Power grid operation risk assessment method, device and equipment for high-proportion new energy power system and medium

Abstract

The application discloses a power grid operation risk assessment method and device for a high-proportion new energy power system, equipment and a medium, relates to the field of power analysis, and comprises the following steps: determining discrete sample values and discrete sample probabilities corresponding to a target random variable based on probability distribution parameters; determining quantum probability amplitudes of the discrete sample probabilities, determining target power functions of the discrete sample values based on a power mapping relationship, determining target origin moments by using a target quantum search operator, and generating target power flow scenarios and corresponding scenario weights; determining a target shared coefficient matrix and a target right end item based on target iterative power flow equations of each target power flow scenario, and configuring a target circuit to determine a target correction amount of the target power flow scenario; and iterating the target power flow scenario, and determining a power grid operation state corresponding to the target power system based on the scenario weights when the target power flow scenario converges. The application improves the efficiency and accuracy of power grid operation risk assessment.

Description

Technical Field

[0001] This invention relates to the field of power analysis, and in particular to a method, apparatus, equipment and medium for assessing the power grid operation risk of a high proportion of new energy power systems. Background Technology

[0002] The large-scale integration of wind and solar power has significantly increased the number and uncertainty of random injections into the power system, transforming bus voltage, branch power flow, and equipment load rate from deterministic values ​​to probabilistic distributions. Probabilistic power flow can quantify the impact of renewable energy fluctuations on the grid's operating state and is an important technical means to identify voltage overruns, branch congestion, and low-probability tail risks. Existing quantum power flow typically submits each iterative power flow equation to a quantum linear equation solving circuit. When there are many renewable energy random variables, the number of representative scenarios increases, and scenario-by-scenario processing requires repeated circuit configuration, queuing to access cloud quantum processing units, and performing measurements, creating new time delay bottlenecks. Multiple circuit executions also accumulate quantum noise, slowing down the overall analysis due to the slowest convergence scenario.

[0003] Therefore, how to improve the efficiency and accuracy of power grid operation risk assessment in scenarios with many random variables in new energy sources is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for assessing grid operation risks in high-proportion renewable energy power systems, which can improve the efficiency and accuracy of grid operation risk assessment. The specific solution is as follows: Firstly, this application discloses a method for assessing grid operation risks in high-proportion renewable energy power systems, including: The probability distribution parameters and power mapping relationships corresponding to each target random variable of the target power system are determined, and the discrete sample values ​​and discrete sample probabilities corresponding to the target random variables are determined based on the probability distribution parameters; the target random variables are new energy random variables in the target power system. The quantum probability amplitude corresponding to the discrete sample probability is determined, the target power function corresponding to the discrete sample value is determined based on the power mapping relationship, and the target origin moment corresponding to the target random variable is determined based on the quantum probability amplitude and the target power function using the target quantum search operator, so as to generate the target power flow scenario and the corresponding scenario weights based on the target origin moment and the preset moment matching conditions. Based on the target iterative power flow equations corresponding to each target power flow scenario, a target shared coefficient matrix and a target right-hand side term corresponding to each target power flow scenario are determined. A target circuit is then configured based on the target shared coefficient matrix to determine the target correction amount corresponding to the target power flow scenario using the target right-hand side term. The target right-hand side term is the right-hand side term of the imbalance quantity that varies with the target power flow scenario. The target correction amount includes voltage correction and phase angle correction. The target circuit is a reusable quantum linear equation solving circuit. The target power flow scenario is iterated based on the target correction amount. When the target power flow scenario meets the preset scenario convergence condition, the bus voltage statistical moment and branch power flow statistical moment corresponding to the target power flow scenario are determined based on the scenario weight. The grid operation state corresponding to the target power system is determined based on the bus voltage statistical moment and branch power flow statistical moment. An operation risk assessment result is generated based on the grid operation state.

[0005] Optionally, determining the discrete sample values ​​and discrete sample probabilities corresponding to the target random variable based on the probability distribution parameters includes: The continuous domain of the target random variable is divided to determine the intervals after division. The representative value corresponding to each interval after division is determined as the discrete sample value. Based on the difference of the cumulative distribution function corresponding to the boundary points of adjacent intervals after division, the discrete sample probability corresponding to each interval after division is determined. Accordingly, determining the quantum probability amplitude corresponding to the discrete sample probability includes: The discrete sample probability is loaded onto a first qubit using a state preparation operator to determine the probability quantum state corresponding to the discrete sample probability, and the probability amplitude corresponding to the probability quantum state is determined as the quantum probability amplitude corresponding to the discrete sample probability; wherein, the first qubit is used to carry the probability distribution of the discrete sample probability in the form of amplitude, and the number of the first qubit is equal to the logarithm of the number of the segmented intervals to the base 2.

[0006] Optionally, determining the target origin moment corresponding to the target random variable using the target quantum search operator based on the quantum probability amplitude and the target power function includes: The target power function is normalized to determine a normalized power function. The normalized power function is then used to determine the normalized power value corresponding to the discrete sample value. The normalized power value is then encoded into a second qubit using a power function encoding operator. The second qubit is used to carry the function value of the target power function in the form of amplitude. A target quantum search operator is constructed using the state preparation operator and the first inverse operator corresponding to the state preparation operator, the power function encoding operator and the second inverse operator corresponding to the power function encoding operator, and the target reflection operator; the target reflection operator includes a first reflection operator with respect to the initial zero state and a second reflection operator with respect to the target subspace, the target subspace being spanned by the target state corresponding to the second qubit; The operator phase corresponding to the target quantum search operator is determined, and the target amplitude corresponding to the second qubit is determined based on the operator phase, the quantum probability amplitude, and the target power function using the target quantum search operator, so as to determine the target origin moment corresponding to the target random variable based on the target amplitude.

[0007] Optionally, the power function encoding operator includes a first-order power function encoding operator, a second-order power function encoding operator, a third-order power function encoding operator, and a fourth-order power function encoding operator, and the target origin moment includes the corresponding first-order origin moment, second-order origin moment, third-order origin moment, and fourth-order origin moment, and the power function encoding operators of each order are constructed based on the same state preparation operator.

[0008] Optionally, generating the target power flow scene and corresponding scene weights using the target origin moment based on preset moment matching conditions includes: The number of power flow scenarios is determined based on the number of variables in the target random variable; A target power flow scenario is generated using the target origin moment based on preset moment matching conditions, and the scenario weight of the target power flow scenario is determined based on the position weight of the location point corresponding to the target power flow scenario; the preset moment matching conditions are moment matching conditions based on three-point estimation.

[0009] Optionally, determining the target shared coefficient matrix and the target right-hand side terms corresponding to each target power flow scenario based on the target iterative power flow equations for each target power flow scenario includes: The network parameters of the target power system are obtained, and the target iterative power flow equations corresponding to each target power flow scenario are determined based on the network parameters and the renewable energy injection power corresponding to each target power flow scenario. The target iterative power flow equations are fast decoupled AC power flow equations, and the target iterative power flow equations include active power-phase subproblem equations and reactive power-voltage subproblem equations. The target shared coefficient matrix is ​​determined based on the coefficient matrix corresponding to each target iterative power flow equation, and the target right-hand term corresponding to the target power flow scenario is determined based on the power imbalance corresponding to the target power flow scenario. Accordingly, configuring the target circuit based on the target shared coefficient matrix to determine the target correction amount corresponding to the target power flow scenario using the target circuit based on the target right-hand side includes: Based on the target shared coefficient matrix, a quantum phase estimation module circuit, a controlled rotation module circuit, and an inverse quantum phase estimation module circuit are constructed, and a spectrum estimation register is configured based on the target shared coefficient matrix, so as to configure the target circuit using the quantum phase estimation module circuit, the controlled rotation module circuit, the inverse quantum phase estimation module circuit, and the spectrum estimation register; Configure a scenario marker register for each target power flow scenario, and use the scenario marker register to multiplex the target circuit based on the target right-hand term to determine the target correction amount corresponding to the target power flow scenario; the scenario marker register is used to store the scenario marker corresponding to the target power flow scenario, and the scenario marker does not change the spectral information of the target shared coefficient matrix.

[0010] Optionally, determining the grid operating state corresponding to the target power system based on the bus voltage statistical moments and the branch power flow statistical moments includes: The bus voltage statistical moments and the branch power flow statistical moments are expanded using Gram-Charlier series to reconstruct the target cumulative distribution function, which includes the bus voltage cumulative distribution function and the branch power flow cumulative distribution function. Based on the target cumulative distribution function, the voltage upper and lower limits and branch capacity limits corresponding to the target power system, the voltage over-limit probability, branch overload probability and quantile risk index corresponding to the target power system are determined; the quantile risk index includes the voltage quantile risk index under the first confidence level and the load rate quantile risk index under the second confidence level.

[0011] Secondly, this application discloses a power grid operation risk assessment device for high-proportion renewable energy power systems, comprising: The parameter discretization module is used to determine the probability distribution parameters and power mapping relationships corresponding to each target random variable of the target power system, and to determine the discrete sample values ​​and discrete sample probabilities corresponding to the target random variables based on the probability distribution parameters; the target random variables are new energy random variables in the target power system. The power flow scenario determination module is used to determine the quantum probability amplitude corresponding to the discrete sample probability, determine the target power function corresponding to the discrete sample value based on the power mapping relationship, and use the target quantum search operator to determine the target origin moment corresponding to the target random variable based on the quantum probability amplitude and the target power function, so as to generate the target power flow scenario and the corresponding scenario weights based on the target origin moment and the preset moment matching conditions. The correction amount determination module is used to determine the target shared coefficient matrix and the target right-hand side term corresponding to each target power flow scenario based on the target iterative power flow equation corresponding to each target power flow scenario, and to configure a target circuit based on the target shared coefficient matrix, so as to use the target circuit to determine the target correction amount corresponding to the target power flow scenario based on the target right-hand side term; the target right-hand side term is the right-hand side term of the imbalance quantity that varies with the target power flow scenario; the target correction amount includes voltage correction amount and phase angle correction amount; the target circuit is a reusable quantum linear equation solving circuit; The operation risk assessment module is used to iterate the target power flow scenario based on the target correction amount, and when the target power flow scenario meets the preset scenario convergence condition, determine the bus voltage statistical moment and branch power flow statistical moment corresponding to the target power flow scenario based on the scenario weight, so as to determine the grid operation state corresponding to the target power system based on the bus voltage statistical moment and branch power flow statistical moment, and generate operation risk assessment results based on the grid operation state.

[0012] Thirdly, this application discloses an electronic device, including: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for assessing grid operation risks for high-proportion renewable energy power systems.

[0013] Fourthly, this application discloses a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned grid operation risk assessment method for high-proportion renewable energy power systems.

[0014] In this application, when monitoring the grid operation status of a high-proportion renewable energy power system, the probability distribution parameters and power mapping relationships corresponding to each target random variable of the target power system are determined. Based on the probability distribution parameters, discrete sample values ​​and discrete sample probabilities corresponding to the target random variables are determined. The target random variables are renewable energy random variables in the target power system. The quantum probability amplitude corresponding to the discrete sample probabilities is determined. Based on the power mapping relationship, the target power function corresponding to the discrete sample values ​​is determined. A target quantum search operator is used to determine the target origin moment corresponding to the target random variable based on the quantum probability amplitude and the target power function. The target origin moment is then used to generate target power flow scenarios and corresponding scenario weights based on preset moment matching conditions. The target shared coefficient moments are determined based on the target iterative power flow equations corresponding to each target power flow scenario. The system uses a target circuit to determine the target correction amount corresponding to the target power flow scenario based on the target shared coefficient matrix and the target right-hand side terms corresponding to each target power flow scenario. The target right-hand side terms are the right-hand side terms of the imbalance quantity that change with the target power flow scenario. The target correction amount includes voltage correction amount and phase angle correction amount. The target circuit is a reusable quantum linear equation solving circuit. The system iterates the target power flow scenario based on the target correction amount. When the target power flow scenario meets the preset scenario convergence condition, the system determines the bus voltage statistical moment and branch power flow statistical moment corresponding to the target power flow scenario based on the scenario weight. The system determines the grid operation state corresponding to the target power system based on the bus voltage statistical moment and branch power flow statistical moment. The system generates an operation risk assessment result based on the grid operation state. As can be seen, this application provides a stable high-order moment input for three-point estimation by jointly encoding the probability distribution of new energy sources and power mapping into quantum states and obtaining the target origin moment using the target quantum search operator; by mapping the target origin moment, target power flow scenario, scenario weight, target right-hand side term, and power flow result together according to the same scenario, the consistency of scenario data is ensured; by utilizing the reusable quantum linear equation solving circuit with a structure that is invariant in each target power flow scenario through fast decoupling of the power flow coefficient matrix, the overhead of repetitive circuit configuration and quantum processing unit calls is reduced; finally, the probability distribution is reconstructed from the weighted scenario power flow result and the operational risk is output, thereby accurately and efficiently determining the grid operation status of a high-proportion new energy power system while maintaining power flow accuracy, and thus achieving a precise assessment of grid operation risk. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This application discloses a flowchart of a power grid operation risk assessment method for high-proportion renewable energy power systems. Figure 2 This is a schematic diagram of a specific classical-quantum cooperative quantum probabilistic power flow architecture disclosed in this application; Figure 3 This is a schematic diagram of the statistical moment calculation process for a specific quantum-enhanced three-point estimation disclosed in this application; Figure 4 This is a schematic diagram of a specific multi-scenario quantum power flow solution framework disclosed in this application; Figure 5 This is a schematic diagram of a power grid operation risk assessment device for a high-proportion renewable energy power system disclosed in this application. Figure 6 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The large-scale integration of wind and solar power has significantly increased the number and uncertainty of random injections into the power system, transforming bus voltage, branch power flow, and equipment load rate from deterministic values ​​to probabilistic distributions. Probabilistic power flow can quantify the impact of renewable energy fluctuations on the grid's operating status and is an important technical means to identify voltage overruns, branch congestion, and low-probability tail risks. Existing quantum power flow typically submits each iterative power flow equation to a quantum linear equation solving circuit. When there are many renewable energy random variables, the number of representative scenarios increases, and scenario-by-scenario processing requires repeated circuit configuration, queuing to access cloud quantum processing units, and performing measurements, creating new time delay bottlenecks. Multiple circuit executions also accumulate quantum noise, slowing down the overall analysis due to the slowest convergence scenario. To address these technical problems, this application discloses a grid operation risk assessment method for high-proportion renewable energy power systems, which can improve the efficiency and accuracy of grid operation risk assessment.

[0019] See Figure 1 As shown in the figure, this invention discloses a method for assessing grid operation risks in high-proportion renewable energy power systems, including: Step S11: Determine the probability distribution parameters and power mapping relationships corresponding to each target random variable of the target power system, and determine the discrete sample values ​​and discrete sample probabilities corresponding to the target random variables based on the probability distribution parameters; the target random variables are new energy random variables in the target power system.

[0020] In this embodiment, the target power system is a high-proportion renewable energy power system whose grid operation status is to be monitored, and the target random variable is the random variable corresponding to the random injection of renewable energy sources such as wind power and photovoltaic power into the target power system, i.e., the output of renewable energy units. It is understood that the method described in this application is derived from... Figure 2 The classical-quantum collaborative computing environment shown is executed. In one specific implementation, this computing environment includes a classical computing module, a quantum task orchestration module, and a quantum processing module. The classical computing module is used to read the power grid topology, branch parameters, node types, deterministic loads, and new energy probability distribution parameters, and to complete discrete sample generation, three-point scenario construction, power flow imbalance calculation, and risk index analysis. The quantum task orchestration module is used to generate quantum circuits, allocate registers, submit quantum tasks, and save scenario indexes. The quantum processing module is used to execute quantum amplitude estimation circuits and multi-scenario quantum linear equation solving circuits, which can be implemented by a real quantum computer, a quantum processing unit provided by a quantum cloud service, or a quantum simulator.

[0021] Specifically, the classical computation module establishes an input record for each target random variable. This input record includes at least the random variable number, access node, rated power, probability distribution type and parameters, upper and lower limits of the domain, and power mapping function. It also establishes a network record for the target power system, including at least the node admittance matrix, slack nodes, PV nodes, PQ nodes, baseline capacity, and convergence threshold. Subsequent target power flow scenarios all use the same network record, only changing the random injection of new energy sources and the right-hand side of the resulting power flow equations. Furthermore, the quantum task orchestration module maintains a statistical moment task table and a power flow task table. The statistical moment task table records the random variable number, statistical moment order, number of qubits, and number of measurements; the power flow task table records the scenario number, scenario weight, subproblem type, number of iterations, convergence state, and the position of the right-hand side in the extended quantum state. This ensures that quantum measurement results can be written back to the corresponding target random variable and target power flow scenario, avoiding mismatches in scenario weights during quantum task submission and measurement write-back.

[0022] In this embodiment, determining the discrete sample value and discrete sample probability corresponding to the target random variable based on the probability distribution parameters includes: dividing the continuous domain of the target random variable to determine the divided intervals, determining the representative value corresponding to each divided interval as the discrete sample value, and determining the discrete sample probability corresponding to each divided interval based on the cumulative distribution function difference corresponding to the boundary points of adjacent divided intervals.

[0023] In one specific implementation, suppose the target power system contains T mutually independent target random variables. , For the t-th objective random variable, its cumulative distribution function is: The upper and lower boundaries of the i-th interval are respectively and ,according to The lower limit of discreteness can be determined for the engineering operation range. and discrete upper limit The range is divided into two parts according to equal probability or according to a preset boundary. Non-overlapping partitioned intervals. The discrete sample probability of the i-th partitioned interval is the difference between the cumulative distribution function values ​​at adjacent boundary points, and the sum of the discrete sample probabilities is 1, i.e.: ; The representative value of this segmented interval can be taken as the midpoint of the interval, the conditional expectation, or the value corresponding to the inverse cumulative distribution function, and used as the discrete sample value. .

[0024] In this embodiment, when determining the probability distribution parameters and power mapping relationship, for wind power input, the Weibull distribution is used to describe the wind speed, and a piecewise wind power function is constructed according to the cut-in wind speed, rated wind speed, and cut-out wind speed. As a power mapping relationship: power is zero when wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed; power increases with wind speed when wind speed is between the cut-in wind speed and the rated wind speed; and power remains at the rated power when wind speed is between the rated wind speed and the cut-out wind speed. For photovoltaic input, the normalized solar irradiance is described using a Beta distribution and multiplied by the rated photovoltaic power to obtain the photovoltaic power. If there are load forecasting errors, energy storage output, or other random power sources in the target power system, they are replaced with the corresponding probability distribution and power mapping relationship without changing the subsequent processing flow.

[0025] Step S12: Determine the quantum probability amplitude corresponding to the discrete sample probability, determine the target power function corresponding to the discrete sample value based on the power mapping relationship, and use the target quantum search operator to determine the target origin moment corresponding to the target random variable based on the quantum probability amplitude and the target power function, so as to generate the target power flow scene and the corresponding scene weight based on the preset moment matching conditions using the target origin moment.

[0026] In this embodiment, when determining the quantum probability amplitude corresponding to the discrete sample probability, a state preparation operator is used to load the discrete sample probability onto the first qubit to determine the probability quantum state corresponding to the discrete sample probability, and the probability amplitude corresponding to the probability quantum state is determined as the quantum probability amplitude corresponding to the discrete sample probability. The first qubit is used to carry the probability distribution of the discrete sample probability in the form of amplitude, and the number of first qubits is equal to the base-2 logarithm of the number of partitioned intervals. That is, when the number of partitioned intervals is... At that time, the number of the first quantum bits is m.

[0027] In one specific implementation, the discrete sample probabilities are loaded into the first qubit using a state preparation operator. After normalizing the probabilities of each discrete sample, the quantum task orchestration module generates a state preparation operator T0, so that the m first qubits (i.e., the sample register qubits) are in a probabilistic quantum state: ; in, That is, discrete sample probability The corresponding quantum probability amplitude, quantum state The measured probability is consistent with the probability distribution of the target random variable. Before submitting the statistical moments task, the classical computation module checks the deviation of the sum of the probabilities of each discrete sample from 1 and records the discrete tail interval to avoid losing the tail probabilities required for higher-order moments due to insufficient truncation range.

[0028] In this embodiment, the target origin moment corresponding to the target random variable is determined using a target quantum search operator based on quantum probability amplitude and target power function. This includes: normalizing the target power function to determine a normalized power function; using the normalized power function to determine the normalized power value corresponding to the discrete sample value; and using a power function encoding operator to encode the normalized power value into a second qubit. The second qubit is used to carry the function value of the target power function in amplitude form. A target quantum search operator is constructed using a state preparation operator and its corresponding first inverse operator, a power function encoding operator and its corresponding second inverse operator, and a target reflection operator. The target reflection operator includes a first reflection operator about the initial zero state and a second reflection operator about the target subspace, which is spanned by the target state corresponding to the second qubit. The operator phase corresponding to the target quantum search operator is determined, and the target amplitude corresponding to the second qubit is determined using the target quantum search operator based on the operator phase, quantum probability amplitude, and target power function, so as to determine the target origin moment corresponding to the target random variable based on the target amplitude.

[0029] It should be noted that the target power function is determined by the power mapping relationship and is used to characterize the new energy power and its power corresponding to each discrete sample value. The power function encoding operator includes a first-order power function encoding operator, a second-order power function encoding operator, a third-order power function encoding operator, and a fourth-order power function encoding operator, corresponding to the statistical moment orders z=1, 2, 3, and 4, respectively. Each order of power function encoding operator is constructed based on the same state preparation operator T0. That is, the probability state preparation operator T0 is reused for the same target random variable, and power function encoding operators T1 are constructed sequentially for z=1, 2, 3, and 4, respectively, resulting in the first-order, second-order, third-order, and fourth-order raw moments. Each order of power function encoding operator is constructed based on the same state preparation operator.

[0030] Specifically, let's set As the power reference used for normalization, preferably taken as the rated power of new energy sources, the normalized power function of order z is then: That is, the target power function The ratio of the z-th power of the power reference to the z-th power of the power reference. The normalized power value corresponding to each discrete sample value. Limited to the range [0,1], and controlled rotation will Encoding the second qubit in the target state The probability is obtained as follows: ; Understandably, the second quantum bit is in The total probability of the state is , which is exactly the expected value of the z-th power of the normalized power function under the discrete probability distribution. Therefore, by estimating this probability, the corresponding order of the original moment can be obtained.

[0031] In this embodiment, (Note:) This indicates that the state preparation operator T0 is executed first, followed by the power function encoding operator T1; correspondingly, ,in Prepare the first inverse operator corresponding to the state operator. This is the second inverse operator corresponding to the power function encoding operator. The target reflection operator includes a first reflection operator f0 with respect to the initial zero state and a second reflection operator f1 with respect to the target subspace, which is composed of the target state of the second qubit. Zhang Cheng. Based on this, the target quantum search operator, namely the Grover operator, is constructed: ; By performing quantum amplitude estimation on the target quantum search operator, the first to fourth order origin moments of the new energy power, i.e., the target origin moments, can be obtained with a query complexity lower than that of classical random sampling. The target quantum search operator G performs deterministic rotations in the two-dimensional space spanned by the target subspace and the non-target subspace. The rotation angle is determined by the target amplitude of the second qubit, thus transforming the problem of estimating the target amplitude into the problem of estimating the characteristic phase of the target quantum search operator.

[0032] In this embodiment, n phase estimation qubits are configured, and a Hadamard gate and a controlled power operator with the j-th phase estimation qubit as the control bit are executed sequentially on the phase estimation qubits. And the inverse quantum Fourier transform, measuring the phase estimation qubit to obtain the operator phase corresponding to the target quantum search operator. According to the operator phase Reconstruct the total probability of the target state of the second qubit and calculate the target origin moment: ; When two phase measurement peaks exist at the same order, the equivalent amplitude is selected using their symmetry about 1 / 2. If the measurement accuracy does not reach the preset threshold, the number of phase estimation qubits or the number of measurements is increased before re-execution. After the target origin moments of four orders are calculated, the classical calculation module checks... And the obtained variance is non-negative; if this is not satisfied, the discretization accuracy or quantum amplitude estimation accuracy is increased before recalculation. It should be noted that, under the condition that state preparation and reversible power function calculation are achievable, quantum amplitude estimation... A single query is sufficient to achieve the required error. Classical independent random sampling typically requires This embodiment utilizes the advantages of this query to improve the stability of the third and fourth-order raw moments upon which skewness and kurtosis depend, thereby providing reliable input for subsequent three-point estimation.

[0033] In this embodiment, target power flow scenarios and corresponding scenario weights are generated using target origin moments based on preset moment matching conditions. This includes: determining the number of power flow scenarios based on the number of variables of the target random variables; generating target power flow scenarios using target origin moments based on preset moment matching conditions, and determining the scenario weights of the target power flow scenarios based on the position weights of the corresponding location points; the preset moment matching conditions are moment matching conditions based on three-point estimation. Specifically, the mean, variance, skewness, and kurtosis of the target random variables are calculated using target origin moments, and target power flow scenarios and corresponding scenario weights are generated according to the three-point estimated moment matching conditions (i.e., preset moment matching conditions). For T mutually independent target random variables, L=2T+1 target power flow scenarios are formed using a full-mean scenario and two non-mean scenarios for each target random variable to avoid scenario explosion caused by the complete Cartesian product. Thus, the continuous probabilistic power flow is transformed into the calculation of L weighted deterministic AC power flows.

[0034] In one specific implementation, such as Figure 3 As shown, the classical calculation module calculates the mean of the t-th objective random variable based on the first to fourth order objective raw moments. Standard deviation Skewness and kurtosis, and form a normalized central moment: ; Three standardized locations are set for each target random variable. and corresponding position weights The moment matching relation for the zeroth to fourth orders, which satisfies the three-point estimation, is as follows: ; After obtaining the position and weight, the target random variable is restored to its original state. If the location point exceeds the physical operating boundary of the new energy, it is projected onto the operating boundary and the corresponding weights are renormalized; if the obtained location or weight is not a real number, or the moment matching residual exceeds the preset threshold, the accuracy of the target origin moment is increased, or a constraint solution method that satisfies the same moment matching condition is used to solve it again.

[0035] In this embodiment, the number of power flow scenarios is determined based on the number T of target random variables. For T independent target random variables, instead of constructing a complete Cartesian product at three locations, a central scenario is established where all target random variables take the mean. Two non-mean location scenarios are then created for each target random variable, while the remaining target random variables still take the mean, thus forming L = 2T + 1 target power flow scenarios. In the l-th target power flow scenario, the value of the t-th target random variable is... The scene weight of this scene The location weights of the corresponding target random variables are determined by the combination of these weights, with all scenario weights normalized to 1. Each target power flow scenario stores a scenario number, a random injection vector, and scenario weights. The classic calculation module writes the active and reactive power injections from renewable energy sources for each target power flow scenario into the corresponding access nodes and superimposes them with deterministic loads to form the scenario node injection vector. If renewable energy sources operate at a constant power factor, reactive power injection is calculated synchronously based on active power output and power factor; if renewable energy sources adopt voltage control mode, they are handled according to PV node constraints.

[0036] Step S13: Determine the target shared coefficient matrix and the target right-hand side term corresponding to each target power flow scenario based on the target iterative power flow equations corresponding to each target power flow scenario, and configure the target circuit based on the target shared coefficient matrix to determine the target correction amount corresponding to the target power flow scenario based on the target right-hand side term; the target right-hand side term is the right-hand side term of the imbalance quantity that changes with the target power flow scenario; the target correction amount includes voltage correction amount and phase angle correction amount; the target circuit is a reusable quantum linear equation solving circuit.

[0037] In this embodiment, determining the target shared coefficient matrix and the target right-hand side terms corresponding to each target power flow scenario based on the target iterative power flow equations corresponding to each target power flow scenario includes: obtaining the network parameters of the target power system, and determining the target iterative power flow equations corresponding to each target power flow scenario based on the network parameters and the renewable energy injection power corresponding to each target power flow scenario; the target iterative power flow equations are fast decoupled AC power flow equations, and the target iterative power flow equations include active power-phase subproblem equations and reactive power-voltage subproblem equations; determining the target shared coefficient matrix based on the coefficient matrix corresponding to each target iterative power flow equation, and determining the target right-hand side terms corresponding to the target power flow scenario based on the power imbalance amount corresponding to the target power flow scenario.

[0038] In one specific implementation, a fast decoupled AC power flow equation is established as the target iterative power flow equation for each target power flow scenario. The coefficient matrices B' and B'' of the fast decoupled AC power flow equation are determined by the network topology and branch parameters, and remain unchanged in each target power flow scenario, thus forming a target shared coefficient matrix; while the right-hand side is determined by the power imbalance of the current scenario, and changes with the target power flow scenario and the number of iterations, forming the target right-hand side. L active power-phase subproblems with the same coefficient matrix B' are grouped into one group, and L reactive power-voltage subproblems with the same coefficient matrix B'' are grouped into another group, and the mapping between scenario number, scenario weight, and right-hand side is saved.

[0039] Understandably, in this embodiment, coefficient matrices B' and B'' for fast decoupling of power flow are established based on network parameters such as grid topology and branch reactance. Rows and columns corresponding to slack nodes are removed, and the dimensions of the active power-phase subproblem equations and reactive power-voltage subproblem equations are determined according to the PV node and PQ node sets. For the same network topology and node type set, B' and B'' remain unchanged in all target power flow scenarios, thus forming the target shared coefficient matrices for the active power-phase subproblem and reactive power-voltage subproblem, respectively.

[0040] In the k-th iteration, the active power imbalance for each target power flow scenario is calculated. and reactive power imbalance And according to the voltage normalization rule for fast decoupling power flow, the target right-hand side terms b'(l,k) and b''(l,k) are formed. The active-phase subproblem equation and reactive-voltage subproblem equation for the l-th target power flow scenario in the k-th iteration are respectively: ; in, This is the phase angle correction amount. The voltage correction is calculated using the active power-phase angle sub-problem equation, and the voltage correction is calculated using the reactive power-voltage sub-problem equation.

[0041] It should be noted that in this embodiment, a quantum phase estimation module circuit, a controlled rotation module circuit, and an inverse quantum phase estimation module circuit can be constructed based on the target shared coefficient matrix, and a spectral estimation register can be configured based on the target shared coefficient matrix to configure the target circuit using the quantum phase estimation module circuit, the controlled rotation module circuit, the inverse quantum phase estimation module circuit, and the spectral estimation register. A scenario marker register is configured for each target power flow scenario, and the scenario marker register is used to multiplex the target circuit based on the target right-hand side to determine the target correction amount corresponding to the target power flow scenario. The scenario marker register is used to store the scenario markers corresponding to the target power flow scenarios, and the scenario markers do not change the spectral information of the target shared coefficient matrix. In the specific implementation process, B' or B'' of the same subproblem in each target power flow scenario remains unchanged, while the target right-hand side changes with the scenario and the number of iterations. After each round of iteration, if the maximum active power imbalance and the maximum reactive power imbalance of the target power flow scenario are both less than a preset threshold, the scenario is marked as converged; otherwise, it is retained in the next round of tasks. If reactive power exceeds the limit during the calculation process and causes a change in node type, the target power flow scenarios with the same node type are regrouped, and a target shared coefficient matrix is ​​constructed for each group to ensure the invariance of the coefficient matrix within each group.

[0042] In this embodiment, for the B' subproblem or B'' subproblem, the dimension of the target shared coefficient matrix B is set to D. First, B is scale-normalized so that its eigenvalues ​​lie within the range representable by quantum phase estimation; when B is not a Hermitian matrix, a... Hermitian embeddings of the form; then D zeros are filled to The preprocessing process records the indices of the valid state components. This preprocessing is only used for the target power flow scenario obtained from the three-point estimation and does not classify or integrate arbitrary power flow equation sets according to the number of qubits or the range of eigenvalues. In particular, when B' and B'' are completely identical, B can be... This allows two equations to reuse a single quantum circuit simultaneously.

[0043] In this embodiment, the target circuit is a reusable quantum linear equation solving circuit configured based on the target shared coefficient matrix. This circuit reuses the quantum phase estimation, controlled rotation, and inverse quantum phase estimation modules determined by the target shared coefficient matrix. It can load the target right-hand side terms of each target power flow scenario sequentially in a scenario-by-scenario mode, or load the target right-hand side terms of multiple active scenarios at once in a shared solving method with scenario marking. By measuring and analyzing the solution states output by the quantum linear equation solving circuit, the phase angle correction of each target power flow scenario can be obtained. and voltage correction amount .

[0044] This embodiment allows the target circuit to employ two execution modes. The first is the scenario-by-scenario mode: keeping the circuit template corresponding to the target shared coefficient matrix B unchanged, the right-hand side of each target power flow scenario is updated sequentially. And it performs the solution of quantum linear equations. The second method is a shared solution method with scene tags: see [link to solution]. Figure 4 As shown, for the currently non-converged... For each target flow scenario (i.e., activity scenario), set the scenario flag register and normalize the target right-hand side item. With scene markers The joint preparation is a scenario-right-end term joint quantum state, while retaining the scenario weights obtained from the three-point estimation in the classical task table. : ; in, Used to satisfy the normalization requirements of the joint quantum state, but does not replace the scene weights used in probabilistic power flow calculations. An identity operation is applied to the scene marker register, and an evolution determined by the target shared coefficient matrix B is applied to the state register, forming a quantum evolution operator: ; Since all target power flow scenarios use the same target shared coefficient matrix B, and the scenario label does not change the spectral information of B, only one set of N-bit spectral estimation registers is needed to enable the active scenarios generated by the three-point estimation to reuse the quantum phase estimation, controlled rotation, and inverse quantum phase estimation modules. Unlike directly constructing an integrated matrix of any number of different power flow equations, the sharing relationship in this embodiment is defined by the three-point estimation scenario, the common network topology, and the common target shared coefficient matrix B.

[0045] Under the shared solution method, the following is adopted: Each qubit stores a scenario marker and a state component, where The function is the floor function. The quantum linear equation solving circuit is configured with one auxiliary qubit, N spectrum estimation register qubits, and M' scene-state register qubits; each active scene reuses the quantum phase estimation, controlled rotation, and inverse quantum phase estimation modules determined by B.

[0046] Specifically, the quantum linear equation solving circuit performs the following operations in sequence: preparing the scenario-right-hand side joint quantum state. ; utilizing controlled Perform quantum phase estimation to obtain eigenvalue estimates of the target shared coefficient matrix; based on the estimated eigenvalues... Execution and The corresponding auxiliary qubit is rotated in a controlled manner, where C is not greater than the absolute value of the minimum eigenvalue; inverse quantum phase estimation is performed to untangle the spectral estimation register; the target state of the auxiliary qubit is post-selected or amplified to obtain the solution state with scene labeling.

[0047] The quantum mission orchestration module measures the solution states according to scene markers and state indices. For the voltage correction component that needs to be read, its amplitude can be obtained through repeated measurements, quantum amplitude estimation, or observable estimation, and then the classical value and sign can be recovered by combining the normalization coefficient of the right-hand side and the scaling coefficient of the quantum linear equation. The recovered phase angle correction is then calculated. or voltage correction amount Write back to the classic trend iteration according to the original scene number, and then continue to use the corresponding scene weight. Calculate and output statistical moments.

[0048] For target power flow scenarios that have already converged, their right-hand side terms will not be prepared in the next round. The quantum task orchestration module rearranges the non-converged scenarios according to the active scenario mask, masks the converged scenarios from the next shared solution task, and saves the mapping between the active scenario position, the original scenario number, the scenario weight, and the quantum measurement results, thereby maintaining the measurement results and the original scenario weights. The correspondence is established. After all target power flow scenarios converge, the multi-scenario power flow iteration ends.

[0049] In terms of quantum resources, a scenario-by-scenario solution is employed. One scenario requires an appointment The usage of each quantum bit and Sub-quantum processing unit call; shared solution method only requires One qubit and one quantum processing unit call. Ignoring state preparation overhead, and considering the sparsity s and condition number of each scenario... and phase estimation accuracy If the scenarios are identical, the additional scenario marker qubits do not increase the circuit depth of quantum phase estimation, controlled rotation, and inverse quantum phase estimation; when the number of scenarios increases, it mainly expands the scenario-state register without changing the structure of the common spectrum estimation module. It should be noted that this shared solution method belongs to the weighted target power flow scenario generated by the three-point estimation and is not a general scheme for the integrated calculation of arbitrary multiple power flow equations.

[0050] Step S14: Iterate the target power flow scenario based on the target correction amount, and when the target power flow scenario meets the preset scenario convergence condition, determine the bus voltage statistical moment and branch power flow statistical moment corresponding to the target power flow scenario based on the scenario weight, and determine the grid operation state corresponding to the target power system based on the bus voltage statistical moment and branch power flow statistical moment, and generate an operation risk assessment result based on the grid operation state.

[0051] In this embodiment, the voltage amplitude and phase angle of each target power flow scenario are updated based on the target correction amount obtained from quantum measurement results. The power imbalance is recalculated and the next iteration begins until the maximum active power imbalance and maximum reactive power imbalance of each target power flow scenario are both less than a preset threshold, thus satisfying the preset scenario convergence condition. For each converged target power flow scenario, the output quantities such as bus voltage amplitude, phase angle, branch active power flow, reactive power flow, and load rate are calculated. The first to fourth order statistical moments of each output quantity are calculated according to the scenario weight to obtain the bus voltage statistical moment and branch power flow statistical moment.

[0052] During the iterative process of the target power flow scenario, the bus voltage magnitude, phase angle, branch active power flow, reactive power flow, and load factor are calculated for each converged target power flow scenario. Let the output function of the l-th target power flow scenario be... , Scene weight is Then the output z-order origin moment is: ; The mean, standard deviation, skewness, and kurtosis of each output quantity are calculated from the first to fourth order output origin moments to obtain the bus voltage statistical moments and branch power flow statistical moments. The branch power flow statistical moments include the statistical moments of branch active power flow, reactive power flow, or load rate.

[0053] In one specific implementation, the power grid operating state corresponding to the target power system is determined based on the bus voltage statistical moments and branch power flow statistical moments, including: expanding the bus voltage statistical moments and branch power flow statistical moments using Gram-Charlier series to reconstruct the target cumulative distribution function; the target cumulative distribution function includes the bus voltage cumulative distribution function and the branch power flow cumulative distribution function; based on the target cumulative distribution function, the voltage upper and lower limits and branch capacity limits corresponding to the target power system, the voltage over-limit probability, branch overload probability and quantile risk index corresponding to the target power system are determined; the quantile risk index includes the voltage quantile risk index under the first confidence level and the load rate quantile risk index under the second confidence level.

[0054] It should be noted that, in this embodiment, in addition to reconstructing the target cumulative distribution function through Gram-Charlier series expansion, which includes the bus voltage cumulative distribution function and the branch power flow cumulative distribution function, other distribution reconstruction methods satisfying the same moment constraints can also be used. The target cumulative distribution function is compared with the upper and lower voltage limits and branch capacity limits of the target power system to obtain the voltage exceedance probability, branch overload probability, and quantile risk index. The quantile risk index includes the voltage quantile risk index at the first confidence level and the load rate quantile risk index at the second confidence level. The first and second confidence levels are preset confidence levels, which can be the same or different, for example, both can be set to 95%. The classical calculation module outputs scenario convergence records, quantum task call records, a bus voltage risk table, and a branch congestion risk table, and sorts the risks according to exceedance probability or quantile margin, serving as the grid operating status corresponding to the target power system. The above grid operating status can be used for new energy grid connection planning evaluation, day-ahead and intraday safety verification, distribution network voltage risk assessment, and transmission section congestion screening.

[0055] Understandably, this embodiment uses the first qubit to carry discrete sample probabilities and the second qubit to carry normalized power values. It constructs a target quantum search operator using state preparation operators, power function encoding operators, their inverse operators, and reflection operators. It obtains the first to fourth order target origin moments using quantum phase estimation. The target origin moments are transformed into 2T+1 weighted target power flow scenarios through three-point estimation. By rapidly decoupling the AC power flow equations, a reusable quantum linear equation solving circuit is configured in the target shared coefficient matrix that remains unchanged in each target power flow scenario. It also supports scenario-by-scenario mode and shared solution method with scenario labels. Finally, the target cumulative distribution function is reconstructed through Gram-Charlier series expansion, and the out-of-limit probability and quantile risk index are output. This realizes a complete technical link from random input to multi-scenario calculation and then to probability risk output.

[0056] To verify the effectiveness of the power grid operation status monitoring method provided in this embodiment, a simplified verification embodiment based on an improved IEEE-30 node system is given below.

[0057] In the IEEE-30 node system, wind farms with a rated power of 20MW are connected to nodes 7, 11, 15, and 30, respectively, and a photovoltaic power station with a rated power of 25MW is connected to node 8. The number of target random variables is T=5. Wind speed is described using a Weibull distribution, and irradiance is described using a Beta distribution. The average penetration rate of new energy sources is approximately 20%, with a peak rate of approximately 40%. The classical end uses an Intel Core i5-14400 processor and 16GB of memory to run a quantum simulator as the quantum processing module.

[0058] Using n=11 quantum amplitude estimation qubits (i.e., phase estimation qubits), the first to fourth order raw moments of five objective random variables are calculated, generating L=2T+1=11 weighted objective power flow scenarios. The B' subproblem and B'' subproblem of the 11 objective power flow scenarios reuse their respective objective shared coefficient matrices; the single-scenario state register has M=5 qubits, while the scenario-state register has M'=9 qubits for shared solution, and N=12 common spectrum estimation register qubits are configured, requiring a total of 22 qubits including one auxiliary qubit. The main verification results are shown in Table 1 below.

[0059] Table 1 Simplified Validation Table

[0060] In the higher-order moment estimation stage, the first-order raw moment is obtained when n=11. The error is Fourth-order origin moment The error is The errors of the first to fourth order raw moments stabilize at In terms of scale, during the scene generation phase, 5 target random variables generate 11 target power flow scenes, achieving the replacement of the continuous probability domain with 2T+1 scenes.

[0061] In the multi-scenario computing resource allocation stage, the total number of qubits used for shared solution methods is 1908, and the total number of quantum gates is [missing information]. The total number of quantum processing unit calls was 88; the total number of qubits used in the scenario-by-scenario approach was 15984; and the total number of quantum gates was [missing information]. The total number of calls to the quantum processing unit was 888; the shared solution method reduced the number of calls by 88.06%, 89.70%, and 90.09% respectively compared to the scenario-by-scenario method. Regarding the power flow solution accuracy, the power flow results obtained in this embodiment have an error of no more than [percentage missing] compared to the classical fast decoupled power flow. No visible additional error was introduced. In the distribution and efficiency stage, the goodness of fit of the cumulative distribution function of the bus voltage was... The goodness of fit of the cumulative distribution function of the branch power flow is about 99%, and the total computation time is reduced by 47.1%, indicating that this embodiment can reproduce the probability distribution and its tail characteristics.

[0062] After reconstructing the probability distribution, the average load factor of branches 21-22 is 94.07%, and capacity verification should be prioritized. Branches 15-18, under the extreme 5% scenario with high renewable energy injection, can see their load factor increase by approximately 12%, exhibiting a clear right-tailed characteristic. The 95th percentile voltage error is... With a load factor error of 0.46% at the 95th percentile level (PU), this embodiment can identify tail risks that are difficult to reveal with average values. As seen in the simplified example above, the preparation of new energy probabilistic states and quantum amplitude estimation provide stable high-order moments for the generation of three-point scenarios. After reusing the target shared coefficient matrix and spectral estimation circuit for 11 weighted target power flow scenarios, the number of qubits used, the total number of quantum gates, and the number of quantum processing unit calls all significantly decreased, while the power flow error remained within acceptable limits. Within; the weighted output moments and cumulative distribution reconstruction further form tail risk results that can be used for power grid safety verification, verifying the complete execution process from random input to multi-scenario calculation and then to probabilistic risk output.

[0063] As can be seen, this application provides a stable high-order moment input for three-point estimation by jointly encoding the probability distribution of new energy sources and power mapping into quantum states and obtaining the target origin moment using the target quantum search operator; by mapping the target origin moment, target power flow scenario, scenario weight, target right-hand side term, and power flow result together according to the same scenario, the consistency of scenario data is ensured; by utilizing the reusable quantum linear equation solving circuit with a structure that is invariant in each target power flow scenario through fast decoupling of the power flow coefficient matrix, the overhead of repetitive circuit configuration and quantum processing unit calls is reduced; finally, the probability distribution is reconstructed from the weighted scenario power flow result and the operational risk is output, thereby accurately and efficiently determining the grid operation status of a high-proportion new energy power system while maintaining power flow accuracy, and thus achieving a precise assessment of grid operation risk.

[0064] See Figure 5 As shown, this application discloses a power grid operation risk assessment device for high-proportion renewable energy power systems, comprising: The parameter discrete processing module 11 is used to determine the probability distribution parameters and power mapping relationships corresponding to each target random variable of the target power system, and to determine the discrete sample values ​​and discrete sample probabilities corresponding to the target random variables based on the probability distribution parameters; the target random variables are new energy random variables in the target power system. The power flow scene determination module 12 is used to determine the quantum probability amplitude corresponding to the discrete sample probability, determine the target power function corresponding to the discrete sample value based on the power mapping relationship, and use the target quantum search operator to determine the target origin moment corresponding to the target random variable based on the quantum probability amplitude and the target power function, so as to generate the target power flow scene and the corresponding scene weight based on the target origin moment and the preset moment matching conditions. The correction amount determination module 13 is used to determine the target shared coefficient matrix and the target right-hand side term corresponding to each target power flow scenario based on the target iterative power flow equation corresponding to each target power flow scenario, and to configure the target circuit based on the target shared coefficient matrix so as to use the target circuit to determine the target correction amount corresponding to the target power flow scenario based on the target right-hand side term; the target right-hand side term is the right-hand side term of the imbalance quantity that changes with the target power flow scenario; the target correction amount includes voltage correction amount and phase angle correction amount; the target circuit is a reusable quantum linear equation solving circuit; The operation risk assessment module 14 is used to iterate the target power flow scenario based on the target correction amount, and when the target power flow scenario meets the preset scenario convergence condition, determine the bus voltage statistical moment and branch power flow statistical moment corresponding to the target power flow scenario based on the scenario weight, so as to determine the grid operation state corresponding to the target power system based on the bus voltage statistical moment and branch power flow statistical moment, and generate operation risk assessment results based on the grid operation state.

[0065] As can be seen, this application provides a stable high-order moment input for three-point estimation by jointly encoding the probability distribution of new energy sources and power mapping into quantum states and obtaining the target origin moment using the target quantum search operator; by mapping the target origin moment, target power flow scenario, scenario weight, target right-hand side term, and power flow result together according to the same scenario, the consistency of scenario data is ensured; by utilizing the reusable quantum linear equation solving circuit with a structure that is invariant in each target power flow scenario through fast decoupling of the power flow coefficient matrix, the overhead of repetitive circuit configuration and quantum processing unit calls is reduced; finally, the probability distribution is reconstructed from the weighted scenario power flow result and the operational risk is output, thereby accurately and efficiently determining the grid operation status of a high-proportion new energy power system while maintaining power flow accuracy, and thus achieving a precise assessment of grid operation risk.

[0066] In one specific implementation, the parameter discretization processing module 11 may include: The parameter discrete processing unit is used to divide the continuous domain of the target random variable to determine the divided intervals, determine the representative value corresponding to each divided interval as a discrete sample value, and determine the discrete sample probability corresponding to each divided interval based on the cumulative distribution function difference corresponding to the boundary points of adjacent divided intervals. Accordingly, the current flow scenario determination module 12 may specifically include: A quantum probability amplitude determination unit is used to load the discrete sample probability onto a first qubit using a state preparation operator to determine the probability quantum state corresponding to the discrete sample probability, and to determine the probability amplitude corresponding to the probability quantum state as the quantum probability amplitude corresponding to the discrete sample probability; wherein, the first qubit is used to carry the probability distribution of the discrete sample probability in the form of amplitude, and the number of the first qubit is equal to the logarithm to the base 2 of the number of the segmented intervals.

[0067] In one specific implementation, the power flow scenario determination module 12 may include: A power value encoding unit is used to normalize the target power function to determine a normalized power function, use the normalized power function to determine the normalized power value corresponding to the discrete sample value, and use a power function encoding operator to encode the normalized power value into a second quantum bit; the second quantum bit is used to carry the function value of the target power function in the form of amplitude; The search operator construction unit is used to construct a target quantum search operator by utilizing the state preparation operator and the first inverse operator corresponding to the state preparation operator, the power function encoding operator and the second inverse operator corresponding to the power function encoding operator, and the target reflection operator; the target reflection operator includes a first reflection operator with respect to the initial zero state and a second reflection operator with respect to the target subspace, the target subspace being spanned by the target state corresponding to the second qubit; The origin moment determination unit is used to determine the operator phase corresponding to the target quantum search operator, and to determine the target amplitude corresponding to the second qubit based on the operator phase, the quantum probability amplitude and the target power function, so as to determine the target origin moment corresponding to the target random variable based on the target amplitude.

[0068] In one specific implementation, the power flow scenario determination module 12 may include: The scenario number determination unit is used to determine the number of power flow scenarios based on the number of variables of the target random variable; The scene weight determination unit is used to generate a target current flow scene based on a preset moment matching condition using the target origin moment, and to determine the scene weight of the target current flow scene based on the position weight of the position point corresponding to the target current flow scene; the preset moment matching condition is a moment matching condition based on three-point estimation.

[0069] In one specific implementation, the correction amount determination module 13 may include: The power flow equation determination unit is used to obtain the network parameters of the target power system and determine the target iterative power flow equation corresponding to each target power flow scenario based on the network parameters and the renewable energy injection power corresponding to each target power flow scenario. The target iterative power flow equation is a fast decoupled AC power flow equation, and the target iterative power flow equation includes the active power-phase subproblem equation and the reactive power-voltage subproblem equation. The right-hand term determination unit is used to determine the target shared coefficient matrix based on the coefficient matrix corresponding to each target iterative power flow equation, and to determine the target right-hand term corresponding to the target power flow scenario based on the power imbalance amount corresponding to the target power flow scenario. Accordingly, the correction amount determination module 13 may specifically include: A target circuit configuration unit is used to construct a quantum phase estimation module circuit, a controlled rotation module circuit, and an inverse quantum phase estimation module circuit based on the target shared coefficient matrix, and to configure a spectrum estimation register based on the target shared coefficient matrix, so as to configure the target circuit using the quantum phase estimation module circuit, the controlled rotation module circuit, the inverse quantum phase estimation module circuit, and the spectrum estimation register; The correction amount determination unit is used to configure a scene marker register for each of the target power flow scenarios, and use the scene marker register to multiplex the target circuit based on the target right-hand term to determine the target correction amount corresponding to the target power flow scenario; the scene marker register is used to store the scene marker corresponding to the target power flow scenario, and the scene marker does not change the spectral information of the target shared coefficient matrix.

[0070] In one specific implementation, the operational risk assessment module 14 may include: The distribution function reconstruction unit is used to reconstruct the target cumulative distribution function by expanding the bus voltage statistical moments and the branch power flow statistical moments using Gram-Charlier series expansion; the target cumulative distribution function includes the bus voltage cumulative distribution function and the branch power flow cumulative distribution function. The risk indicator determination unit is used to determine the voltage over-limit probability, branch overload probability, and quantile risk indicators corresponding to the target power system based on the target cumulative distribution function, the voltage upper and lower limits and branch capacity limits corresponding to the target power system; the quantile risk indicators include the voltage quantile risk indicator under the first confidence level and the load rate quantile risk indicator under the second confidence level.

[0071] Furthermore, embodiments of this application also disclose an electronic device, Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0072] Figure 6 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the grid operation risk assessment method for high-proportion renewable energy power systems disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0073] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0074] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0075] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the grid operation risk assessment method for high-proportion renewable energy power systems disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0076] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned disclosed method for assessing grid operation risks in high-proportion renewable energy power systems. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0078] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0079] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0080] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0081] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for assessing grid operation risk in high-proportion renewable energy power systems, characterized in that, include: The probability distribution parameters and power mapping relationships corresponding to each target random variable of the target power system are determined, and the discrete sample values ​​and discrete sample probabilities corresponding to the target random variables are determined based on the probability distribution parameters; the target random variables are new energy random variables in the target power system. The quantum probability amplitude corresponding to the discrete sample probability is determined, the target power function corresponding to the discrete sample value is determined based on the power mapping relationship, and the target origin moment corresponding to the target random variable is determined based on the quantum probability amplitude and the target power function using the target quantum search operator, so as to generate the target power flow scenario and the corresponding scenario weights based on the target origin moment and the preset moment matching conditions. Based on the target iterative power flow equations corresponding to each target power flow scenario, a target shared coefficient matrix and a target right-hand side term corresponding to each target power flow scenario are determined. A target circuit is then configured based on the target shared coefficient matrix to determine the target correction amount corresponding to the target power flow scenario using the target right-hand side term. The target right-hand side term is the right-hand side term of the imbalance quantity that varies with the target power flow scenario. The target correction amount includes voltage correction and phase angle correction. The target circuit is a reusable quantum linear equation solving circuit. The target power flow scenario is iterated based on the target correction amount. When the target power flow scenario meets the preset scenario convergence condition, the bus voltage statistical moment and branch power flow statistical moment corresponding to the target power flow scenario are determined based on the scenario weight. The grid operation state corresponding to the target power system is determined based on the bus voltage statistical moment and branch power flow statistical moment. An operation risk assessment result is generated based on the grid operation state.

2. The grid operation risk assessment method for high-proportion renewable energy power systems according to claim 1, characterized in that, The step of determining the discrete sample values ​​and discrete sample probabilities corresponding to the target random variable based on the probability distribution parameters includes: The continuous domain of the target random variable is divided to determine the intervals after division. The representative value corresponding to each interval after division is determined as the discrete sample value. Based on the difference of the cumulative distribution function corresponding to the boundary points of adjacent intervals after division, the discrete sample probability corresponding to each interval after division is determined. Accordingly, determining the quantum probability amplitude corresponding to the discrete sample probability includes: The discrete sample probability is loaded onto a first qubit using a state preparation operator to determine the probability quantum state corresponding to the discrete sample probability, and the probability amplitude corresponding to the probability quantum state is determined as the quantum probability amplitude corresponding to the discrete sample probability; wherein, the first qubit is used to carry the probability distribution of the discrete sample probability in the form of amplitude, and the number of the first qubit is equal to the logarithm of the number of the segmented intervals to the base 2.

3. The grid operation risk assessment method for high-proportion renewable energy power systems according to claim 2, characterized in that, The step of determining the target origin moment corresponding to the target random variable based on the quantum probability amplitude and the target power function using the target quantum search operator includes: The target power function is normalized to determine a normalized power function. The normalized power function is then used to determine the normalized power value corresponding to the discrete sample value. The normalized power value is then encoded into a second qubit using a power function encoding operator. The second qubit is used to carry the function value of the target power function in the form of amplitude. A target quantum search operator is constructed using the state preparation operator and the first inverse operator corresponding to the state preparation operator, the power function encoding operator and the second inverse operator corresponding to the power function encoding operator, and the target reflection operator; the target reflection operator includes a first reflection operator with respect to the initial zero state and a second reflection operator with respect to the target subspace, the target subspace being spanned by the target state corresponding to the second qubit; The operator phase corresponding to the target quantum search operator is determined, and the target amplitude corresponding to the second qubit is determined based on the operator phase, the quantum probability amplitude, and the target power function using the target quantum search operator, so as to determine the target origin moment corresponding to the target random variable based on the target amplitude.

4. The grid operation risk assessment method for high-proportion renewable energy power systems according to claim 3, characterized in that, The power function encoding operator includes a first-order power function encoding operator, a second-order power function encoding operator, a third-order power function encoding operator, and a fourth-order power function encoding operator. The target origin moment includes the corresponding first-order origin moment, second-order origin moment, third-order origin moment, and fourth-order origin moment. The power function encoding operators of each order are constructed based on the same state preparation operator.

5. The grid operation risk assessment method for high-proportion renewable energy power systems according to claim 1, characterized in that, The step of generating a target power flow scene and corresponding scene weights based on preset moment matching conditions using the target origin moment includes: The number of power flow scenarios is determined based on the number of variables in the target random variable; A target power flow scenario is generated using the target origin moment based on preset moment matching conditions, and the scenario weight of the target power flow scenario is determined based on the position weight of the location point corresponding to the target power flow scenario; the preset moment matching conditions are moment matching conditions based on three-point estimation.

6. The grid operation risk assessment method for high-proportion renewable energy power systems according to claim 1, characterized in that, The determination of the target shared coefficient matrix and the target right-hand side terms corresponding to each target power flow scenario based on the target iterative power flow equations for each target power flow scenario includes: The network parameters of the target power system are obtained, and the target iterative power flow equations corresponding to each target power flow scenario are determined based on the network parameters and the renewable energy injection power corresponding to each target power flow scenario. The target iterative power flow equations are fast decoupled AC power flow equations, and the target iterative power flow equations include active power-phase subproblem equations and reactive power-voltage subproblem equations. The target shared coefficient matrix is ​​determined based on the coefficient matrix corresponding to each target iterative power flow equation, and the target right-hand term corresponding to the target power flow scenario is determined based on the power imbalance corresponding to the target power flow scenario. Accordingly, configuring the target circuit based on the target shared coefficient matrix to determine the target correction amount corresponding to the target power flow scenario using the target circuit based on the target right-hand side includes: Based on the target shared coefficient matrix, a quantum phase estimation module circuit, a controlled rotation module circuit, and an inverse quantum phase estimation module circuit are constructed, and a spectrum estimation register is configured based on the target shared coefficient matrix, so as to configure the target circuit using the quantum phase estimation module circuit, the controlled rotation module circuit, the inverse quantum phase estimation module circuit, and the spectrum estimation register; Configure a scenario marker register for each target power flow scenario, and use the scenario marker register to multiplex the target circuit based on the target right-hand term to determine the target correction amount corresponding to the target power flow scenario; the scenario marker register is used to store the scenario marker corresponding to the target power flow scenario, and the scenario marker does not change the spectral information of the target shared coefficient matrix.

7. The grid operation risk assessment method for high-proportion renewable energy power systems according to any one of claims 1 to 6, characterized in that, The process of determining the grid operating state corresponding to the target power system based on the bus voltage statistical moments and the branch power flow statistical moments includes: The bus voltage statistical moments and the branch power flow statistical moments are expanded using Gram-Charlier series to reconstruct the target cumulative distribution function, which includes the bus voltage cumulative distribution function and the branch power flow cumulative distribution function. Based on the target cumulative distribution function, the voltage upper and lower limits and branch capacity limits corresponding to the target power system, the voltage over-limit probability, branch overload probability and quantile risk index corresponding to the target power system are determined; the quantile risk index includes the voltage quantile risk index under the first confidence level and the load rate quantile risk index under the second confidence level.

8. A power grid operation risk assessment device for high-proportion renewable energy power systems, characterized in that, include: The parameter discretization module is used to determine the probability distribution parameters and power mapping relationships corresponding to each target random variable of the target power system, and to determine the discrete sample values ​​and discrete sample probabilities corresponding to the target random variables based on the probability distribution parameters; the target random variables are new energy random variables in the target power system. The power flow scenario determination module is used to determine the quantum probability amplitude corresponding to the discrete sample probability, determine the target power function corresponding to the discrete sample value based on the power mapping relationship, and use the target quantum search operator to determine the target origin moment corresponding to the target random variable based on the quantum probability amplitude and the target power function, so as to generate the target power flow scenario and the corresponding scenario weights based on the target origin moment and the preset moment matching conditions. The correction amount determination module is used to determine the target shared coefficient matrix and the target right-hand side term corresponding to each target power flow scenario based on the target iterative power flow equation corresponding to each target power flow scenario, and to configure a target circuit based on the target shared coefficient matrix, so as to use the target circuit to determine the target correction amount corresponding to the target power flow scenario based on the target right-hand side term; the target right-hand side term is the right-hand side term of the imbalance quantity that varies with the target power flow scenario; the target correction amount includes voltage correction amount and phase angle correction amount; the target circuit is a reusable quantum linear equation solving circuit; The operation risk assessment module is used to iterate the target power flow scenario based on the target correction amount, and when the target power flow scenario meets the preset scenario convergence condition, determine the bus voltage statistical moment and branch power flow statistical moment corresponding to the target power flow scenario based on the scenario weight, so as to determine the grid operation state corresponding to the target power system based on the bus voltage statistical moment and branch power flow statistical moment, and generate operation risk assessment results based on the grid operation state.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the grid operation risk assessment method for high-proportion renewable energy power systems as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the grid operation risk assessment method for high-proportion renewable energy power systems as described in any one of claims 1 to 7.

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