Power system planning method based on multivariable dynamic analysis

Abstract

The invention relates to the technical field of power system analysis, in particular to a power system planning method based on multivariable dynamic analysis, and the method comprises the steps: obtaining the related data of a power system, and constructing a DC optimal power flow model with the minimum power generation cost; converting the DC optimal power flow model based on multi-parameter planning; considering the influence of the dynamic change parameters on the power system and constructing a critical region; and solving a planning result according to the critical region and the mapping relation between the dynamic change parameters and the power system planning result. The objective of the invention is to solve the limitation caused by the change of technical and economic parameters during the planning and deductive analysis of a power system, achieve the deductive analysis of continuously changing technical and economic parameters, and improve the reliability of a planning result.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and in particular to a power system planning method based on multivariable dynamic analysis. Background Art

[0002] As the proportion of renewable energy power generation technology in power systems increases significantly, it becomes crucial to ensure the economic operation of the system and rationally plan future renewable energy access; therefore, planning and simulation analysis of power systems is particularly important.

[0003] Current technical means mainly rely on methods based on electricity market pricing mechanisms or clearing rules to predict and evaluate the system behavior after the integration of renewable energy into the power grid. Although these methods can provide relatively accurate analysis results for scenarios under specific conditions. However, in the face of the continuous changes in technical and economic parameters over time, policies and other factors in reality, the existing single scenario analysis methods have obvious limitations; in particular, existing technologies usually assume relatively static boundary conditions. When encountering dynamic factors such as cost reduction, efficiency improvement or market rule adjustment in actual operations, traditional methods are difficult to quickly adapt and accurately reflect the impact of these dynamic factors on long-term planning decisions. In addition, re-executing the entire planning and deduction analysis process every time a key parameter changes is not only time-consuming and labor-intensive, but frequent iterations may also introduce additional errors, affecting the reliability of the analysis results. Summary of the invention

[0004] In order to solve the limitations caused by changes in technical and economic parameters during power system planning and deduction analysis, realize the deduction analysis of continuously changing technical and economic parameters, and improve the reliability of planning results, the present invention provides a power system planning method based on multivariable dynamic analysis, and the technical solutions adopted are as follows:

[0005] The present invention provides a power system planning method based on multivariable dynamic analysis, the method comprising:

[0006] Obtain power system related data and build a DC optimal power flow model to minimize power generation cost;

[0007] Transform the DC optimal power flow model based on multi-parameter planning;

[0008] Consider the impact of dynamically changing parameters on the power system and construct critical areas;

[0009] The planning results are solved based on the mapping relationship between critical areas and dynamically changing parameters and power system planning results.

[0010] Furthermore, obtaining power system related data and constructing a DC optimal power flow model with minimum power generation cost includes:

[0011] Obtain the electrical parameters of each branch of the power system and extract the branch inductive reactance matrix;

[0012] Obtain the voltage phase angle of each node in the power system and extract the voltage phase angle vector;

[0013] The branch power flow equation is constructed based on the branch inductance matrix and the voltage phase angle vector.

[0014] Furthermore, obtaining power system related data and constructing a DC optimal power flow model with minimized power generation cost also includes:

[0015] Construct the generator group association matrix and the new energy association matrix according to the connection relationship between the power system nodes and the generator groups and new energy equipment;

[0016] Obtaining the active power vector, active load vector and admittance matrix of the power system;

[0017] The power balance equation is constructed based on the generator set correlation matrix, new energy correlation matrix, active power vector, active load vector and admittance matrix.

[0018] Furthermore, the constraints of the DC optimal power flow model include:

[0019] The branch flow constraint condition can be expressed as:

[0020]

[0021] In the formula, and represents the dual variable of the branch power flow constraint at time t, Indicates the minimum active power flow limit of branch j, PL jt represents the active power flow limit of branch j at time t, Indicates the maximum active power flow limit of branch j, Ω L Represents a branch set;

[0022] The power balance constraint can be expressed as:

[0023]

[0024] In the formula, and The dual variable representing the active power constraint at time t, Indicates the minimum active power limit of generator set i, PG i,t represents the active power of generator set i at time t, Indicates the maximum active power limit of generator set i, Ω G Represents a collection of generator sets.

[0025] Furthermore, the conversion of the DC optimal power flow model based on multi-parameter planning includes:

[0026] Extract multi-parameter vectors and decision variable vectors;

[0027] The objective function and constraints of minimizing power generation cost are redefined based on the multi-parameter vector and decision variable vector, which can be expressed as:

[0028] minω T PG+e

[0029] st[δ dual ]:H·x≤c

[0030] In the formula, ω represents the dynamic parameter vector; T represents the transposition; PG represents the active power vector of the generator set; st represents the constraint condition; δ dual represents the dual variable vector, including the active power constraint dual variable and the branch flow constraint dual variable; x represents the decision variable vector, x=[RE], RE represents the new energy active power; e represents the fixed cost constant; H represents the constraint matrix, including the branch flow constraint and the generation active power constraint; c represents the constraint vector.

[0031] Furthermore, the impact of dynamically changing parameters on the power system is considered and critical areas are constructed, including:

[0032] Establish a KKT conditional relationship based on the dynamically changing parameters, the static optimal decision variables and the static optimal dual variables, and convert the KKT conditional relationship into a block matrix;

[0033] Solve the linear relationship between the dynamic optimal decision variables and the dynamic optimal dual variables with respect to the dynamically changing parameters based on the block matrix;

[0034] Inactive constraints are defined, and the critical region set is determined based on the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables.

[0035] Furthermore, a KKT conditional relationship is established according to the dynamically changing parameters, the static optimal decision variables and the static optimal dual variables, and the KKT conditional relationship is converted into a block matrix including:

[0036] Establish the KKT conditional relationship, which can be expressed as:

[0037]

[0038] H ac ·x * -c ac =0

[0039] Where ω represents the dynamic change parameter vector; H acrepresents the submatrix in the constraint matrix H; represents the static optimal dual variable; T represents transposition; c ac represents a subvector in the constraint vector c; x * represents the static optimal decision variable;

[0040] Converting the KKT condition into a block matrix can be expressed as:

[0041]

[0042] In the formula, H extend represents the extended matrix, including the submatrix in the constraint matrix H and its transpose; represents the static optimal solution vector, including the static optimal decision variables and the optimal dual variables; is the right-hand side vector, including the sub-matrix in the constraint matrix H and the negative dynamic change parameter vector.

[0043] Furthermore, solving the linear relationship between the dynamic optimal decision variable and the dynamic optimal dual variable with respect to the dynamically changing parameter based on the block matrix includes:

[0044] When the block matrix is ​​non-singular, the inverse matrix of the expanded matrix is ​​solved. When the block matrix is ​​singular, the expanded pseudo-inverse matrix is ​​solved. The linear relationship between the decision variables and the dual variables with respect to the dynamically changing parameters can be expressed as:

[0045] x * (ω)=H x ω+c x

[0046]

[0047] In the formula, x * (ω) represents the dynamic optimal decision variable; represents the dynamic optimal dual variable; H x represents the first coefficient matrix; c x represents the first constant vector; c δ represents the second constant vector; H δ represents the second coefficient matrix; H x 、c x 、c δ , H δ , can be solved by expanding the matrix H extend The inverse matrix of is obtained.

[0048] Furthermore, the definition of inactivation constraints includes:

[0049] Extract the inactive constraint submatrix from the constraint matrix;

[0050] Extracting an inactive constraint subvector from the constraint vector;

[0051] According to the inactive constraint sub-matrix and inactive constraint sub-vector, the inactive constraint is established, which can be expressed as:

[0052] H inac ·x * <c inac

[0053] In the formula, H inac represents the inactive constraint submatrix; c inac Represents an inactive constraint subvector.

[0054] Further, determining the critical region set according to the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables includes:

[0055] Introducing the dynamic optimal decision variable into the inactive constraint, we get the equivalent form of the inactive constraint, which can be expressed as:

[0056] H x ·ω <c inac -H inac ·c x

[0057] In the formula, the explicit expression of the dynamic optimal dual variable is: The complementary relaxation condition is, H δ ω+c δ ≥0;

[0058] The critical region set is determined according to the equivalent form of the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables, which can be expressed as:

[0059] CR={ω|H inac ·x * <c inac , H x ·ω <c inac -H inac ·c x )

[0060] Where CR represents the critical region set, i.e., the region where the optimal solution of dynamically changing parameters remains unchanged.

[0061] The present invention has the following beneficial effects:

[0062] The power system planning method based on multivariable dynamic analysis provided by the present invention transforms the model through multi-parameter planning, considers the influence of dynamically changing parameters and constructs critical areas, and finally solves the planning results according to the mapping relationship between the critical areas and the dynamically changing parameters and the planning results. It can handle continuously changing technical and economic parameters, avoids repeated solutions to optimization problems, comprehensively evaluates the influence of different technical and economic parameters on power system planning, quickly updates planning results, and provides efficient and accurate planning and analysis means for complex and changeable power system environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A method flow chart of a power system planning method based on multivariable dynamic analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of a power system planning method based on multivariable dynamic analysis proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0066] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0067] A specific scheme of a power system planning method based on multivariable dynamic analysis provided by the present invention is described in detail below with reference to the accompanying drawings.

[0068] See also Figure 1 , which shows a method flow chart of a power system planning method based on multivariable dynamic analysis provided by an embodiment of the present invention, the method comprising:

[0069] Step S100: Acquire power system related data, and construct a DC optimal power flow model with minimized power generation cost; specifically, the power system related data at least includes: acquiring relevant information of all generator sets in the power system: including rated power, minimum / maximum power output limit, power generation efficiency curve, etc. For each generator set, determine its technical and economic parameters at different times, such as power generation cost coefficient (fuel cost, operation and maintenance cost and other parameters are proportionally converted to the cost per unit power generation), investment recovery coefficient (considering the cost of generator set construction investment allocated to power generation during its life cycle) and other technical and economic parameters; Grid topology data: determine the network structure of the power system, including node information (such as substations, power plant access points, etc.) and branch information (such as transmission lines, transformers, etc.), and obtain electrical parameters such as branch impedance and admittance; Load data: collect load power demand information of each node at different times, including active power and reactive power demand. Active power demand data can be obtained through power metering equipment (such as electric meters), and reactive power demand can be obtained through power factor measurement combined with active power calculation, or using monitoring data of reactive power compensation equipment; The objective function of the DC optimal power flow model to minimize the power generation cost can be expressed as:

[0070]

[0071] In formula (1), Ω G Represents a collection of generator sets; a i,t represents the technical and economic parameters of generator set i at time t; PG i,t represents the active power of generator set i at time t;

[0072] Step S100 specifically includes:

[0073] Step S110: constructing a DC power flow equation, specifically:

[0074] Step S111: Obtain the electrical parameters of each branch of the power system and extract the branch inductive reactance matrix B f , B f is a symmetric matrix whose elements represent the reactance between nodes.

[0075] Step S112: obtaining the voltage phase angle of each node in the power system and extracting the voltage phase angle vector;

[0076] Step S113: construct a branch power flow equation according to the branch inductance matrix and the voltage phase angle vector, which can be expressed as:

[0077]

[0078] In formula (2), PL t represents the branch power flow vector at time t; Bf represents the branch inductive reactance matrix; θ t Represents the voltage phase angle vector at time t; Ω T Represents a time set;

[0079] The branch flow constraint condition can be expressed as:

[0080]

[0081] In formula (3), and represents the dual variable of the branch power flow constraint at time t, Indicates the minimum active power flow limit of branch j, PL jt represents the active power flow limit of branch j at time t, Indicates the maximum active power flow limit of branch j, Ω L Represents a set of branches; step S110 accurately describes the relationship between the active power flow of each branch in the power system and the node voltage phase angle by constructing a DC power flow equation, and takes into account the upper and lower limit constraints of the branch flow. This step can ensure the physical feasibility of the model and provide a basis for subsequent multi-parameter planning and critical area construction.

[0082] Step S120: construct a power balance equation, specifically:

[0083] Step S121: constructing a generator group association matrix and a new energy association matrix according to the connection relationship between the power system nodes and the generator groups and the new energy equipment;

[0084] Step S122: Obtain the active power vector, active load vector and admittance matrix of the power system; specifically, obtain the active power output of all generator sets and new energy equipment (distributed photovoltaic, offshore new energy) at time t, and construct the active power vector; obtain the active power demand of all load nodes at time t, and construct the active load vector; construct the admittance matrix according to the network topology of the power system to represent the admittance relationship between system nodes;

[0085] Step S123: construct a power balance equation according to the generator group association matrix, the new energy association matrix, the active power vector, the active load vector and the admittance matrix, which can be expressed as:

[0086]

[0087] In formula (4), [λ t ] represents the dual variable of the power balance equation at time t; A G Represents the generator group association matrix, that is, the connection relationship between the generator group and the system node; PG tA represents the active power vector of the power system at time t, that is, the active power output of all generator sets; RE Represents the new energy association matrix, that is, the connection relationship between new energy and system nodes; RE t Represents the active power vector of renewable energy at time t, that is, the active power output of all renewable energy sources; PD t represents the active load vector of the power system at time t, that is, the active power demand of all load nodes; B represents the admittance matrix, that is, the admittance relationship between system nodes; θ t represents the voltage phase angle vector at time t;

[0088] The power balance constraint can be expressed as:

[0089]

[0090] In formula (5), and The dual variable representing the active power constraint at time t, Indicates the minimum active power limit of generator set i, PG i,t represents the active power of generator set i at time t, Indicates the maximum active power limit of generator set i, Ω G Represents a collection of generator sets; step S120 comprehensively considers the power balance relationship between generator sets, new energy equipment and load nodes by constructing a power balance equation, and introduces upper and lower limits of active power of generator sets; thereby ensuring the power supply and demand balance of the power system at different time points, and providing key constraints for subsequent multi-parameter planning and critical area construction.

[0091] In summary, step S100 constructs a DC optimal power flow (DC-OPF) model to minimize the power generation cost. By solving the above model using mathematical optimization software, the impact of technical and economic parameters on distributed photovoltaic and offshore renewable energy can be analyzed. However, since the DC-OPF model usually assumes that all parameters (such as technical and economic parameters, load demand, renewable energy output, etc.) are known and fixed, it cannot handle the uncertainty of technical and economic parameters. When the technical and economic parameters change, the optimization problem needs to be solved again, which leads to low calculation efficiency.

[0092] Step S200: converting the DC optimal power flow model based on multi-parameter planning;

[0093] Step S200 specifically includes:

[0094] Step S210: extracting a multi-parameter vector and a decision variable vector;

[0095] Step S220: Redefine the objective function and constraints of minimizing power generation cost according to the multi-parameter vector and the decision variable vector, which can be expressed as:

[0096]

[0097] In formula (6), ω represents the dynamic parameter vector; T represents the transposition; PG represents the active power vector of the generator set; st represents the constraint condition; δ dual represents a dual variable vector, including active power constraint dual variables and branch flow constraint dual variables; x represents a decision variable vector, x = [RE]; e represents a fixed cost constant; H represents a constraint matrix, including branch flow constraints and generation active power constraints; c represents a constraint vector; Step S200 transforms the DC optimal power flow model based on multi-parameter planning, incorporates dynamically changing parameters into the optimization model, eliminates the assumption of parameter fixity in the traditional DC-OPF model, and can handle the uncertainty of technical and economic parameters, avoids the inefficiency of resolving the optimization problem every time the parameters change, and significantly improves the computational efficiency and model flexibility. Through the multi-parameter planning method, the linear relationship between the decision variables and the dynamic parameters can be solved at one time, providing an efficient method for real-time optimization and planning of power systems.

[0098] Step S300: Consider the impact of dynamically changing parameters on the power system and construct critical regions; specifically, since many technical and economic parameters (such as fuel prices, load demand, new energy output, etc.) are uncertain in power system planning and operation, they will change over time and market conditions. The traditional DC optimal power flow model (DC-OPF) usually assumes that these parameters are known and fixed, which is unrealistic in practical applications, and resolving the optimization problem every time the parameters change is a huge waste of computing resources, especially in large-scale power systems, where the computational complexity and time cost are very high; this embodiment can handle the uncertainty of these parameters by considering the impact of dynamically changing parameters on the power system and constructing critical regions. The construction of critical regions enables the model to quickly determine whether it is necessary to resolve the optimization problem when the parameters change. If the parameter changes are still within a critical region, the optimal solution will not change, thereby avoiding unnecessary calculations, significantly improving computing efficiency, enhancing the robustness of the model, supporting real-time optimization and decision-making, and optimizing long-term planning;

[0099] Step S300 specifically includes:

[0100] Step S310: Establish a KKT conditional relationship according to the dynamically changing parameters, the static optimal decision variables and the static optimal dual variables, and convert the KKT conditional relationship into a block matrix; specifically, the KKT (Karush-Kuhn-Tucker) condition is a set of necessary conditions in the optimization problem, which is used to ensure that all constraints are met at the optimal solution; wherein, establishing the KKT conditional relationship can be expressed as:

[0101]

[0102] H ac ·x * -c ac =0 (7b)

[0103] In formula (7a) and (7b), ω represents the dynamic change parameter vector; H ac represents the submatrix in the constraint matrix H; represents the static optimal dual variable; T represents transposition; c ac represents a subvector in the constraint vector c; x * represents the static optimal decision variable; by establishing the KKT conditional relationship, the influence of dynamically changing parameters on the power system can be systematically handled. This step not only ensures that all constraints are met at the optimal solution, but also provides a mathematical basis for the subsequent critical area division.

[0104] Converting the KKT condition into a block matrix can be expressed as:

[0105]

[0106] In formula (8a) and (8b), H extend represents the extended matrix, including the submatrix in the constraint matrix H and its transpose; represents the static optimal solution vector, including the static optimal decision variables and the optimal dual variables; is the right-hand side vector, including the submatrix in the constraint matrix H and the negative dynamic change parameter vector; by converting the KKT condition into a block matrix, it is more convenient to solve and analyze the optimization problem, which improves the accuracy and computational efficiency of the model;

[0107] Step S320: Solve the linear relationship between the dynamic optimal decision variable and the dynamic optimal dual variable with respect to the dynamically changing parameter based on the block matrix; specifically, the determinant of the extended matrix can be calculated. If the determinant is not zero, the matrix is ​​non-singular; if the determinant is zero, the matrix is ​​singular; when the block matrix is ​​non-singular, solve the inverse matrix of the extended matrix; when the block matrix is ​​singular, solve the extended pseudo-inverse matrix, and the pseudo-inverse matrix can be calculated by singular value decomposition (SVD) or other numerical methods;

[0108] The linear relationship between the decision variable and the dual variable with respect to the dynamically changing parameters can be expressed as:

[0109] x * (ω)=H x ω+c x (9a)

[0110]

[0111] In formula (9a) and (9b), x * (ω) represents the dynamic optimal decision variable; represents the dynamic optimal dual variable; H x represents the first coefficient matrix; c x represents the first constant vector; c δ represents the second constant vector; H δ represents the second coefficient matrix; H x 、c x 、c δ , H δ , can be solved by expanding the matrix H extend The inverse matrix is ​​obtained; step S320 can systematically handle the impact of parameter changes on the power system by solving the linear relationship between the dynamic optimal decision variables and the dynamic optimal dual variables with respect to the dynamically changing parameters based on the block matrix. This step not only provides the optimal solution under dynamically changing parameters, but also provides a mathematical basis for subsequent critical area division and optimization problem solving. By solving the inverse matrix or pseudo-inverse matrix of the extended matrix, the dynamic optimal solution can be efficiently calculated, which improves the accuracy and computational efficiency of the model. This method is of great significance in dealing with complex and changeable power systems, and provides strong theoretical support and technical means for the optimal operation and planning of power systems.

[0112] Step S330: define inactive constraints, and determine the critical region set based on the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables; inactive constraints refer to constraints that have not reached their boundary values ​​at the optimal solution. These constraints are relaxed near the optimal solution and have no direct impact on the optimal solution; active constraints refer to constraints that reach their boundary values ​​at the optimal solution. These constraints are tight near the optimal solution and have a direct impact on the optimal solution, ensuring that the optimal solution satisfies these constraints; inactive constraints enable the model to better adapt to parameter changes, avoiding the situation where the optimal solution changes significantly due to slight changes in parameters.

[0113] Step S330 specifically includes:

[0114] Step S331: extracting an inactive constraint sub-matrix from the constraint matrix;

[0115] Step S332: extracting an inactive constraint subvector from the constraint vector;

[0116] Step S333: Establish an inactive constraint according to the inactive constraint sub-matrix and the inactive constraint sub-vector, which can be expressed as:

[0117] H inac ·x * <c inac (10)

[0118] In formula (10), H inac represents the inactive constraint submatrix; c inac Represents an inactive constraint subvector.

[0119] Step S334: Introduce the dynamic optimal decision variable into the inactive constraint to obtain an equivalent form of the inactive constraint, which can be expressed as:

[0120] H x ·ω <c inac -H inac ·c x (11)

[0121] In formula (11), the explicit expression of the dynamic optimal dual variable is: The complementary relaxation condition is, H δ ω+c δ ≥0;

[0122] Step S335: Determine the critical region set according to the equivalent form of the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables, which can be expressed as:

[0123] CR={ω|H inac ·x * <c inac ,H x ·ω <c inac -H inac ·c x} (13)

[0124] In formula (13), CR represents the critical region set, that is, the region where the optimal solution of the dynamically changing parameters remains unchanged;

[0125] Step S400: Solve the planning result according to the mapping relationship between the critical area and the dynamically changing parameters and the power system planning result; specifically, the critical area set CR obtained in step S300 is used to divide the technical and economic parameter space into multiple critical areas, and the optimal solution in each critical area remains unchanged; in each critical area, there is a linear relationship between the technical and economic parameters and the power system planning result, and the mapping function is constructed using the multi-parameter planning theory. After the construction is completed, the continuously changing technical and economic parameters can be evaluated at the same time; the increment of the technical and economic parameters to each distributed photovoltaic and offshore new energy output can be analyzed, and the mapping function can be expressed as:

[0126] RE t,* =F(a 1,t ) (14)

[0127] In formula (14), RE t,* represents the optimal planning result of the system; F(·) is the mapping function between the output of new energy and technical and economic parameters; a 1,i represents technical and economic parameters, ω=[a 1,0 ,…,a 1,n ];

[0128] Define the variable technical and economic parameters obtained profile =[ω i ,…,ω l ], where l is the number of data points in the variable technical and economic parameters; the variable technical and economic parameter matrix is ​​input into the model, and the optimal decision variables under each technical and economic parameter can be solved according to the critical region and linear relationship to obtain the optimal solution sequence x profile =[x * (ω i ),…,x * (ω l )], where x * (ω i ) represents the technical and economic parameters ω i The optimal decision variables under the condition of the above mentioned conditions; step S400 solves the planning results according to the mapping relationship between the critical area and the dynamically changing parameters and the power system planning results, and can systematically deal with the impact of changes in technical and economic parameters on the power system; this step not only improves the accuracy and computational efficiency of the model, but also provides a scientific basis for multi-scenario analysis and long-term planning. By constructing the critical area and mapping function, the optimal solution under different technical and economic parameters can be quickly evaluated, supporting real-time optimization and decision-making, and enhancing the flexibility and adaptability of the power system.

[0129] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0130] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A power system planning method based on multivariable dynamic analysis, characterized in that: The method comprises: Obtain power system related data and build a DC optimal power flow model to minimize power generation cost; Transform the DC optimal power flow model based on multi-parameter planning; Consider the impact of dynamically changing parameters on the power system and construct critical areas; The planning results are solved based on the mapping relationship between critical areas and dynamically changing parameters and power system planning results.

2. The power system planning method based on multivariable dynamic analysis according to claim 1, characterized in that: Obtaining power system related data and constructing a DC optimal power flow model with minimal power generation cost includes: Obtain the electrical parameters of each branch of the power system and extract the branch inductive reactance matrix; Obtain the voltage phase angle of each node in the power system and extract the voltage phase angle vector; The branch power flow equation is constructed based on the branch inductance matrix and the voltage phase angle vector.

3. The power system planning method based on multivariable dynamic analysis according to claim 2, characterized in that: Acquiring power system related data and constructing a DC optimal power flow model with minimum power generation cost also includes: Construct the generator group association matrix and the new energy association matrix according to the connection relationship between the power system nodes and the generator groups and new energy equipment; Obtaining the active power vector, active load vector and admittance matrix of the power system; The power balance equation is constructed based on the generator set correlation matrix, new energy correlation matrix, active power vector, active load vector and admittance matrix.

4. The power system planning method based on multivariable dynamic analysis according to claim 3, characterized in that: The constraints of the DC optimal power flow model include: The branch flow constraint condition can be expressed as: In the formula, and represents the dual variable of the branch power flow constraint at time t, Indicates the minimum active power flow limit of branch j, PL jt represents the active power flow limit of branch j at time t, Indicates the maximum active power flow limit of branch j, Ω L Represents a branch set; The power balance constraint can be expressed as: In the formula, and The dual variable representing the active power constraint at time t, Indicates the minimum active power limit of generator set i, PG i，t represents the active power of generator set i at time t, Indicates the maximum active power limit of generator set i, Ω G Represents a collection of generator sets.

5. The power system planning method based on multivariable dynamic analysis according to claim 1, characterized in that: The conversion of the DC optimal power flow model based on multi-parameter planning includes: Extract multi-parameter vectors and decision variable vectors; The objective function and constraints of minimizing power generation cost are redefined based on the multi-parameter vector and decision variable vector, which can be expressed as: min ω T PG+e s.t. [δ dual ]:H·x≤c In the formula, ω represents the dynamic parameter vector; T represents the transposition; PG represents the active power vector of the generator set; st represents the constraint condition; δ dual represents the dual variable vector, including the active power constraint dual variable and the branch flow constraint dual variable; x represents the decision variable vector, x=[RE], RE represents the new energy active power; e represents the fixed cost constant; H represents the constraint matrix, including the branch flow constraint and the generation active power constraint; c represents the constraint vector.

6. The power system planning method based on multivariable dynamic analysis according to any one of claims 1 to 5, characterized in that: Considering the impact of dynamically changing parameters on the power system and constructing critical areas include: Establish a KKT conditional relationship based on the dynamically changing parameters, the static optimal decision variables and the static optimal dual variables, and convert the KKT conditional relationship into a block matrix; Solve the linear relationship between the dynamic optimal decision variables and the dynamic optimal dual variables with respect to the dynamically changing parameters based on the block matrix; Inactive constraints are defined, and the critical region set is determined based on the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables.

7. The power system planning method based on multivariable dynamic analysis according to claim 6, characterized in that: The KKT conditional relationship is established according to the dynamically changing parameters, the static optimal decision variables and the static optimal dual variables, and the KKT conditional relationship is converted into a block matrix including: Establish the KKT conditional relationship, which can be expressed as: H ac ·x * -c ac =0 Where ω represents the dynamic change parameter vector; H ac represents the submatrix in the constraint matrix H; represents the static optimal dual variable; T represents transposition; c ac represents a subvector in the constraint vector c; x * represents the static optimal decision variable; Converting the KKT condition into a block matrix can be expressed as: In the formula, H extend represents the extended matrix, including the submatrix in the constraint matrix H and its transpose; represents the static optimal solution vector, including the static optimal decision variables and the optimal dual variables; is the right-hand side vector, including the sub-matrix in the constraint matrix H and the negative dynamic change parameter vector.

8. The power system planning method based on multivariable dynamic analysis according to claim 7, characterized in that: Solving the linear relationship between the dynamic optimal decision variables and the dynamic optimal dual variables with respect to the dynamically changing parameters based on the block matrix includes: When the block matrix is ​​non-singular, the inverse matrix of the expanded matrix is ​​solved. When the block matrix is ​​singular, the expanded pseudo-inverse matrix is ​​solved. The linear relationship between the decision variables and the dual variables with respect to the dynamically changing parameters can be expressed as: x * (ω)=H x ·ω+c x In the formula, x * (ω) represents the dynamic optimal decision variable; represents the dynamic optimal dual variable; H x represents the first coefficient matrix; c x represents the first constant vector; c δ represents the second constant vector; H δ represents the second coefficient matrix; H x 、c x 、c δ , H δ , can be solved by expanding the matrix H extend The inverse matrix of is obtained.

9. The method for power system planning based on multivariable dynamic analysis according to claim 8, characterized in that: Defining inactive constraints includes: Extract the inactive constraint submatrix from the constraint matrix; Extracting an inactive constraint subvector from the constraint vector; According to the inactive constraint sub-matrix and inactive constraint sub-vector, the inactive constraint is established, which can be expressed as: H inac ·x * <c inac In the formula, H inac represents the inactive constraint submatrix; c inac Represents an inactive constraint subvector.

10. The power system planning method based on multivariable dynamic analysis according to claim 9, characterized in that: The critical region set is determined based on the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables, including: Introducing the dynamic optimal decision variable into the inactive constraint, we get the equivalent form of the inactive constraint, which can be expressed as: H x ·ω<c inac -H inac ·c x In the formula, the explicit expression of the dynamic optimal dual variable is: The complementary relaxation condition is, H δ ω+c δ ≥0; The critical region set is determined according to the equivalent form of the inactive constraints and the complementary relaxation conditions of the dynamic optimal dual variables, which can be expressed as: CR={ω|H inac ·x * <c inac ,H x ·ω<c inac -H inac ·c x } Where CR represents the critical region set, i.e., the region where the optimal solution of dynamically changing parameters remains unchanged.

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