Power distribution network adjustable resource cooperative control method and system considering uncertainty

Abstract

The invention discloses a power distribution network adjustable resource cooperative control method and system considering uncertainty, and belongs to the technical field of power distribution network control, and the method comprises the following steps: collecting the real-time operation data of a power distribution network, and building a physical model of the power distribution network; uncertain factors possibly occurring in the power distribution network are analyzed based on the physical model; establishing an optimization model considering uncertainty factors; determining a cooperative control strategy of each adjustable resource according to the optimization model; monitoring the running state of the power distribution network in real time, dynamically adjusting parameters of the optimization model according to a monitoring result, and recalculating the optimization model to obtain a new cooperative control strategy; and adjusting the operation state of each adjustable resource according to the new cooperative control strategy. According to the invention, by comprehensively considering various uncertain factors and a cooperative control strategy of adjustable resources, on the basis that the power distribution system is kept stable, the energy utilization rate is improved to the maximum extent, the influence on the environment is reduced, and the safe and stable operation of the power distribution network is ensured.

Description

Technical Field

[0001] The present invention relates to a method and system for collaboratively controlling adjustable resources of a distribution network taking uncertainty into consideration, and belongs to the technical field of distribution network control. Background Art

[0002] With the development of new energy technologies and the massive access of distributed generation (DG), modern distribution networks are becoming more and more complex and uncertain.

[0003] At present, the control technology of distribution network mainly focuses on the following two aspects: 1. Static optimization: such as power flow optimization, economic dispatch, etc., mainly for optimization problems under deterministic conditions, and it is difficult to cope with real-time changes and uncertainties. 2. Distributed control: Some studies have begun to focus on distributed control strategies, but most of them are local optimization and lack global coordination.

[0004] Traditional distribution network control methods are difficult to cope with these new challenges. Therefore, it is particularly important to study a coordinated control technology for adjustable resources in distribution networks that can effectively consider uncertainties. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a method and system for collaborative control of adjustable resources in a distribution network taking uncertainty into consideration, which can ensure the safe and stable operation of the distribution network.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] In a first aspect, an embodiment of the present invention provides a method for collaboratively controlling adjustable resources in a distribution network considering uncertainty, comprising the following steps:

[0008] Step 1: collect real-time operation data of the distribution network and establish a physical model of the distribution network, wherein the physical model includes the topological structure of the distribution network, the power flow path and the distribution of adjustable resources;

[0009] Step 2: analyzing possible uncertainties in the distribution network based on the physical model, wherein the uncertainties include load fluctuations, fluctuations in the output of distributed power sources, and equipment failures;

[0010] Step 3, establishing an optimization model that takes uncertainty factors into consideration, wherein the optimization model includes an objective function and constraints, wherein the objective function is used to minimize the operating cost or loss of the distribution network, and the constraints include voltage stability, power balance, and equipment operation restrictions of the distribution network;

[0011] Step 4: determining a coordinated control strategy for each adjustable resource according to the optimization model, wherein the coordinated control strategy includes start / stop, output and power regulation of the adjustable resources;

[0012] Step 5: monitor the operating status of the distribution network in real time, dynamically adjust the optimization model parameters according to the monitoring results, and recalculate the optimization model to obtain a new collaborative control strategy;

[0013] Step 6: Adjust the operating status of each adjustable resource according to the new collaborative control strategy.

[0014] As a possible implementation of this embodiment, step 2 includes the following steps:

[0015] Collecting real-time data of all nodes in the distribution network, wherein the real-time data of the nodes includes the voltage and current of the nodes;

[0016] Collect all line parameters in the distribution network, including resistance, reactance and line length;

[0017] Construct a topological diagram of the distribution network based on the node and line information in the distribution network;

[0018] Calculate the injected power of each node according to the real-time data of the node, wherein the injected power includes active power and reactive power;

[0019] The power flow equations are established by using the node injection power and the balance equations, and the power flow equations are solved by numerical iteration method to determine the node voltage and phase angle, and then determine the power flow path;

[0020] Determine the specific location of the adjustable resource in the distribution network through the real-time data of the node, and collect the characteristic parameters of each adjustable resource, including rated power, operating status, and response time;

[0021] A mathematical model of the adjustable resources is established according to the characteristic parameters of the adjustable resources to form a physical model of the distribution network.

[0022] As a possible implementation of this embodiment, the physical model of the distribution network is:

[0023] P DG =P DG，max f(V, I, θ);

[0024] Among them, P DG Represents the actual output power of distributed power generation, P DG,max represents the rated maximum output power of the distributed power supply, V, I, θ represent the node voltage, node current, and the phase difference between the voltage and current, respectively, and f() represents the correlation function.

[0025] As a possible implementation of this embodiment, determining the coordinated control strategy of each adjustable resource according to the optimization model includes the following steps:

[0026] Determine the start-stop strategy of each adjustable resource according to the distribution network optimization model, wherein the start-stop strategy includes using the start-stop cost function, comprehensively considering the startup cost, shutdown cost and operation efficiency, combining the load forecast, the output forecast of the distributed power source and the historical operation data, and dynamically adjusting the start-stop state of the adjustable resource;

[0027] The output strategy of each adjustable resource is determined according to the distribution network optimization model. The output strategy includes applying an economic dispatch algorithm, comprehensively considering the maximum output, minimum output and output cost function of the adjustable resources, and determining the power output allocation of each resource by optimizing the objective function.

[0028] As a possible implementation of this embodiment, the start and stop strategy of each adjustable resource is:

[0029]

[0030] Among them, C start-stop (t) represents the total start-stop cost at time t, N represents the total number of adjustable resources, A binary variable indicating whether the adjustable resource i is started at time t, C start,i (t) represents the startup cost of adjustable resource i at time t, A binary variable indicating whether the adjustable resource i is stopped at time t, C stop,i (t) represents the stopping cost of adjustable resource i at time t.

[0031] As a possible implementation of this embodiment, the output strategy of each adjustable resource is:

[0032]

[0033] Among them, C gen (t) represents the output strategy of each adjustable resource, N represents the total number of adjustable resources, C gen,i represents the output cost function, P i (t) represents the output of adjustable resource i at time t.

[0034] As a possible implementation of this embodiment, the optimization model considering uncertainty factors is:

[0035]

[0036] Among them, C total represents the total cost, T represents the time period, C buy (t) represents the electricity purchase cost at time t, Cgen (t) represents the power generation cost at time t, C res (t) represents the adjustable resource operation and maintenance cost at time t, C loss (t) represents the electricity loss cost at time t.

[0037] In a second aspect, an embodiment of the present invention provides a distribution network adjustable resource collaborative control system considering uncertainty, including:

[0038] A physical model building module, used to collect real-time operation data of the distribution network and build a physical model of the distribution network, wherein the physical model includes the topological structure of the distribution network, the power flow path and the distribution of adjustable resources;

[0039] An uncertainty factor analysis module, used to analyze possible uncertainty factors in the distribution network based on the physical model, wherein the uncertainty factors include load fluctuation, output fluctuation of distributed power sources and equipment failure;

[0040] An optimization model building module is used to build an optimization model that takes uncertainty factors into consideration, wherein the optimization model includes an objective function and constraints, wherein the objective function is used to minimize the operating cost or loss of the distribution network, and the constraints include voltage stability, power balance, and equipment operation restrictions of the distribution network;

[0041] A collaborative control strategy determination module is used to determine the collaborative control strategy of each adjustable resource according to the optimization model, wherein the collaborative control strategy includes the start and stop, output and power regulation of the adjustable resources;

[0042] The optimization model parameter adjustment module is used to monitor the operating status of the distribution network in real time, dynamically adjust the optimization model parameters according to the monitoring results, and recalculate the optimization model to obtain a new collaborative control strategy;

[0043] The operating state adjustment module is used to adjust the operating state of each adjustable resource according to the new collaborative control strategy.

[0044] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions to perform any steps of the above-mentioned method for collaborative control of adjustable resources in a distribution network taking into account uncertainty.

[0045] In a fourth aspect, an embodiment of the present invention provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for coordinated control of adjustable resources in a distribution network taking into account uncertainty are executed.

[0046] The beneficial effects of the technical solution of the embodiment of the present invention are as follows:

[0047] The present invention comprehensively considers various uncertain factors and a coordinated control strategy of adjustable resources, so that the distribution system can maximize energy utilization and reduce environmental impact while maintaining stability, thereby ensuring safe and stable operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of a method for collaboratively controlling adjustable resources in a distribution network considering uncertainty according to an exemplary embodiment;

[0049] Figure 2 It is a schematic diagram of the structure of a coordinated control system of adjustable resources in a distribution network taking uncertainty into consideration according to an exemplary embodiment. DETAILED DESCRIPTION

[0050] In order to more clearly illustrate the technical features of the solution of the present invention, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0051] like Figure 1 As shown, an embodiment of the present invention provides a method for collaboratively controlling adjustable resources in a distribution network considering uncertainty, comprising the following steps:

[0052] Step 1: collect real-time operation data of the distribution network and establish a physical model of the distribution network, wherein the physical model includes the topological structure of the distribution network, the power flow path and the distribution of adjustable resources;

[0053] Step 2: analyzing possible uncertainties in the distribution network based on the physical model, wherein the uncertainties include load fluctuations, fluctuations in the output of distributed power sources, and equipment failures;

[0054] Step 3, establishing an optimization model that takes uncertainty factors into consideration, wherein the optimization model includes an objective function and constraints, wherein the objective function is used to minimize the operating cost or loss of the distribution network, and the constraints include voltage stability, power balance, and equipment operation restrictions of the distribution network;

[0055] Step 4: determining a coordinated control strategy for each adjustable resource according to the optimization model, wherein the coordinated control strategy includes start / stop, output and power regulation of the adjustable resources;

[0056] Step 5: monitor the operating status of the distribution network in real time, dynamically adjust the optimization model parameters according to the monitoring results, and recalculate the optimization model to obtain a new collaborative control strategy;

[0057] Step 6: Adjust the operating status of each adjustable resource according to the new collaborative control strategy, so as to achieve collaborative control of the adjustable resources of the distribution network to cope with the influence of uncertain factors and ensure the safe and stable operation of the distribution network.

[0058] This embodiment solves the problems that may be caused by various uncertain factors in the distribution network, and ensures the normal, efficient and safe operation of the distribution network.

[0059] As a possible implementation of this embodiment, step 2 includes the following steps:

[0060] Collecting real-time data of all nodes in the distribution network, wherein the real-time data of the nodes includes the voltage and current of the nodes;

[0061] Collect all line parameters in the distribution network, including resistance, reactance and line length;

[0062] Construct a topological diagram of the distribution network based on the node and line information in the distribution network;

[0063] Calculate the injected power of each node according to the real-time data of the node, wherein the injected power includes active power and reactive power;

[0064] The power flow equations are established by using the node injection power and the balance equations, and the power flow equations are solved by numerical iteration method to determine the node voltage and phase angle, and then determine the power flow path;

[0065] Determine the specific location of the adjustable resource in the distribution network through the real-time data of the node, and collect the characteristic parameters of each adjustable resource, including rated power, operating status, and response time;

[0066] The mathematical model of adjustable resources is established according to the characteristic parameters of adjustable resources to form the physical model of the distribution network. The physical model is combined with the actual distribution network to capture various uncertain factors that may affect the performance of the distribution network.

[0067] As a possible implementation of this embodiment, the physical model of the distribution network is:

[0068] P DG =P DG，max f(V, I, θ);

[0069] Among them, P DG Represents the actual output power of distributed power generation, P DG,max represents the rated maximum output power of the distributed power supply, V, I, θ represent the node voltage, node current, and the phase difference between the voltage and current, respectively, and f() represents the correlation function.

[0070] As a possible implementation of this embodiment, determining the coordinated control strategy of each adjustable resource according to the optimization model includes the following steps:

[0071] Determine the start-stop strategy of each adjustable resource according to the distribution network optimization model, wherein the start-stop strategy includes using the start-stop cost function, comprehensively considering the startup cost, shutdown cost and operation efficiency, combining the load forecast, the output forecast of the distributed power source and the historical operation data, and dynamically adjusting the start-stop state of the adjustable resource;

[0072] The output strategy of each adjustable resource is determined according to the distribution network optimization model. The output strategy includes applying the economic dispatch algorithm, comprehensively considering the maximum output, minimum output and output cost function of the adjustable resources, and determining the power output allocation of each resource by optimizing the objective function. The collaborative control strategy dynamically adjusts the start and stop, output and power of the adjustable resources to make more efficient use of these resources to meet the ever-changing power demand.

[0073] As a possible implementation of this embodiment, the start and stop strategy of each adjustable resource is:

[0074]

[0075] Among them, C start-stop (t) represents the total start-stop cost at time t, N represents the total number of adjustable resources, A binary variable indicating whether the adjustable resource i is started at time t, C start,i (t) represents the startup cost of adjustable resource i at time t, A binary variable indicating whether the adjustable resource i is stopped at time t, C stop,i (t) represents the stopping cost of adjustable resource i at time t.

[0076] As a possible implementation of this embodiment, the output strategy of each adjustable resource is:

[0077]

[0078] Among them, C gen (t) represents the output strategy of each adjustable resource, N represents the total number of adjustable resources, C gen,i represents the output cost function, P i (t) represents the output of adjustable resource i at time t.

[0079] As a possible implementation of this embodiment, the optimization model considering uncertainty factors is:

[0080]

[0081] Among them, Ctotal represents the total cost, T represents the time period, C buy (t) represents the electricity purchase cost at time t, C gen (t) represents the power generation cost at time t, C res (t) represents the adjustable resource operation and maintenance cost at time t, C loss (t) represents the electricity loss cost at time t.

[0082] In a second aspect, an embodiment of the present invention provides a distribution network adjustable resource collaborative control system considering uncertainty, including:

[0083] A physical model building module, used to collect real-time operation data of the distribution network and build a physical model of the distribution network, wherein the physical model includes the topological structure of the distribution network, the power flow path and the distribution of adjustable resources;

[0084] An uncertainty factor analysis module, used to analyze possible uncertainty factors in the distribution network based on the physical model, wherein the uncertainty factors include load fluctuation, output fluctuation of distributed power sources and equipment failure;

[0085] An optimization model building module is used to build an optimization model that takes uncertainty factors into consideration, wherein the optimization model includes an objective function and constraints, wherein the objective function is used to minimize the operating cost or loss of the distribution network, and the constraints include voltage stability, power balance, and equipment operation restrictions of the distribution network;

[0086] A collaborative control strategy determination module is used to determine the collaborative control strategy of each adjustable resource according to the optimization model, wherein the collaborative control strategy includes the start and stop, output and power regulation of the adjustable resources;

[0087] The optimization model parameter adjustment module is used to monitor the operating status of the distribution network in real time, dynamically adjust the optimization model parameters according to the monitoring results, and recalculate the optimization model to obtain a new collaborative control strategy;

[0088] The operating status adjustment module is used to adjust the operating status of each adjustable resource according to the new collaborative control strategy, so as to realize the collaborative control of the adjustable resources of the distribution network to cope with the influence of uncertain factors and ensure the safe and stable operation of the distribution network.

[0089] This embodiment collects real-time operation data of the distribution network, establishes a corresponding physical model, and then analyzes possible uncertainties, including but not limited to load fluctuations, fluctuations in the output of distributed power sources, and equipment failures.

[0090] After determining the uncertainty factors, this embodiment will determine the coordinated control strategies of various adjustable resources according to the preset optimization model. These coordinated control strategies dynamically adjust the start and stop, output and power of the adjustable resources to more effectively utilize these resources to meet the ever-changing power demand.

[0091] This embodiment also provides a function of real-time monitoring of the distribution network operation status. Through monitoring, changes in the distribution network operation status can be discovered in a timely manner, and according to these changes, the parameters of the optimization model can be dynamically adjusted, and then the optimization model can be recalculated to obtain a new and more optimized collaborative control strategy.

[0092] Under the new collaborative control strategy, the operating status of various adjustable resources can be further adjusted to better adapt to the influence of uncertain factors, thereby ensuring the safe and stable operation of the distribution network.

[0093] In general, this embodiment solves the problems that may be caused by various uncertain factors in the distribution network, and ensures the normal, efficient and safe operation of the distribution network.

[0094] An electronic device provided by an embodiment of the present invention includes a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, and when the device is running, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions to perform any steps of the above-mentioned method for collaborative control of adjustable resources in a distribution network taking into account uncertainty.

[0095] Specifically, the above-mentioned memory and processor can be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer program stored in the memory, it can execute the above-mentioned distribution network adjustable resource collaborative control method considering uncertainty.

[0096] Those skilled in the art will appreciate that the structure of the electronic device does not limit the electronic device and may include more or fewer components than shown in the figure, or combine or split certain components, or arrange the components differently.

[0097] In some embodiments, the electronic device may also include a touch screen that can be used to display a graphical user interface (e.g., a startup interface of an application) and receive user operations on the graphical user interface (e.g., startup operations on an application). The specific touch screen may include a display panel and a touch panel. The display panel may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. The touch panel may collect the user's contact or non-contact operations on or near it, and generate pre-set operation instructions, for example, the user uses any suitable object such as a finger, stylus, or accessories on or near the touch panel. In addition, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation and posture, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into information that the processor can process, and then sends it to the processor, and can receive and execute commands from the processor. In addition, the touch panel can be implemented by various types such as resistive, capacitive, infrared and surface acoustic wave, and any technology developed in the future can also be used to implement the touch panel. Further, the touch panel can cover the display panel, and the user can operate on or near the touch panel covered on the display panel according to the graphical user interface displayed on the display panel. After the touch panel detects the operation on or near it, it is transmitted to the processor to determine the user input, and then the processor provides corresponding visual output on the display panel in response to the user input. In addition, the touch panel and the display panel can be implemented as two independent components or integrated.

[0098] Corresponding to the method for starting the above-mentioned application, an embodiment of the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for collaborative control of adjustable resources in a distribution network taking into account uncertainty are executed.

[0099] The application startup device provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0100] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0101] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0102] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, each functional module in the embodiments provided in the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0104] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for coordinated control of adjustable resources in a distribution network considering uncertainty, characterized in that: The steps include: Step 1: collect real-time operation data of the distribution network and establish a physical model of the distribution network, wherein the physical model includes the topological structure of the distribution network, the power flow path and the distribution of adjustable resources; Step 2: analyzing possible uncertainties in the distribution network based on the physical model, wherein the uncertainties include load fluctuations, fluctuations in the output of distributed power sources, and equipment failures; Step 3, establishing an optimization model that takes uncertainty factors into consideration, wherein the optimization model includes an objective function and constraints, wherein the objective function is used to minimize the operating cost or loss of the distribution network, and the constraints include voltage stability, power balance, and equipment operation restrictions of the distribution network; Step 4: determining a coordinated control strategy for each adjustable resource according to the optimization model, wherein the coordinated control strategy includes start / stop, output and power regulation of the adjustable resources; Step 5: monitor the operating status of the distribution network in real time, dynamically adjust the optimization model parameters according to the monitoring results, and recalculate the optimization model to obtain a new collaborative control strategy; Step 6: Adjust the operating status of each adjustable resource according to the new collaborative control strategy.

2. The method for coordinated control of adjustable resources in a distribution network considering uncertainty according to claim 1, characterized in that: The step 2 comprises the following steps: Collecting real-time data of all nodes in the distribution network, wherein the real-time data of the nodes includes the voltage and current of the nodes; Collect all line parameters in the distribution network, including resistance, reactance and line length; Construct a topological diagram of the distribution network based on the node and line information in the distribution network; Calculate the injected power of each node according to the real-time data of the node, wherein the injected power includes active power and reactive power; The power flow equations are established by using the node injection power and the balance equations, and the power flow equations are solved by numerical iteration method to determine the node voltage and phase angle, and then determine the power flow path; Determine the specific location of the adjustable resource in the distribution network through the real-time data of the node, and collect the characteristic parameters of each adjustable resource, including rated power, operating status, and response time; A mathematical model of the adjustable resources is established according to the characteristic parameters of the adjustable resources to form a physical model of the distribution network.

3. The method for coordinated control of adjustable resources in a distribution network considering uncertainty according to claim 2, characterized in that: The physical model of the distribution network is: P DG =P DG，max ·f(V,I,θ); Among them, P DG Represents the actual output power of distributed power generation, P DG,max represents the rated maximum output power of the distributed power supply, V, I, θ represent the node voltage, node current, and the phase difference between the voltage and current, respectively, and f() represents the correlation function.

4. The method for coordinated control of adjustable resources in a distribution network considering uncertainty according to claim 1, characterized in that: Determining the coordinated control strategy of each adjustable resource according to the optimization model includes the following steps: Determine the start-stop strategy of each adjustable resource according to the distribution network optimization model, wherein the start-stop strategy includes using the start-stop cost function, comprehensively considering the startup cost, shutdown cost and operation efficiency, combining the load forecast, the output forecast of the distributed power source and the historical operation data, and dynamically adjusting the start-stop state of the adjustable resource; The output strategy of each adjustable resource is determined according to the distribution network optimization model. The output strategy includes applying an economic dispatch algorithm, comprehensively considering the maximum output, minimum output and output cost function of the adjustable resources, and determining the power output allocation of each resource by optimizing the objective function.

5. The method for coordinated control of adjustable resources in a distribution network considering uncertainty according to claim 4, characterized in that: The start and stop strategies of the adjustable resources are: Among them, C start-stop (t) represents the total start-stop cost at time t, N represents the total number of adjustable resources, A binary variable indicating whether the adjustable resource i is started at time t, C start,i (t) represents the startup cost of adjustable resource i at time t, A binary variable indicating whether the adjustable resource i is stopped at time t, C stop,i (t) represents the stopping cost of adjustable resource i at time t.

6. The method for coordinated control of adjustable resources in a distribution network considering uncertainty according to claim 4, characterized in that: The output strategies of the adjustable resources are: Among them, C gen (t) represents the output strategy of each adjustable resource, N represents the total number of adjustable resources, C gen,i represents the output cost function, P i (t) represents the output of adjustable resource i at time t.

7. The method for coordinated control of adjustable resources in a distribution network considering uncertainty according to any one of claims 1 to 6, characterized in that: The optimization model considering uncertainty factors is: Among them, C total represents the total cost, T represents the time period, C buy (t) represents the electricity purchase cost at time t, C gen (t) represents the power generation cost at time t, C res (t) represents the adjustable resource operation and maintenance cost at time t, C loss (t) represents the electricity loss cost at time t.

8. A coordinated control system of adjustable resources in a distribution network considering uncertainty, characterized in that: include: A physical model building module, used to collect real-time operation data of the distribution network and build a physical model of the distribution network, wherein the physical model includes the topological structure of the distribution network, the power flow path and the distribution of adjustable resources; An uncertainty factor analysis module, used to analyze possible uncertainty factors in the distribution network based on the physical model, wherein the uncertainty factors include load fluctuation, output fluctuation of distributed power sources and equipment failure; An optimization model building module is used to build an optimization model that takes uncertainty factors into consideration, wherein the optimization model includes an objective function and constraints, wherein the objective function is used to minimize the operating cost or loss of the distribution network, and the constraints include voltage stability, power balance, and equipment operation restrictions of the distribution network; A collaborative control strategy determination module is used to determine the collaborative control strategy of each adjustable resource according to the optimization model, wherein the collaborative control strategy includes the start and stop, output and power regulation of the adjustable resources; The optimization model parameter adjustment module is used to monitor the operating status of the distribution network in real time, dynamically adjust the optimization model parameters according to the monitoring results, and recalculate the optimization model to obtain a new collaborative control strategy; The operating state adjustment module is used to adjust the operating state of each adjustable resource according to the new collaborative control strategy.

9. An electronic device, characterized in that: It includes a processor, a memory and a bus, the memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the method for collaborative control of adjustable resources in a distribution network considering uncertainty as described in any one of claims 1-8.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, executes the steps of the method for collaborative control of adjustable resources in a distribution network taking into account uncertainty as described in any one of claims 1 to 8.

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