Power flow unsolvable adjustment strategy generation method and device and electronic equipment

Abstract

The invention discloses a power flow unsolvable adjustment strategy generation method and device and electronic equipment, and relates to the field of intelligent power grids or other related technical fields, and the method comprises the following steps: a parameter configuration step: configuring an adjustment direction vector and a target adjustment step length for each node in a non-convergence power flow section; an equation construction step: establishing an optimization model, and constructing a power flow equation containing operation constraints and a target function; an iterative calculation step: performing iterative solution on the power flow equation containing the operation constraint by using an optimal multiplier Newton method based on the initial value of the adjustment step length, and judging whether the power flow equation containing the operation constraint is converged or not according to the value of the target function and the value of the optimal multiplier to obtain a judgment result; and a strategy generation step: determining a target adjustment step length according to a judgment result, and generating a power flow unsolvable adjustment strategy based on the adjustment direction vector and the target adjustment step length. According to the invention, the technical problem that the adjustment efficiency is low when a sensitivity-based tidal current non-solution adjustment mode is adopted in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of smart grids or other related technical fields. Specifically, it relates to a method and device for generating an adjustment strategy for unsolvable power flow, and an electronic device. Background Art

[0002] With the continuous increase in the scale of the power system, especially the high requirements of large - area interconnected operation and advanced applications for refined modeling of wide - area systems, power flow calculation, as a basic technology in modern power grid energy management systems, is facing unprecedented challenges. Power flow calculation is a core tool used in power system operation and planning to determine the steady - state characteristics of the system under specific operating conditions. It calculates parameters such as voltage, power flow, and phase angle in the network based on Kirchhoff's laws, which is crucial for the stable operation and optimization of the power system. However, traditional power flow calculation methods often encounter convergence problems when dealing with large - scale and complex power grid models, and traditional manual power flow adjustment methods have low efficiency and cannot converge. In order to improve the intelligent application level of the dispatching system, it is urgent to provide a power flow restoration adjustment strategy calculation for unsolvable sections to achieve the function of automatically restoring and adjusting the unsolvable power flow.

[0003] In related technologies, a sensitivity - based power flow adjustment method for unsolvable cases is adopted to find weak areas of the power grid from the perspective of stability, and then power flow adjustment is carried out for the weak areas. An approximate solution of the power flow of the system is found by reducing the output, and at the same time, different indicators are combined with sensitivity analysis to find weak nodes of the system, and targeted power flow adjustment is carried out for these weak nodes. However, the power flow adjustment method for weak areas has a much larger computational amount compared to the initial power flow solution problem, resulting in low adjustment efficiency. In addition, there is no specific conclusion on how to identify weak points and how to reasonably determine the size of the power adjustment amount at weak points. When the power grid scale is large, non - convergence or low convergence rate often occurs.

[0004] For the above - mentioned problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide a method and device for generating an adjustment strategy for unsolvable power flow, and an electronic device, so as to at least solve the technical problem of low adjustment efficiency existing in the sensitivity - based power flow adjustment method for unsolvable cases in related technologies.

[0006] According to one aspect of an embodiment of the present invention, a method for generating an adjustment strategy for unsolvable power flow is provided, including: a parameter configuration step of identifying a non-convergent power flow section and configuring an adjustment direction vector and a target adjustment step size for each node in the non-convergent power flow section; an equation construction step of establishing an optimization model based on the adjustment direction vector and the target adjustment step size of each node, and constructing a power flow equation and an objective function with operation constraints according to the optimization model; an iterative calculation step of initializing the target adjustment step size to obtain an initial value of the adjustment step size, and iteratively solving the power flow equation with operation constraints by using the optimal multiplier Newton method based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, it is determined whether the power flow equation with operation constraints converges according to the value of the objective function and the value of the optimal multiplier, and a determination result is obtained; a strategy generation step of, when the determination result indicates that the power flow equation with operation constraints converges, obtaining the target adjustment step size corresponding to each node obtained by iterative calculation, calculating the step size increment of each node, comparing the step size increment with the target adjustment step size to obtain a comparison result, and when the comparison result indicates that all the step size increments are smaller than the target adjustment step size, generating an adjustment strategy for unsolvable power flow based on the adjustment direction vector and the target adjustment step size of each node.

[0007] Further, after obtaining the determination result, the iterative calculation step further includes: when the determination result indicates that the power flow equation with operation constraints does not converge, calculating the step size increment of each node according to the step size increment calculation formula; obtaining the target adjustment step size corresponding to each node obtained by iterative calculation, and updating the initial value of the adjustment step size based on the step size increment of each node and the target adjustment step size corresponding to each node obtained by iterative calculation to obtain an updated initial value of the adjustment step size; repeating the iterative calculation step based on the updated initial value of the adjustment step size until the power flow equation with operation constraints converges.

[0008] Further, after obtaining the comparison result, it further includes: when the comparison result indicates that any of the step size increments is greater than or equal to the target adjustment step size, performing a halving and fallback operation on the step size increment to obtain a fallback step size increment; repeating the iterative calculation step and the strategy generation step based on the updated initial value of the adjustment step size until the comparison result indicates that all the step size increments are smaller than the target adjustment step size.

[0009] Further, the optimization model is expressed as: where f Pi (V,θ) is the sum of the active power at node i, f Qi (V,θ) is the sum of the reactive power at node i, P giThe active power injected at node i, Q gi The reactive power injected at node i, P di The active load at node i, Q di The reactive load at node i, V is the magnitude of the node voltage, and θ is the node phase angle.

[0010] Furthermore, the power flow equation with operating constraints is expressed as: f(x,λ) = f(x) + λb = S, where S = S g -S d , S is the power injection at the node in the current state, f(x) is the node power equation, f(x,λ) is the parameterized node power equation, S g is the power injection for node generation, S d is the power consumed by the node load.

[0011] Furthermore, the objective function is expressed as: where F(x) represents the objective function value, S is the power injection at the node in the current state, S = S g -S d , S g is the power injection for node generation, S d is the power consumed by the node load, and f(x,λ) is the parameterized node power equation.

[0012] Furthermore, the calculation formula for the step size increment is expressed as: where Δλ represents the step size increment, b represents the adjustment direction vector of each node, S is the injection power vector of each node, S * represents the injection power vector corresponding to the point on the solution plane of the power flow equation with constraint conditions that is closest to S, ω * represents the zero eigenvector of the singular point of the Jacobian matrix.

[0013] According to another aspect of the embodiments of the present invention, there is also provided an adjustment strategy generation device for unsolvable power flow, including: a configuration unit for performing a parameter configuration step to identify a non-convergent power flow section and configure an adjustment direction vector and a target adjustment step size for each node in the non-convergent power flow section; a construction unit for performing an equation construction step to establish an optimization model based on the adjustment direction vectors and the target adjustment step sizes of the nodes, and construct a power flow equation and an objective function with operating constraints according to the optimization model; a calculation unit for performing an iterative calculation step to initialize the target adjustment step size to obtain an initial value of the adjustment step size, and iteratively solve the power flow equation with operating constraints using the optimal multiplier Newton method based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, determine whether the power flow equation with operating constraints converges according to the value of the objective function and the value of the optimal multiplier to obtain a determination result; a generation unit for performing a strategy generation step. When the determination result indicates that the power flow equation with operating constraints converges, obtain the target adjustment step sizes corresponding to each node obtained by iterative calculation, calculate the step size increments of each node, compare the step size increments with the target adjustment step sizes to obtain a comparison result. When the comparison result indicates that all the step size increments are smaller than the target adjustment step sizes, generate an adjustment strategy for unsolvable power flow based on the adjustment direction vectors and the target adjustment step sizes of each node.

[0014] Optionally, the calculation unit includes: a first calculation module for calculating the step size increments of each node according to the step size increment calculation formula when the determination result indicates that the power flow equation with operating constraints does not converge; a first update module for obtaining the target adjustment step sizes corresponding to each node obtained by iterative calculation, and updating the initial value of the adjustment step size based on the step size increments of each node and the target adjustment step sizes corresponding to each node obtained by iterative calculation to obtain an updated initial value of the adjustment step size; a first repetition module for repeatedly performing the iterative calculation step based on the updated initial value of the adjustment step size until the power flow equation with operating constraints converges.

[0015] Further, the adjustment strategy generation device for unsolvable power flow further includes: a first execution module for performing a halving and fallback operation on the step size increment when the comparison result indicates that any of the step size increments is greater than or equal to the target adjustment step size to obtain a fallback step size increment; a second repetition module for repeatedly performing the iterative calculation step and the strategy generation step based on the updated initial value of the adjustment step size until the comparison result indicates that all the step size increments are smaller than the target adjustment step sizes.

[0016] Further, the optimization model is expressed as: where f Pi(V, θ) is the sum of the active power at node i, f Qi (V, θ) is the sum of the reactive power at node i, P gi is the active power injected at node i, Q gi is the reactive power injected at node i, P di is the active load at node i, Q di is the reactive load at node i, V is the magnitude of the node voltage, and θ is the node phase angle.

[0017] Furthermore, the power flow equation with operating constraints is expressed as: f(x, λ) = f(x) + λb = S, where S = S g -S d , S is the power injected at the nodes in the current state, f(x) is the node power equation, f(x, λ) is the parameterized node power equation, S g is the power injected by node generation, S d is the power consumed by node load.

[0018] Furthermore, the objective function is expressed as: where F(x) represents the objective function value, S is the power injected at the nodes in the current state, S = S g -S d , S g is the power injected by node generation, S d is the power consumed by node load, and f(x, λ) is the parameterized node power equation.

[0019] Furthermore, the calculation formula for the step size increment is expressed as: where Δλ represents the step size increment, b represents the adjustment direction vector of each node, S is the injection power vector of each node, S * represents the injection power vector corresponding to the point on the solution plane of the power flow equation with constraint conditions that is closest to S, ω * represents the zero eigenvector of the singular point of the Jacobian matrix.

[0020] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, the computer-readable storage medium includes a stored computer program, wherein, when the computer program runs, it controls the device where the computer-readable storage medium is located to execute any one of the above adjustment strategy generation methods for unsolvable power flow.

[0021] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including one or more processors and a memory, the memory is used to store one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement any one of the above adjustment strategy generation methods for unsolvable power flow.

[0022] In this application, an adjustment strategy for unsolvable power flow is generated through the following steps: a parameter configuration step, identifying non-convergent power flow sections and configuring an adjustment direction vector and a target adjustment step size for each node in the non-convergent power flow sections; an equation construction step, establishing an optimization model based on the adjustment direction vectors and the target adjustment step sizes of each node, and constructing a power flow equation and an objective function with operation constraints according to the optimization model; an iterative calculation step, initializing the target adjustment step size to obtain an initial value of the adjustment step size, and iteratively solving the power flow equation with operation constraints using the optimal multiplier Newton method based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, it is determined whether the power flow equation with operation constraints converges according to the value of the objective function and the value of the optimal multiplier, and a determination result is obtained; a strategy generation step, when the determination result indicates that the power flow equation with operation constraints converges, obtaining the target adjustment step sizes corresponding to each node obtained by iterative calculation, calculating the step size increments of each node, comparing the step size increments with the target adjustment step sizes to obtain a comparison result, and when the comparison result indicates that the step size increments are all smaller than the target adjustment step sizes, generating an adjustment strategy for unsolvable power flow based on the adjustment direction vectors and the target adjustment step sizes of each node.

[0023] In this application, by establishing an optimization model and constructing a power flow equation and an objective function with constrained operation, and using the optimal multiplier Newton method for the recovery of unsolvable power flow, it is possible to significantly improve the convergence speed of power flow calculation while fully considering system operation constraints and retaining accuracy. The dynamic adjustment of the optimal multiplier ensures that the calculation process steadily advances towards the optimal solution, avoiding possible repeated iterations or non-convergence phenomena. By using intelligent means to quickly and accurately generate an adjustment strategy to achieve the purpose of quickly and accurately restoring the solvability of power flow calculation, the technical effect of improving the adjustment efficiency of unsolvable power flow is achieved. Furthermore, it solves the technical problem of low adjustment efficiency in the related art when using a sensitivity-based power flow unsolvable adjustment method. Description of the Drawings

[0024] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0025] Figure 1 is a flowchart of an optional method for generating an adjustment strategy for unsolvable power flow according to an embodiment of the present invention;

[0026] Figure 2 is a schematic diagram for solving the step size increment according to an embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of a generation process of an optional adjustment strategy for unsolvable power flow according to an embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of an apparatus for generating an optional adjustment strategy for unsolvable power flow according to an embodiment of the present invention;

[0029] Figure 5 It is a hardware structure block diagram of an electronic device (or mobile device) for executing a method for generating an adjustment strategy for unsolvable power flow according to an embodiment of the present invention. Detailed implementation manners

[0030] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0032] It should be noted that the method and apparatus for generating an adjustment strategy for unsolvable power flow in the present application can be used in the field of smart grid when generating an adjustment strategy for unsolvable power flow, and can also be used in any field other than the field of smart grid when generating an adjustment strategy for unsolvable power flow. The application field of the method and apparatus for generating an adjustment strategy for unsolvable power flow in the present application is not limited.

[0033] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties. Moreover, the processing of the relevant data, such as collection, storage, use, processing, transmission, provision, disclosure, and application, complies with the relevant laws, regulations, and standards of the relevant regions, adopts necessary confidentiality measures, does not violate public order and good customs, and provides corresponding operation entrances for users to choose to authorize or refuse. For example, an interface is set between this system and relevant users or institutions. Before obtaining relevant information, a request for obtaining information needs to be sent to the aforementioned users or institutions through the interface, and after receiving the consent information feedback from the aforementioned users or institutions, the relevant information is obtained.

[0034] It should be noted that when collecting and analyzing customer information in this application, a corresponding operation entrance is provided for users to choose to agree or refuse the automated decision-making results; if the user chooses to refuse, the expert decision-making process will be entered.

[0035] The following embodiments of the present invention can be applied to various adjustment strategy generation systems / applications / devices with unsolvable power flows. For the power flow non-convergent section of the present invention, based on the given adjustment control direction, the minimum control adjustment amount for restoring to the power flow solvable domain along the specified adjustment direction is calculated. At the same time, in order to avoid the problem of power flow pathologies, the optimal multiplier Newton method is used as the basic method for calculating the power flow restoration correction control problem, which greatly improves both the power flow calculation speed and convergence. Based on the basic principle of the optimal multiplier Newton method, the power flow non-convergent section is solved. The information of the critical point Jacobian matrix is calculated through the optimal multiplier Newton method for the calculation of the adjustment amount, and the target adjustment step size is continuously updated through iterative calculation. Furthermore, based on the final target adjustment step size, a restoration adjustment strategy for the unsolvable power flow is generated, improving the adjustment efficiency of the unsolvable power flow.

[0036] The present invention will be described in detail below in conjunction with each embodiment.

[0037] Embodiment 1

[0038] According to an embodiment of the present invention, an embodiment of a method for generating an adjustment strategy for an unsolvable power flow is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0039] Figure 1 is a flowchart of an optional method for generating an adjustment strategy for an unsolvable power flow according to an embodiment of the present invention, as Figure 1 shown, and the method includes the following steps:

[0040] Step S101, parameter configuration step, identify the non-convergent power flow section, and configure the adjustment direction vector and the target adjustment step size for each node in the non-convergent power flow section.

[0041] It should be noted that in a power system, power flow refers to the flow of electric energy (including active power and reactive power) in transmission lines, transformers, and other power network components. Power flow analysis is a basic task in power systems, used to determine the voltages at each node in the power network and the power passing through each component under given generation and load conditions. Power flow analysis can help power system engineers and dispatchers understand the operating state of the system, evaluate the stability of the network, plan system operation and maintenance, and conduct fault analysis, etc.

[0042] Power flow analysis is usually implemented based on a set of non-linear equations, namely the so-called "power flow equations". The power flow equations describe the active power and reactive power balance at the nodes in the power network. When the power flow analysis does not converge, it means that the network cannot reach a stable state under the given conditions. This may be because the operating point of the power system exceeds its stable operating range, or due to problems in the network structure of the power system (such as the connection mode of lines and transformers). Therefore, when encountering the situation where the power flow analysis does not converge, some strategies usually need to be adopted for adjustment, such as changing the generator output, adjusting the load, modifying the network structure parameters, etc., so that the power system can return to a state where power flow calculation can be performed.

[0043] In the above step S101, first retrieve the power flow calculation data of the power system. The power flow calculation data includes the load and generator output power of all nodes in the system, as well as information such as the node voltage amplitude and phase angle. Reading the power flow calculation data is to establish an initial state model of the power system for power flow calculation. Then, use the standard power flow calculation method to calculate the section data to identify the steady-state operating state of the system. The purpose of power flow calculation is to check whether the system can meet the electrical characteristics such as electric power balance and node voltage constraints under the given operating mode. During the power flow calculation process, if the algorithm still cannot converge to a solution that satisfies the electrical constraint conditions after multiple iterations, the section is identified as a non-convergent power flow section. The non-convergent section is actually the power network result with power flow non-convergence in the power system, which contains multiple nodes, and the nodes can be power components such as generators and loads. The non-convergent power flow section may be caused by various factors, including extreme load or generation distribution, network structure problems, or insufficient control strategies, etc.

[0044] Furthermore, for the identified non-convergent power flow section, it is necessary to configure an adjustment direction vector for each node therein. The adjustment direction vector reflects the adjustment direction of node power (including active power and reactive power), and can be configured based on the type of node (such as PQ node, PV node or balanced node) and the system state. The purpose of configuring the adjustment direction vector is to determine the optimal direction of power adjustment, so as to gradually adjust the system state and restore it to the range where the power flow calculation is solvable. After configuring the adjustment direction vector, it is also necessary to set a minimum adjustment step (i.e., the target adjustment step), and the minimum adjustment step is directly related to the convergence speed and efficiency of the power flow calculation. An initial adjustment step will be set for each node, and this step will be used as the basis for adjusting the power. The purpose of setting the target adjustment step is to minimize the power adjustment amount of the system while ensuring that the electrical constraint conditions are met, so as to reduce the impact on the system operation.

[0045] For the adjustment direction vector b, considering adjustable engines and load shedding, there are different configuration methods for the adjustment direction at node i according to different node types.

[0046] Define the adjustment direction vector of active power as:

[0047]

[0048] Define the adjustment direction of reactive power as:

[0049]

[0050] Among them, b i is the adjustment direction vector corresponding to the active power and reactive power at node i, ΔP ig , ΔQ ig are the active power generation and reactive power generation involved in the adjustment at node i, ΔP id , ΔQ id are the active power load and reactive power load involved in the adjustment at node i. A PQ node (also known as a load node or passive node) represents a node in the power system where both the active power (P) and reactive power (Q) demand or injection are known, but the voltage magnitude (V) and phase angle (θ) are unknown, and the node voltage magnitude and phase angle need to be determined through power flow calculation. A PQ node represents a node in the power system where the active power (P) and node voltage (V) are known, the reactive power (Q) and node voltage phase angle (θ) are unknown, and a balanced node represents a node in the power system where the voltage magnitude (V) and phase angle (θ) are known while the active power (P) and reactive power (Q) demand or injection are unknown.

[0051] Step S102, Equation Construction Step: Based on the adjustment direction vectors and target adjustment step lengths of each node, an optimization model is established, and a power flow equation and an objective function with operating constraints are constructed according to the optimization model.

[0052] In the above Step S102, by establishing an optimization model, the problem of unsolvable power flow is transformed into an optimization problem of solving the target adjustment step length. An optimization model can be established based on the predefined target adjustment step lengths of each node and the established adjustment directions. The goal of the optimization model is to find the minimum control adjustment amount (i.e., the target adjustment step length) to restore the system from the current unsolvable state to the power flow solvable domain.

[0053] Based on the optimization model, a power flow equation with operating constraints is further constructed. The power flow equation describes the active and reactive power balance relationships of the nodes and the electrical connection between the node voltages and phase angles. At the same time, according to the optimization goal (minimizing the control adjustment amount), an objective function is constructed. The objective function quantifies the relationship between the power flow adjustment effect and the system operating state and is the core guiding the iterative process and the generation of adjustment strategies.

[0054] When constructing the power flow equation, the operating constraints of the power system need to be considered, such as the node voltage range, the capacity limit of transmission lines, the output upper limit of generators, etc. These constraint conditions are necessary conditions to ensure the safe and stable operation of the system and are also the limitations that must be satisfied when solving the optimization model.

[0055] obj.minλ

[0056] Furthermore, the optimization model is expressed as: s.t.P gi -P di -f Pi (V,θ)-λb i =0; where, f Pi (V,θ) is

[0057] Q gi -Q di -f Qi (V,θ)-λb i =0

[0058] The sum of the active powers at node i, f Qi (V,θ) is the sum of the reactive powers at node i, P gi is the active power injected at node i, Q gi is the reactive power injected at node i, P di is the active load at node i, Q di is the reactive load at node i, V is the node voltage amplitude, and θ is the node phase angle.

[0059] Specifically, the optimization objective of the optimization model is to minimize the target adjustment step size, that is, to find a set of minimum adjustment amounts to restore the power system from the current unsolvable state to a solvable state. The constraint condition of the optimization model is the power balance constraint, which is related to the active power, reactive power and adjustment amount of each node. Through the optimization model, when the power flow calculation encounters an extreme situation of non-convergence, the power system can timely adjust its operating state and restore to a stable and feasible operating point, thus ensuring the intelligent dispatching and safe operation of the power system.

[0060] Furthermore, the objective function is expressed as: where F(x) represents the value of the objective function, S is the node injection power in the current state, S = S g - S d , S g is the node generation injection power, S d is the node load consumption power, and f(x,λ) is the parameterized node power equation.

[0061] Furthermore, the power flow equation with operation constraints is expressed as: f(x,λ) = f(x) + λb = S, where S = S g - S d , S is the node injection power in the current state, f(x) is the node power equation, f(x,λ) is the parameterized node power equation, S g is the node generation injection power, S d is the node load consumption power.

[0062] Specifically, after giving the initial target adjustment step size, the power flow equation with operation constraints in the optimization model is solved using the optimal multiplier method with parameters, and the simplified power flow equation with operation constraints can be obtained as: f(x,λ) = f(x) + λb = S. In the above formula, S = S g - S d , S is the node injection power in the current state, f(x) is the node power equation, f(x,λ) is the parameterized node power equation, S g is the node generation injection power, S d is the node load consumption power.

[0063] According to the above power flow equation with operation constraints, the following objective function is constructed:

[0064]

[0065] For the above objective function, if the power flow of the system has a solution, the function F(x) has a minimum value of 0. If F(x) ≠ 0, the equality constraints represented by the power flow equation with operating constraints cannot be satisfied. In this way, the objective function of the optimization model is combined with the power flow equation with operating constraints, and the problem of solving the power flow equation with parameter λ is transformed into a nonlinear programming problem of solving the optimal parameters. The solution of this problem aims to find a set of control variables such that when these control variables are applied to the system, the system can satisfy all electrical constraints and the power flow calculation can converge.

[0066] Step S103, the iterative calculation step, initializes the target adjustment step size to obtain the initial value of the adjustment step size, and uses the optimal multiplier Newton method to iteratively solve the power flow equation with operating constraints based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, it is determined whether the power flow equation with operating constraints converges according to the value of the objective function and the value of the optimal multiplier, and a determination result is obtained.

[0067] In the above step S103, after establishing the objective equation, the optimal multiplier Newton method is used to solve the objective equation. By setting the iteration number threshold and the convergence criterion, the optimal multiplier Newton method can determine whether the algorithm has successfully found a power flow solution that satisfies the constraint conditions, thereby obtaining an adjustment strategy for recovering from the unsolvable state to the power flow solvable domain. When performing the calculation, first initialize the target adjustment step size to obtain the initial value of the adjustment step size, providing a starting point for the iterative process so that the algorithm can start from a known point to find the minimum adjustment amount that satisfies all constraint conditions.

[0068] Furthermore, the optimal multiplier Newton method is an algorithm that combines the efficiency of the Newton iteration method and the constraint handling ability of the multiplier method. It can continuously adjust the control variables during the iterative process until a set of solutions is found, making the objective function value minimum and simultaneously satisfying all operating constraint conditions. The iterative solution process includes:

[0069] Determine the initial values of the iteration, including the initial value of the target adjustment step size and the initial values of other state variables in the power system;

[0070] Calculate the Jacobian matrix and the gradient of the objective function to update the state variables in the next iteration;

[0071] After each iteration, calculate the value of the objective function and the value of the optimal multiplier. Determine whether the power flow converges and obtains a solution based on the value of the objective function and the value of the optimal multiplier. If the value of the objective function drops to 0 after several iterations of calculation and the optimal multiplier stabilizes near 1.0, then the power flow converges and a solution is obtained. If the value of the objective function drops and stabilizes at a positive value after several iterations and the optimal multiplier approaches 0, it indicates that the power flow is unsolvable and the calculation stops. If the power flow calculation does not converge and the solution does not satisfy the power flow equality constraint conditions, it means that the given target adjustment step size does not satisfy the power flow equality constraint conditions. When performing iterative calculations, in order to prevent the algorithm from falling into an infinite loop, it is necessary to set an iteration number threshold N. When the iteration number reaches N, whether the convergence condition is satisfied or not, the algorithm will stop iterating and give the corresponding result;

[0072] Reset the target adjustment step size and repeat the above process until the convergence condition is satisfied.

[0073] By initializing the target adjustment step size and using the optimal multiplier Newton method to iteratively solve the power flow equation with operating constraints, it is possible to efficiently and accurately determine whether the system power flow is solvable under the condition of satisfying the constraint conditions, and obtain a restoration strategy based on the minimum adjustment step size. It not only avoids the problems of low power flow adjustment efficiency and non-convergence in traditional methods, but also improves the robustness and engineering practical applicability of the algorithm.

[0074] Step S104, strategy generation step. When the determination result indicates that the power flow equation with operating constraints converges, obtain the target adjustment step size corresponding to each node obtained by iterative calculation, calculate the step size increment of each node, compare the step size increment with the target adjustment step size to obtain a comparison result. When the comparison result indicates that the step size increments are all smaller than the target adjustment step size, generate an adjustment strategy for unsolvable power flow based on the adjustment direction vector and the target adjustment step size of each node.

[0075] It should be noted that if the determination result after iteratively solving the power flow equation with operating constraints based on the optimal multiplier Newton method is that the power flow equation with operating constraints converges, that is, after iterative calculation, the value of the objective function drops to a preset target value (such as 0), and the optimal multiplier stabilizes near the optimal multiplier target value (such as 1.0), calculate the target increment for the next adjustment. If the target increment satisfies the convergence criterion, it is considered that the final solution is obtained. The convergence criterion is specifically that the target increment of each node is smaller than the target adjustment step size set in this loop iteration. When the power flow equation converges and the target increment satisfies the convergence criterion, generate an adjustment strategy for unsolvable power flow based on the adjustment direction vector and the target adjustment step size of each node.

[0076] Further, after obtaining the determination result, the iterative calculation step further includes: when the determination result indicates that the power flow equation with operation constraints does not converge, calculating the step length increment of each node according to the step length increment calculation formula; obtaining the target adjustment step length corresponding to each node obtained by iterative calculation, and updating the initial value of the adjustment step length based on the step length increment of each node and the target adjustment step length corresponding to each node obtained by iterative calculation to obtain the updated initial value of the adjustment step length; repeating the iterative calculation step based on the updated initial value of the adjustment step length until the power flow equation with operation constraints converges.

[0077] It should be noted that if the determination result after iterative solution of the power flow equation with operation constraints based on the optimal multiplier Newton method is that the power flow equation with operation constraints does not converge, that is, after a preset number of iterations, the value of the objective function decreases and stabilizes at a positive value, and the optimal multiplier approaches 0, it means that the power flow is unsolvable, indicating that the target adjustment step length parameter λ given in this iterative calculation does not satisfy the power flow equality constraint condition. At this time, the calculation stops, the left eigenvector of the singular point is calculated according to the eigenvector of the Jacobian matrix at the critical point, and the step length increment corresponding to the target adjustment step length is calculated, and the target adjustment step length is updated according to the step length increment to be used as the initial parameter for the next round of iteration.

[0078] Further, the step length increment calculation formula is expressed as: where Δλ represents the step length increment, b represents the adjustment direction vector of each node, S is the injection power vector of each node, and S * represents the injection power vector corresponding to the point with the smallest distance from S on the solution plane of the power flow equation with constraint conditions, and ω * represents the zero eigenvector of the singular point of the Jacobian matrix.

[0079] In some embodiments, for the target adjustment step length λ, when it is judged to be non-convergent in the iterative solution of the optimal multiplier Newton method, the step length increment Δλ needs to be calculated. Figure 2 It is an optional schematic diagram for solving the step length increment according to an embodiment of the present invention. As Figure 2 shown, assume that the interface between the solution domain and the non-solution domain of the power flow equation with constraint conditions is Σ, the adjustment direction vector is defined as b, the node injection power vector in the current state is S, and the point S * nearest to S on the solution plane is obtained by calculation, the critical point solution (optimal solution) is x * , the tangent plane of Σ at S * is Σ0, and x * can be obtained from the optimal multiplier Newton method. For the Jacobian matrix J(x * ), which is singular, its corresponding zero eigenvector ω * is orthogonal to Σ0 at S * . Define S mi as Σ at S *The point closest to the tangent plane distance S, S λ is the intersection point of the tangent plane and b. Calculate Δλ according to the trigonometric relationship to obtain: Update the target adjustment step size through the step size increment Δλ, repeat the iterative calculation steps, enter the next iteration, until the determination result of the iterative calculation indicates that the power flow equation converges, and obtain the target adjustment step size under the convergence condition.

[0080] The purpose of this update mechanism is to avoid the iterative process falling into a local optimal solution. By updating the target adjustment step size and performing the next iterative calculation, the search in the next iteration cycle can start from a position closer to the feasible solution domain, thereby increasing the possibility of converging to the global optimal solution.

[0081] Furthermore, after obtaining the comparison result, it also includes: when the comparison result indicates that any step size increment is greater than or equal to the target adjustment step size, perform a halving backtracking operation on the step size increment to obtain the backtracked step size increment; repeat the iterative calculation steps and the strategy generation steps based on the updated adjustment step size initial value until the comparison result indicates that all step size increments are less than the target adjustment step size.

[0082] In some embodiments, after the power flow equation converges, it is also necessary to determine whether the step size increment satisfies the convergence criterion. If Δλ does not satisfy the convergence criterion Δλ < λ min (i.e., the minimum adjustment step size of this iteration, corresponding to the above target adjustment step size), it indicates that during this iteration, near the optimal multiplier power flow solution is the "concave surface" of ∑, indicating that the iterative algorithm may need to adopt specific strategies (such as reducing the step size, changing the direction, etc.) to ensure that it can effectively converge to the optimal power flow solution instead of stagnating at a non-optimal solution or falling into a cyclic iteration. At this time, perform a halving backtracking on the step size increment Δλ, that is, let Δλ = (λ0 - λ) / 2, where λ0 represents the target adjustment step size of the previous iterative calculation, and λ represents the target adjustment step size of this iterative calculation. Update the target adjustment step size λ = λ + Δλ according to the halved backtracked step size increment, and use it as the target adjustment step size for the next iterative calculation to perform the iterative calculation.

[0083] Through the above steps, first identify the non-convergent power flow section, configure the adjustment direction vector and the target adjustment step size for each node in the non-convergent power flow section, establish an optimization model based on the adjustment direction vector and the target adjustment step size of each node, and construct a power flow equation and an objective function with operating constraints according to the optimization model; then initialize the target adjustment step size to obtain the initial value of the adjustment step size, and use the optimal multiplier Newton method to iteratively solve the power flow equation with operating constraints based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, determine whether the power flow equation with operating constraints converges according to the value of the objective function and the value of the optimal multiplier to obtain a determination result. Finally, when the determination result indicates that the power flow equation with operating constraints converges, obtain the target adjustment step size corresponding to each node obtained by iterative calculation, calculate the step size increment of each node, compare the step size increment with the target adjustment step size to obtain a comparison result. When the comparison result indicates that the step size increments are all smaller than the target adjustment step size, generate an adjustment strategy for the unsolvable power flow based on the adjustment direction vector and the target adjustment step size of each node.

[0084] In this embodiment, by establishing an optimization model and constructing a power flow equation and an objective function with constrained operation, and using the optimal multiplier Newton method to recover the unsolvable power flow, it is possible to significantly improve the convergence speed of power flow calculation while fully considering the system operation constraints and retaining the accuracy. The dynamic adjustment of the optimal multiplier ensures that the calculation process steadily advances towards the optimal solution, avoiding possible repeated iterations or non-convergence phenomena. By using intelligent means to quickly and accurately generate an adjustment strategy to achieve the purpose of quickly and accurately restoring the solvability of power flow calculation, the technical effect of improving the adjustment efficiency of unsolvable power flow is achieved. Furthermore, it solves the technical problem of low adjustment efficiency in the related art when using the sensitivity-based power flow unsolvable adjustment method.

[0085] The following is a detailed description in combination with another optional specific implementation manner.

[0086] Figure 3 is a schematic diagram of an optional generation process of an adjustment strategy for unsolvable power flow according to an embodiment of the present invention. As Figure 3 shown, the generation process of the adjustment strategy for unsolvable power flow includes:

[0087] Step 1, read in the power flow section data;

[0088] First, read in the power flow section data, including system load and generator output (i.e., generator power), node voltage amplitude and phase angle, power flow calculation node type (PQ, PV or balanced node), etc. data;

[0089] Step 2, perform a power flow calculation. If the power flow calculation converges, execute Step 8 to end this process. If the power flow calculation does not converge, identify the non-convergent power flow section and execute Step 3;

[0090] Step 3: Set the initial values of the adjustment step sizes for each node of the non-convergent section.

[0091] Given the calculation parameters, set the adjustment direction vectors b for the generator and the load, and set the initial value of the minimum adjustment step size (i.e., the target adjustment step size) λ0 = λ = 0. λ0 is used to save the value of the minimum adjustment step size λ in the previous iterative calculation.

[0092] Convert the unsolvable problem of the ill-conditioned power flow calculation into an optimization problem of solving the minimum adjustment step size λ. Define the minimum adjustment step size λ and the established adjustment direction b, and establish an optimization model:

[0093]

[0094] In the formula: f Pi (V,θ) is the sum of the active powers at node i, and f Qi (V,θ) is the sum of the reactive powers at node i, P gi is the active power injected at node i, Q gi is the reactive power injected at node i, P di is the active load at node i, Q di is the reactive load at node i, V is the node voltage amplitude, and θ is the node phase angle.

[0095] For the adjustment direction b, considering the adjustable engines and the reducible loads, the adjustment direction at node i is set differently according to the node type. Specifically,

[0096] Define the active power adjustment direction as:

[0097]

[0098] Define the reactive power adjustment direction as:

[0099]

[0100] In the formula: b i is the adjustment direction vector of the active power and reactive power corresponding to node i, ΔP ig , ΔQ ig are the active power generation and reactive power generation participating in the adjustment at node i, ΔP id , ΔQ id are the active load quantity and reactive load quantity participating in the adjustment at node i.

[0101] After the initial adjustment step size λ is given, use the optimal multiplier method with parameters to solve the power flow equation with operation constraints in the optimization model, and the simplified power flow equation with operation constraints can be obtained:

[0102] f(x,λ) = f(x) + λb = S,

[0103] S = S g -S d ;

[0104] Where: S is the node injection power in the current state, f(x) is the node power equation, f(x,λ) is the parameterized node power equation, S g is the node generation injection power, and S d is the node load consumption power.

[0105] Construct the following objective function according to the above power flow equation with operation constraints:

[0106]

[0107] If the power flow of the system has a solution, the function F(x) has a minimum value of 0. If F(x) ≠ 0, the power flow equation with operation constraints cannot meet the system operation constraint conditions. Therefore, the problem of solving the power flow equation with parameter λ is transformed into a nonlinear programming problem of solving the optimal parameters.

[0108] Step four, calculate using the Newton method with parameterized optimal multipliers;

[0109] To avoid the problem of ill-conditioned power flow during the power flow adjustment process, when solving the power flow, use the optimal Newton method with parameters to solve the power flow equation. Specifically,

[0110] When using the Newton method with optimal multipliers to solve the power flow equation with operation constraints, the correction equation can be obtained as:

[0111] Δx k = -J -1 (x k ,λ)(f(x k ,λ) - S),

[0112] Where: Δx k is the corrected state variable, and J(x k ,λ) is the Jacobian matrix in the k-th iteration during the iterative calculation.

[0113] The iterative recurrence formula is as follows:

[0114] x k+1 = x k + μ k Δx k ;

[0115] Where: x k+1 is the state variable of the (k + 1)-th iteration, and μ k is the optimal multiplier in the k-th iteration. To determine the optimal multiplier μ, the objective function can be abbreviated as:

[0116]

[0117] A = S - f(x k-1 , λ),

[0118] B = -J(x k-1 , λ)Δx k ,

[0119] C = -f(Δx k , λ);

[0120] In the formula: n is the number of power flow equations, and k is the number of iterations. According to the gradient the value of the optimal multiplier μ can be determined.

[0121] Step Five: Based on the calculation results of the optimal multiplier Newton method, perform power flow convergence judgment. If the power flow does not converge, calculate the step size increment, update the target adjustment step size according to the step size increment, and then repeat Steps Four to Five until the power flow converges. Otherwise, if the power flow converges, execute Step Six;

[0122] When using the optimal multiplier Newton method for power flow calculation, starting from a certain initial value, if the value of the objective function drops to 0 after several iterative calculations and the value of the optimal multiplier μ stabilizes near 1.0, the power flow converges to a solution. If the value of the objective function drops and stabilizes at a positive value after several iterations and the optimal multiplier μ approaches 0, it means that the power flow has no solution and the calculation stops.

[0123] If the power flow calculation does not converge and the solution does not satisfy the power flow equality constraint conditions, it means that the given adjustment step size parameter λ does not satisfy the power flow equality constraint conditions. Calculate the left eigenvector of the singular point according to the eigenvector of the Jacobian matrix at the critical point, and calculate the adjustment amount Δλ of the adjustment step size parameter λ to update the target adjustment step size λ. If Δλ already satisfies the convergence criterion, it is considered that the final solution is obtained, and calculate the adjustment strategy for restoring the power flow solution in the established direction according to the final adjustment step size λ.

[0124] For the calculation method of the iterative step size Δλ when the adjustment parameter λ is judged as non - convergent by the optimal multiplier Newton method: Assume that the interface between the solution domain and the non - solution domain of the system is Σ, define the power adjustment direction as b, the node injection power vector at the current state is S, and calculate the point S * nearest to S on the solution plane, the critical point solution (optimal solution) is x * , the tangent plane of Σ at S * is Σ0. From the optimal multiplier Newton method, x * For the Jacobian matrix J(x * ) being singular, its corresponding zero eigenvector ω * and Σ0 at S *is orthogonal at. Define S min as the point on the tangent plane of Σ closest to S at S * , and S λ is the intersection point of the tangent plane and b. Calculate the step size increment Δλ according to the trigonometric relationship:

[0125] Step 6: Determine whether the step size increment reaches the preset condition. If not, roll back to calculate the step size increment, update the target adjustment step size according to the rolled-back step size increment, and repeat Steps 4 to 5. Otherwise, if so, execute Step 7;

[0126] If the step size increment Δλ does not satisfy the convergence criterion Δλ < λ min (i.e., the target adjustment step size), it indicates that during this iteration, the vicinity of the optimal multiplier power flow solution is at the "concave surface" of Σ. Halve and roll back Δλ (i.e., let Δλ = (λ0 - λ) / 2, where λ0 represents the target adjustment step size calculated in the previous iteration and λ represents the target adjustment step size calculated in this iteration), and update the target adjustment step size λ = λ + Δλ according to the rolled-back step size increment calculated, and perform the next round of iterative calculation;

[0127] Step 7: Generate an adjustment strategy for restoring the power flow solution according to the target adjustment step size λ determined by the iterative calculation;

[0128] Step 8: End.

[0129] In the embodiment of the present invention, for the power flow non-convergent section, based on the given adjustment control direction, calculate the minimum control adjustment amount for restoring to the power flow solvable region along the specified adjustment direction. At the same time, in order to avoid the problem of power flow ill-conditioning, the optimal multiplier Newton method is used as the basic method for calculating the power flow restoration correction control problem, which greatly improves both the power flow calculation speed and convergence. Solve the power flow unsolvable section based on the basic principle of the optimal multiplier Newton method, calculate the adjustment amount by calculating the critical point Jacobian matrix information through the optimal multiplier Newton method, and continuously update the target adjustment step size through iterative calculation. Furthermore, generate a restoration adjustment strategy for the power flow unsolvable based on the final target adjustment step size, improving the adjustment efficiency of the power flow unsolvable.

[0130] The following is a detailed description in combination with another embodiment.

[0131] Embodiment 2

[0132] An adjustment strategy generation device for power flow unsolvable provided in this embodiment includes multiple implementation units, and each implementation unit corresponds to each implementation step in Embodiment 1 above. Its specific implementation manner and beneficial effects can be referred to the foregoing method embodiment and will not be elaborated here.

[0133] Figure 4It is a schematic diagram of an optional adjustment strategy generation device for unsolvable power flow according to an embodiment of the present invention. As Figure 4 shown, the adjustment strategy generation device for unsolvable power flow may include: a configuration unit 41, a construction unit 42, a calculation unit 43, and a generation unit 44. Among them,

[0134] The configuration unit 41 is configured to execute a parameter configuration step, identify a non-convergent power flow section, and configure an adjustment direction vector and a target adjustment step size for each node in the non-convergent power flow section;

[0135] The construction unit 42 is configured to execute an equation construction step, establish an optimization model based on the adjustment direction vectors and target adjustment step sizes of each node, and construct a power flow equation and an objective function with operation constraints according to the optimization model;

[0136] The calculation unit 43 is configured to execute an iterative calculation step, initialize the target adjustment step size to obtain an initial value of the adjustment step size, and perform iterative solution of the power flow equation with operation constraints using the optimal multiplier Newton method based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, determine whether the power flow equation with operation constraints converges according to the value of the objective function and the value of the optimal multiplier, and obtain a determination result;

[0137] The generation unit 44 is configured to execute a strategy generation step. When the determination result indicates that the power flow equation with operation constraints converges, obtain the target adjustment step size corresponding to each node obtained by iterative calculation, calculate the step size increment of each node, compare the step size increment with the target adjustment step size to obtain a comparison result. When the comparison result indicates that the step size increments are all smaller than the target adjustment step size, generate an adjustment strategy for unsolvable power flow based on the adjustment direction vectors and target adjustment step sizes of each node.

[0138] The above-mentioned adjustment strategy generation device for unsolvable power flow executes a parameter configuration step through a configuration unit 41 to identify an unconverged power flow section, and configures an adjustment direction vector and a target adjustment step size for each node in the unconverged power flow section; executes an equation construction step through a construction unit 42 to establish an optimization model based on the adjustment direction vectors and target adjustment step sizes of each node, and constructs a power flow equation and an objective function with operation constraints according to the optimization model; executes an iterative calculation step through a calculation unit 43 to initialize the target adjustment step size to obtain an initial value of the adjustment step size, and iteratively solve the power flow equation with operation constraints by using the optimal multiplier Newton method based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, determine whether the power flow equation with operation constraints converges according to the value of the objective function and the value of the optimal multiplier to obtain a determination result; executes a strategy generation step through a generation unit 44. When the determination result indicates that the power flow equation with operation constraints converges, obtain the target adjustment step sizes corresponding to each node obtained by iterative calculation, calculate the step size increments of each node, compare the step size increments with the target adjustment step sizes to obtain a comparison result. When the comparison result indicates that the step size increments are all smaller than the target adjustment step sizes, generate an adjustment strategy for unsolvable power flow based on the adjustment direction vectors and target adjustment step sizes of each node.

[0139] In this embodiment, by establishing an optimization model and constructing a power flow equation and an objective function with constrained operation, and using the optimal multiplier Newton method for the recovery of unsolvable power flow, it is possible to significantly improve the convergence speed of power flow calculation while fully considering system operation constraints and retaining accuracy. The dynamic adjustment of the optimal multiplier ensures that the calculation process steadily advances towards the optimal solution, avoiding possible repeated iterations or non-convergence phenomena. By generating an adjustment strategy quickly and accurately through intelligent means to achieve the purpose of quickly and accurately restoring the solvability of power flow calculation, the technical effect of improving the adjustment efficiency of unsolvable power flow is achieved. Furthermore, it solves the technical problem of low adjustment efficiency in the related art when using a sensitivity-based adjustment method for unsolvable power flow.

[0140] Optionally, the calculation unit includes: a first calculation module, configured to calculate the step size increments of each node according to the step size increment calculation formula when the determination result indicates that the power flow equation with operation constraints does not converge; a first update module, configured to obtain the target adjustment step sizes corresponding to each node obtained by iterative calculation, and update the initial value of the adjustment step size based on the step size increments of each node and the target adjustment step sizes corresponding to each node obtained by iterative calculation to obtain an updated initial value of the adjustment step size; a first repetition module, configured to repeat the iterative calculation step based on the updated initial value of the adjustment step size until the power flow equation with operation constraints converges.

[0141] Further, the adjustment strategy generation device for unsolvable power flow further includes: a first execution module, configured to perform a halving and fallback operation on the step size increment to obtain a fallback step size increment when the comparison result indicates that any step size increment is greater than or equal to the target adjustment step size; a second repetition module, configured to repeatedly execute the iterative calculation step and the strategy generation step based on the updated initial value of the adjustment step size until the comparison result indicates that all step size increments are less than the target adjustment step size.

[0142] Further, the optimization model is expressed as: where f Pi (V,θ) is the sum of the active power at node i, and f Qi (V,θ) is the sum of the reactive power at node i, P gi is the active power injected at node i, Q gi is the reactive power injected at node i, P di is the active load at node i, Q di is the reactive load at node i, V is the node voltage amplitude, and θ is the node phase angle.

[0143] Further, the power flow equation with operating constraints is expressed as: f(x,λ) = f(x) + λb = S, where S = S g -S d , S is the node injection power in the current state, f(x) is the node power equation, f(x,λ) is the parameterized node power equation, S g is the node generation injection power, and S d is the node load consumption power.

[0144] Further, the objective function is expressed as: where F(x) represents the objective function value, S is the node injection power in the current state, S = S g -S d , S g is the node generation injection power, S d is the node load consumption power, and f(x,λ) is the parameterized node power equation.

[0145] Further, the step size increment calculation formula is expressed as: where Δλ represents the step size increment, b represents the adjustment direction vector of each node, S is the injection power vector of each node, and S * represents the injection power vector corresponding to the point with the smallest distance from S on the solution plane of the power flow equation with constraint conditions, and ω * represents the zero eigenvector of the singular point of the Jacobian matrix.

[0146] The above-mentioned adjustment strategy generation device for unsolvable power flow may further include a processor and a memory. The above-mentioned configuration unit 41, construction unit 42, calculation unit 43, generation unit 44, etc. are all stored in the memory as program units, and the corresponding functions are implemented by the processor executing the above-mentioned program units stored in the memory.

[0147] The above-mentioned processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the adjustment strategy for unsolvable power flow is generated by adjusting the kernel parameters.

[0148] The above-mentioned memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory includes at least one storage chip.

[0149] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute any one of the above-mentioned adjustment strategy generation methods for unsolvable power flow.

[0150] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, which includes one or more processors and a memory. The memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement any one of the above-mentioned adjustment strategy generation methods for unsolvable power flow.

[0151] According to another aspect of the embodiments of the present invention, there is also provided a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements any one of the above-mentioned adjustment strategy generation methods for unsolvable power flow.

[0152] The present application also provides a computer program product which, when executed on a data processing device, is adapted to execute a program initialized with the following method steps: a parameter configuration step of identifying a non-convergent power flow section and configuring an adjustment direction vector and a target adjustment step size for each node in the non-convergent power flow section; an equation construction step of establishing an optimization model based on the adjustment direction vectors and target adjustment step sizes of the nodes and constructing a power flow equation and an objective function with operating constraints according to the optimization model; an iterative calculation step of initializing the target adjustment step size to obtain an initial value of the adjustment step size, and iteratively solving the power flow equation with operating constraints by using the optimal multiplier Newton method based on the initial value of the adjustment step size, and determining whether the power flow equation with operating constraints converges according to the value of the objective function and the value of the optimal multiplier when the number of iterations reaches the iteration number threshold to obtain a determination result; a strategy generation step of, when the determination result indicates that the power flow equation with operating constraints converges, obtaining the target adjustment step sizes corresponding to the respective nodes obtained by iterative calculation, calculating the step size increments of the respective nodes, comparing the step size increments with the target adjustment step sizes to obtain a comparison result, and generating an adjustment strategy for unsolvable power flow based on the adjustment direction vectors and target adjustment step sizes of the respective nodes when the comparison result indicates that the step size increments are all smaller than the target adjustment step sizes.

[0153] The present application also provides a computer program product which, when executed on a data processing device, is further adapted to execute a program initialized with the following method steps: after obtaining the determination result, the iterative calculation step further includes: when the determination result indicates that the power flow equation with operating constraints does not converge, calculating the step size increments of the respective nodes according to the step size increment calculation formula; obtaining the target adjustment step sizes corresponding to the respective nodes obtained by iterative calculation, and updating the initial value of the adjustment step size based on the step size increments of the respective nodes and the target adjustment step sizes corresponding to the respective nodes obtained by iterative calculation to obtain an updated initial value of the adjustment step size; and repeatedly executing the iterative calculation step based on the updated initial value of the adjustment step size until the power flow equation with operating constraints converges.

[0154] The present application also provides a computer program product which, when executed on a data processing device, is further adapted to execute a program initialized with the following method steps: after obtaining the comparison result, it further includes: when the comparison result indicates that any step size increment is greater than or equal to the target adjustment step size, performing a halving and fallback operation on the step size increment to obtain a fallback step size increment; and repeatedly executing the iterative calculation step and the strategy generation step based on the updated initial value of the adjustment step size until the comparison result indicates that the step size increments are all smaller than the target adjustment step sizes.

[0155] The present application also provides a computer program product which, when executed on a data processing device, is further adapted to execute a program initialized with the following method steps: The optimization model is expressed as: Among them, f Pi (V,θ) is the sum of the active power at node i, f Qi (V,θ) is the sum of the reactive power at node i, P gi is the active power injected at node i, Q gi is the reactive power injected at node i, P di is the active load at node i, Q di is the reactive load at node i, V is the node voltage amplitude, and θ is the node phase angle.

[0156] This application also provides a computer program product, which is also adapted to execute a program initialized with the following method steps when executed on a data processing device: The power flow equation with operating constraints is expressed as: f(x,λ) = f(x) + λb = S, where S = S g -S d , S is the node injection power in the current state, f(x) is the node power equation, f(x,λ) is the parameterized node power equation, S g is the node generation injection power, S d is the node load consumption power.

[0157] This application also provides a computer program product, which is also adapted to execute a program initialized with the following method steps when executed on a data processing device: The objective function is expressed as: Among them, F(x) represents the objective function value, S is the node injection power in the current state, S = S g -S d , S g is the node generation injection power, S d is the node load consumption power, and f(x,λ) is the parameterized node power equation.

[0158] This application also provides a computer program product, which is also adapted to execute a program initialized with the following method steps when executed on a data processing device: The step size increment calculation formula is expressed as: Among them, Δλ represents the step size increment, b represents the adjustment direction vector of each node, S is the injection power vector of each node, S * represents the injection power vector corresponding to the point with the minimum distance from S on the solution plane of the power flow equation containing the constraint conditions, ω * represents the zero eigenvector of the singular point of the Jacobian matrix.

[0159] Figure 5 is the hardware structure block diagram of an electronic device (or mobile device) for implementing the adjustment strategy generation method for unsolvable power flow according to an embodiment of the present invention. As Figure 5 shown, the electronic device may include one or more processors ( Figure 5In the figure, 502a, 502b, ……, 502n are used to show that the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 504 for storing data. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply, and / or a camera. Those of ordinary skill in the art can understand that Figure 5 The structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the electronic device may further include more or fewer components than those shown Figure 5 in the figure, or have a different configuration from that Figure 5 shown in the figure.

[0160] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0161] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0162] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units may be a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other may be through some interfaces, and the indirect couplings or communication connections of the units or modules may be in electrical or other forms.

[0163] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, in each embodiment of the present invention, the functional units may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated units may be implemented in the form of hardware or in the form of software functional units.

[0165] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0166] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for generating an adjustment strategy for an unsolvable trend, characterized in that Including: A parameter configuration step of identifying a non-convergent power flow section and configuring an adjustment direction vector and a target adjustment step size for each node in the non-convergent power flow section; An equation construction step of establishing an optimization model based on the adjustment direction vectors and the target adjustment step sizes of each node, and constructing a power flow equation with operating constraints and an objective function according to the optimization model; An iterative calculation step of initializing the target adjustment step size to obtain an initial value of the adjustment step size, and iteratively solving the power flow equation with operating constraints by using the optimal multiplier Newton method based on the initial value of the adjustment step size. When the number of iterations reaches the iteration number threshold, determining whether the power flow equation with operating constraints converges according to the value of the objective function and the value of the optimal multiplier, and obtaining a determination result; A strategy generation step of, when the determination result indicates that the power flow equation with operating constraints converges, obtaining the target adjustment step sizes corresponding to each node obtained by iterative calculation, calculating the step size increments of each node, comparing the step size increments with the target adjustment step sizes to obtain a comparison result, and when the comparison result indicates that all the step size increments are smaller than the target adjustment step sizes, generating an adjustment strategy for unsolvable power flow based on the adjustment direction vectors and the target adjustment step sizes of each node.

2. The method according to claim 1, wherein After obtaining the determination result, the iterative calculation step further includes: When the determination result indicates that the power flow equation with operating constraints does not converge, calculating the step size increments of each node according to the step size increment calculation formula; Obtaining the target adjustment step sizes corresponding to each node obtained by iterative calculation, and updating the initial value of the adjustment step size based on the step size increments of each node and the target adjustment step sizes corresponding to each node obtained by iterative calculation to obtain an updated initial value of the adjustment step size; Repeating the iterative calculation step based on the updated initial value of the adjustment step size until the power flow equation with operating constraints converges.

3. The method according to claim 1, wherein After obtaining the comparison result, it further includes: When the comparison result indicates that any of the step size increments is greater than or equal to the target adjustment step size, performing a halving and fallback operation on the step size increment to obtain a fallback step size increment; Repeating the iterative calculation step and the strategy generation step based on the updated initial value of the adjustment step size until the comparison result indicates that all the step size increments are smaller than the target adjustment step sizes.

4. The method according to claim 1, wherein The optimization model is expressed as: where, f Pi (V,θ) is the sum of the active power at node i, f Qi (V,θ) is the sum of the reactive power at node i, P gi is the active power injected at node i, Q gi is the reactive power injected at node i, P di is the active load at node i, Q di is the reactive load at node i, V is the magnitude of the node voltage, and θ is the node phase angle.

5. The method according to claim 1, wherein The power flow equation with operating constraints is expressed as: f(x,λ) = f(x) + λb = S, where S = S g - Sd, S is the node injection power in the current state, f(x) is the node power equation, f(x,λ) is the parameterized node power equation, S g is the node generation injection power, S d is the node load consumption power.

6. The method according to claim 5, wherein The objective function is expressed as: Among them, F(x) represents the objective function value, S is the node injection power in the current state, and S = S g - Sd, where S g is the node power generation injection power, and S d is the node load consumption power. f(x,λ) is a parameterized nodal power equation.

7. The method according to claim 2, characterized in that The step size increment calculation formula is expressed as: Among them, Δλ represents the step size increment, b represents the adjustment direction vector of each node, S is the injection power vector of each node, and S * represents the injection power vector corresponding to the point with the minimum distance from S on the solution plane of the power flow equation containing the constraint conditions, and ω * represents the zero eigenvector of the singular point of the Jacobian matrix.

8. An adjustment strategy generation device for an insoluble trend, characterized in that, Including: A configuration unit for performing the parameter configuration step of identifying a non-convergent power flow section and configuring an adjustment direction vector and a target adjustment step size for each node in the non-convergent power flow section; A construction unit for performing the equation construction step of establishing an optimization model based on the adjustment direction vectors and the target adjustment step sizes of each node, and constructing a power flow equation with operating constraints and an objective function according to the optimization model; A calculation unit for performing an iterative calculation step, initializing the target adjustment step size to obtain an initial value of the adjustment step size, and iteratively solving the power flow equation with operating constraints based on the initial value of the adjustment step size using the optimal multiplier Newton method. When the number of iterations reaches the iteration number threshold, it determines whether the power flow equation with operating constraints converges according to the value of the objective function and the value of the optimal multiplier, and obtains a determination result; A generation unit for performing a policy generation step. When the determination result indicates that the power flow equation with operating constraints converges, it obtains the target adjustment step size corresponding to each node obtained by iterative calculation, calculates the step size increment of each node, compares the step size increment with the target adjustment step size to obtain a comparison result. When the comparison result indicates that the step size increments are all smaller than the target adjustment step size, it generates an adjustment policy for unsolvable power flow based on the adjustment direction vector and the target adjustment step size of each node.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the method for generating an adjustment policy for unsolvable power flow according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, and the memory is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating an adjustment policy for unsolvable power flow according to any one of claims 1 to 7.

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