Operation mode optimization method and device based on optimal power flow algorithm

Abstract

The invention discloses an operation mode optimization method and device based on an optimal power flow algorithm. The method comprises the following steps: sending a basic operation mode to an analysis module, and generating operation modes in corresponding scenes according to the basic mode through different scene boundary conditions; sequentially verifying whether the operation modes converge or not through a Newton-Raphson method; constructing an AC power flow original model, and obtaining a linear form of a power flow equation to form a linearized AC power flow algorithm engine; and establishing a four-step priority adjustment rule based on the linearized alternating current power flow algorithm engine, and adjusting non-convergent operation modes step by step until all the operation modes converge. According to the invention, through a high-precision high-convergence load flow calculation engine, a load flow non-convergence automatic adjustment algorithm is constructed, and the problem of non-convergence is accurately positioned; forming an optimal power flow algorithm according to different scene boundaries; whether the power flow converges or not is verified through Newton-Raphson method circulation, and finally convergent operation modes in different scenes are generated, so that the convergence stability is enhanced.

Description

Technical Field

[0001] This invention relates to the field of automation technology, and in particular to a method and apparatus for optimizing operation modes based on the optimal power flow algorithm. Background Technology

[0002] With the construction of new power systems, the power source side faces increased uncertainty and changes in grid connection characteristics due to power electronic devices, while the load side faces pressure from a large number of new loads connecting to the grid and flexible interaction between supply and demand. The power grid is becoming larger and more complex, urgently requiring improvements in energy supply and system security and stability. The rapid development of power sources, grids, and loads presents new challenges to power supply and demand, flexible interaction, and stable operation mechanisms.

[0003] Optimal power flow calculation is fundamental to power system operation optimization. Due to the nonlinear nature of power flow equations, the optimal power flow problem is non-convex, and solution algorithms cannot guarantee convergence or the global optimality of the optimization results. Data errors and active and reactive power imbalances are the main causes of power flow non-convergence.

[0004] In existing technologies, optimal power flow solutions can be categorized into strict AC optimal power flow methods, convex relaxation methods, and optimal power flow methods using linear network models. Strict AC optimal power flow methods refer to optimal power flow algorithms that strictly satisfy the AC power flow equations during the solution process. These can be solved using nonlinear optimization algorithms, including Newton's method, linear optimization methods, artificial intelligence algorithms, and interior-point methods. However, because these algorithms cannot guarantee convergence or global optimality of the solution, schedulers cannot apply them to scenarios with high convergence requirements, such as market clearing and day-ahead / intraday scheduling. Convex relaxation methods relax the power flow equations from equality to inequality. These algorithms can guarantee convergence, but since the feasible region of the convex relaxation model includes the feasible region of the original optimal power flow problem, if the convex relaxation problem has no solution, the original optimal power flow problem is also infeasible. Optimal power flow methods using linear network models achieve linear modeling of the optimal power flow problem through the linearization of the AC power flow equations. The goal of this type of method is not to obtain the optimal solution to the original optimal power flow problem, but to obtain an optimized result that is close enough to the optimal solution of the original optimal power flow problem through reasonable approximation of the power flow equations. Among them, the DC optimal power flow method may obtain an uneconomical or even unsafe scheduling scheme because it uses a lot of approximations in the power flow equations. Summary of the Invention

[0005] The main objective of this invention is to provide a method and apparatus for optimizing operation based on the optimal power flow algorithm, so as to solve the technical problems of slow response speed and low accuracy of some network threat information in the prior art.

[0006] To achieve the above objectives, this invention provides an optimization method for operation modes based on the optimal power flow algorithm. The method includes the following steps: S10, inputting a basic operation mode into the analysis module, and generating corresponding operation modes for different scenarios through different scenario boundary conditions; S20, verifying the convergence of the operation modes sequentially using the Newton-Lambert method. If the verification result is convergent, the result is directly output; if the verification result is not convergent, subsequent operations are performed; S30, constructing the original AC power flow model and obtaining the linear form of the power flow equation to form a linearized AC power flow algorithm engine; S40, based on the linearized AC power flow algorithm engine, establishing a four-step priority adjustment rule to form the optimal power flow algorithm, progressively adjusting non-convergent operation modes until all operation modes converge.

[0007] Optionally, step S30 includes the following steps: S310, obtaining the original active and reactive power flow on line (i, j) based on the polar coordinate power flow equation; S320, constructing a nonlinear AC power flow network model; S330, obtaining the node power balance equation; S340, realizing a refined nonlinear representation of the power flow equation based on the nonlinear AC power flow model.

[0008] Optionally, the polar coordinate power flow equation can be expressed as:

[0009]

[0010] Where v is the voltage amplitude; θ is the voltage phase angle; P ij and Q ij Represent the active power flow and reactive power flow on line (i,j), respectively; g ij and b ij Let v represent the conductance and susceptance of line (i,j) respectively; i and v j θ represents the voltage magnitudes at node i and node j, respectively; ij This represents the phase angle difference between the first and last nodes of the line (i,j).

[0011] Optionally, step S320 includes the following steps: S3210, processing the trigonometric function terms in the original power flow equation based on second-order Taylor series expansion; S3220, processing the voltage magnitude and phase angle cross terms in the original power flow equation; S3230, processing the voltage nonlinear terms in the original power flow equation; S3240, constructing a nonlinear AC power flow network model.

[0012] Optionally, the high-precision linearized form of the power flow equation is as follows:

[0013]

[0014] Among them, g ijP and b ij P This represents the coefficients of the active power balance equation in the hot-start model; g ij Q and b ij Q θ represents the coefficients of the reactive power balance equation in the hot-start model. ij,0 This represents the initial value of the phase angle difference between the first and last nodes of line (i,j); This represents the linearization term for the voltage component of the line (i,j).

[0015] Optionally, the node power balance equation is as follows:

[0016]

[0017] Among them, P i and Q i Represent the active power and reactive power at node i, respectively; g ii and b ii κ represents the conductance and susceptance of the grounding branch connected to node i, respectively; i This represents the set of branches connected to node i.

[0018] Optionally, step S40 includes the following steps: S410, optimizing the non-convergent operating modes after different scenario boundaries, establishing a four-step priority adjustment rule based on the high-precision linearized AC power flow algorithm engine, and adjusting the non-convergent operating modes step by step; S420, based on the high-precision high-convergence power flow calculation engine, locating the problem causing the power flow non-convergence, combining node power imbalance information, and according to the priority adjustment rule, performing distribution comparison and iteration step by step, and outputting the adjusted operating mode; S430, recalculating the power flow using the Newton-Lambert method after each adjustment of the operating mode, and transmitting the result through the system. The steps include initializing the baseline state, calculating the Jacobian matrix, forming the correction vector, and updating the state variables, and checking the convergence of the running mode; S440, if the change in the state variables is less than the preset tolerance error, stop the iteration; if the change in the state variables is not less than the preset tolerance error, return to calculate the Jacobian matrix and continue the iteration; S450, verify whether the power flow has converged using the Newton-Lambert method; if the power flow has converged, output the optimization result and use the output optimization result as the final result generated by the running mode optimization; if it still does not converge, continue to adjust the power flow non-convergence until the final output power flow has converged.

[0019] Optionally, the stepwise adjustment of the non-convergent operating mode includes the following steps: S4110, automatically correcting the model parameters of the ill-conditioned nodes, the model parameters including line impedance and transformer ratio; S4120, switching the node type; S4130, adjusting the generator / load power; S4140, adjusting the reference node voltage amplitude.

[0020] Furthermore, to achieve the above objectives, this application embodiment also provides an operation mode optimization device based on the optimal power flow algorithm. The device includes: an analysis module, used to input a basic operation mode into the analysis module, and generate operation modes under corresponding scenarios through different scenario boundary conditions; a verification module, used to verify whether the operation modes converge using the Newton-Lambert method. If the verification result is converged, the result is directly output; if the verification result is not converged, subsequent operations are performed; a model construction module, used to construct the original AC power flow model and obtain the linear form of the power flow equation to form a linearized AC power flow algorithm engine; and a convergence module, used to establish a four-step priority adjustment rule based on the linearized AC power flow algorithm engine, and adjust the non-convergent operation modes step by step until all operation modes converge.

[0021] In addition, to achieve the above objectives, embodiments of this application also provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the operation optimization method based on the optimal power flow algorithm described in any embodiment of this application.

[0022] Furthermore, to achieve the above objectives, embodiments of this application also provide a computing device, the computing device comprising: at least one processor, a memory, and an input / output unit; wherein, the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the operation mode optimization method based on the optimal power flow algorithm described in any embodiment of this application.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0024] The method for optimizing the operation mode based on the optimal power flow algorithm provided in this application constructs an original AC power flow model and obtains the linear form of the power flow equation, thereby forming a linearized AC power flow algorithm engine. By reducing the order of the power flow equation through nonlinear mapping, the kernel of the power flow algorithm and various parameters required for calculation by the power flow algorithm calling framework are flexibly set, constructing a high-precision, high-convergence AC power flow calculation engine. Through this high-precision, high-convergence power flow calculation engine, an automatic adjustment algorithm for power flow non-convergence is constructed. By establishing a four-step priority adjustment rule, non-convergent operation modes are adjusted step by step. Considering priority omissions, iterative steps are taken to provide a reliable convergent operation mode profile, significantly improving the accuracy and efficiency of locating power flow non-convergence problems. The convergence of the power flow is verified through the Newton-Lambert method, achieving automatic generation of high-precision, high-convergence operation modes and enhancing convergence stability. Attached Figure Description

[0025] Figure 1 A flowchart illustrating the operation optimization method based on the optimal power flow algorithm provided in this application embodiment;

[0026] Figure 2 A structural block diagram of the operation mode optimization device based on the optimal power flow algorithm provided in the embodiments of this application;

[0027] Figure 3 This is a schematic diagram of the structure of the medium provided in the embodiments of this application;

[0028] Figure 4 A schematic diagram of the structure of a computing device provided in an embodiment of this application.

[0029] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0030] It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the application. Rather, these embodiments are provided to make the disclosure more thorough and complete, and to fully convey the scope of the disclosure to those skilled in the art.

[0031] To address the aforementioned technical problems, embodiments of this application provide a method for optimizing the operation mode based on an optimal power flow algorithm, such as... Figure 1 As shown, the method may include the following steps:

[0032] S10: Send a basic operating mode to the analysis module, and generate the corresponding operating mode under different scenario boundary conditions based on the basic mode.

[0033] Specifically, the different scenarios include five scenarios: [normal operation mode], [high thermal power generation, abundant water], [high thermal power generation, low water], [high renewable energy generation, abundant water], and [high renewable energy generation, low water]. This embodiment generates the operation mode under the corresponding scenario by using the selected scenario boundary conditions to generate the basic mode of complete operation under the baseline state.

[0034] Furthermore, the base mode refers to the way a typical power flow calculation system operates completely under baseline conditions. All other operating modes in all scenarios (such as high water, low water, etc.) are generated by modifying specific boundary conditions on this "base mode".

[0035] S20: Verify the convergence of the running mode using the Newton-Lambert method. If the verification result is converged, output the result directly. If the verification result is not converged, proceed with subsequent operations.

[0036] Specifically, the Newton-Raphson method is the core algorithm used for power flow calculation in power system analysis. It is an efficient iterative method for solving nonlinear equations.

[0037] Furthermore, verifying the convergence of the operating mode using the Newton-Lambert method can be achieved through the following steps:

[0038] Step 1: Initialize the system baseline state;

[0039] Set the initial values ​​for the state variables of all nodes in the system:

[0040] PQ node (load node): Voltage amplitude v i,0 Set to 1.0.pu, voltage phase angle θ i,0 Set to 0°

[0041] PV node (generator node): Voltage amplitude v i,0 Set to a specified value, voltage phase angle θ i,0 Set to 0°

[0042] Balance node (relaxation node): Voltage amplitude v i,0 and voltage phase angle θ i,0 Set all to the specified values

[0043] Initial state variable x i,0 =[θ i,0 ,v i,0 ] T .

[0044] Step 2: Calculate the Jacobian matrix;

[0045] The Jacobian matrix is ​​a sparse square matrix with dimension (2n). p +n q )×(2n p +n q ), n p Let n be the number of nodes in PQ+PV. q The number of PQ nodes. The state variable value x based on the current iteration step k. k =[θ k ,v k ] T Calculate the Jacobian matrix J k

[0046]

[0047] The four sub-matrices represent the sensitivities of active power to phase angle, active power to voltage, reactive power to phase angle, and reactive power to voltage, respectively.

[0048] Step 3: Form the correction vector;

[0049] Calculate power deviation: P k and Q k Indicates based on the current state x kThe node injection power is calculated based on the network parameters.

[0050] Solve the system of linear equations: J k Δx k =ΔS k Solve this equation using sparse matrix solving techniques (such as LU decomposition) to obtain the corrected vector Δx. k =[Δθ k ,Δv k ] T This vector represents the amount of change required for the state variable at the current point.

[0051] Step 4: Update the state variables.

[0052] x k+1 =x k +Δx k ,Right now

[0053] Where, θ k θ represents the voltage phase angle at the current iteration step k; k+1 This represents the voltage phase angle in the next iteration step k+1; v k This represents the voltage amplitude at the current iteration step k; v k+1 Δθ represents the voltage magnitude at the next iteration step k+1. k and Δv k This represents the voltage phase angle correction and voltage amplitude correction for the current iteration step k.

[0054] Output the new state variable x k+1 This is used for the next iteration.

[0055] If the change in the state variable is less than the preset tolerance error, the algorithm is considered to have converged and the iteration stops; otherwise, the algorithm returns to calculate the Jacobian matrix and continues to iterate.

[0056] S30: Construct the original AC power flow model and obtain the linear form of the power flow equation to form a linearized AC power flow algorithm engine.

[0057] The original AC power flow model is based on the fundamental laws of circuits (Kirchhoff's laws) and energy conservation, and describes the relationship between voltage, current and power at each node in the system through node voltage equations.

[0058] In an exemplary embodiment, step S30 includes the following steps:

[0059] S310, based on the polar coordinate power flow equation, obtains the original active and reactive power flow of line (i, j);

[0060] S320, constructing a nonlinear AC power flow network model;

[0061] S330, obtain the node power balance equation;

[0062] S340 is based on a nonlinear AC power flow model to achieve a refined nonlinear representation of the power flow equations.

[0063] Specifically, polar coordinate power flow equations are mathematical models based on the polar coordinate system used in power system power flow calculations to describe the relationship between node voltage magnitude, phase angle, and system power balance. Their core principle is to replace the real / imaginary parts of rectangular coordinates with polar coordinates (voltage magnitude V and phase angle θ), thus better reflecting the physical characteristics of power systems.

[0064] It's important to understand that by treating the square of v as an independent variable, the nodal power balance equations become linear, but the power flow equations remain nonlinear, albeit with a significantly reduced nonlinearity.

[0065] Furthermore, the polar coordinate power flow equation can be expressed as:

[0066]

[0067] Where v is the voltage amplitude; θ is the voltage phase angle; P ij and Q ij Represent the active power flow and reactive power flow on line (i,j), respectively; g ij and b ij Let v represent the conductance and susceptance of line (i,j) respectively; i and v j θ represents the voltage magnitudes at node i and node j, respectively; ij This represents the phase angle difference between the first and last nodes of the line (i,j).

[0068] In an exemplary embodiment, step S320 includes the following steps: S3210, processing the trigonometric function terms in the original power flow equation based on second-order Taylor series expansion; S3220, processing the voltage magnitude and phase angle cross terms in the original power flow equation; S3230, processing the voltage nonlinear terms in the original power flow equation; S3240, constructing a nonlinear AC power flow network model.

[0069] Specifically, in the derivation of the low-nonlinearity power flow equations, the quadratic terms are first retained, and then the handling of network losses is discussed. In the low-nonlinearity network model, network loss should be the only and unavoidable source of nonlinearity. First, a second-order Taylor series expansion is used to handle the trigonometric function terms in the original power flow equations. Since the phase angle difference between the two ends of a line in the transmission network is very small, θ... ij ≈0. Therefore, the second-order Taylor expansions of the sine and cosine functions near 0 are as follows:

[0070]

[0071] Substituting it into the original equation, we get:

[0072]

[0073] Regarding the handling of the voltage magnitude and phase angle cross terms in the equation, v i and θ ij The latter two are tightly coupled, with v i v j and θ ij / θ 2 ij Treating them as independent variables, using initial values ​​(v) i,0 v j,0 ,θ ij,0 The Taylor series expansion near the voltage decouples the voltage and phase angle:

[0074] v i v j θ ij ≈v i，0 v j，0 θ ij +(v i v j -v i，0 v j，0 )θ ij，0

[0075]

[0076] Among them, v i,0 and v j,0 θ represents the initial values ​​of the voltage amplitude at node i and node j, respectively; ij,0 This represents the initial value of the phase angle difference between the first and last nodes of line (i,j).

[0077] 3) Let's further discuss the handling of the voltage nonlinear term in the equation. For the nonlinear term v... i v j The processing of voltage nonlinear terms directly affects the modeling accuracy of voltage amplitude in low nonlinear power flow equations. The following mathematical transformation is used to process the voltage nonlinear terms:

[0078]

[0079] Among them, the first item For v 2 The linear function, since the voltage amplitude difference between the two ends of a power grid line is usually small, although the second term It is non-linear, but accounts for a small proportion. Therefore, v 2 Treating them as independent variables, the nonlinear voltage term is decomposed into linear and quadratic terms through variable substitution without loss of accuracy. The quadratic term characterizes the impact of voltage amplitude on network loss.

[0080] 4) Further mathematical transformation is performed using the following formula to convert the squared difference of voltage amplitudes into v. 2 The function is substituted to obtain the voltage nonlinear term v. i v j Approximate expression:

[0081]

[0082] in, and These represent the active power loss and reactive power loss on line (i,j), respectively.

[0083]

[0084]

[0085] Based on the derivation of the above formulas, the nodal power balance equations can be obtained.

[0086] In an exemplary embodiment, the node power balance equation is as follows:

[0087]

[0088] Among them, P i and Q i Represent the active power and reactive power at node i, respectively; g ii and b ii κ represents the conductance and susceptance of the grounding branch connected to node i, respectively; i This represents the set of branches connected to node i.

[0089] Through the node power balance equations described in the above embodiments, P can be obtained. ij and Q ij (i.e., low-nonlinear AC power flow model) In this model, network loss is the only source of nonlinearity. By transforming the node power balance equations and treating the squares of voltage amplitudes as independent variables, the original equations can be transformed into linear expressions. Based on this, and combined with high-quality initial values ​​provided by subsequent hot starts, a linear approximation of network loss can be achieved, thus ultimately forming a linearized power flow equation model, i.e., a linear AC power flow model.

[0090] The linear AC power flow model constructed using the above steps (retaining voltage amplitude / phase angle coupling and ignoring higher-order terms) quickly identifies the top K ill-conditioned nodes with the largest power deviations by calculating the node power imbalance ΔS, and classifies the problem types, including parameter errors, node type conflicts, and power exceeding limits. Due to the high efficiency of linear optimization methods, this patent employs a successive linear approach. During iteration, the optimization results of the previous iteration provide initial values ​​for the next optimization model. In each iteration, the power flow equations are further improved in accuracy based on the initial values ​​of the previous iteration. Therefore, based on the initial values ​​(v... k ,θ k A hot-start model is used to achieve a linear approximation of network losses, thereby realizing a high-precision linearized representation of the power flow equations. The specific process is as follows:

[0091] 1) First, using the initial values ​​(v0, θ0), we can obtain the Taylor expansion expressions for the sine and cosine functions as shown below:

[0092]

[0093] In the formula, and The coefficients are the Taylor expansion expression of the sine function; and are the coefficients of the Taylor expansion expression of the cosine function.

[0094]

[0095] 2) In the hot start model, by performing a first-order Taylor expansion of the sine and cosine functions and substituting it into the polar coordinate power flow equations, we obtain:

[0096]

[0097] In the above formula, v and θ are in v i v j θ ij Tightly coupled.

[0098] 3) Based on initial values, the warm-start model can adjust v i v j θ ij The term uses a more accurate Taylor expansion approximation:

[0099] v i v j θ ij ≈v i，0 v j,0 θ ij +(v i v j -v i，0 v j,0 )θ ij，0

[0100] Substituting into the above formula, we get:

[0101]

[0102] in, and These are the coefficients of the active power balance equation for the hot-start model; and These are the coefficients of the reactive power balance equation in the hot-start model.

[0103] From the above formula, we can obtain:

[0104]

[0105] 4) The hot start model is similar to the cold start model, and the following formula is still used for the voltage amplitude nonlinear term v. i v j Perform mathematical transformations:

[0106]

[0107] v 2 Treating v as an independent variable, the warm-start model uses the following method to implement v. 2 ij Linearization:

[0108]

[0109] Among them, v ij,0 This represents the initial value of the voltage magnitude difference between the first and last nodes of line (i,j); v ij This represents the voltage magnitude difference between the first and last nodes of the line (i,j); This represents the linearization term for the voltage component of the line (i,j).

[0110] Nonlinear voltage term v in the hot start model i v j The linear approximation is as follows:

[0111]

[0112] In an exemplary embodiment, the high-precision linearized form of the power flow equation is as follows:

[0113]

[0114] The above derivation reveals that the mathematical changes and variable substitutions of the nonlinear voltage terms are the main features that distinguish this patented model from existing models and methods, and are also the key to ensuring high accuracy and high linearity of the power flow equations.

[0115] Among them, the hot start model is a high-precision linearization technique based on iterative optimization. Its core lies in using the result of the previous iteration as the initial value, dynamically updating the linearization benchmark point, and specifically approximating the network loss term.

[0116] S40. Based on the linearized AC power flow algorithm engine, a four-step priority adjustment rule is established to adjust the non-convergent operating modes step by step until all operating modes converge.

[0117] In an exemplary embodiment, step S40 includes the following steps:

[0118] S410 optimizes the operation mode by considering non-convergence patterns after different scenario boundaries. Based on the high-precision linearized AC power flow algorithm engine, a four-step priority adjustment rule is established to progressively adjust non-convergence patterns. The four-step priority adjustment rule is as follows:

[0119] Level 1: Automatically corrects model parameters of ill-conditioned nodes (such as line impedance and transformer turns ratio);

[0120] Level 2: Convert node type (e.g., PV→PQ node to resolve reactive power limit exceedance);

[0121] Level 3: Adjust generator / load power (prioritize increasing power generation from units with high economic efficiency);

[0122] Level 4: Adjust the reference node voltage amplitude;

[0123] S420, based on a high-precision, high-convergence power flow calculation engine, identifies the problem causing power flow non-convergence, combines node power imbalance information, and according to the priority adjustment rules, performs distribution comparison and iteration to output the adjusted operating mode.

[0124] S430: After each adjustment of the operating mode, the power flow is recalculated using the Newton-Lager method. The convergence of the operating mode is checked by the steps of initializing the system baseline state, calculating the Jacobian matrix, forming the correction vector, and updating the state variables.

[0125] S440, if the change in the state variable is less than the preset tolerance error, stop the iteration; if the change in the state variable is not less than the preset tolerance error, return to calculate the Jacobian matrix and continue the iteration.

[0126] S450 verifies whether the power flow has converged using the Newton-Lambert method. If the power flow has converged, the optimization result is output and used as the final result generated by the operation mode optimization. If it still does not converge, the power flow non-convergence adjustment continues until the final output power flow has converged.

[0127] The Jacobian matrix is ​​the optimal linear approximation of a vector-valued function from n-dimensional space to m-dimensional space at a certain point.

[0128] Specifically, in step S440, if the change in the state variable is less than the preset allowable error, the Newton-Lager method is considered to have converged. Convergence refers to whether the iterative process of the Newton-Lager method has reached the termination condition. Power flow convergence refers to whether there is a feasible solution in the power system that satisfies all power balance equations and operating constraints. Algorithm convergence focuses on the numerical stability of the Newton-Lager method iteration, while power flow convergence focuses on the physical equilibrium of the system. Algorithm convergence is the computational basis of power flow convergence, and power flow convergence is the ultimate goal of algorithm convergence.

[0129] Furthermore, in step S450, the process of verifying whether the power flow has converged using the Newton-Lambert method is as follows: when the change in the state variable is less than the preset allowable error, the numerical iteration is determined to be converged; when the active / reactive power deviations of all nodes are less than the engineering allowable value, the power flow is converged.

[0130] Furthermore, the steps for initializing the system baseline state, calculating the Jacobian matrix, forming the correction vector, and updating the state variables are as follows:

[0131] Step 1: Initialize the system baseline state;

[0132] Set the initial values ​​for the state variables of all nodes in the system:

[0133] PQ node (load node): Voltage amplitude v i,0 Set to 1.0.pu, voltage phase angle θ i,0 Set to 0°

[0134] PV node (generator node): Voltage amplitude v i,0 Set to a specified value, voltage phase angle θ i,0 Set to 0°

[0135] Balance node (relaxation node): Voltage amplitude v i,0 and voltage phase angle θ i,0 Set all to the specified values

[0136] Initial state variable x i,0 =[θ i,0 ,v i,0 ] T .

[0137] Step 2: Calculate the Jacobian matrix;

[0138] The Jacobian matrix is ​​a sparse square matrix with dimension (2n). p +n q )×(2n p +n q ), n p Let n be the number of nodes in PQ+PV. q The number of PQ nodes. The state variable value x based on the current iteration step k.k =[θ k ,v k ] T Calculate the Jacobian matrix J k

[0139]

[0140] The four sub-matrices represent the sensitivities of active power to phase angle, active power to voltage, reactive power to phase angle, and reactive power to voltage, respectively.

[0141] Step 3: Form the correction vector;

[0142] Calculate power deviation: P k and Q k Indicates based on the current state x k The node injection power is calculated based on the network parameters.

[0143] Solve the system of linear equations: J k Δx k =ΔS k Solve this equation using sparse matrix solving techniques (such as LU decomposition) to obtain the corrected vector Δx. k =[Δθ k ,Δv k ] T This vector represents the amount of change required for the state variable at the current point.

[0144] Step 4: Update the state variables.

[0145] x k+1 =x k +Δx k ,Right now

[0146] Where, θ k θ represents the voltage phase angle at the current iteration step k; k+1 This represents the voltage phase angle in the next iteration step k+1; v k This represents the voltage amplitude at the current iteration step k; v k+1 Δθ represents the voltage magnitude at the next iteration step k+1. k and Δv k This represents the voltage phase angle correction and voltage amplitude correction for the current iteration step k.

[0147] Output the new state variable x k+1 This is used for the next iteration.

[0148] In an exemplary embodiment, the stepwise adjustment of the non-convergent operating mode includes the following steps:

[0149] S4110, automatically corrects the model parameters of ill-conditioned nodes, the model parameters including line impedance and transformer turns ratio;

[0150] S4120, Change node type;

[0151] S4130, adjust generator / load power;

[0152] S4140, adjusts the reference node voltage amplitude.

[0153] Specifically, this embodiment quickly identifies the top K ill-conditioned nodes with the largest power deviations by calculating the node power imbalance ΔS, and classifies the problem types, including parameter errors, node type conflicts, and power exceeding limits; then, by combining the local linear approximation characteristics of the Jacobian matrix, the model parameters of the ill-conditioned nodes are dynamically corrected through sensitivity analysis.

[0154] The method for optimizing the operation mode based on the optimal power flow algorithm provided in this application constructs an original AC power flow model and obtains the linear form of the power flow equation, thereby forming a linearized AC power flow algorithm engine. By reducing the order of the power flow equation through nonlinear mapping, the kernel of the power flow algorithm and various parameters required for calculation by the power flow algorithm calling framework are flexibly set, constructing a high-precision, high-convergence AC power flow calculation engine. Through this high-precision, high-convergence power flow calculation engine, an automatic adjustment algorithm for power flow non-convergence is constructed. By establishing a four-step priority adjustment rule, non-convergent operation modes are adjusted step by step. Considering priority omissions, iterative steps are taken to provide a reliable convergent operation mode profile, significantly improving the accuracy and efficiency of locating power flow non-convergence problems. The convergence of the power flow is verified through the Newton-Lambert method, achieving automatic generation of high-precision, high-convergence operation modes and enhancing convergence stability.

[0155] Based on the above embodiments, refer to Figure 2 Another embodiment of this application also provides an operation mode optimization device based on the optimal power flow algorithm. The operation mode optimization device 200 based on the optimal power flow algorithm may include the following modules:

[0156] Analysis module 210 is used to send a basic operating mode to the analysis module, and generate the corresponding operating mode under different scenario boundary conditions based on the basic mode;

[0157] The verification module 220 is used to verify whether the running mode converges sequentially using the Newton-Lambert method. If the verification result is converged, the result is output directly; if the verification result is not converged, subsequent operations are performed.

[0158] The model building module 230 is used to build the original AC power flow model and obtain the linear form of the power flow equation to form a linearized AC power flow algorithm engine.

[0159] The convergence module 240 is used to establish a four-step priority adjustment rule based on the linearized AC power flow algorithm engine, and adjust the non-convergent operation mode step by step until all operation modes converge.

[0160] Based on the above embodiments, this application also provides a computer-readable storage medium, see reference. Figure 3 The computer-readable storage medium shown is an optical disc 50, on which a computer program (i.e., a program product) is stored. When the computer program is run by the processor, it implements the steps described in the above method implementation. For example, S10, a basic operating mode is sent to the analysis module, and the basic mode is used to generate operating modes under different scenario boundary conditions; S20, the convergence of the operating modes is verified sequentially using the Newton-Lambert method. If the verification result is converged, the result is directly output; if the verification result is not converged, subsequent operations are performed; S30, the original AC power flow model is constructed, and the linear form of the power flow equation is obtained to form a linearized AC power flow algorithm engine; S40, based on the linearized AC power flow algorithm engine, a four-step priority adjustment rule is established to adjust the non-convergent operating modes step by step until all operating modes converge. The specific implementation of each step will not be repeated here.

[0161] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0162] In addition to the above embodiments, this application also provides a computing device. Figure 4 A block diagram is shown of an exemplary computing device 60 suitable for implementing embodiments of the present application. The computing device 60 may be a computer system or a server. Figure 4 The computing device 60 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0163] like Figure 4 As shown, the components of computing device 60 may include, but are not limited to: one or more processors or processing units 601, system memory 602, and bus 603 connecting different system components (including system memory 602 and processing unit 601).

[0164] The computing device 60 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 60, including volatile and non-volatile media, removable and non-removable media.

[0165] System memory 602 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 6021 and / or cache memory 6022. Computing device 60 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 6023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 4 Not shown in the image (usually referred to as a "hard drive"). Although not shown in Figure 4 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to a bus 603 that connects different system components via one or more data media interfaces. The system memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0166] A program / utility 6025 having a set (at least one) of program modules 6024 may be stored, for example, in system memory 602, and such program modules 6024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 6024 typically perform the functions and / or methods described in the embodiments of this application.

[0167] The computing device 60 can also communicate with one or more external devices 604 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 605. Furthermore, the computing device 60 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 606. Figure 4 As shown, network adapter 606 communicates with other modules of computing device 60 (such as processing unit 601, etc.) via bus 603, which connects different system components. It should be understood that, although... Figure 4 Other hardware and / or software modules may be used in conjunction with computing device 60, as not shown in the diagram.

[0168] The processing unit 601 executes various functional applications and data processing by running programs stored in the system memory 602. For example, in S10, a basic operating mode is sent to the analysis module, and the basic mode is used to generate operating modes for different scenarios based on different scenario boundary conditions; in S20, the convergence of the operating modes is verified sequentially using the Newton-Lambert method. If the verification result is converged, the result is directly output; if the verification result is not converged, subsequent operations are performed; in S30, the original AC power flow model is constructed, and the linear form of the power flow equation is obtained to form a linearized AC power flow algorithm engine; in S40, based on the linearized AC power flow algorithm engine, a four-step priority adjustment rule is established to adjust the non-convergent operating modes step by step until all operating modes converge. The specific implementation of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the operating mode optimization device based on the optimal power flow algorithm are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of a unit / module described above can be further divided into multiple units / modules for specificity.

[0169] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0174] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0175] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

[0176] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. A method for optimizing the operation mode based on the optimal power flow algorithm, characterized in that, The method includes the following steps: S10, a basic operating mode is sent to the analysis module, and the basic mode is used to generate the corresponding operating mode under different scenario boundary conditions; S20: Verify whether the running method converges using the Newton-Lager method. If the verification result is converged, output the result directly. If the verification result is not converged, proceed with subsequent operations. S30: Construct the original AC power flow model, and solve the power flow equation linearly through the nonlinear mapping degradation method to form a linearized AC power flow algorithm engine. S40, Based on the linearized AC power flow algorithm engine, establish a four-step priority adjustment rule, adjust the non-convergent operation mode step by step until the operation mode converges, and generate the optimized operation mode.

2. The method for optimizing the operation mode based on the optimal power flow algorithm according to claim 1, characterized in that, Step S30 includes the following steps: S310, based on the polar coordinate power flow equation, obtains the original active and reactive power flow of line (i, j); S320, constructing a nonlinear AC power flow network model; S330, obtain the node power balance equation; S340 is based on a nonlinear AC power flow model to achieve a refined nonlinear representation of the power flow equations.

3. The method for optimizing the operation mode based on the optimal power flow algorithm according to claim 2, characterized in that, The polar coordinate power flow equation can be expressed as: Where v is the voltage amplitude; θ is the voltage phase angle; P ij and Q ij Represent the active power flow and reactive power flow on line (i,j), respectively; g ij and b ij Let v represent the conductance and susceptance of line (i,j) respectively; i and v j θ represents the voltage magnitudes at node i and node j, respectively; ij This represents the phase angle difference between the first and last nodes of the line (i,j).

4. The method for optimizing the operation mode based on the optimal power flow algorithm according to claim 1, characterized in that, Step S320 includes the following steps: S3210, based on second-order Taylor series expansion, handles the trigonometric function terms in the original power flow equations; S3220, Process the voltage magnitude and phase angle cross terms in the original power flow equation; S3230, Process the voltage nonlinearity term in the original power flow equation; S3240, construct a nonlinear AC power flow network model.

5. The method for optimizing the operation mode based on the optimal power flow algorithm according to claim 2, characterized in that, The high-precision linearized form of the power flow equation is as follows: Among them, g ij P and b ij P This represents the coefficients of the active power balance equation in the hot-start model; g ij Q and b ij Q θ represents the coefficients of the reactive power balance equation in the hot-start model. ij,0 This represents the initial value of the phase angle difference between the first and last nodes of line (i,j); This represents the linearization term for the voltage component of the line (i,j).

6. The method for optimizing the operation mode based on the optimal power flow algorithm according to claim 2, characterized in that, The node power balance equations are as follows: Among them, P i and Q i Represent the active power and reactive power at node i, respectively; g ii and b ii κ represents the conductance and susceptance of the grounding branch connected to node i, respectively; i This represents the set of branches connected to node i.

7. The method for optimizing the operation mode based on the optimal power flow algorithm according to claim 1, characterized in that, Step S40 includes the following steps: S410 optimizes the non-convergent operation mode after different scenario boundaries. Based on the high-precision linearized AC power flow algorithm engine, a four-step priority adjustment rule is established to form the optimal power flow algorithm and adjust the non-convergent operation mode step by step. S420, based on a high-precision, high-convergence power flow calculation engine, identifies the problems that cause power flow non-convergence, combines node power imbalance information, adjusts the rules according to the optimal power flow algorithm, compares distributions and iterates successively, and outputs the adjusted operating mode. S430: After each adjustment of the operating mode, the power flow is recalculated using the Newton-Lager method. The convergence of the operating mode is checked by the steps of initializing the system baseline state, calculating the Jacobian matrix, forming the correction vector, and updating the state variables. S440, if the change in the state variable is less than the preset tolerance error, stop the iteration; if the change in the state variable is not less than the preset tolerance error, return to calculate the Jacobian matrix and continue the iteration. S450 verifies whether the power flow has converged using the Newton-Lambert method. If the power flow has converged, the optimization result is output and used as the final result generated by the running mode optimization. If it still does not converge, the power flow non-convergence adjustment is continued using the optimal power flow algorithm until the final output power flow has converged.

8. The method for optimizing the operation mode based on the optimal power flow algorithm according to claim 7, characterized in that, The optimal power flow algorithm, which adjusts the non-convergent operation mode step by step, includes the following steps: S4110, automatically corrects the model parameters of ill-conditioned nodes, the model parameters including line impedance and transformer turns ratio; S4120, Change node type; S4130, adjust generator / load power; S4140, adjusts the reference node voltage amplitude.

9. An operation mode optimization device based on optimal power flow algorithm, characterized in that, include: The analysis module is used to input a basic operating mode and generate operating modes for the corresponding scenarios based on different scenario boundary conditions. The verification module is used to verify whether the running mode converges sequentially using the Newton-Lambert method. If the verification result is converged, the result is output directly; if the verification result is not converged, subsequent operations are performed. The model building module is used to build the original AC power flow model and obtain the linear form of the power flow equation to form a linearized AC power flow algorithm engine. The convergence module is used to establish a four-step priority adjustment rule based on the linearized AC power flow algorithm engine, form the optimal power flow algorithm, and adjust the non-convergent running modes step by step until all running modes converge, and output the optimized running mode.

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