Power flow calculation method and system for power system

Abstract

The invention discloses a power flow calculation method and system for a power system, and aims to relax key variables such as part of generator output and node voltage from equality constraints to inequality constraints in order to solve the problem that part of generator output and node voltage are difficult to give due to large-range change of an operation mode in a novel power system; after the inequality constraint is introduced, the power flow model is changed into a novel power system constraint power flow model from an original nonlinear equation set solving problem; converting an inequality in the constraint power flow model into an equation by adopting a method of constructing an auxiliary function, and then converting the constraint power flow model into a bounded constraint least square problem; aiming at the problem that a traditional optimization algorithm is difficult to converge, a trust region algorithm is adopted for solving, and the adjustment amount of variables such as generator output and node voltage and a final power flow solution are obtained. The method can effectively solve the problem that the power flow has no solution due to the fact that the initial power flow value is difficult to give, and theoretical support is provided for planning and operation of a power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system flow calculation, and in particular relates to a power system flow calculation method and system. Background Art

[0002] Given the network topology and load, we have been focusing on finding a reasonable way to start the power system by setting the generator output and switching on and off of related equipment, so that the power system can operate in an efficient, economical and reliable manner. By reasonably setting the output and voltage and other related variables of the generator, and then evaluating the parameters such as voltage, phase angle and current of each node in the power system through flow calculation, if the flow has a solution, we can judge whether it is a feasible solution by observing whether the state quantity crosses the boundary. If the flow has no solution, we hope to find parameter adjustment measures to make the flow converge by adjusting the generator output, reactive compensation equipment, transformer taps and other equipment, so as to obtain a reasonable operation mode.

[0003] In the new power system, fluctuating power sources such as photovoltaic and wind power generation are introduced, battery energy storage is connected to the grid, and many power electronic devices such as DC transmission, flexible AC transmission and load control are adopted. All these will lead to huge changes in the structure and operation of the power system. Traditional power system flow models and algorithms are difficult to fully describe these new characteristics, so it is necessary to rethink and optimize the power system flow calculation method.

[0004] The proportion of fluctuating power sources such as photovoltaic and wind power generation is increasing. The grid connection of these fluctuating power sources will introduce uncertainty and time-variability, which makes the operation of power systems significantly diversified and complicated. Traditional power flow models and calculation methods can no longer solve the new power system well. Therefore, in order to adapt to the operating characteristics of the new power system, it is particularly important to deeply study and improve the power flow model and algorithm of the power system.

[0005] A large number of power electronic devices, such as inverters and converters, have been introduced into the new power system to realize the grid connection of photovoltaic, wind power generation and battery energy storage, as well as DC transmission and other functions. The existence of these power electronic devices makes the power system power flow calculation model more complicated. The traditional power flow calculation method based on transmission line parameters can no longer accurately describe the operating status of the system. The nonlinear characteristics of power electronic devices and the complexity of control strategies require more sophisticated modeling and solution methods for analysis and optimization. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a power system flow calculation method and system in view of the deficiencies in the above-mentioned prior art, which is used to solve the technical problem that the initial value of the flow is difficult to give and the flow has no solution, and is used to improve the situation in which traditional flow in new power systems is difficult to converge or even fails.

[0007] The present invention adopts the following technical solutions:

[0008] A method for calculating power system flow, comprising the following steps:

[0009] Establish a new power constraint power flow model;

[0010] The inequalities in the new power constraint power flow model are transformed into equalities by constructing auxiliary functions.

[0011] Convert the equality equation into a least squares problem with bounded constraints;

[0012] The trust region method is used to solve the obtained least squares problem to obtain the adjustment amount of variables such as power system generator output, node voltage, and the final power flow solution.

[0013] Preferably, in the new power constraint flow model, the equality constraint is expressed as:

[0014]

[0015] The inequality constraints in the constrained power flow model are expressed as:

[0016]

[0017]

[0018] x l ≤x≤x u

[0019] Where N represents the set of all nodes, A represents the set of nodes that relax the active output of the generator, B represents the set of nodes that relax the reactive output of the generator, and C represents the set of nodes that relax the node voltage amplitude. For nodes i, e i represents the real part of voltage, a i represents the real part of the current, f i represents the imaginary part of voltage, b i Represents the imaginary part of the current, P Li Indicates the active power absorbed by the load, P Gi Indicates the active power generated by the generator, Q Li Indicates the reactive power absorbed by the load, Q Gi Indicates the reactive power generated by the generator. Represents a given voltage amplitude, xl Represents the lower bound of the iteration variable, x u represents the upper bound of the iteration variable, G ij represents the real part of the node admittance matrix, B ij represents the real part of the node admittance matrix.

[0020] Preferably, the inequality in the new power constraint power flow model is converted into an equation as follows:

[0021] The constrained power flow model is transformed into a set of nonlinear inequalities and nonlinear equality equations under standard bounded constraints, where the bilateral constraints of the upper and lower bounds are transformed into two unilateral constraints;

[0022] Construct a penalty function to convert inequalities into equalities.

[0023] Preferably, the penalty function is constructed as follows:

[0024] An auxiliary function of a continuously differentiable function is constructed based on the idea of ​​penalty function. By adding a penalty term related to the degree of constraint violation to the objective function, the value of the objective function increases when the constraint is violated.

[0025] Preferably, the equality equation is transformed into a least squares problem with bounded constraints as follows:

[0026] Based on the construction of auxiliary functions, firstly, according to the input equation vector C E and inequality vector C I Form the vector function h and its corresponding Jacobian matrix J0;

[0027] Then, the vector function h and its corresponding Jacobian matrix J0 are modified according to the constructed auxiliary function to obtain F and its corresponding Jacobian matrix J.

[0028] Preferably, the bounded constrained least squares problem is as follows:

[0029]

[0030] Among them, x is the iteration variable, l is the lower bound of the iteration variable, u is the upper bound of the iteration variable, F(x) is the power flow equations after the inequality is transformed into an equation, and f(x) is the objective function.

[0031] Preferably, the block representation of the vector function h and J0 is as follows:

[0032] h=[C E C I ] T =[h e1 h e2 h i1 h i2 h i3h i4 h i5 ] T

[0033] J0=[J 0e1 J 0e2 J 0i1 J 0i2 J 0i3 J 0i4 J 0i5 ] T

[0034] Among them, h e represents the equation vector; J e Indicates h e The corresponding Jacobian matrix; h i represents the inequality vector; J i Indicates h i The corresponding Jacobian matrix;

[0035] h e and h i The elements in the voltage deviation ΔU 2 , active power deviation ΔP, reactive power deviation ΔQ, lower voltage deviation Lower bound active power deviation ΔP l , lower bound reactive power deviation ΔQ l , Upper voltage deviation Upper bound active power deviation ΔP u , upper bound reactive power deviation ΔQ u composition;

[0036] There are 2n1 equal constraints for the active power deviation ΔP and reactive power deviation ΔQ of the PQ node. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0037]

[0038]

[0039] There are n2-m1 equal constraints for the active power deviation ΔP of the P node. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0040]

[0041]

[0042] The lower voltage amplitude inequality constraint of PQ node and upper voltage amplitude inequality constraints There are 2n1 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as:

[0043]

[0044]

[0045] The lower bound active power deviation of the node ΔP l and the upper bound active power deviation ΔP u There are 2m1 inequality constraints in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0046]

[0047]

[0048] P nodes and The lower bound reactive power deviation of the node ΔQ l and upper bound reactive power deviation ΔQ u There are 2n2 inequality constraints in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0049]

[0050]

[0051] P nodes and Node lower voltage amplitude inequality constraint and upper voltage amplitude inequality constraints There are 2n2 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as:

[0052]

[0053]

[0054] The lower bound active power deviation ΔP of the Vθ node l , lower bound reactive power deviation ΔQ l , upper bound active power deviation ΔP u , upper bound reactive power deviation ΔQ u There are 4 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as:

[0055] h i5 =[ΔP l,n ΔP u,n ΔQ l,n ΔQ u,n ] T

[0056]

[0057] Preferably, the gradient corresponding to f is:

[0058]

[0059] in, is the gradient of the objective function f, J T is the transpose of the Jacobian matrix of the power flow equation vector F after the inequality is transformed into an equation, and F is the power flow equation vector after the inequality is transformed into an equation.

[0060] Preferably, the vector function h and its corresponding Jacobian matrix J0 are modified as follows:

[0061] F=h

[0062] ee

[0063] F i =[h i ] +

[0064] J e =J 0e

[0065] (J i ) i =max{(h i ) i ,0}(J 0i ) i

[0066] Among them, F e is the equation vector of the power flow equation after the inequality is transformed into an equation, h e is the equation vector of the power flow equation, F i is the power flow inequality equation vector after the inequality is transformed into an equation, h i is the power flow inequality equation vector, J e is the Jacobian matrix of the equation vector of the power flow equation after the inequality is transformed into an equation, J 0e is the Jacobian matrix of the power flow equation vector, J i is the Jacobian matrix of the power flow inequality equation vector after the inequality is transformed into an equation.

[0067] In a second aspect, an embodiment of the present invention provides a power system flow calculation system, including:

[0068] Building modules to establish a new power constraint flow model;

[0069] The first conversion module converts the inequalities in the new power constraint power flow model into equalities by constructing auxiliary functions;

[0070] The second conversion module converts the equality equation into a least squares problem with bounded constraints;

[0071] The calculation module uses the trust region method to solve the obtained least squares problem and obtains the adjustment amount of variables such as the power system generator output and node voltage as well as the final power flow solution.

[0072] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned power system flow calculation method when executing the computer program.

[0073] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which implements the steps of the above-mentioned power system flow calculation method when executed by a processor.

[0074] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned power system flow calculation method when executing the computer program.

[0075] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which implements the steps of the above-mentioned power system flow calculation method when executed by the electronic device.

[0076] Compared with the prior art, the present invention has at least the following beneficial effects:

[0077] A power system power flow calculation method can expand the power flow solution space and convergence domain through relaxation variables, so that the ill-conditioned power flow problem that the original Newton-Raphson algorithm cannot converge can be solved. For the case where the power flow has no solution, the generator output is infinitely relaxed to obtain a mechanism for a reasonable operation mode. Compared with the traditional power flow, the constrained power flow requires fewer known quantities and can expand the power flow solution space, so that it can well deal with the situation where the output of some generators and the voltage amplitude are difficult to give when calculating the power flow of the new power system; the constrained power flow requires fewer known quantities than the traditional power flow. For the values ​​that are difficult to give in the traditional power flow, we only need to give a range in the constrained power flow. On the basis of the three node types in the traditional power flow, 6 new node types are expanded, so that we can reasonably realize the flexible setting of known and unknown quantities. At the same time, on the basis of the relaxation variables, reasonable constraints are imposed on them, which can ensure that the solution process moves towards the desired result, avoiding the situation where the result of the traditional power flow calculation is not ideal after convergence, so that a more efficient operation mode of the power system can be achieved.

[0078] Furthermore, the benefits of converting the equation into a bounded least squares problem are as follows: restricting each variable to a specific range can be used as a specification of the mapping domain; secondly, restricting the iterative variables to the feasible domain can prevent the generation of infeasible iteration points outside the feasible domain or infeasible iteration directions during the iteration process; and, converting all single-variable simple bound constraints into equations and placing them in the objective function will cause the dimension of the objective function to be very large and reduce the computational efficiency. Therefore, we only convert the inequality constraints into equations and place them in the objective function, while retaining the simple bound constraints of the variables.

[0079] Furthermore, based on the mathematical model of the constrained power flow model, the trust region algorithm is adopted for solving. Compared with the Newton method, the trust region algorithm has higher robustness. For the ill-posed problems that are difficult to solve by the Newton method, the trust region algorithm may converge to a reasonable solution. At the same time, the trust region algorithm can solve well-posed, under-determined and over-determined problems, so that the setting of node types can be flexibly realized. The present invention provides the mathematical analytical expressions and corresponding iterative formats of the power flow model under different conditions. The embodiments prove that the trust region algorithm has good computational efficiency and robustness.

[0080] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0081] In summary, the present invention obtains a new model and algorithm for power system power flow calculation. The proposed method can effectively deal with the problem of traditional power flow convergence difficulty or even convergence failure.

[0082] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a general implementation flow chart of the method of the present invention;

[0084] Figure 2 A flowchart for the specific implementation of the trust region algorithm in solving the new power system constrained power flow model;

[0085] Figure 3 This is a schematic diagram of the basic idea of ​​the trust region algorithm;

[0086] Figure 4 Schematic diagram of expanding the solution space for the relaxation variables of the constrained power flow model;

[0087] Figure 5 To obtain the constrained power flow model and algorithm system for the new power system operation mode;

[0088] Figure 6 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0089] Figure 7 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0092] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0093] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0094] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0095] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0096] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0097] The present invention provides a method for calculating power system flow. On the basis of the traditional power flow model, the generator output of some generator nodes is relaxed and constrained based on the actual system. At the same time, the voltage amplitude of the node without a given voltage amplitude is constrained to obtain a new power system constrained flow model. The constrained flow model is converted into a least squares problem and solved using a trust region algorithm. The specific conversion method and the process of solving the trust region algorithm are given. This research result is expected to provide certain theoretical guidance for the calculation of new power flow.

[0098] See also Figure 1 The present invention provides a method for calculating power flow in a power system, comprising the following steps:

[0099] S1. Establish a new power constraint flow model;

[0100] In order to solve the problem that the output of some generators and node voltages are difficult to determine due to large-scale changes in operating modes in new power systems, key variables such as the output of some generators and node voltages are relaxed from equality constraints to inequality constraints. After introducing inequality constraints, the power flow model is transformed from the original nonlinear equation solution problem to a new power system constrained power flow model.

[0101] In view of the problem that the initial values ​​in traditional power flow models and algorithms are difficult to give or the power flow algorithms are difficult to converge when the initial values ​​are forcibly given due to the introduction of fluctuating power sources, direct current transmission and power electronics technology in new power systems, the present invention studies and researches constrained power flow models.

[0102] For power flow calculation, there are three cases in total: well-conditioned power flow, ill-conditioned power flow, and power flow with no solution.

[0103] For a well-conditioned power flow, we hope that the power flow calculation converges quickly;

[0104] For ill-conditioned power flows, it is hoped that the constrained power flow model can expand the power flow solution space and converge to a feasible solution;

[0105] When there is no solution to the power flow, the constrained power flow model can expand the power flow solution space by infinitely relaxing the generator output so that the power flow converges. According to the power flow convergence result and the degree of out-of-bounds of the infinitely relaxed generator, the load scale, the power generation of the generator and the configuration of the reactive compensation equipment are appropriately adjusted to make the power flow solution return to the feasible domain.

[0106] The node voltage and active output of wind, solar and other power sources in new power systems and some generator nodes in DC transmission systems cannot be given. Therefore, the requirements are relaxed on the basis of the traditional power flow algorithm. Only a range is given relative to the given value. To prevent the voltage amplitude in the power flow result from being too high or too low, resulting in unqualified voltage quality, or the generator output from exceeding the limit to obtain an infeasible solution that does not conform to the actual system, the relevant state variables are constrained according to the actual system, so that the result obtained by solving the problem must be a feasible solution.

[0107] On the basis of the traditional power flow model, the generator output of some generator nodes is relaxed and constrained based on the actual system. At the same time, the voltage amplitude of the nodes without given voltage amplitude is constrained, so as to obtain a new power system constrained power flow model.

[0108] Note that there are three types of nodes in the traditional power flow model: PQ nodes, PV nodes, and balancing nodes. Since the voltage of each node is unknown before the power flow calculation and the active and reactive losses cannot be determined, the active output and reactive output of the balancing node are not given. Since the Newton method cannot solve the problem of inequality constraints, the output of the balancing machine is not constrained. Instead, after the power flow converges, it is judged whether the power flow solution is within the feasible domain based on whether the output of the balancing node generator exceeds the boundary. That is, the balancing node in the traditional power flow model is equivalent to the infinitely relaxed generator node in the constrained power flow model.

[0109] In the constrained power flow model, there are multiple relaxed generator nodes, and there is no concept of balance node. The Vθ node in the constrained power flow model no longer represents the concept of balance node, but only refers to the voltage reference node with given voltage amplitude and phase angle.

[0110] That is, in the present invention, the voltage amplitudes of all nodes except the Vθ node are relaxed, the active output of some PV nodes is selectively relaxed, and the reactive output of all PV nodes is relaxed.

[0111] Therefore, based on the three node types of the traditional power flow model - PQ node, PV node and Vθ node, the constrained power flow model has expanded new node types, P node, Q node, V node, QV node, PQV node, Node, where P, Q, U, and θ are all to be determined. In the new power system constrained power flow model, there are multiple relaxed generators, so there is no concept of balancing machine in the constrained power flow model. The Vθ node only represents the voltage reference node with given voltage amplitude and voltage phase.

[0112] The node numbering of the present invention is specifically set as shown in the following table:

[0113] Table 1 Node number settings

[0114]

[0115] Under the above node number, the iteration variables are:

[0116] x=[e1,f1,…,e n-1 ,f n-1 ] T (1)

[0117] After relaxing some variables, in order to ensure that the relaxed variables do not exceed the bounds in the power flow calculation results, constraints are imposed on the relaxed variables. Thus, the traditional power flow model consisting of a set of nonlinear inequality equations is transformed into a constrained power flow model consisting of nonlinear equations and nonlinear inequalities.

[0118] Then the equality constraint in the constrained power flow model is expressed as:

[0119]

[0120] The inequality constraints in the constrained power flow model are expressed as:

[0121]

[0122]

[0123] x l ≤x≤x u

[0124] Wherein, N represents the set of all nodes, A represents the set of nodes that relax the active output of the generator, B represents the set of nodes that relax the reactive output of the generator, and C represents the set of nodes that relax the node voltage amplitude.

[0125] S2, converting the inequality in the new power constraint power flow model obtained in step S1 into an equality equation by constructing an auxiliary function;

[0126] The constrained power flow model is essentially composed of a series of equations containing nonlinear equalities and nonlinear inequalities. In order to solve such a set of equations, numerical optimization theory is usually used to solve it. Therefore, it is necessary to first convert the inequalities into equalities, and then convert the nonlinear equalities and nonlinear inequalities in the constrained power flow model into a least squares problem with bound constraints for effective solution.

[0127] S201, converting the constrained power flow model into a set of nonlinear inequalities and nonlinear equality equations under standard bounded constraints, wherein the bilateral constraints of the upper and lower bounds are converted into two unilateral constraints;

[0128] S202. According to two commonly used conversion methods, the inequality is converted into an equation, thereby converting the problem of solving the nonlinear inequality and nonlinear equation system into a bounded constrained least squares problem represented by the following equation, so as to facilitate solving using a numerical optimization algorithm.

[0129]

[0130] First, the constrained power flow model is standardized as:

[0131]

[0132] Among them, the bilateral constraints of the upper and lower bounds are transformed into two unilateral constraints, and both are expressed as less than 0.

[0133] use represents the equation vector, represents the inequality vector, L represents the lower bound vector of the variable, and U represents the upper bound vector of the variable, so formula (6) is expressed as:

[0134]

[0135] For the constrained power flow model, C E and C I In order to transform equation (7) into a least squares problem, two methods are used: introducing slack variables and constructing penalty functions.

[0136] The first approach is to introduce an additional slack variable s, convert the inequality into an equality, and add the corresponding boundary constraints.

[0137] Specifically, for (7), a non-negative slack variable should be introduced The non-negativity of s is determined by the inequality constraint. At this time, the iteration variable in the least squares problem is x = (v T ,s T ) T , Given by:

[0138]

[0139] It can be seen that the core idea of ​​adding slack variables is to transfer the "inequality sign" of the inequality to the variable, thereby converting the nonlinear inequality and equality equations into a simple variable constraint equality equation.

[0140] The biggest advantage of constructing the least squares problem by adding slack variables is that the objective function is relatively simple, so the calculation of the Jacobian matrix during the iteration process is also relatively simple. However, this algorithm also has its shortcomings, because adding slack variables will increase the dimension of the iterative variables in the calculation process, which will increase the amount of calculation to a certain extent.

[0141] In addition to adding slack variables, another common transformation method is to construct an auxiliary function of the continuously differentiable function, which is constructed as follows:

[0142] [t] + =max{t,0} 2 / 2 (9)

[0143] At this time, the iteration variable x = v T , Given by:

[0144]

[0145] When c i When (x)≤0, [c i (v)] + is equal to 0, so The contribution of is 0, which will make f(x) move towards 0;

[0146] On the contrary, when c i When (x)>0, [c i (v)] + If it is greater than zero, it will give F(x) a positive component, so that f(x) moves in the direction of increase, that is, away from 0.

[0147] Conversely, constructing continuously differentiable functions also has advantages and disadvantages relative to adding slack variables.

[0148] First, there is no need to add additional variables when constructing auxiliary functions, so fewer iterative variables need to be solved during the iteration process. However, relatively speaking, when constructing auxiliary functions, F(x) and its corresponding Jacobian matrix will be more complicated, which will also increase the amount of calculation to a certain extent.

[0149] S3, converting the equation obtained in step S2 into a least squares problem with bounded constraints;

[0150] The two algorithms for converting inequalities into equalities in step S2 have similar computational complexity. The present invention uses the algorithm for constructing an auxiliary function as an example to illustrate the process of establishing the least squares problem.

[0151] In the iterative process, the formation of the objective function and the Jacobian matrix is ​​very important. Based on the construction of auxiliary functions, the present invention firstly calculates the input equation vector C Eand inequality vector C I A vector function h and its corresponding Jacobian matrix J0 are formed; then the vector function h and its corresponding Jacobian matrix J0 are modified according to the constructed auxiliary function, so as to obtain F and its corresponding Jacobian matrix J.

[0152] The block representation of the vector function h and J0 in the present invention is shown in formula (11) and formula (12):

[0153] h=[C E C I ] T =[h e1 h e2 h i1 h i2 h i3 h i4 h i5 ] T (11)

[0154] J0=[J 0e1 J 0e2 J 0i1 J 0i2 J 0i3 J 0i4 J 0i5 ] T (12)

[0155] Among them, h e represents the equation vector; J e Indicates h e The corresponding Jacobian matrix; h i represents the inequality vector; J i Indicates h i The corresponding Jacobian matrix.

[0156] h e and h i The elements in the voltage deviation ΔU 2 , active power deviation ΔP, reactive power deviation ΔQ, lower voltage deviation Lower bound active power deviation ΔP l , lower bound reactive power deviation ΔQ l , Upper voltage deviation Upper bound active power deviation ΔP u , upper bound reactive power deviation ΔQ u The matrix block representation is for the convenience of programming and modification.

[0157] 1) There are 2n1 equal constraints for the active power deviation ΔP and reactive power deviation ΔQ of the PQ node. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0158]

[0159]

[0160] 2) There are n2-m1 equal constraints for the active power deviation ΔP of the P node. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0161]

[0162]

[0163] 3) Inequality constraint on the lower voltage amplitude of the PQ node and upper voltage amplitude inequality constraints There are 2n1 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as:

[0164]

[0165]

[0166] 4) The lower bound active power deviation of the node ΔP l and the upper bound active power deviation ΔP u There are 2m1 inequality constraints in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0167]

[0168]

[0169] 5) P nodes and The lower bound reactive power deviation of the node ΔQ l and upper bound reactive power deviation ΔQ u There are 2n2 inequality constraints in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows:

[0170]

[0171]

[0172] 6) P nodes and Node lower voltage amplitude inequality constraint and upper voltage amplitude inequality constraints There are 2n2 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as:

[0173]

[0174]

[0175] 7) The lower bound active power deviation ΔP of the Vθ node l , lower bound reactive power deviation ΔQ l , upper bound active power deviation ΔP u , upper bound reactive power deviation ΔQ u There are 4 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as:

[0176] h i5 =[ΔP l,n ΔP u,n ΔQ l,n ΔQ u,n ] T (25)

[0177]

[0178] The expressions of all elements are shown in formulas (28) and (29).

[0179]

[0180]

[0181] On the basis of forming the vector functions h and J0, the vector functions h and J0 are modified to obtain the vector functions F and J in the least squares problem. The specific modifications are as follows:

[0182] F e =h e (29)

[0184] F i =[h i ] + (30)

[0185] J e =J 0e (31)

[0186] (J i ) i =max{(h i ) i ,0}(J 0i ) i (32)

[0187] At this point, the vector function F and its corresponding Jacobian matrix J in the least squares problem are formed, then the gradient corresponding to f is:

[0188]

[0189] S4. Use the trust region method to solve the least squares problem obtained in step S3 to obtain the adjustment amount of variables such as the power system generator output and node voltage and the final power flow solution.

[0190] In another embodiment of the present invention, a power system flow calculation system is provided, which can be used to implement the above-mentioned power system flow calculation method. Specifically, the power system flow calculation system includes a construction module, a first conversion module, a second conversion module and a calculation module.

[0191] Among them, the construction module establishes a new power constraint power flow model;

[0192] The first conversion module converts the inequalities in the new power constraint power flow model into equalities by constructing auxiliary functions;

[0193] The second conversion module converts the equality equation into a least squares problem with bounded constraints;

[0194] The calculation module uses the trust region method to solve the obtained least squares problem and obtains the adjustment amount of variables such as the power system generator output and node voltage as well as the final power flow solution.

[0195] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the power system flow calculation method, including:

[0196] A new power constraint flow model is established; the inequalities in the new power constraint flow model are converted into equality equations by constructing auxiliary functions; the equality equations are converted into bounded constrained least squares problems; the trust region method is used to solve the obtained least squares problem to obtain the adjustment amounts of variables such as the power system generator output and node voltage as well as the final power flow solution.

[0197] See also Figure 6 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the power system flow calculation method in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the power system flow calculation system in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0198] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 6 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0199] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0200] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0201] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0202] See also Figure 7 , the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to execute the above-mentioned power system flow calculation method.

[0203] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, for example, wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0204] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0205] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the power system flow calculation method in the above embodiment; the processor may load and execute the following steps:

[0206] A new power constraint flow model is established; the inequalities in the new power constraint flow model are converted into equality equations by constructing auxiliary functions; the equality equations are converted into bounded constrained least squares problems; the trust region method is used to solve the obtained least squares problem to obtain the adjustment amounts of variables such as the power system generator output and node voltage as well as the final power flow solution.

[0207] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0208] The present invention uses Matlab program as a carrier to implement a constrained power flow model and algorithm system for obtaining a new power system operation mode. The IEEE30 node system and the IEEE118 node system are calculated and analyzed, and the case30 file and case118 file in the MATPOWER package are read by Matlab to obtain the data of the IEEE30 system and the IEEE118 system. Different variable relaxations and different upper and lower limits of relaxation variables are set to form different constrained power flow models. At the same time, the results calculated by the Newton-Raphson method of MATPOWER are compared. The results prove the effectiveness of the constrained power flow model in expanding the solution space and study the model transformation mechanism for obtaining a reasonable operation method.

[0209] The embodiments of the present invention for calculation and analysis are the IEEE30 node system and the IEEE118 node system. The IEEE30 and IEEE118 node systems are standard test systems commonly used in the field of power systems, formulated by the Institute of Electrical and Electronics Engineers (IEEE). They are intended to provide a standardized platform for power system research, simulation and testing. Both systems are built based on a simplified power structure model and include key power system components such as generators, transformers, transmission lines and loads. These components are interconnected by wires and together constitute the overall architecture of a power network.

[0210] The IEEE 30-node system has 6 generators, 41 branches, and a power base value of 100MVA; the IEEE 118-node system has 54 generators, 186 branches, and a power base value of 100MVA. If there is no special instruction, the maximum number of iterations K is set by default. max =100, convergence accuracy ε = 1×10 -5 .

[0211] The constrained power flow model is obtained by relaxing some variables on the basis of the traditional power flow model. Different constrained power flow models can be obtained by relaxing different numbers and types of variables. The solution space of power system power flow refers to the set of possible voltage amplitude and voltage phase angle solutions for a given system in power flow calculation.

[0212] When solving power system power flow problems, the power flow may not converge. This is generally caused by the following two reasons:

[0213] 1) Ill-conditioned power flow problem: The power flow itself has a solution, but the algorithm is not good enough to find the solution;

[0214] 2) No solution for power flow: Due to improper load distribution and generator output configuration in the power system, it is impossible to obtain a reasonable real number solution when performing power flow calculations in the power system.

[0215] The constrained power flow model studied in this invention is aimed at the situation that the Newton-Raphson algorithm cannot converge in solving the new power system. It is hoped that for the pathological power flow problem of the Newton-Raphson algorithm, the constrained power flow model can expand the solution space and make it have a reasonable solution; when facing the situation that there is no solution to the power flow, the constrained power flow can greatly relax the constraints on the output of individual generators and infinitely expand the output range of the relaxed generators, that is, infinitely relax the output of individual generators, so that the power flow calculation can converge. Afterwards, by observing the output size of the infinitely relaxed generator, further analysis can be performed and effective measures can be found to reasonably adjust the system parameters, such as adjusting the given output of other generators or reducing the load.

[0216] For the same system, the smaller the load, the easier it is to converge, because at this time you only need to reduce the generator output. When the load is larger, the tidal current is closer to the pathological state or even the tidal current itself has no solution, because the maximum output of the generator is limited, it is not easy to achieve power balance, and therefore it is difficult to find a suitable tidal current solution. The present invention increases the system load to form a pathological tidal current problem, and observes whether the tidal current solution space exists under the pathological tidal current of the same load for different constrained tidal current models, thereby analyzing the effectiveness of the constrained tidal current model in expanding the solution space. Under the pathological tidal current of the same load, if the constrained tidal current model I converges, but the tidal current model II diverges, it can be explained that the solution space of the tidal current model I is larger than the solution space of the tidal current model II; similarly, for the same system, different constrained tidal current models can also be used to analyze the effectiveness of the constrained tidal current model in expanding the solution space according to the maximum load increase allowed when the different constrained tidal current models converge.

[0217] Finite relaxation of all relaxed generators’ active output P and reactive output Q, the upper and lower bounds of the constraints are the corresponding upper and lower bounds of each generator in the actual system.

[0218] For the IEEE 30-node system, the number of generator nodes in the finite relaxation part corresponding to the relaxation of variables and the constraint rule d) is 1, 2, and 4, respectively, forming three constrained power flow models. By increasing the loads of 3, 7, 15, and 30 load nodes and the total load respectively, it is observed whether different constrained power flow models have solutions, and the effectiveness of expanding the solution space of the constrained power flow model is analyzed. The maximum load increase allowed when different constrained power flow models of the IEEE 30-node system converge is shown in Table 1.

[0219] Table 1 Maximum load increase of different IEEE30 models

[0220]

[0221] For the IEEE118 system, the number of generator nodes in the finite relaxation part corresponding to the relaxation of variables and the constraint rule d) is 1, 14, and 24, respectively, forming three constrained power flow models. By increasing the loads of 2, 52, and 101 load nodes and the total load, we observe whether different constrained power flow models have solutions and analyze the effectiveness of expanding the solution space of the constrained power flow model. The maximum load increase allowed when different constrained power flow models of the IEEE118 node system converge is shown in the table below. As can be seen from the table below, the more relaxed the number of generators, the greater the maximum load increase allowed by the system when the model converges. Therefore, the ill-conditioned problems that cannot be solved by the model with a small number of relaxed active generators under heavy loads can be well solved by the model with a large number of relaxed active generators, that is, increasing the number of relaxed active generators may increase the solution space of the power flow and solve the ill-conditioned problems that were originally unsolvable. Therefore, we can conclude that the more relaxed the number of generators, the larger the power flow solution space of the system. In traditional power flow calculations, only the generator output of the Vθ node is infinitely relaxed, that is, the number of relaxed generators in the traditional power flow model is 1. However, in the constrained power flow model, the number of relaxed generators can be reasonably increased significantly, which can expand the solution space of the power flow and thus solve the pathological power flow that cannot be solved by the traditional power flow.

[0222] From the above analysis, it can be seen that increasing the number of slack variables may expand the solution space of the power flow. In addition, different constraints on slack variables will result in different sizes of solution spaces. It is obvious that the wider the upper and lower bounds of the slack variables, the easier the power flow should converge and the larger the solution space of the power flow.

[0223] Table 2 Maximum load increase of different IEEE118 models

[0224]

[0225] Table 3 shows the convergence of IEEE118 under different constraint conditions in different constraint models. As can be seen from Table 3, the more the number of relaxed generators is, the wider the upper and lower bounds of the relaxed variables are, the easier the power flow is to converge, and the larger the solution space of the power flow is.

[0226] Table 3 Convergence of IEEE118 under different constraint models

[0227]

[0228] Therefore, when the power flow does not converge, we can increase the solution space of the power flow calculation by increasing the number of slack variables and relaxing the constraints, so that the power flow converges.

[0229] From the above analysis, it can be seen that relaxing constraints or adding slack variables can increase the convergence domain of the power flow, so that the power flow calculation that originally could not converge can converge. For the case of non-convergence of the power flow, if it is a pathological power flow with a solution, we can increase the number of slack variables, such as increasing the number of relaxed generators, and impose reasonable upper and lower bounds on the relaxed generators, so that the power flow calculation converges to a feasible solution; however, before the power flow calculation, we do not know whether the system has a solution under the current topological structure, generator output, load level and other given parameters. If the power flow itself has no solution, we may still not be able to make the power flow converge by relaxing variables and adding constraints to the slack variables. At this time, we can select some generator nodes for infinite relaxation. The advantage of this is that we can greatly expand the solution space of the power flow, which may make the system with no solution to the power flow have a solution, but the solution at this time may not be a feasible solution, because in the actual system, the output of the generator is limited, and the output of the infinitely relaxed generator may exceed its actual output upper and lower bounds and is not in line with reality. Therefore, we need to adjust the system parameters by infinitely relaxing the output of the generator node beyond the limit, such as adjusting the given generator output, adjusting the transformer tap and reactive compensation equipment. Generally speaking, when there is no solution to the constrained power flow with upper and lower bounds, first select the active output of the generator node for infinite relaxation, and then adjust the nearby generator output according to the degree of the active output of the generator with infinite relaxation after the power flow converges, so that the active output of the generator with infinite relaxation returns to the actual upper and lower bounds when the power flow converges, so that the power flow solution returns to the feasible domain, thereby obtaining a reasonable operation mode; if the power flow solution still cannot return to the feasible domain, consider shedding the load to reduce the load level. Usually, the active output P of the generator node is selected for infinite relaxation, while the reactive output Q of the generator node is not selected for infinite relaxation. The reason is that the active power in the power system is easy to balance, but the reactive power is not easy to balance, so it is often easier to adjust the active power than the reactive power.

[0230] The present invention increases the load of the load node to make the constrained power flow unsolvable, and converges the power flow by infinitely relaxing the active output of the generator. Then, according to the output crossing degree of the infinitely relaxed generator with active output, the output of other generators is adjusted nearby to make the power flow return to the feasible domain, thereby illustrating the model transformation mechanism for obtaining a reasonable operation mode.

[0231] For the IEEE30 system, infinite relaxation is applied to generator 1 and finite relaxation is applied to generator 25 in the solution model, so that the required load power of node 3 is increased proportionally, that is, the load power of node 3 is set to S L3=k×(0.024+j0.012), when k=31, the power flow converges, but it is observed that the output of the generator at node 1 is 0.82061+j0.32448, that is, the active output exceeds the upper limit of 0.8. This is not a feasible solution, nor is it a feasible startup method. At this time, it is only necessary to adjust the output of the generator near node 3. At the same time, it is observed that the active power of generator 22 is sufficient, so the active output of generator 22 is increased. For example, let P Gset22 From the original 0.2195 to 0.35, let P g22 =P Gset22 , and other parts remain unchanged. By solving the model, we can get the generator output of node 1 as 0.67231+j0.022464, which returns to the feasible domain.

[0232] For the IEEE118 system, infinite relaxation is applied to generator 1 in the solution model, and finite relaxation is applied to generators 4, 6, and 69, so that the load power of node 67 is increased proportionally, that is, the load power of node 67 is set to S L67 =k×(0.19+j0.02), when k=5, the power flow converges. At this time, it is observed that the output of the generator at node 1 is 1.1132+j0.15165, that is, the active output exceeds the upper limit 1, which is not a feasible solution. At this time, it is necessary to adjust the output of the generator near node 67, so increase the active output of generator node 8, for example, let P g8 By increasing it from the original 0 to 0.5 and keeping other parts unchanged, the model is solved and the output of the generator at node 1 can be obtained as 0.6395+j0.15045, which returns to the feasible domain.

[0233] When the traditional load flow (TLF) uses the Newton-Raphson algorithm for flow calculation, a generator with a larger capacity is set as a balancing node to balance the power, and the rest of the system's generator nodes are PV nodes with given active output and voltage amplitude. In the constrained load flow model (CLF), a generator node with a given voltage amplitude is selected as the Vθ node. For other generator nodes, if the active output and voltage amplitude of the generator can be given, the node is set as a PV node; if the active output of the generator can be given but the voltage amplitude is not easy to give, the voltage amplitude of the generator is relaxed and the Vθ node is set. The node is P node; if the generator output and voltage amplitude of this node are not easy to give, then this node is set as PQVθ node.

[0234] When calculating the power system flow, it may be difficult to give the active output of some generator nodes and the node voltage amplitude. If the Newton-Raphson algorithm is used for flow calculation, it is necessary to forcibly give the active output of some generator nodes and the node voltage amplitude that are difficult to give to form the PV node. This may cause the node voltage amplitude to be too high or too low in the flow calculation results. Too high or too low node voltage amplitude may cause the following problems:

[0235] 1) Voltage amplitude that is too high or too low may cause equipment overload, equipment startup difficulty, shorten equipment life, or even damage the equipment;

[0236] 2) Abnormal increase or decrease in voltage value will reduce the stability of the power system. If the situation is serious, it may cause voltage collapse or even paralysis of the entire system, leading to large-scale power outages and equipment damage;

[0237] 3) Low voltage may also cause relay protection to operate, thereby changing the grid topology and reducing the transmission limit;

[0238] 4) Voltage that is too high or too low will reduce the quality of power, thereby reducing the efficiency of equipment and leading to reduced factory production capacity.

[0239] For the IEEE30 system, the traditional power flow uses the Newton-Raphson method to solve the node voltage amplitude in the interval [0.9606,1.0000] when the voltage amplitude of the PV node is given as 1 and the active output is given. The voltage quality is good and the power flow solution is a feasible solution. However, for new energy sources such as wind, light, and solar energy, due to their volatility, their voltage amplitude and output cannot be accurately given. If the PV node voltage amplitude is given too low or too high, the voltage amplitude of the solution result will be too low or too high.

[0240] For example, in the IEEE30 system, using the traditional power flow algorithm, if the voltage amplitude of the given PV nodes is 0.98, it will be found that the voltage amplitude of many PQ nodes in the solution is lower than 0.95, the voltage quality is unqualified, and the solution result is not a feasible solution.

[0241] In the constrained power flow model CLF, we do not give the voltage amplitude of the PV node, but give its upper and lower bounds as [0.95,1.0 5], alsoWithout giving its active output, the corresponding upper and lower bounds are given, that is, all PV nodes are relaxed to PQVθ nodes. To ensure the feasibility of the solution, the voltage amplitudes of all PQ nodes are constrained to [0.95, 1.05]. If the infinitely relaxed generator node output exceeds the bounds after the solution, there are generally two solutions. The first is to adjust the nearby generators according to the degree of the infinitely relaxed generator node exceeding the bounds. The second is to add the output of the infinitely relaxed generator node generator as a constraint to the original model.

[0242] The second solution is adopted here; by solving the constrained power flow model, the voltage amplitude range of all nodes is obtained to be [0.9671, 1.0148], and the output of each generator does not exceed the limit, which is a feasible solution and an efficient operation mode. The specific solution results are compared in Table 4:

[0243] Table 4 Calculation results under two IEEE30 models

[0244]

[0245] For the IEEE118 system, as shown in Table 5, there are similar conclusions. In the traditional Newton-Raphson algorithm, if the given voltage of the PV node is low, such as 0.98, the voltage amplitude obtained is in the interval [0.9399, 1.035], and there are many PQ node voltage amplitudes lower than 0.95, and the power quality is poor. After adopting the constrained power flow model, we have finitely relaxed some generators (since there are 54 generators in the 118 system, without loss of generality, 24 generators are relaxed here), and the voltage amplitudes obtained are all between [0.9571, 1.0500], and the output of each relaxed generator does not cross the boundary, and the result is a feasible solution.

[0246] Table 5 Calculation results under two IEEE118 models

[0247]

[0248] From the above analysis, it can be seen that constrained power flow requires fewer known quantities than traditional power flow. For values ​​that are difficult to give in traditional power flow, we only need to give a range in constrained power flow. Based on the three node types in traditional power flow, six new node types have been expanded, so we can reasonably realize the flexible setting of known and unknown quantities. At the same time, based on the slack variables, we can reasonably constrain them to ensure that the solution process moves towards the desired results, avoiding the situation where the results of traditional power flow calculations are not ideal after convergence, so that a more efficient operation mode of the power system can be achieved.

[0249] In new power systems, constrained power flow has better performance in solving pathological power flow and finding measures to adjust parameters when there is no solution to the power flow. Constrained power flow can expand the solution space of the power flow and solve problems that traditional power flow cannot solve.

[0250] However, constrained power flow also has shortcomings compared to traditional power flow. In a well-conditioned power flow with a good initial value, the Newton-Raphson algorithm can converge quickly, while the number of iterations and calculation time of constrained power flow are slightly more than those of the Newton-Raphson algorithm. The main reasons are as follows: First, when constrained power flow imposes upper and lower bounds on the slack variables, two one-sided inequality constraints will be introduced or an equation in the traditional power flow will be converted into two one-sided inequalities. For example, when the voltage amplitude of each PQ node in the traditional power flow is constrained by upper and lower bounds, 2n1 one-sided inequality constraints will be introduced, which will greatly increase the number of equations in the constrained power flow, thereby affecting the calculation speed and number of iterations; second, when the initial value is good, that is, when the initial value is in the neighborhood of the power flow solution, the Newton method has a quadratic convergence characteristic, which will make the power flow converge quickly, but the trust region algorithm cannot guarantee that each step uses a Newton step, thereby increasing the number of iterations.

[0251] In summary, the present invention provides a power system flow calculation method and system. In view of the fact that traditional power flow models and algorithms are difficult to converge in the context of new power systems, based on the traditional power flow model, some variables are relaxed and constrained to obtain a new power system constrained power flow model, and the trust region algorithm is used to solve the model. The main research and conclusions are as follows:

[0252] 1) A new type of power system constrained power flow model is studied. This model breaks through the limitation of traditional power flow models that can only relax the output of one generator, thereby expanding the solution space of the power flow. Inequality constraints are added to the power flow model to reasonably constrain the control variables and state variables to ensure that the power flow converges to a reasonable solution.

[0253] 2) A trust region algorithm that can efficiently solve inequalities and equations is studied, and the algorithm is used to solve a new power system constrained power flow model. The analysis of the implementation example proves the effectiveness of the trust region algorithm in solving the new power system constrained power flow model.

[0254] 3) A power system flow calculation program was written and an example analysis was performed. The results show that for well-conditioned flow, the constrained flow model can quickly converge to a reasonable solution; for the pathological flow problem faced by traditional flow, the constrained flow can expand the solution space of the flow and find a feasible solution for the flow; for the case where there is no solution to the flow, the constrained flow can greatly expand the flow solution space by infinitely relaxing the output of some generators, thereby making the flow converge. According to the degree of out-of-bounds of the infinitely relaxed generators and the network topology, parameter adjustment measures are given to make the flow return to the feasible domain.

[0255] In new power systems, the idea of ​​relaxation and constraint of constrained power flow variables proposed in this study can provide certain theoretical guidance for solving pathological power flows and adjusting parameters when there is no solution to the power flow.

[0256] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0257] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0258] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0259] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0260] 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 on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0261] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0262] If the integrated module / 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 present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0263] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0264] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0265] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0266] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for calculating power system flow, characterized in that: The following steps are involved: Establish a new power constraint power flow model; The inequalities in the new power constraint power flow model are transformed into equalities by constructing auxiliary functions. Transform the equality equation into a least squares problem with bounded constraints; The trust region method is used to solve the obtained least squares problem to obtain the adjustment amount of variables such as power system generator output and node voltage as well as the final power flow solution.

2. The power system flow calculation method according to claim 1, characterized in that: In the new power constraint flow model, the equality constraint is expressed as: The inequality constraints in the constrained power flow model are expressed as: x l ≤x≤x u Where N represents the set of all nodes, A represents the set of nodes that relax the active output of the generator, B represents the set of nodes that relax the reactive output of the generator, and C represents the set of nodes that relax the node voltage amplitude. For nodes i, e i represents the real part of voltage, a i represents the real part of the current, f i represents the imaginary part of voltage, b i Represents the imaginary part of the current, P Li Indicates the active power absorbed by the load, P Gi Indicates the active power generated by the generator, Q Li Indicates the reactive power absorbed by the load, Q Gi Indicates the reactive power generated by the generator. Represents a given voltage amplitude, x l Represents the lower bound of the iteration variable, x u represents the upper bound of the iteration variable, G ij represents the real part of the node admittance matrix, B ij represents the real part of the node admittance matrix.

3. The power system flow calculation method according to claim 1, characterized in that: The inequalities in the new power constraint flow model are converted into equalities as follows: The constrained power flow model is transformed into a set of nonlinear inequalities and nonlinear equality equations under standard bounded constraints, where the bilateral constraints of the upper and lower bounds are transformed into two unilateral constraints; Construct a penalty function to convert inequalities into equalities.

4. The power system flow calculation method according to claim 3, characterized in that: The penalty function is constructed as follows: An auxiliary function of a continuously differentiable function is constructed based on the idea of ​​penalty function. By adding a penalty term related to the degree of constraint violation to the objective function, the value of the objective function increases when the constraint is violated.

5. The power system flow calculation method according to claim 1, characterized in that: The equation is transformed into a least squares problem with bounded constraints: Based on the construction of auxiliary functions, firstly, according to the input equation vector C E and inequality vector C I Form the vector function h and its corresponding Jacobian matrix J0; Then, the vector function h and its corresponding Jacobian matrix J0 are modified according to the constructed auxiliary function to obtain F and its corresponding Jacobian matrix J.

6. The power system flow calculation method according to claim 5, characterized in that: The least squares problem with bounded constraints is as follows: Among them, x is the iteration variable, l is the lower bound of the iteration variable, u is the upper bound of the iteration variable, F(x) is the power flow equations after the inequality is transformed into an equation, and f(x) is the objective function.

7. The method for calculating power system flow according to claim 5, characterized in that: The block representation of the vector function h and J0 is as follows: Among them, h e represents the equation vector; J e Indicates h e The corresponding Jacobian matrix; h i represents the inequality vector; J i Indicates h i The corresponding Jacobian matrix; h e and h i The elements in are determined by the voltage deviation ΔU 2 , active power deviation ΔP, reactive power deviation ΔQ, lower voltage deviation Lower bound active power deviation ΔP l , lower bound reactive power deviation ΔQ l , Upper voltage deviation Upper active power deviation ΔP u , upper bound reactive power deviation ΔQ u composition; There are 2n1 equal constraints for the active power deviation ΔP and reactive power deviation ΔQ of the PQ node. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows: There are n2-m1 equal constraints for the active power deviation ΔP of the P node. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows: The lower voltage amplitude inequality constraint of PQ node and upper voltage amplitude inequality constraints There are 2n1 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as: The lower bound active power deviation of the node ΔP l and the upper bound active power deviation ΔP u There are 2m1 inequality constraints in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows: P nodes and The lower bound reactive power deviation of the node ΔQ l and upper bound reactive power deviation ΔQ u There are 2n2 inequality constraints in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as follows: P nodes and Node lower voltage amplitude inequality constraint and upper voltage amplitude inequality constraints There are 2n2 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as: The lower bound active power deviation ΔP of the Vθ node l , lower bound reactive power deviation ΔQ l , upper bound active power deviation ΔP u , upper bound reactive power deviation ΔQ u There are 4 in total. The vector function h represented by blocks and the corresponding Jacobian matrix J are expressed as: h i5 =[ΔP l,n ΔP u,n ΔQ l,n ΔQ u,n ] T 8. The method for calculating power system flow according to claim 5, characterized in that: The gradient corresponding to f is: in, is the gradient of the objective function f, J T is the transpose of the Jacobian matrix of the power flow equation vector F after the inequality is transformed into an equation, and F is the power flow equation vector after the inequality is transformed into an equation.

9. The method for calculating power system flow according to claim 8, characterized in that: The specific modification of the vector function h and its corresponding Jacobian matrix J0 is: F=h ee F i =[h i ] + I e =J 0e (J i ) i =max{(h i ) i ,0}(J 0i ) i Among them, F e is the equation vector of the power flow equation after the inequality is transformed into an equation, h e is the equation vector of the power flow equation, F i is the power flow inequality equation vector after the inequality is transformed into an equation, h i is the power flow inequality equation vector, J e is the Jacobian matrix of the equation vector of the power flow equation after the inequality is transformed into an equation, J 0e is the Jacobian matrix of the power flow equation vector, J i is the Jacobian matrix of the power flow inequality equation vector after the inequality is transformed into an equation.

10. A power system flow calculation system, characterized in that: include: Building modules to establish a new power constraint flow model; The first conversion module converts the inequalities in the new power constraint power flow model into equalities by constructing auxiliary functions; The second conversion module converts the equality equation into a least squares problem with bounded constraints; The calculation module uses the trust region method to solve the obtained least squares problem and obtains the adjustment amount of variables such as the power system generator output and node voltage as well as the final power flow solution.

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