4 is characterized in that: In step S3, when the optimal output power obtained by the solution is uploaded to the cloud computing platform after each iterative solution, the total operating cost of the flexible resources in the distribution station area is also uploaded to the cloud computing platform. After receiving the optimal output power, the cloud computing platform compares the total operating cost of the flexible resources in the distribution station area of ​​the optimal output power received this time with the total operating cost of the flexible resources in the distribution station area of ​​the optimal output power received last time. If the total operating cost of the flexible resources in the distribution station area of ​​the optimal output power received this time is lower, a judgment result that the optimal output power has not yet converged is generated and sent to the edge computing device; otherwise, a judgment result that the optimal output power has converged is generated, and the optimal output power received last time is used as the global optimal output power and the judgment result is sent to the edge computing device.

8. A flexible resource control system for distribution substations based on cloud-edge collaboration, comprising interconnected microprocessors and memories, characterized in that: The microprocessor is programmed or configured to execute the flexible resource control method for distribution station areas based on cloud-edge collaboration as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the flexible resource control method for distribution station areas based on cloud-edge collaboration as described in any one of claims 1 to 7 through a processor.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the flexible resource control method for distribution station areas based on cloud-edge collaboration as described in any one of claims 1 to 7 through a processor.

Images