Quantum computing embedded power grid elasticity evaluation method considering all potential risks

Abstract

The invention belongs to the technical field of power system analysis and evaluation, and relates to a quantum computing embedded power grid elasticity evaluation method considering all potential risks, which comprises the following steps: 1, acquiring typhoon meteorological disaster information possibly occurring in a period of time in the future; 2, generating probability distribution based on vulnerability analysis by using typhoon early warning data information; 3, quickly evaluating system fault risk loss under short-time rolling risk early warning; 4, simulating and evaluating the fault risk loss in the whole process; 5, judging whether the disaster-affected line is repaired at the tk moment or not; 6, judging whether Monte Carlo simulation meets a convergence condition or not; according to the method, the line fault probability can be estimated in an accelerated manner by introducing the quantum amplitude estimation algorithm, the evaluation efficiency can be remarkably improved under the condition that the precision consistency is ensured by utilizing the rapid multi-scene parallel evaluation capability supported by quantum calculation and combining the dynamic repair and quantum embedding elastic evaluation process, and the defects of the existing theory and technology are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system analysis and evaluation, and relates to a quantum computing embedded power grid resilience evaluation method that takes into account all potential risks. Background Art

[0002] The core of resilience evaluation is to construct reasonable resilience evaluation indicators and accurate and efficient evaluation methods, so as to quantify the resilience level of the power system, that is, the prevention, resistance, and recovery capabilities under extreme events, and provide a quantitative analysis means for planning and operation oriented to resilience improvement. Currently, the construction of resilience evaluation indicators mainly focuses on the prevention, resistance, and recovery capabilities of the power system under extreme events, covering multiple spatio-temporal dimensions from pre-disaster - during-disaster - post-disaster and source - grid - load - storage and cyber-physical coupling. For resilience evaluation methods, the mainstream methods include the analytical method and the simulation method. The analytical method obtains component or system resilience indicators through theoretical derivation methods. The advantage is that the model is rigorous, but it is mainly applicable to small-scale power grids or the identification of weak links in the system under specific disaster scenarios. The simulation method conducts a large number of random scenario simulations, randomly generates fault scenarios based on Monte Carlo simulation, simulates the dynamic response of the system, and finally calculates statistical indicators by counting the system resilience performance.

[0003] However, although the existing theories and technologies have formed a certain foundation in meteorological prediction, vulnerability modeling, and Monte Carlo simulation, there are defects such as low computational efficiency, incomplete risk measurement, and insufficient dynamic repair modeling.

[0004] Therefore, an evaluation method with high computational efficiency and more comprehensive risk measurement is needed to solve the above technical problems. Summary of the Invention

[0005] To solve the above problems, the present invention can accelerate the estimation of line fault probability by introducing the quantum amplitude estimation algorithm, utilize the fast multi-scenario parallel evaluation ability supported by quantum computing, and combine dynamic repair with the quantum-embedded resilience evaluation process to significantly improve the evaluation efficiency while ensuring consistent accuracy, making up for the shortcomings of the existing theories and technologies. In addition, the present invention also introduces measurement indicators such as conditional value at risk (CVaR) to fully quantify the high-loss tail risk of extreme events.

[0006] The technical solution adopted by the present invention to solve the technical problems is: a quantum computing embedded power grid resilience evaluation method that takes into account all potential risks, including the following steps:

[0007] Step 1: Obtain typhoon meteorological disaster information that may occur in a future period of time. After information acquisition and induction, give typhoon warning data information on the typhoon disaster distribution and probability trend in the future time period, providing basic data for subsequent power system vulnerability assessment and resilience analysis;

[0008] Step 2: Generate a probability distribution based on vulnerability analysis using typhoon warning data information;

[0009] Step 3: Rapidly evaluate the risk loss of system failures under short-term rolling risk warnings; After obtaining the average failure probability P of transmission branch l l,t , construct a quantum circuit to evaluate the failure risk brought by typhoon disasters to the power system considering all potential risks;

[0010] Step 4: Simulate and evaluate the risk loss of failures throughout the process; According to the failure probabilities of power line components obtained in Step 2, start the s-th Monte Carlo simulation, generate a failure set of power line t k at time through sampling method, and then conduct an operating state analysis based on the power system response restoration model;

[0011] Step 5: Judge whether the affected line at time t k has been repaired. If it has been repaired, record the operating state of the system at time t k ~t k+n under the s-th Monte Carlo simulation, update the result value of the operating state of the system after the s-th Monte Carlo simulation. If it has not been repaired, go to the next time for simulation and repeat Step 5;

[0012] Step 6: Judge whether the Monte Carlo simulation meets the convergence condition. If it meets the convergence condition, establish key resilience indicators based on the recorded operating state of the system and conduct quantitative analysis; If it does not meet the convergence condition, update the Monte Carlo times s, set the initial time t k of t k = T0, and go to Step 4 for the next Monte Carlo simulation.

[0013] Preferably, in the above Step 1, the typhoon meteorological disaster information includes: the type, intensity, path, and influence range of the typhoon meteorological disaster; The acquisition methods include: meteorological forecasts, historical data, and model predictions.

[0014] Preferably, Step 2 specifically includes: constructing a power line vulnerability model to simulate the failure probability under a specific wind speed, and comprehensively analyzing the failure probability of affected power lines using the quantum amplitude estimation algorithm; The power line vulnerability model is:

[0015]

[0016] In formula (1), P L (v) represents the failure probability function of power line L at wind speed v, represents the design value of the failure probability under normal conditions, v critical represents the critical wind speed, above which the failure probability begins to increase significantly; v collapse represents the collapse wind speed, when the wind speed reaches or exceeds this value, the line will surely fail.

[0017] Preferably, in step 2, the quantum amplitude estimation algorithm and Monte Carlo simulation are used to comprehensively analyze the failure probability of the affected power lines. The specific method includes the following steps:

[0018] Step 2-1: Load the probability distribution function of typhoons under different wind speeds v; the initial quantum state passes through the operator can output the superposition quantum state, including the wind speed v and the corresponding probability distribution information:

[0019]

[0020] In formula (2), |v i > n represents the n-bit quantum state of the wind speed v, represents the transformation of the initial quantum state into a new quantum state, represents the corresponding probability amplitude;

[0021] Step 2-2: Construct the operator to load the failure probability information under different wind speeds. The operator acts on the quantum circuit to generate the quantum state |P L (v)>. The quantum state representing the failure probability of the affected power line is:

[0022]

[0023] In formula (3), represents the quantum state of the average failure probability of the line L, |P L (v i )> represents the quantum state of the failure probability of the line L at the wind speed v i ; the operator is constructed as a quantum operator containing n qubits and R y rotation gates. The variable v i is mapped to v i = 2 n-1 x n-1 + 2 n-2 x n-2 + … + 2 0 x0 and is further encoded into the quantum state |v i > n = |x n- 1x n-2 …x0>, x k ∈ {0,1}; after the action of the operator , the output quantum state bit is |v i > n [cos(P L (v i))|0> + sin(P L (v i ))|1>];

[0024] Step 2 - 3: Construct an operator to amplify the total fault probability estimate value, and apply the application operator 2 s times, gradually amplifying the observability of the target state in quantum measurement, and achieving comprehensive and efficient estimation of the total fault probability of the affected power line.

[0025] Preferably, when performing Step 3, first set different primary reinforcement schemes for power lines according to the power line fault probability, and then weigh the VaR α (LS) and CVaR α (LS) index results, and output the power line reinforcement ranking scheme.

[0026] Preferably, in the said Step 4, the specific process of generating the fault set of the power line at time t k includes: generating a random number r between 0 and 1, and comparing it with the average total fault probability of the line l observed in Step 2 If the random number r is less than then it is considered that the line l will fail and stop operating. If the random number r is greater than then the line l does not fail.

[0027] Preferably, in the said Step 4, the power system response recovery model is:

[0028] P G,min ≤P t s，G ≤P G,max (15)

[0030]

[0031] In Equations (15) to (20), P t s,G represents the generator output power at time t under the s - th Monte Carlo, and P G,min , P G ,max are respectively the minimum and maximum limits of the generator output power; represents the load power of the i - th node and the initial required load power of the i - th node at time t under the s - th Monte Carlo; represents the transmission power on the l - th line at time t under the s - th Monte Carlo, represents the line l state variable at time t under the s - th Monte Carlo, respectively represent the susceptance of line l and the phase angle of node i at time t under the s-th Monte Carlo simulation, represent the phase angle of node j at time t under the s-th Monte Carlo simulation, M represents a sufficiently large constant value; P l EL,max represents the maximum transmission power limit of line l; represent the minimum and maximum value limits of the phase angle of node i; respectively represent the incidence matrices of power system bus nodes with generators and transmission lines, represent the incidence matrix of power system bus nodes with electrical loads, respectively represent the vectors of power system load shedding power and initial required load power, P G ,P EL respectively represent the vectors of power system generator output power and line transmission power.

[0032] The beneficial effects of the present invention are:

[0033] 1. The present invention can simultaneously achieve rapid assessment of fault risk losses under short-term rolling risk warning and full-process simulation assessment of fault risk losses, utilize the advantage of quantum parallel computing to simulate the fault probability under all potential risk scenarios, and has a square acceleration convergence advantage compared with classical Monte Carlo simulation, which can solve the problem of low simulation efficiency of classical calculation methods.

[0034] 2. The present invention constructs ΦΛΕΠ evaluation index, comprehensive resilience evaluation index (CRI), and resilience evaluation index based on tail high-loss risk measurement based on the system performance curve, which can make up for the shortcomings of the existing quantification of high-loss tail risks of extreme events being incomplete. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flowchart of a quantum computing embedded power grid resilience evaluation method that takes into account all potential risks of the present invention;

[0036] Figure 2 is a schematic diagram of a quantum circuit of the quantum amplitude estimation algorithm of the present invention;

[0037] Figure 3 is a quantum circuit diagram of the present invention for evaluating and analyzing fault risk losses;

[0038] Figure 4 is an illustration diagram of a typhoon scenario example of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0039] Next, the related technologies in the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0040] Reference Figures 1 to 4 As shown, the process of this specific embodiment includes steps 1 to 7, and the detailed process block diagram is as Figure 1 shown.

[0041] Step 1: Through means such as meteorological forecasts, historical data, and model predictions, obtain information such as the type, intensity, path, and impact range of meteorological disasters that may occur within the next T hours. Taking typhoon disasters as an example, after information acquisition and induction, prediction information on the typhoon disaster distribution and probability trend under the T-hour warning is given, such as Figure 1 shown, providing basic data for subsequent power system vulnerability assessment and resilience analysis.

[0042] Step 2: Generate a probability distribution based on vulnerability analysis using typhoon warning data. First, construct a power line vulnerability model to simulate the failure probability under a specific wind speed, and comprehensively analyze the failure probability of affected power lines using the quantum amplitude estimation algorithm.

[0043] The constructed power line vulnerability model is as follows:

[0044]

[0045] In the formula, P L (v) represents the failure probability function of power line L under wind speed v, which is obtained from numerical simulation experiments. is the designed value of the failure probability under normal environmental conditions. Actual empirical data can improve these vulnerability models to reflect the performance of simulated lines under real typhoons. v critical represents the critical wind speed, above which the failure probability begins to increase significantly. v collapse represents the collapse wind speed, when the wind speed reaches or exceeds this value, the line will surely fail.

[0046] In order to obtain the average failure probability of each faulty line quickly in parallel, the quantum amplitude estimation algorithm and Monte Carlo simulation can be used to comprehensively analyze the failure probability of affected power lines. The specific method is as follows:

[0047] Step 2.1: Load the probability distribution function of typhoons under different wind speeds v. In order to utilize the potential parallel acceleration advantage of the quantum amplitude estimation algorithm, n qubits are needed to map the random variable v that describes the wind speed i to the discretized interval {0, 1,..., 2n -1}. As shown in Figure 2 , the initial quantum state can output a superposition quantum state through the operator , including the wind speed v and the corresponding probability distribution information, as shown in Equation (2).

[0048]

[0049] In the formula, |v i > n represents the n-bit quantum state of the wind speed v, and a total of 2 n possible values of the wind speed v can be represented. i ∈ {0, 1,..., 2 n -1}.

[0050] Step 2.2: Construct the operator to load the failure probability information at different wind speeds. The operator acts on the quantum circuit to generate the quantum state |P L (v)>. Therefore, the quantum state representing the failure probability of the affected power line can be written as

[0051]

[0052] In Equation (3), represents the quantum state of the average failure probability of the line L, and |P L (v i )> represents the quantum state of the failure probability of the line L at the wind speed v i ; the operator can be constructed as a quantum operator containing n qubits and R y rotation gates. The variable v i can be mapped to v i = 2 n-1 x n-1 + 2 n-2 x n-2 + … + 2 0 x0 by the binary 0 / 1 variable x. Further, it can be encoded as the quantum state |v i > n = |x n-1 x n-2 …x0>, where x k ∈ {0, 1}. After the action of the operator , the output quantum state bit is |v i > n [cos(P L (v i ))|0> + sin(P L (v i ))|1>].

[0053] Step 2.3: Construct the operator Apply the application operator to amplify the estimated value of the full-fault probability 2 s times, gradually amplifying the observability of the target state in quantum measurement, and achieving comprehensive and efficient estimation of the full-fault probability of the affected power line. Specifically, taking the 2-qubit circuit model as an example, as shown Figure 2 as follows.

[0054] According to what is shown in the figure, construct the operator according to Equation (4)

[0055]

[0056] In the formula, represents the target quantum state vector, represents the quantum model for constructing the target quantum state of, is the conjugate transpose of the quantum model of, respectively represent the quantum models that take the opposite (multiply by -1) of the quantum states |0>, |1>.

[0057] By appropriately selecting the number of times k of the unitary operator acting, the amplitude of the target quantum state can be amplified to the required level, facilitating accurate amplitude estimation during the measurement process. Mapping and converting the measurement probability value, the

[0058]

[0059] According to Steps 2.1 to 2.3, obtain the fault probabilities of each section of the power line considering all potential risks The transmission branch l consists of multiple sections of power lines L. The average fault probability of the transmission branch l is obtained according to the following model

[0060]

[0061] In the formula, N L,l represents the number of sections of power lines L included in the transmission branch l.

[0062] Step 3: Rapidly evaluate the fault risk loss of the system under short-term rolling risk warning. After obtaining the average fault probability of the transmission branch l, construct the quantum circuit shown Figure 3 to evaluate the fault risk brought by typhoon disasters to the power system considering all potential risks.

[0063] The average fault probability Construct the operator P with known parameters to simulate the probability distribution of the power line fault state. The superposition quantum state can be expressed as the following formula.

[0064]

[0065] After passing through the operator R0, the load shedding loss value under each power line fault state is stored in the K-bit quantum register, and the quantum state changes as follows.

[0066]

[0067] Next, set the M-bit register to represent the comparison value LS α , in order to screen out the values of the load shedding loss under the fault state that are less than LS α , define the function and construct the quantum comparator module R x . At this time, the probability that the measurement auxiliary qubit is |1> is

[0068]

[0069] The definition of Value at Risk (VaR) is that for a fixed execution level α ∈ [0, 1], find the minimum threshold LS α such that under the threshold, it satisfies At this time, VaR α (LS) = LS α . The total number of values of LS is K = 2 n Therefore, continuously measure the probability that the auxiliary bit is in |1> Through classical binary search, it only takes at most n steps to find the minimum LS that satisfies the condition α .

[0070] The conditional value at risk is the expected value of LS restricted to {0, 1,..., LS α}, which is defined as equation (11). After obtaining VaR α (LS) = LS α , the K-bit register and the auxiliary qubit are used as control bits, define the function f(LS) as equation (12), and construct the operator R1 and apply it to another auxiliary qubit.

[0071]

[0072] The probability that the measurement auxiliary qubit is |1> is According to equation (11), the actual value of the conditional value at risk can be deduced backward. According to the construction of the operator Q in step 2.3 and repeating it s times, the measurement probability of the target state is expanded to obtain an accurate target probability value.

[0073] When performing this step, first, according to the power line fault probability, different primary reinforcement schemes for power lines are set, and then the VaR α (LS) and CVaR α (LS) index results are weighed to output the reinforcement ranking scheme for power lines.

[0074] Step 4: Simulate and evaluate the fault risk loss throughout the process. According to the power line component fault probabilities obtained in Step 2, start the s-th Monte Carlo simulation. Generate a fault set at time t of the power line through the sampling method, and then perform an operating state analysis based on the power system response restoration model. k The specific process of generating the fault set through the sampling method is as follows: Generate a random number r between 0 and 1, and compare it with the average full fault probability of line l observed in Step 2

[0075] If the random number r is less than then it is considered that line l will fail and stop operating. If the random number r is greater than then this line l does not fail.

[0076]

[0077] In the formula, is the state variable of line l at time t in the s-th Monte Carlo simulation. The value is 0 when it fails, and the normal operating value is 1 when it does not fail.

[0078] The specific power system response restoration model is as follows:

[0079] P G,min ≤P i s,G ≤P G,max (15)

[0081]

[0082] In the formula, P t s,G represents the generator output power at time t in the s-th Monte Carlo simulation. P G,min , P G,max are the minimum and maximum limits of the generator output power respectively; represents the load power at the i-th node and the initial required load power at the i-th node at time t in the s-th Monte Carlo simulation; represents the transmission power on the l-th line at time t in the s-th Monte Carlo simulation, represents the state variable of line l at time t in the s-th Monte Carlo simulation, represent the susceptance of line l and the phase angle of node i at time t in the s-th Monte Carlo simulation respectively,​ denotes the phase angle of node j at time t under the s-th Monte Carlo simulation, M represents a sufficiently large constant value; P l EL,max denotes the maximum transmission power limit of line l; denotes the minimum and maximum limits of the phase angle of node i; respectively denote the incidence matrices of the power system bus nodes with generators and transmission lines, denotes the incidence matrix of the power system bus nodes with electrical loads, respectively denote the vectors of the power system load shedding power and the initial required load power, P G , P EL respectively denote the vectors of the power system generator output power and the line transmission power.

[0083] Step 5: Determine whether the damaged lines are repaired at time t. If they are repaired, record the system operation status at time t under the s-th Monte Carlo simulation, and update the system operation status result value after s Monte Carlo simulations. If they are not repaired, go to the next time simulation and repeat Step 5. k If they are repaired, record the system operation status at time t under the s-th Monte Carlo simulation, and update the system operation status result value after s Monte Carlo simulations. If they are not repaired, go to the next time simulation and repeat Step 5. k ~t k+n If they are not repaired, go to the next time simulation and repeat Step 5.

[0084] The recovery time of transmission lines depends on the specific severity of the post-disaster damage. In the resilience assessment, it is difficult to predict the recovery time with the existing emergency repair plans and statistical data. Therefore, the damage degree caused by typhoons in this paper is divided into three levels (i.e., low, medium, and severe). For each transmission line, according to the three different damage degrees, k w reflects the impact of typhoons on the repair time. Therefore, the repair time (TTR) of each damaged line can be expressed by the following formula,

[0085] TTR = k w ×TTR normal ,

[0086]

[0087] In the formula, k w denotes the influence coefficient of wind speed on TTR. When the wind speed or rainfall reaches the corresponding range, are respectively random numbers generated according to the uniform distribution

[0088] Step 6: Determine whether the Monte Carlo simulation meets the convergence condition. If it meets the convergence condition, establish key resilience indicators based on the recorded system operation status and conduct quantitative analysis; if it does not meet the convergence condition, update the Monte Carlo number s, and set the initial time t k of t k ​= T0, go to step 4 for the next Monte Carlo simulation.

[0089] If the convergence condition is satisfied, construct and analyze the calculation of typical resilience indicators according to the specific system operation status as follows.

[0090] (1) ΦΛEII evaluation index based on the system performance curve

[0091] System load shedding rate Φ:

[0092] System load shedding maximum amplitude Λ:

[0093] System derating operation duration E: E = t r -t pe (24)

[0095] System load recovery rate ∏:

[0096] Among them, are the load level during normal system operation and the lowest load level of the system operation after being damaged by the typhoon respectively, t pe , t e , t r , t pr are the moment when the typhoon disaster has ended, the moment when the typhoon disaster begins to threaten the power grid, the moment when the system starts post-disaster recovery after the typhoon ends, and the moment when the system returns to normal operation after the typhoon respectively.

[0097] (2) Comprehensive resilience evaluation index (CRI): Focus on considering the average system performance under multiple Monte Carlo simulations.

[0098]

[0099] In the formula, N s is the total number of Monte Carlo simulation disasters, T is the resilience evaluation time interval, and N E is the number of power system nodes.

[0100] (3) Resilience evaluation index based on tail high-loss risk measurement

[0101] U(I) = f(D(I)t(I)) (27)

[0103] Γ(ζ) = ∫ U(I)≤ζ p(I)dl (28)

[0105]

[0106] Wherein, U(I) represents the loss of the power system under the random event I, which is composed of the load shedding D(I) and the duration t(I) under the random event I. Γ(ζ) is the cumulative distribution function of the system loss, indicating the probability that the system loss U(I) does not exceed ζ under the whole-process simulation. VaR α (U) represents the maximum system loss U at the probability confidence level α ∈ (0, 1), and CVaR α (U) represents that the loss U exceeds VaR α the average value of the tail risk of the threshold.

[0107] Embodiment

[0108] This embodiment takes the typhoon disaster as an example for pre-disaster warning and resilience assessment. The schematic diagram of the example is as Figure 4 , and the process and schematic plan are as follows.

[0109] First: Data collection, preprocessing and disaster vulnerability modeling. Integrate the typhoon disaster-related data and the power grid operation data, including the historical path, wind speed data obtained from the China Meteorological Administration and the warning data at different times, and construct the power grid vulnerability model under the disaster. Convert the power grid parameters (such as line failure rate) into quantum state amplitudes and input them into the quantum computing module.

[0110] Second: Based on the quantum amplitude estimation algorithm, comprehensively analyze and calculate the component-level fault probability, use the quantum parallelism to accelerate the evaluation of the fault probability of power components (such as lines), and on this basis, design and construct a quantum circuit suitable for quickly evaluating the fault risk loss under short-term rolling risk warning, and further evaluate and analyze the average load shedding loss value under the power system risk warning and its risk value at a certain confidence level.

[0111] Third: Generate the power grid fault scenarios under the typhoon disaster through Monte Carlo simulation, consider the fault scenarios and repairs, and conduct the whole-process simulation to evaluate the fault risk loss.

[0112] Finally: System-level resilience assessment and pre-disaster defense decision-making, quantify the power grid resilience indicators, identify the weak links and formulate defense strategies. Through the ΦΛΕΠ evaluation index based on the system performance curve, the comprehensive resilience evaluation index (CRI), and the resilience evaluation index based on the tail high-loss risk measurement, realize the identification of weak links and the system resilience evaluation under extreme scenarios, cluster the high-loss scenarios and identify the components with high fault probability under extreme scenarios for reinforcement.

[0113] The key points of this embodiment: 1. The quantum computing embedded power grid resilience evaluation process and method that takes into account all potential risks; 2. Comprehensively analyze the fault probability of the affected power lines by using the quantum amplitude estimation algorithm; 3. The quantum simulation circuit for quickly evaluating the system fault risk loss under short-term rolling risk warning.

[0114] In summary, the present invention can simultaneously achieve rapid assessment of failure risk losses under short-term rolling risk warning and full-process simulation assessment of failure risk losses. By leveraging the advantages of quantum parallel computing, it simulates the failure probability under all potential risk scenarios, has a quadratic acceleration convergence advantage compared to classical Monte Carlo simulation, and can solve the problem of low simulation efficiency of classical calculation methods.

[0115] It should be emphasized that the above are only the preferred embodiments of the present invention, and there is no limitation to the present invention in any form. Any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A quantum computing embedded power grid resilience assessment method that takes into account all potential risks, characterized in that It includes the following steps: Step 1: Obtain typhoon meteorological disaster information that may occur in a future period of time. After information acquisition and induction, give typhoon warning data information on the typhoon disaster distribution and probability trend in the future time period; Step 2: Generate a probability distribution based on vulnerability analysis using the typhoon warning data information; Step 3: Rapidly evaluate the system failure risk loss under short-term rolling risk warning; After obtaining the average failure probability of the transmission branch l Construct a quantum circuit to evaluate the failure risk brought by typhoon disasters to the power system considering all potential risks; Step 4: Simulate and evaluate the failure risk loss throughout the process; Based on the fault probability of the power line components obtained in step 2, start the s-th Monte Carlo simulation, and generate the fault set of the power line at time t through the sampling method. Subsequently, perform the operating state analysis according to the power system response restoration model; k At this time, then perform the operating state analysis according to the power system response restoration model; Step 5: Determine whether the damaged line is repaired at time t k If it is repaired, record the system operation status from time t to t at the s-th Monte Carlo simulation, and update the system operation status result value after the s-th Monte Carlo simulation. If it is not repaired, go to the next time simulation and repeat Step 5; k ~t k+n ​ Step 6: Judge whether the Monte Carlo simulation meets the convergence condition. If it meets the convergence condition, establish key resilience indicators based on the recorded system operation status and conduct quantitative analysis; If the convergence condition is not satisfied, update the number of Monte Carlo times s and set the initial time t of k k to t k = T0, and go to step 4 for the next Monte Carlo simulation.

2. A method for evaluating the resilience of a quantum computing embedded power grid that takes into account all potential risks, characterized in that, In the said Step 1, the typhoon meteorological disaster information includes: the type, intensity, path, and influence range of the typhoon meteorological disaster; the acquisition methods include: meteorological forecasts, historical data, and model predictions.

3. A quantum computing embedded power grid resilience evaluation method that takes into account all potential risks according to claim 1, characterized in that The said Step 2 specifically includes: constructing a power line vulnerability model to simulate the failure probability under a specific wind speed, and comprehensively analyzing the failure probability of the affected power lines using the quantum amplitude estimation algorithm; the power line vulnerability model is: In formula (1), P L (v) represents the failure probability function of the power line L at the wind speed v, represents the design value of the failure probability under normal conditions, v critical represents the critical wind speed, v collapse represents the collapse wind speed.

4. A method for evaluating the resilience of a quantum computing embedded power grid considering all potential risks according to claim 3, characterized in that, In the said Step 2, the failure probability of the affected power lines is comprehensively analyzed using the quantum amplitude estimation algorithm and the Monte Carlo simulation. The specific method includes the following steps: Step 2-1: Load the probability distribution function of typhoons under different wind speeds v; the initial quantum state can output a superposition quantum state through the operator including the wind speed v and the corresponding probability distribution information: In formula (2), |v i > n represents the n-bit quantum state of the wind speed v, represents that the initial quantum state is transformed into a new quantum state, represents the corresponding probability amplitude; Step 2-2: Construct an operator to load the failure probability information at different wind speeds, the operator acts on the quantum circuit to generate the quantum state |P L (v)>, and the quantum state representing the failure probability of the affected power line is: In formula (3), The quantum state representing the average fault probability of line L, |P L (v i )> represents the quantum state of the fault probability of line L at wind speed v i ; Operator is constructed to include n qubits and R y rotation gates, and the variable v i is mapped to v by the binary 0 / 1 variable x i = 2 n-1 x n-1 + 2 n-2 x n-2 + … + 2 0 x0, and is further encoded into the quantum state |v i > n = |x n-1 x n-2 …x0>, where x k ∈ {0, 1}; after the action of the operator , the output quantum state bit is |v i > n [cos(P L (v i ))|0> + sin(P L (v i ))|1>]; Step 2-3: Construct an operator To amplify the total fault probability estimate value, apply the application operator 2 s times, gradually amplifying the observability of the target state in quantum measurement, and achieving comprehensive and efficient estimation of the total fault probability of the affected power line 5. A quantum computing embedded power grid resilience evaluation method that takes into account all potential risks, characterized in that When performing step 3, first set different preliminary reinforcement plans for power lines according to the power line fault probability, and then weigh the VaR α (LS) and CVaR α (LS) index results, and output the reinforcement ranking plan for power lines.

6. A method for evaluating the resilience of a quantum computing embedded power grid considering all potential risks according to claim 1, characterized in that In the said step 4, the specific process of generating the fault set at time t of the power line by the sampling method k includes: generating a random number r between 0 and 1, and comparing it with the average total fault probability of the line l observed in step 2 If the random number r is less than it is considered that the line l will fail and stop operation. If the random number r is greater than then the line l does not fail.

7. A method for evaluating the resilience of a quantum computing embedded power grid considering all potential risks according to claim 1, characterized in that In the said Step 4, the power system response and recovery model is: In formulas (15) to (20), represents the generator output power at time t under the s-th Monte Carlo simulation, P G,min , P G,max are respectively the minimum and maximum limits of the generator output power; represents the load power at the i-th node and the initial required load power at the i-th node at time t under the s-th Monte Carlo simulation; represents the transmission power on the l-th line at time t under the s-th Monte Carlo simulation, represents the line l state variable at time t under the s-th Monte Carlo simulation, respectively represent the susceptance of the l-th line and the phase angle of node i at time t under the s-th Monte Carlo simulation, represents the phase angle of node j at time t under the s-th Monte Carlo simulation, and M represents a sufficiently large constant value; represents the maximum transmission power limit of line l; represents the minimum and maximum limits of the phase angle of node i; respectively represent the incidence matrices of the power system bus nodes with generators and transmission lines, represents the incidence matrix of the power system bus nodes with electrical loads, respectively represent the vectors of the load shedding power and the initial required load power of the power system, P G , P EL respectively represent the vectors of the generator output power and the line transmission power of the power system.

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