A two-stage reactive power optimization method, system, device and medium considering switching of capacitive reactance

Abstract

The application discloses a kind of two-stage reactive power optimization method, system, device and medium considering capacitor reactance switching.The method comprises: obtaining network model and initial section, constructs optimal power flow optimization model with the minimum weighted sum of unit reactive power adjustment and voltage deviation;Set control variable as generator reactive power and capacitor reactance, solve the optimization model, obtain the section after first round of reactive power optimization;Discretize the total amount of capacitor reactor adjustment of each calculation node after first stage reactive power optimization, analyze capacitor reactance switching strategy;Modify the switching state of capacitor reactance in initial section, obtain the reconstructed section;Set control variable as generator reactive power, solve the optimization model considering the flow constraint of reconstructed section, obtain the section after second round of reactive power optimization;Output generator reactive power adjustment strategy and capacitor reactance switching strategy.The application is used as the reactive power optimization scheme for improving capacitor reactance switching, easy to deploy, with the condition of application in actual power system.

Description

Technical Field

[0001] This invention belongs to the field of power system load regulation technology, specifically relating to a two-stage reactive power optimization method, system, equipment, and medium that takes into account capacitor reactance switching. Background Technology

[0002] Optimal Power Flow (OPF) is a core theoretical tool and method for achieving global optimization decisions in reactive power optimization of power systems. By constructing an optimization model with a specific objective, and considering constraints such as power flow constraints, node voltage constraints, and generator reactive power output constraints under the condition of ensuring safe system operation, it coordinates and optimizes the switching of generator reactive power output and capacitor / reactor equipment to achieve optimized correction of over-limit node voltages. The aforementioned optimal power flow containing discrete variables is a difficult mixed-integer nonlinear programming problem to solve. To facilitate the solution, most optimal power flow models treat these discrete variables as continuous variables during calculation. After calculation, an optimal continuous solution of the optimal power flow is obtained, and then these continuous discrete variables are restored to the actual discrete equipment states to obtain a specific capacitor / reactor switching strategy. However, when operating the capacitor / reactor switching strategy obtained in this way, the actual connected capacity of the capacitor / reactor differs from the optimal connected capacity calculated by the optimal power flow at that point. This often leads to problems such as the adjusted system operating point deviating from the true global optimal solution, or the system operating point no longer satisfying all constraints.

[0003] Therefore, it is necessary to design a reactive power optimization method suitable for engineering, which reduces the impact of capacitor and reactor discretization on the system without changing the original solution method, and ensures that the system operating point meets all constraints after the calculation is completed. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a two-stage reactive power optimization method, device, equipment and medium that takes into account capacitor reactance switching, so as to solve the problem that the system operating point deviates from the global optimal solution or no longer meets all constraints due to capacitor reactance discretization during the reactive power optimization process.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] According to a first aspect of the present invention, a two-stage reactive power optimization method considering capacitor reactance switching is provided, comprising the following steps:

[0007] Obtain the network model and the initial section A to be adjusted, and construct an optimal power flow optimization model with the objective of minimizing the weighted sum of reactive power adjustment and voltage deviation of the unit.

[0008] The first stage of reactive power optimization is carried out based on the initial section A. The control variables are set as generator reactive power and capacitor reactance. The optimization model considering the power flow constraints of the initial section A is solved. After the calculation is completed, the section B after the first round of reactive power optimization is obtained.

[0009] Based on section B after the first round of reactive power optimization, the total adjustment amount of capacitors and reactors of each calculation node after the first stage of reactive power optimization is discretized, the capacitor and reactor switching strategy is analyzed, and the switching state of capacitors and reactors in the initial section A is modified according to the analyzed capacitor and reactor switching strategy to obtain the reconstructed section C.

[0010] The second stage of reactive power optimization is carried out based on the reconstructed section C. The control variable is set as generator reactive power. The optimization model considering the power flow constraints of the reconstructed section C is solved. After the calculation is completed, the section D after the second round of reactive power optimization is obtained.

[0011] Based on the reconstructed section C and the section D after the second round of reactive power optimization, the generator reactive power adjustment strategy and capacitor reactance switching strategy are output.

[0012] Furthermore, the objective function of the optimal power flow optimization model, which aims to minimize the weighted sum of reactive power adjustment and voltage deviation of the generating units, is expressed as:

[0013] ;

[0014] The constraints are as follows:

[0015] ;

[0016] in The penalty factor for the reactive power adjustment of the generator. This is the penalty factor for node voltage deviation. This refers to the reactive power adjustment of the generator. For nodes voltage amplitude, For nodes voltage amplitude, For nodes Target voltage amplitude, where n is the number of nodes. For nodes Generator active power For nodes Generator reactive power For nodes Active load, For nodes reactive load, For nodes in the node admittance matrix and nodes The electrical conductance between them For nodes in the node admittance matrix and nodes The susceptance between them For nodes and nodes The phase angle difference between them Represents a node The lower limit of voltage, Represents a node The upper limit of voltage, This represents the lower limit of the generator's reactive power output. This represents the upper limit of the generator's reactive power output.

[0017] The constraints also include constraints on the reactive power input of the capacitor reactance at each calculation node of the connected capacitor reactance:

[0018] ;

[0019] Represents a computing node The total reactive power of the capacitors and reactances invested in; Representation and computation node The capacity of reactors that are connected; Representation and computation node The capacitance of capacitors that are connected together.

[0020] Furthermore, obtaining the initial cross-section A to be adjusted includes:

[0021] Record the reactive power output of each generator in section A to be adjusted as follows: Terminal voltage ,in This refers to the number of generators.

[0022] Record the switching status of the capacitor in section A to be adjusted. Reactor switching status ,in The number of capacitors. This refers to the number of reactors;

[0023] Record the total reactive power connected to the capacitor reactance at each calculation node in section A to be adjusted. ,in To calculate the number of nodes.

[0024] Furthermore, the first stage of reactive power optimization adopts a flat start, with the voltage amplitude and phase angle of each node set to 1 and 0, respectively;

[0025] The target voltage range for the first stage of reactive power optimization is set as follows: By adjusting the reactive power output of the generators and the switching of capacitor reactance, the voltage of each computation node is constrained to the target range; the interior point method is used to solve the optimization model considering the power flow constraints of the initial section A, and section B is obtained.

[0026] In section B, the total reactive power connected to the capacitor reactance at each calculation node is recorded as follows: ;

[0027] Record the voltage magnitude and phase angle at each calculation node in section B. , , To calculate the number of nodes.

[0028] Furthermore, based on section B after the first round of reactive power optimization, the total adjustment amount of capacitors and reactors at each calculation node after the first stage of reactive power optimization is discretized, and the capacitor and reactor switching strategy is analyzed, including:

[0029] compute nodes The total amount of capacitor reactance adjustment is , This represents the total reactive power of the capacitors and reactors connected to the calculation node nq at section A; This represents the total reactive power of the capacitors and reactors connected at the calculation node nq of section B.

[0030] The total adjustment of capacitor reactance for each computing node is analyzed to obtain the specific switching strategy for capacitor reactance. The analysis process is as follows:

[0031] (1) Obtain the status and capacity of capacitors and reactors that are connected to each computing node;

[0032] (2) Perform the analysis operation on the capacitors and reactors with the connection relationship on each calculation node in turn. After the analysis and judgment of the current capacitor and reactor is completed, continue to compare the capacity of the next capacitor and reactor until the total amount of capacitor and reactor adjustment on the calculation node has been allocated or all capacitors and reactors have been compared. The analysis process ends.

[0033] Among them, a single capacitor reactor The method for analyzing and judging the throwing and cutting strategy has two cases, as follows:

[0034] Scenario 1: When selecting a computing node The capacity of the last unloaded capacitor or loaded reactor was... :

[0035] when When the capacitor is engaged or the reactor is disengaged, the capacitor is engaged or disengaged. At that time, if If the capacitor is switched on or the reactor is switched off, then the capacitor should be switched on or the reactor should be switched off. If so, the capacitor reactor will not be operated;

[0036] Scenario 2: When When selecting a computing node The capacity of the last capacitor put into operation or the last reactor not put into operation was... :when If so, then the reactor is switched on or the capacitor is switched off; if If the reactor is switched on or the capacitor is switched off, then the reactor is switched on or the capacitor is switched off. If so, the capacitor reactor will not be operated.

[0037] Furthermore, the second stage of reactive power optimization employs a hot start, with the voltage amplitude and phase angle of each node being as follows: , , To calculate the number of nodes;

[0038] The target voltage range for the second stage of reactive power optimization is set as follows: The target voltage range for reactive power optimization in the second stage is larger than the target voltage range for reactive power optimization in the first node. , The interior point method is used to solve the optimization model for power flow constraints at section C, and section D is obtained.

[0039] Record the reactive power output of each generator in section D as follows Terminal voltage .

[0040] Furthermore, based on the reconstructed section C and the section D after the second round of reactive power optimization, the generator reactive power adjustment strategy and capacitor reactance switching strategy are output, including:

[0041] Generator tuning strategies are divided into generator tuning strategies for PV nodes and generator tuning strategies for PQ nodes:

[0042] For generator i at the PV node, the generator terminal voltage adjustment is: , Let i be the terminal voltage of generator i in section D. Let i be the terminal voltage of generator i in section A, when When the specified threshold condition is met, the generator's adjustment strategy is output as terminal voltage adjustment. ;

[0043] For generator j at node PQ, the generator reactive power adjustment is: , For the reactive power output of generator i in section D, For generator i, the reactive power output in section A is when When the specified threshold condition is met, the generator's adjustment strategy is output as generator reactive power adjustment. ;

[0044] The switching strategy for the output of the capacitor-reactor is the capacitor-reactor set. , With sets , The difference value, and Let A and B be the sets of switching states of the capacitor at cross-sections C and A, respectively. and These are the sets of switching states of the reactor in sections C and A, respectively.

[0045] According to a second aspect of the present invention, a two-stage reactive power optimization system considering capacitor reactance switching is provided, comprising:

[0046] The optimization model building module is used to obtain the network model and the initial section A to be adjusted, and to build an optimal power flow optimization model with the goal of minimizing the weighted sum of the reactive power adjustment and voltage deviation of the unit.

[0047] The first optimization module is used to perform the first stage of reactive power optimization based on the initial section A. The control variables are set as generator reactive power and capacitor reactance. The optimization model considering the power flow constraints of the initial section A is solved. After the calculation is completed, the section B after the first round of reactive power optimization is obtained.

[0048] The section reconstruction module is used to discretize the total adjustment of capacitors and reactors of each calculation node after the first stage of reactive power optimization based on section B after the first round of reactive power optimization, analyze the capacitor and reactor switching strategy, and modify the switching state of capacitors and reactors in the initial section A according to the analyzed capacitor and reactor switching strategy to obtain the reconstructed section C.

[0049] The second optimization module is used to perform the second stage of reactive power optimization based on the reconfigured section C. The control variable is set as generator reactive power, and the optimization model considering the power flow constraints of the reconfigured section C is solved. After the calculation is completed, the section D after the second round of reactive power optimization is obtained.

[0050] The strategy output module is used to output the generator reactive power adjustment strategy and capacitor reactance switching strategy based on the reconstructed section C and the section D after the second round of reactive power optimization.

[0051] According to a third aspect of the present invention, an electronic device is provided, the device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs, when executed by the processors, implementing the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described in the first aspect of the present invention.

[0052] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described in the first aspect of the present invention.

[0053] According to a fifth aspect of the present invention, a computer program product is provided, comprising a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described in the first aspect of the present invention.

[0054] Beneficial Effects: This invention acquires a network model and the section A to be adjusted, constructing an optimal power flow optimization model with the objective of minimizing the weighted sum of reactive power adjustment and voltage deviation. In the first stage of reactive power optimization, the control variables are set as generator reactive power and capacitor reactance. The interior-point method is used to solve the optimization model considering the initial power flow constraints of section A. After calculation, section B after the first round of reactive power optimization is obtained. The total adjustment amount of capacitors and reactors at each calculation node after the first stage of reactive power optimization is discretized, and the capacitor reactance switching strategy is analyzed. The switching state of capacitors and reactors in section A is modified to obtain section C. In the second stage of reactive power optimization, the control variable is set as generator reactive power. The interior-point method is used to solve the optimization model considering the power flow constraints of section C. After calculation, section D after the second round of reactive power optimization is obtained. The generator reactive power adjustment strategy and capacitor reactance switching strategy are output. The method of this invention, as an improved reactive power optimization method considering capacitor reactance switching, is easy to deploy, has the conditions for application in actual power systems, and has good application prospects. The system, equipment, and medium provided by this invention can achieve the corresponding technical effects based on the above method. Attached Figure Description

[0055] Figure 1 This is a flowchart of a two-stage reactive power optimization method considering capacitor reactance switching provided by an embodiment of the present invention;

[0056] Figure 2 This is a flowchart of the capacitor-reactor switching strategy analysis method provided in the embodiments of the present invention. Detailed Implementation

[0057] The technical solution of the present invention will be further described below with reference to the accompanying drawings. It should be understood that the embodiments provided below are merely for the purpose of fully and completely disclosing the present invention and fully conveying the technical concept of the invention to those skilled in the art. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the invention.

[0058] like Figure 1As shown, this embodiment of the invention provides a two-stage reactive power optimization method considering capacitor reactance switching, including the following steps:

[0059] S1. Obtain the network model and the section A to be adjusted, and construct an optimal power flow optimization model with the goal of minimizing the weighted sum of the reactive power adjustment and voltage deviation of the unit.

[0060] The constructed optimization model is as follows:

[0061] Objective function:

[0062] Constraints:

[0063] in The penalty factor for the reactive power adjustment of the generator. This is the penalty factor for node voltage deviation. This refers to the reactive power adjustment of the generator. For nodes voltage amplitude, For nodes voltage amplitude, For nodes Target voltage amplitude, where n is the number of nodes. For nodes Generator active power For nodes Generator reactive power For nodes Active load, For nodes reactive load, For nodes in the node admittance matrix and nodes The electrical conductance between them For nodes in the node admittance matrix and nodes The susceptance between them For nodes and nodes The phase angle difference between them Represents a node The lower limit of voltage, Represents a node The upper limit of voltage, This represents the lower limit of the generator's reactive power output. This is the upper limit of the generator's reactive power output.

[0064] In addition, the constraints also include the reactive power input constraint for the capacitor reactance at each computing node connected to the capacitor reactance:

[0065]

[0066] Represents a computing node The above refers to the total reactive power of the capacitor reactance. Representation and computation node The capacity of reactors that are connected; Representation and computation node The capacitance of capacitors that are connected together.

[0067] Record the reactive power output of each generator in section A to be adjusted as follows: Terminal voltage ,in This refers to the number of generators.

[0068] Record the switching status of the capacitor in section A to be adjusted. Reactor switching status ,in The number of capacitors. This refers to the number of reactors;

[0069] Record the total reactive power connected to the capacitor reactance at each calculation node in section A to be adjusted. ;in To calculate the number of nodes.

[0070] S2. First stage reactive power optimization: Set the control variables as generator reactive power and capacitor reactance, and use the interior point method to solve the optimization model considering the power flow constraints of the initial section A. After the calculation is completed, the section B after the first round of reactive power optimization is obtained.

[0071] The first stage of reactive power optimization adopts a flat start, with the voltage amplitude and phase angle of each node set to 1 and 0 respectively.

[0072] The target voltage range for the first stage of reactive power optimization is set as follows: The voltage constraints of each computational node are brought within the target range by adjusting the reactive power output of generators and the switching capacity of capacitors and reactors. The optimization model considering the initial power flow constraints at section A is solved using the interior-point method. The specific process is as follows: Using the system operating state at section A as the initial value for iteration, a logarithmic barrier function is introduced to handle inequality constraints such as node voltage and unit output, transforming the original problem into a sequence of continuously differentiable approximate subproblems. In each iteration, the gradient of the Lagrange function and the Hessian matrix are derived to solve the linear equation system to obtain the correction directions for the control variables (reactive power output of generators, switching capacity of capacitors and reactors) and the state variable (bus voltage). An internal feasible step size is selected to update the variables, and the barrier parameters are gradually reduced. Iteration continues until the convergence criteria are met. Finally, the solver outputs the optimal setpoint values ​​for the control variables, based on which the new system operating state satisfying all constraints is calculated, i.e., the optimized operating section B.

[0073] In section B, the total reactive power connected to the capacitor reactance at each calculation node is recorded as follows: ;

[0074] Record the voltage magnitude and phase angle at each calculation node in section B. , , To calculate the number of nodes.

[0075] S3. Adjust the total amount of capacitors and reactors in each computing node after the first stage of reactive power optimization in discretization, and analyze the capacitor and reactor switching strategy.

[0076] compute nodes The total amount of capacitor reactance adjustment is . This represents the total reactive power of the capacitors and reactors connected to the calculation node nq at section A; This represents the total reactive power of the capacitors and reactors connected at the calculation node nq of section B. To calculate the difference in the total reactive power of the capacitor reactance connected to node nq at sections A and B respectively. If This indicates that reactive power increased after the node was adjusted, meaning that capacitors were added or reactors were disconnected; if This indicates that the reactive power decreased after the node was adjusted, meaning that a reactor was added or a capacitor was removed.

[0077] The total adjustment of capacitor reactance for each computing node is analyzed to obtain the specific switching strategy for capacitor reactance. The analysis process is as follows:

[0078] (1) Obtain the status and capacity of capacitors and reactors that are connected to each computing node.

[0079] (2) Perform the analysis operation on the capacitors and reactors that are connected to each calculation node in sequence. After the analysis and judgment of the current capacitor and reactor is completed, continue to compare the capacity of the next capacitor and reactor until the total amount of capacitor and reactor adjustment on the calculation node has been allocated or all capacitors and reactors have been compared. The analysis process ends.

[0080] Among them, a single capacitor reactor The method for analyzing and judging the throwing and cutting strategy has two cases, refer to Figure 2 The details are as follows:

[0081] Scenario 1: When selecting a computing node The capacity of the last unloaded capacitor or loaded reactor was... :

[0082] when When the capacitor is engaged, the remaining amount... ; or disconnect the reactor, leaving the remaining amount ;

[0083] when When, the switching principle of capacitor reactance is as follows: When When the capacitor is engaged or the reactor is disengaged, the capacitor is engaged or disengaged. If this is the case, then the capacitor reactor will not be operated.

[0084] Scenario 2: When When selecting a computing node The capacity of the last capacitor put into operation or the last reactor not put into operation was... :

[0085] when When the time comes, the reactor is activated, and the remaining amount... Or disconnect the capacitor, leaving the remaining amount ;

[0086] when When, the switching principle of capacitor reactance is as follows: When When the reactor is engaged or the capacitor is disengaged, the reactor is engaged or disengaged. If this is the case, then the capacitor reactor will not be operated.

[0087] S4. Based on the analyzed capacitor reactance switching strategy, modify the switching state of the capacitor reactance in section A to obtain the reconstructed section C.

[0088] Record the switching state of each capacitor in section C. Reactor switching status .

[0089] S5. Second stage reactive power optimization: Set the control variable as generator reactive power, and use the interior point method to solve the optimization model considering the power flow constraints of section C. After the calculation is completed, the section D after the second round of reactive power optimization is obtained.

[0090] The second stage of reactive power optimization adopts a hot start, with the voltage amplitude and phase angle of each node being [value missing]. , .

[0091] The target voltage range for the second stage of reactive power optimization is set as follows: If the generator's adjustment range is too small to constrain the voltage exceedance caused by discrete switching of capacitor reactance back to the original range, then the target voltage range for the second stage of reactive power optimization will be larger than the target voltage range for the first stage of reactive power optimization. , The optimization model for considering and reconstructing the power flow constraints at section C is solved using the interior point method. The specific method will not be elaborated here, and the section D after the second round of optimization is obtained.

[0092] After the second stage of reactive power optimization is completed, the reactive power output of each generator in section D is recorded as follows: Terminal voltage .

[0093] S6. Based on the reconstructed section C and the section D after the second round of reactive power optimization, output the generator reactive power adjustment strategy and the capacitor reactance switching strategy.

[0094] Generator tuning strategies are divided into generator tuning strategies for PV nodes and generator tuning strategies for PQ nodes:

[0095] For generator i at the PV node, the generator terminal voltage adjustment is: , Let i be the terminal voltage of generator i in section D. Let i be the terminal voltage of generator i in section A, when At that time, the adjustment strategy for the generator output is terminal voltage adjustment. ;

[0096] For generator j at node PQ, the generator reactive power adjustment is: , For the reactive power output of generator i in section D, For generator i, the reactive power output in section A is when At that time, the adjustment strategy for the generator output is to adjust the generator's reactive power output. , This refers to the rated capacity of the generator;

[0097] The switching strategy for the output of the capacitor-reactor is the capacitor-reactor set. , With sets , The difference value.

[0098] Adjustments can be made based on the generator adjustment strategy and capacitor reactance switching strategy described above to ultimately limit the voltage exceedance in section A. Within the range.

[0099] This invention innovatively designs and optimizes the solution process, leveraging the interior-point method originally used for solving nonlinear programming problems with continuous variables to efficiently solve nonlinear programming problems with integer variables, achieving the expected application results. Specifically, the total capacitance and reactance are treated as continuous variables and embedded into the interior-point method solution process. The solution results for the total adjustment of capacitance and reactance at the computational nodes are discretized and analyzed to obtain the switching strategy of the capacitors and reactors. To address the potential disturbances to the voltage of some nodes in the system caused by the discretization strategy, a second-stage reactive power optimization stage is introduced. By adjusting the generator output, secondary optimization and control of the cross-section are implemented, ultimately ensuring that the system operating point meets all constraints after the calculation is completed.

[0100] Based on the same technical concept as the method embodiment, another embodiment of the present invention also provides a two-stage reactive power optimization system considering capacitor reactance switching, including:

[0101] The optimization model building module is used to obtain the network model and the initial section A to be adjusted, and to build an optimal power flow optimization model with the goal of minimizing the weighted sum of the reactive power adjustment and voltage deviation of the unit.

[0102] The first optimization module is used to perform the first stage of reactive power optimization based on the initial section A. The control variables are set as generator reactive power and capacitor reactance. The optimization model considering the power flow constraints of the initial section A is solved. After the calculation is completed, the section B after the first round of reactive power optimization is obtained.

[0103] The section reconstruction module is used to discretize the total adjustment of capacitors and reactors of each calculation node after the first stage of reactive power optimization based on section B after the first round of reactive power optimization, analyze the capacitor and reactor switching strategy, and modify the switching state of capacitors and reactors in the initial section A according to the analyzed capacitor and reactor switching strategy to obtain the reconstructed section C.

[0104] The second optimization module is used to perform the second stage of reactive power optimization based on the reconfigured section C. The control variable is set as generator reactive power, and the optimization model considering the power flow constraints of the reconfigured section C is solved. After the calculation is completed, the section D after the second round of reactive power optimization is obtained.

[0105] The strategy output module is used to output the generator reactive power adjustment strategy and capacitor reactance switching strategy based on the reconstructed section C and the section D after the second round of reactive power optimization.

[0106] Another embodiment of the present invention provides an electronic device, including: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs, when executed by the processors, implement the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described above.

[0107] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described above.

[0108] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus (systems), computer devices, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] This invention is described with reference to a flowchart of a method according to embodiments of the invention. It should be understood that each step in the flowchart and combinations thereof can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A device for a function specified in one or more processes.

[0110] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 Steps of a specified function in one or more processes.

Claims

1. A two-stage reactive power optimization method considering capacitor reactance switching, characterized in that, The method includes the following steps: Obtain the network model and the initial section A to be adjusted, and construct an optimal power flow optimization model with the objective of minimizing the weighted sum of reactive power adjustment and voltage deviation of the unit. The first stage of reactive power optimization is carried out based on the initial section A. The control variables are set as generator reactive power and capacitor reactance. The optimization model considering the power flow constraints of the initial section A is solved. After the calculation is completed, the section B after the first round of reactive power optimization is obtained. Based on section B after the first round of reactive power optimization, the total adjustment amount of capacitors and reactors of each calculation node after the first stage of reactive power optimization is discretized, the capacitor and reactor switching strategy is analyzed, and the switching state of capacitors and reactors in the initial section A is modified according to the analyzed capacitor and reactor switching strategy to obtain the reconstructed section C. The second stage of reactive power optimization is carried out based on the reconstructed section C. The control variable is set as generator reactive power. The optimization model considering the power flow constraints of the reconstructed section C is solved. After the calculation is completed, the section D after the second round of reactive power optimization is obtained. Based on the reconstructed section C and the section D after the second round of reactive power optimization, the generator reactive power adjustment strategy and capacitor reactance switching strategy are output.

2. The method according to claim 1, characterized in that, The optimal power flow optimization model aims to minimize the weighted sum of reactive power adjustment and voltage deviation of the generating units. The objective function is expressed as: ； The constraints are as follows: ； in The penalty factor for the reactive power adjustment of the generator. This is the penalty factor for node voltage deviation. This refers to the reactive power adjustment of the generator. For nodes voltage amplitude, For nodes voltage amplitude, For nodes Target voltage amplitude, where n is the number of nodes. For nodes Generator active power For nodes Generator reactive power For nodes Active load, For nodes reactive load, For nodes in the node admittance matrix and nodes The electrical conductance between them For nodes in the node admittance matrix and nodes The susceptance between them For nodes and nodes The phase angle difference between them Represents a node The lower limit of voltage, Represents a node The upper limit of voltage, This represents the lower limit of the generator's reactive power output. This represents the upper limit of the generator's reactive power output. The constraints also include constraints on the reactive power input of the capacitor reactance at each calculation node of the connected capacitor reactance: ； Represents a computing node The total reactive power of the capacitors and reactances invested in; Representation and computation node The capacity of reactors that are connected; Representation and computation node The capacitance of capacitors that are connected together.

3. The method according to claim 1, characterized in that, Obtaining the initial cross-section A to be adjusted includes: Record the reactive power output of each generator in section A to be adjusted as follows: Terminal voltage ,in This refers to the number of generators. Record the switching status of the capacitor in section A to be adjusted. Reactor switching status ,in The number of capacitors. This refers to the number of reactors. Record the total reactive power connected to the capacitor reactance at each calculation node in section A to be adjusted. ,in To calculate the number of nodes.

4. The method according to claim 1, characterized in that, The first stage of reactive power optimization adopts a flat start, with the voltage amplitude and phase angle of each node set to 1 and 0 respectively; The target voltage range for the first stage of reactive power optimization is set as follows: By adjusting the reactive power output of the generator and the switching of capacitor reactance, the voltage of each computing node is constrained to the target range; The optimization model considering the power flow constraints of the initial section A is solved using the interior point method to obtain section B; In section B, the total reactive power connected to the capacitor reactance at each calculation node is recorded as follows: ; Record the voltage magnitude and phase angle at each calculation node in section B. , , To calculate the number of nodes.

5. The method according to claim 1, characterized in that, Based on section B after the first round of reactive power optimization, the total adjustment amount of capacitors and reactors at each calculation node after the first stage of reactive power optimization is discretized, and the capacitor and reactor switching strategy is analyzed, including: compute nodes The total amount of capacitor reactance adjustment is , This represents the total reactive power of the capacitors and reactors connected to the calculation node nq at section A; This represents the total reactive power of the capacitors and reactors connected to the calculation node nq at section B. The total adjustment of capacitor reactance for each computing node is analyzed to obtain the specific switching strategy for capacitor reactance. The analysis process is as follows: (1) Obtain the status and capacity of capacitors and reactors that are connected to each computing node; (2) Perform the analysis operation on the capacitors and reactors with the connection relationship on each calculation node in turn. After the analysis and judgment of the current capacitor and reactor is completed, continue to compare the capacity of the next capacitor and reactor until the total amount of capacitor and reactor adjustment on the calculation node has been allocated or all capacitors and reactors have been compared. The analysis process ends. Among them, a single capacitor reactor The method for analyzing and judging the throwing and cutting strategy has two cases, as follows: Scenario 1: When selecting a computing node The capacity of the last unloaded capacitor or loaded reactor was... : when When the capacitor is engaged or the reactor is disengaged, the capacitor is engaged or disengaged. At that time, if If the capacitor is switched on or the reactor is switched off, then the capacitor should be switched on or the reactor should be switched off. If so, the capacitor reactor will not be operated; Scenario 2: When When selecting a computing node The capacity of the last capacitor put into operation or the last reactor not put into operation was... :when If so, then the reactor is switched on or the capacitor is switched off; if If the reactor is switched on or the capacitor is switched off, then the reactor is switched on or the capacitor is switched off. If so, the capacitor reactor will not be operated.

6. The method according to claim 3, characterized in that, The second stage of reactive power optimization adopts a hot start, with the voltage amplitude and phase angle of each node as follows: , , To calculate the number of nodes; The target voltage range for the second stage of reactive power optimization is set as follows: The target voltage range for reactive power optimization in the second stage is larger than the target voltage range for reactive power optimization in the first node. , ; The optimization model for power flow constraints at section C, including the interior point method, is used to solve the problem and obtain section D. Record the reactive power output of each generator in section D as follows Terminal voltage .

7. The method according to claim 1, characterized in that, Based on the reconstructed section C and the section D after the second round of reactive power optimization, the generator reactive power adjustment strategy and capacitor reactance switching strategy are output, including: Generator tuning strategies are divided into generator tuning strategies for PV nodes and generator tuning strategies for PQ nodes: For generator i at the PV node, the generator terminal voltage adjustment is: , Let i be the terminal voltage of generator i in section D. Let i be the terminal voltage of generator i in section A, when When the specified threshold condition is met, the generator's adjustment strategy is output as terminal voltage adjustment. ; For generator j at node PQ, the generator reactive power adjustment is: , For the reactive power output of generator i in section D, For generator i, the reactive power output in section A is when When the specified threshold condition is met, the generator's adjustment strategy is output as generator reactive power adjustment. ; The switching strategy for the output of the capacitor-reactor is the capacitor-reactor set. , With sets , The difference value, and Let A and B be the sets of switching states of the capacitor at cross-sections C and A, respectively. and These are the sets of switching states of the reactor in sections C and A, respectively.

8. A two-stage reactive power optimization system considering capacitor reactance switching, characterized in that, include: The optimization model building module is used to obtain the network model and the initial section A to be adjusted, and to build an optimal power flow optimization model with the goal of minimizing the weighted sum of the reactive power adjustment and voltage deviation of the unit. The first optimization module is used to perform the first stage of reactive power optimization based on the initial section A. The control variables are set as generator reactive power and capacitor reactance. The optimization model considering the power flow constraints of the initial section A is solved. After the calculation is completed, the section B after the first round of reactive power optimization is obtained. The section reconstruction module is used to discretize the total adjustment of capacitors and reactors of each calculation node after the first stage of reactive power optimization based on section B after the first round of reactive power optimization, analyze the capacitor and reactor switching strategy, and modify the switching state of capacitors and reactors in the initial section A according to the analyzed capacitor and reactor switching strategy to obtain the reconstructed section C. The second optimization module is used to perform the second stage of reactive power optimization based on the reconfigured section C. The control variable is set as generator reactive power, and the optimization model considering the power flow constraints of the reconfigured section C is solved. After the calculation is completed, the section D after the second round of reactive power optimization is obtained. The strategy output module is used to output the generator reactive power adjustment strategy and capacitor reactance switching strategy based on the reconstructed section C and the section D after the second round of reactive power optimization.

9. An electronic device, characterized in that, The device includes: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, wherein when the programs are executed by the processors, they implement the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described in any one of claims 1-8.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the two-stage reactive power optimization method considering capacitor reactance switching as described in any one of claims 1-8.

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