Main and distribution network collaborative risk assessment calculation method and system considering uncertainty

Abstract

The invention discloses a main and distribution network collaborative risk assessment calculation method and system considering uncertainty in the technical field of power dispatching, and the method comprises the steps: preliminarily assessing the power difference between a main network dispatching instruction and a distribution network unit response, and analyzing the dispatching risk; comprehensively considering various constraint conditions in the power distribution network, and accurately calculating the scheduling capability through a power flow equation; accurate risk assessment is provided for uncertainty characteristics of each unit in the power distribution network; and according to a real-time risk assessment result, a scheduling optimization suggestion is given in combination with an artificial intelligence model, and a final scheduling scheme is obtained. The method has the advantages that various uncertain factors in cooperative operation of the main network and the distribution network can be systematically evaluated, comprehensive risk evaluation data is provided for power dispatching personnel, and potential risks caused by ignoring uncertainty in a traditional method are avoided; through accurate risk prediction and optimization scheduling strategies, the system can reasonably allocate resources, reduce unnecessary standby power supply requirements, and reduce scheduling cost.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a main distribution network collaborative risk assessment calculation method and system considering uncertainty. Background Art

[0002] In modern power systems, the coordinated operation of the main grid and the distribution network is crucial to ensure the stability and reliability of power supply. In particular, with the widespread access to new energy (such as wind power and photovoltaics) and the rapid development of distributed energy, the operation of the power system faces unprecedented challenges. Traditional power system scheduling relies on deterministic models and ignores the various uncertainties that may arise during the operation of the distribution network, which greatly reduces the system's ability to respond to emergencies.

[0003] In the current power dispatching system, the main grid dispatching and control personnel usually issue regulation instructions based on the load demand and grid status of the power grid to ensure the stable operation of the system. However, due to the differences in the ability of the distribution network to carry loads and respond to regulation instructions, and limited by multiple constraints such as voltage, line capacity, and transformer capacity, the response of the distribution network units to the regulation instructions has lags and errors. Therefore, the power difference between the regulation instructions issued by the main grid dispatching and control personnel and the actual response of the lower-level distribution network units has become an important basis for measuring and evaluating system risks.

[0004] The regulation capacity of the distribution network not only depends on the output capacity of various regulation units in the distribution network (such as distributed generation units, energy storage units, electric vehicles, etc.), but is also affected by the constraints of the power grid flow equation. In addition, the uncertainty factors in the distribution network, especially due to the uncertainty of the various operating units involved (such as wind power, photovoltaics, energy storage equipment, electric vehicle charging and discharging, etc.), bring huge challenges to system scheduling. The operating characteristics and uncertainties of each unit are different, and the high uncertainty of these factors makes it difficult for traditional fixed models to fully describe the complexity of the distribution network. In order to better cope with this uncertainty, the traditional power dispatching model needs to combine the advantages of cloud computing and edge computing to provide a more intelligent and flexible dispatching solution. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above problems existing in the prior art, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a main-distribution network collaborative risk assessment calculation method considering uncertainty, comprising: evaluating the power difference between the main network dispatching instructions and the distribution network unit response, and analyzing the dispatching risk; comprehensively considering various constraints in the distribution network, and accurately calculating the dispatching capacity through the power flow equation; providing accurate risk assessment for the uncertainty characteristics of each unit in the distribution network; and providing dispatching optimization suggestions based on real-time risk assessment results in combination with an artificial intelligence model to obtain a final dispatching plan.

[0008] As a preferred scheme of the main distribution network collaborative risk assessment calculation method considering uncertainty described in the present invention, when the power difference increases, the instability and failure probability of the distribution network increase accordingly; through accurate identification of the power difference, potential risk sources can be discovered in time and effective measures can be taken to intervene, thereby avoiding unstable operation of the power grid due to large power differences.

[0009] As a preferred solution of the main distribution network collaborative risk assessment calculation method considering uncertainty described in the present invention, wherein: the distribution network flow equation performs dynamic calculation of the regulation capacity and monitors factors such as grid load, voltage, and line status in real time.

[0010] As a preferred solution of the main distribution network collaborative risk assessment calculation method considering uncertainty described in the present invention, according to the power flow equation, the regulation capacity of a given node j can be calculated according to the following optimization model:

[0011] P adj，j =max(P j，max -P j，min )

[0012]

[0013] V i，min ≤V i ≤V i，max

[0014]

[0015] Among them, for different time scales, the upward and downward adjustment capabilities of the power generation unit i can be expressed as Give a description;

[0016] The load node can be represented by the active and reactive power P of the i node i and Q i Give a description;

[0017] θ i -θ j , represents the voltage phase angle difference between nodes i and j;

[0018] Vi , represents the voltage amplitude of node i.

[0019] As a preferred solution of the main distribution network collaborative risk assessment calculation method considering uncertainty described in the present invention, wherein: the uncertainty characteristics have complex uncertainty distribution conditions such as multi-peak distribution and long-tail distribution;

[0020] The multi-level uncertainty analysis model based on probability statistics and random processes can adapt to complex dynamic changes and provide accurate risk assessment results.

[0021] As a preferred solution of the main distribution network collaborative risk assessment calculation method considering uncertainty described in the present invention, wherein: by combining the operating characteristics of various units in the distribution network, the uncertainty factors of the system are comprehensively assessed, and the system risk assessment results are provided in real time.

[0022] As a preferred solution of the main and distribution network collaborative risk assessment calculation method considering uncertainty described in the present invention, the artificial intelligence model is a model trained and established according to historical adjustment actions and adjustment results, and can provide scheduling optimization suggestions in a short time through rapid analysis of historical data and real-time data.

[0023] Another object of the present invention is to provide a main distribution network collaborative risk assessment system taking into account uncertainty, aiming to solve the problems of insufficient risk assessment accuracy and difficulty in scheduling optimization in the collaborative scheduling process of the main network and distribution network in the existing power system.

[0024] As a preferred solution of the main-distribution network collaborative risk assessment calculation method considering uncertainty described in the present invention, the following comprises: a power difference risk assessment module: monitoring the power difference between the main network dispatching instruction and the distribution network unit response, and performing risk assessment; a regulation capacity assessment module: evaluating the regulation capacity of the distribution network in combination with multiple constraints of the distribution network; an uncertainty analysis and risk modeling module: establishing a generalized uncertainty description model based on a dynamic modeling method of probability statistics and random processes; an intelligent scheduling optimization and real-time feedback module: generating scheduling optimization suggestions based on real-time risk assessment results; a hybrid communication mechanism module: combining event-driven with timed transmission, and triggering an event-driven mechanism when a key event occurs in the power grid; a risk assessment and scheduling decision support module: performing an overall assessment of the power grid risk based on the assessment results output by each module, and providing scheduling optimization suggestions and risk warnings to the main network dispatching personnel.

[0025] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the main and distribution network collaborative risk assessment calculation method considering uncertainty are implemented.

[0026] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the main and distribution network collaborative risk assessment calculation method considering uncertainty are implemented.

[0027] Beneficial effects of the present invention: The present invention can systematically evaluate various uncertainties in the coordinated operation of the main network and the distribution network, provide comprehensive risk assessment data for power dispatchers, and avoid ignoring the potential risks brought about by uncertainty in traditional methods; through real-time risk assessment and optimized dispatch suggestions, the device can effectively reduce the operating risks of the power system and improve the reliability and safety of the system; it can update the risk assessment results in real time according to the changes in uncertainties, help the dispatch center to make emergency decisions quickly in the event of an emergency, and improve the responsiveness of the system; through accurate risk prediction and optimized dispatch strategies, the system can reasonably allocate resources, reduce unnecessary backup power demand, and reduce dispatch costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0029] Figure 1 A schematic diagram of a flow chart of a main and distribution network collaborative risk assessment calculation method considering uncertainty according to an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of the flow chart of a main and distribution network collaborative risk assessment system considering uncertainty according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0034] Furthermore, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] Example 1

[0036] Reference Figure 1 This embodiment provides a main distribution network collaborative risk assessment calculation method considering uncertainty, including:

[0037] S1: Evaluate the power difference between the main grid dispatching instructions and the distribution network unit response, and analyze the dispatching risk.

[0038] Specifically, by modeling the association between power difference and risk, we can achieve the effect of rapidly increasing risk as the power difference increases, and prevent the instability or imbalance of the power system caused by large differences. Specifically, when the power difference increases, the instability and failure probability of the system also increase sharply. Therefore, by accurately identifying the power difference, potential risk sources can be discovered in time and effective measures can be taken to intervene, thereby avoiding unstable operation of the power grid caused by large power differences, helping the main grid dispatchers to prevent large-scale power imbalances or system collapses, and ensuring the safe operation of the system.

[0039] Example 2

[0040] Reference Figure 1 , which is the second embodiment of the present invention, is based on the previous embodiment, and is different from the previous embodiment in that:

[0041] S2: Comprehensively consider various constraints in the distribution network and accurately calculate the dispatching capacity through the power flow equation;

[0042] Specifically, the calculation of the power flow equation can more accurately reflect the actual regulation capacity of the distribution network, avoiding overestimation of the regulation capacity, which may lead to overload of the system. This constraint-based regulation capacity assessment ensures a reasonable prediction of the distribution network capacity during the dispatch process, thereby reducing the dispatch risk caused by over-reliance on theoretical calculations.

[0043] It should be noted that there are multiple regulation units in the distribution network, including distributed photovoltaics, energy storage units and loads. The regulation capacity of the distribution network is not only determined by the maximum power of each regulation unit, but also affected by multiple constraints such as voltage limit, line capacity, and transformer capacity. When the main grid dispatching and control personnel issue regulation instructions, the distribution network may not be able to directly meet the regulation needs in a simple summing manner, especially when there are long lines or large load fluctuations within the grid, voltage fluctuations may lead to insufficient regulation capacity and form a regulation gap.

[0044] In this embodiment, taking the distributed photovoltaic (capacity 2MW), energy storage unit (capacity 1MW) and load (capacity 3MW) in the distribution network as an example, it is demonstrated how to evaluate the regulation capacity through the device of the present invention and solve the problem of insufficient regulation capacity.

[0045] According to the photovoltaic load forecast results, the regulation capacity of photovoltaic power generation is usually calculated as 10% of its maximum capacity. Therefore, in this example, the regulation capacity of distributed photovoltaic power generation is:

[0046] Photovoltaic regulation capacity = 2MW × 10% = 0.2MW;

[0047] Energy storage unit regulation capacity = 1MW;

[0048] Total regulation capacity = 0.2MW (photovoltaic) + 1MW (energy storage unit) = 1.2MW

[0049] In the above calculation, the regulation capacity of the energy storage unit is the theoretical value that can fully exert its regulation capacity. The total regulation capacity matches the 1.2MW regulation demand proposed by the main grid dispatch instruction. Therefore, in theory, the distribution network can meet the regulation demand.

[0050] Example 3

[0051] Reference Figure 1 , which is the third embodiment of the present invention, and provides a main distribution network collaborative risk assessment calculation method considering uncertainty. This embodiment is based on the previous embodiment, and is different from the previous embodiment in that:

[0052] S3: Provide accurate risk assessment based on the uncertainty characteristics of each unit in the distribution network;

[0053] In fact, the actual regulation capacity of the distribution network cannot be simply evaluated by summing up the capacity of each unit. The lines connected to the energy storage unit are long, which means that when the energy storage unit injects power into the distribution network, the voltage may fluctuate greatly at the intermediate nodes. According to the flow calculation and voltage limit constraints, the injected power of the energy storage unit may cause the voltage of some nodes to exceed the upper limit of 110%, thus failing to meet the voltage constraint requirements.

[0054] If the voltage fluctuation of a node in the power grid exceeds 110% during the 1MW power injection of the energy storage unit, the regulation capacity of the energy storage unit can only provide about 800kW instead of 1MW. Then the actual regulation capacity of the distribution network is:

[0055] Total regulation capacity = 0.2MW (photovoltaic) + 0.8MW (energy storage unit) = 1.0MW

[0056] Compared with the 1.2MW regulation capacity required by the main grid dispatching instructions, there is a 0.2MW regulation gap in the distribution network.

[0057] In order to further optimize the regulation decision, it is necessary to combine the forecast information of photovoltaic output power. Assume that according to weather forecast and historical data analysis, the output power of distributed photovoltaic is expected to increase by 100kW in the next 30 minutes, and the regulation gap will be reduced to 100kW at this time.

[0058] Example 4

[0059] Reference Figure 1 , which is the fourth embodiment of the present invention, and provides a main distribution network collaborative risk assessment calculation method considering uncertainty. This embodiment is based on the previous embodiment, and is different from the previous embodiment in that:

[0060] S4: Based on the real-time risk assessment results, combined with the artificial intelligence model, scheduling optimization suggestions are given to obtain the final scheduling plan.

[0061] Specifically, when a 100kW gap is found, the device monitors the power output of each regulating unit in the distribution network in real time and sends the gap information to the main grid dispatch control center. At the same time, the device continuously receives real-time data from the distribution network (including photovoltaic forecasts, energy storage unit status, voltage fluctuations, etc.) to ensure that dispatch optimization decisions are based on the latest data.

[0062] Preferably, the device uses an artificial intelligence optimization algorithm to correct the regulation gap based on factors such as photovoltaic power generation forecast, energy storage unit status, voltage fluctuation, load change, etc. The algorithm concludes that there is a 100kW gap in the distribution network by matching historical data with current load demand, and gives a corresponding optimization plan.

[0063] Furthermore, based on the results of artificial intelligence optimization, the device recommends that the main grid dispatching and control personnel adjust the original regulation instruction (1.2MW) to 1.1MW. This can prevent the distribution network from insufficient regulation due to voltage fluctuation limitations of the energy storage unit, thereby effectively avoiding the risks brought by the regulation gap. At the same time, the device recommends that the remaining 100kW regulation gap be responded to by other distribution network units. By dispatching part of the regulation demand to other areas or supplementing it through other regulation units (such as other distribution network energy storage units, standby generator sets, etc.), a balance of regulation capacity can be achieved, avoiding system instability due to insufficient regulation capacity.

[0064] Furthermore, after receiving the optimization suggestions, the main grid dispatching and control personnel can re-dispatch the regulation capacity of the distribution network based on the optimization plan and assign the remaining regulation tasks to other distribution network units. This process reduces the risk of regulation gaps and improves dispatching flexibility and response efficiency.

[0065] Finally, through dispatch optimization and reasonable resource allocation, the regulation gap was effectively eliminated, the distribution network was able to meet the dispatching needs of the main grid, and at the same time reduced the system regulation risk.

[0066] Example 5

[0067] Reference Figure 2 , is an embodiment of the present invention, which provides a main distribution network collaborative risk assessment system considering uncertainty, characterized in that it includes: a power difference risk assessment module: monitors the power difference between the main network dispatching instruction and the distribution network unit response, and performs risk assessment; a regulation capacity assessment module: combines multiple constraints of the distribution network to evaluate the regulation capacity of the distribution network; an uncertainty analysis and risk modeling module: establishes a generalized uncertainty description model based on the dynamic modeling method of probability statistics and random processes; an intelligent scheduling optimization and real-time feedback module: generates scheduling optimization suggestions based on real-time risk assessment results; a hybrid communication mechanism module: combines event-driven and timed transmission to trigger the event-driven mechanism when a key event occurs in the power grid; a risk assessment and scheduling decision support module: based on the assessment results output by each module, performs an overall assessment of the power grid risk, and provides scheduling optimization suggestions and risk warnings for the main network dispatchers.

[0068] Specifically, the hybrid communication mechanism module adopts a hybrid communication mechanism that combines event-driven and timed transmission to ensure efficient data transmission. When a critical event occurs in the power grid, such as a change in regulation instructions or an abnormal power difference, the system will send data to the main grid dispatching and control center in a timely manner through an event-driven mechanism. During normal operation, the system adopts a timed transmission mechanism to ensure regular updates of the operating status and regulation capacity information of the distribution network. Under certain communication volume constraints, this mechanism improves communication efficiency, avoids communication congestion, and ensures accurate transmission of real-time data.

[0069] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0072] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logical function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limiting. Although the present invention is described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0073] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A main distribution network collaborative risk assessment calculation method considering uncertainty, characterized by: include, Evaluate the power difference between the main grid dispatching instructions and the distribution network unit response, and analyze the dispatching risk; Comprehensively consider various constraints in the distribution network and accurately calculate the dispatching capacity through the power flow equation; Provide accurate risk assessment based on the uncertainty characteristics of each unit in the distribution network; Based on the real-time risk assessment results, combined with the artificial intelligence model, scheduling optimization suggestions are given to obtain the final scheduling plan.

2. The main distribution network collaborative risk assessment calculation method considering uncertainty according to claim 1 is characterized by: When the power difference increases, the instability and failure probability of the distribution network increase accordingly; By accurately identifying the power difference, potential risk sources can be discovered in a timely manner and effective measures can be taken to intervene, thereby avoiding unstable operation of the power grid due to large power differences.

3. The main distribution network collaborative risk assessment calculation method considering uncertainty according to claim 2 is characterized by: The power flow equation of the distribution network performs dynamic calculation of the regulation capability and monitors factors such as grid load, voltage, and line status in real time.

4. The main distribution network collaborative risk assessment calculation method considering uncertainty according to claim 3 is characterized by: According to the power flow equation, the regulation capacity of a given node j can be calculated according to the following optimization model: P adj，j =max(P j，max -P j，min ) In i，min ≤V i ≤V i，max Among them, for different time scales, the upward and downward adjustment capabilities of the power generation unit i can be expressed as Give a description; The load node can be represented by the active and reactive power P of the i node i and Q i Give a description; θ i -θ j , represents the voltage phase angle difference between nodes i and j; V i , represents the voltage amplitude of node i.

5. The main distribution network collaborative risk assessment calculation method considering uncertainty according to claim 4 is characterized in that: The uncertain characteristics have complex uncertainty distributions such as multi-peak distribution and long-tail distribution; The multi-level uncertainty analysis model based on probability statistics and random processes can adapt to complex dynamic changes and provide accurate risk assessment results.

6. The main distribution network collaborative risk assessment calculation method considering uncertainty according to claim 5 is characterized by: By combining the operating characteristics of various units in the distribution network, the uncertainty factors of the system are comprehensively evaluated and the system risk assessment results are provided in real time.

7. The main distribution network collaborative risk assessment calculation method considering uncertainty according to claim 6 is characterized by: The artificial intelligence model is a model that is trained and established according to historical adjustment actions and adjustment results. Through rapid analysis of historical data and real-time data, it can provide scheduling optimization suggestions in a short time.

8. A system using the main distribution network collaborative risk assessment calculation method considering uncertainty as described in any one of claims 1 to 7, characterized in that: include: Power difference risk assessment module: monitors the power difference between the main network dispatching instructions and the distribution network unit response, and performs risk assessment; Regulation capability evaluation module: evaluates the regulation capability of the distribution network based on multiple constraints of the distribution network; Uncertainty analysis and risk modeling module: Based on probability statistics and dynamic modeling methods of random processes, a universal uncertainty description model is established; Intelligent scheduling optimization and real-time feedback module: Generate scheduling optimization suggestions based on real-time risk assessment results; Hybrid communication mechanism module: combines event-driven and timed transmission to trigger event-driven mechanism when key events occur in the power grid; Risk assessment and dispatch decision support module: Based on the assessment results output by each module, an overall assessment of the power grid risk is conducted, and dispatch optimization suggestions and risk warnings are provided to the main grid dispatchers.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the main and distribution network collaborative risk assessment calculation method considering uncertainty described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the main distribution network collaborative risk assessment calculation method considering uncertainty described in any one of claims 1 to 7 are implemented.

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