Reactive power optimization method and terminal

Abstract

The present invention discloses a reactive power optimization method and terminal. In combination with the characteristics of the actual operation of the power grid, the goal is to minimize the loss in the power grid, minimize the cost of reactive power compensation, and keep the voltage within the limit. The influence of compensation on the network loss of each node is calculated, and the nodes with the largest network loss reduction and the lowest compensation cost after the compensation equipment is put into use and the voltage does not exceed the limit after compensation are found. These nodes are used as nodes to be compensated to construct a new objective function. The Lagrange multiplier method for finding the extreme value under the constraint condition is used to find the objective function with constraint conditions and find the optimal solution. Finally, it is verified whether the compensation capacity is within the specified range. If it is within the range, the optimal solution is the optimal compensation capacity. If it is not within the specified range, the compensation is performed according to the maximum and minimum values ​​that can be compensated in the range. Therefore, while improving the economic benefits, the voltage can be reasonably constrained, thereby improving and enhancing the power supply quality of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power optimization, and in particular to a reactive power optimization method and a terminal. Background Art

[0002] Reactive power compensation technology is to connect capacitive and inductive load devices to each other so that electric energy can be automatically converted between these two types of loads, avoiding the current being transmitted back and forth in the power grid transmission line due to the increase of non-linear loads, so as to reduce the loss of reactive power in the power grid. The application of reactive power compensation technology can reduce the power supply capacity of power generation equipment to reduce investment. For new or expanded power plant projects, it is necessary to focus on the application of reactive power compensation technology in the power supply and distribution network, so as to improve the power factor of the generator set, reduce the design capacity of the generator set, and improve the economic benefits of the power plant by reducing line losses. In the high-voltage power grid, the reactive power generated by the generator set transmitted to the load position through long-distance transmission lines cannot meet the reactive power needs of the inductive load at the end of the power grid. Therefore, a reactive power compensation device with appropriate capacity should be connected to the location where reactive power compensation is required in the power grid to generate reactive power to be absorbed by the non-linear load, so as to reduce the reactive power output of the power grid to the load, that is, to reduce the power loss of the equipment to save electricity.

[0003] Reactive power optimization of power system is an effective means to ensure the safe and economical operation of the system, and is one of the important measures to improve the voltage quality of the power system. The so-called reactive power optimization is to find the reactive power regulation means to achieve the optimal performance of one or more performance indicators of the system under the premise of satisfying all specified constraints by optimizing certain control variables when the structural parameters and load conditions of the system are given.

[0004] The reactive power optimization problem is a branch problem that has gradually differentiated from the development of optimal power flow. The optimal power flow model based on a strict mathematical model was first proposed by French electrical engineer Carpentier in the early 1960s. Over the past few decades, countless scholars have conducted extensive research on the optimal power flow problem. The success of the decoupling algorithm in conventional power flow calculation has enabled people to decompose reactive power and active power optimization into two sub-optimization problems according to the characteristics of the power system itself and solve them separately. The reactive power optimization problem is a complex nonlinear programming problem. Due to the nonlinearity of its objective function and constraints, and the mixed discreteness and continuity of the control variables, there is no practical, fast and perfect reactive power optimization method so far. The key to reactive power optimization focuses on the processing of nonlinear functions, the convergence of the algorithm, and how to solve the problem of discrete variables in the optimization problem. Due to the complexity of the reactive power optimization problem itself, it is not easy to classify reactive power optimization. First, the objective function may differ due to different focuses, mainly the following: ensuring the optimal voltage quality so that the voltage is minimized from the rated voltage; minimizing the system active power loss; minimizing the investment in reactive power compensation equipment; minimizing the number of transformer taps and capacitor switching; multi-objective reactive power optimization that comprehensively considers the above objectives, etc. Secondly, the constraints are different, and the optimization methods are also different. Among them, the optimization model with the minimum system active power loss as the objective function is generally used.

[0005] At present, for the problem of reactive power optimization, the existing technology takes into account the constraints and economic benefits, and takes the reactive power Q of the node compensation capacitor into account when constructing the objective function. c As an independent variable, the calculation formula of economic benefits in the prior art is very simple. There is no direct formula written into and solved for the voltage constraint, and the constructed function cannot organically unify the constraint conditions with the function of maximizing economic benefits. People can only use artificial intelligence algorithms to find the optimal solution. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a reactive power optimization method and terminal, which can reasonably constrain the voltage while improving the economic benefits, thereby improving and enhancing the power supply quality of the power grid.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A reactive power optimization method comprises the following steps:

[0009] Calculate the system power flow, find out the losses of each branch of the system, node voltage and node current, and the active power and reactive power input to each node;

[0010] Construct a mathematical model for reactive power optimization, calculate the impact of compensation on network loss for each node, take the node with the largest network loss reduction after the compensation equipment is put into use as the node to be compensated, and calculate the active network loss saved after the compensation of the node to be compensated;

[0011] The objective function is constructed according to the constraints of minimizing the compensation cost, maximizing the active network loss saved after compensation, and meeting the system voltage, and the objective function with the constraints is calculated by using the Lagrange multiplier method for finding the extreme value under the constraints to find the optimal solution;

[0012] If the optimal solution is within the preset range, compensation is performed according to the optimal solution; if the optimal solution is less than the minimum value within the preset range, compensation is performed according to the minimum value; if the optimal solution is greater than the maximum value within the preset range, compensation is performed according to the maximum value.

[0013] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0014] A reactive power optimization terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of a reactive power optimization method of the above scheme is implemented.

[0015] The beneficial effects of the present invention are as follows: combining the characteristics of the actual operation of the power grid, taking the minimum loss in the power grid and the minimum cost of reactive compensation and the voltage not exceeding the limit as the goal, calculating the impact of compensation for each node on the network loss, finding the nodes that have the largest network loss reduction and the lowest compensation cost after the compensation equipment is put into use, and the voltage does not exceed the limit after compensation, and taking these nodes as nodes to be compensated, constructing a new objective function. The Lagrange multiplier method for finding the extreme value under the constraint condition is used to find the objective function with constraint conditions, and find the optimal solution. Finally, verify whether the compensation capacity is within the specified range. If it is within the range, the optimal solution is the optimal compensation capacity. If it is not within the specified range, it is compensated according to the maximum and minimum values ​​that can be compensated in the range. Therefore, while improving the economic benefits, the voltage can be reasonably constrained, thereby improving and improving the power supply quality of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a reactive power optimization method according to an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of a reactive power optimization terminal according to an embodiment of the present invention;

[0018] Figure 3 A connection diagram for a typical power network connection line;

[0019] Figure 4A flowchart of specific steps of a reactive power optimization method according to an embodiment of the present invention;

[0020] Description of labels:

[0021] 1. A reactive power optimization terminal; 2. A memory; 3. A processor. DETAILED DESCRIPTION

[0022] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0023] Please refer to Figure 1 The embodiment of the present invention provides a reactive power optimization method, comprising the steps of:

[0024] Calculate the system power flow, find out the losses of each branch of the system, node voltage and node current, and the active power and reactive power input to each node;

[0025] Construct a mathematical model for reactive power optimization, calculate the impact of compensation on network loss for each node, take the node with the largest network loss reduction after the compensation equipment is put into use as the node to be compensated, and calculate the active network loss saved after the compensation of the node to be compensated;

[0026] The objective function is constructed according to the constraints of minimizing the compensation cost, maximizing the active network loss saved after compensation, and meeting the system voltage, and the objective function with the constraints is calculated by using the Lagrange multiplier method for finding the extreme value under the constraints to find the optimal solution;

[0027] If the optimal solution is within the preset range, compensation is performed according to the optimal solution; if the optimal solution is less than the minimum value within the preset range, compensation is performed according to the minimum value; if the optimal solution is greater than the maximum value within the preset range, compensation is performed according to the maximum value.

[0028] From the above description, it can be seen that the beneficial effects of the present invention are: combined with the characteristics of the actual operation of the power grid, with the goal of minimizing the loss in the power grid, minimizing the cost of reactive compensation, and not exceeding the voltage limit, the impact of compensation on the network loss of each node is calculated, and the nodes with the largest reduction in network loss and the lowest compensation cost after the compensation equipment is put into use and the voltage does not exceed the limit after compensation are found, and these nodes are used as nodes to be compensated to construct a new objective function. The Lagrange multiplier method for finding the extreme value under the constraint condition is used to find the objective function with constraint conditions and find the optimal solution. Finally, it is verified whether the compensation capacity is within the specified range. If it is within the range, the optimal solution is the optimal compensation capacity. If it is not within the specified range, it is compensated according to the maximum and minimum values ​​that can be compensated in the range. Therefore, while improving the economic benefits, the voltage can be reasonably constrained, thereby improving and improving the power supply quality of the power grid.

[0029] Furthermore, the mathematical model of reactive power optimization is constructed, the influence of compensation of each node on the network loss is calculated, and the node with the largest network loss reduction after the compensation equipment is put into use is taken as the node to be compensated, including:

[0030] The total loss is expressed as the sum of the active losses of each branch in the network:

[0031]

[0032] In the formula, I i represents the total current flowing through branch i, R i represents the resistance of branch i, I ia represents the active current flowing through branch i, I ir represents the reactive current flowing through branch i;

[0033] After the compensation equipment is put into use, the network loss caused by the flow of reactive current is greater than the network loss caused by the flow of active current, and the nodes through which the reactive current flows are taken as nodes to be compensated.

[0034] From the above description, it can be seen that the total active network loss is caused by the flow of active current and reactive current. The active current changes very little during the entire optimization process and can be ignored, while the reactive current is the main object for optimization.

[0035] Further, the calculating of the active network loss saved after the compensation of the node to be compensated includes:

[0036] According to the active power loss P of the branch where the node to be compensated is located loss , reactive power loss Q loss and branch parameters, calculate the reactive current I in the branch r :

[0037]

[0038] Q loss =I 2 X=ΔUI r ;

[0039] In the formula, R represents the branch resistance, I r represents reactive current, and U represents voltage;

[0040] The active power loss caused by the reactive current of the line before compensation is:

[0041]

[0042] When a compensation capacitor is added to a node j, the compensation current generated by the compensation capacitor is I c , after compensation, the active network loss Δp' caused by reactive current flow from the source node to node j r for:

[0043]

[0044] In the formula, m represents the number of preceding branches from the jth node to the power source point;

[0045] The active network loss Δp' saved after compensation is:

[0046]

[0047] From the above description, it can be seen that in the branch where the node to be compensated is located, the change of reactive load on a node only affects the reactive flow on the branches starting from the source node to the node, while the reactive flow on other branches remains basically unchanged. Therefore, the reactive current in the branch, the active loss caused by the reactive current of the line, and the active network loss caused by the reactive current flow can be calculated to calculate the active network loss saved after compensation.

[0048] Furthermore, the objective function is constructed according to the constraint conditions of minimizing the compensation cost, maximizing the active network loss saved after compensation, and meeting the system voltage, including:

[0049] Based on the saved active network loss after compensation, combined with the cost factor of the reactive power compensator invested, and according to the voltage constraint condition, the objective function is constructed:

[0050]

[0051] in:

[0052]

[0053] Where λ represents the node voltage penalty function, B o It represents the cost coefficient of reactive power compensation, in Yuan / Var, Iir represents the reactive current flowing through the branch, Q c =U j I c is the reactive power of the node compensation capacitor, I c is the compensation current generated by the compensation capacitor, R i represents the branch resistance, max and min represent the upper and lower limits of the variable respectively, Uj is the voltage of a node j after the compensation capacitor is added to the node, T is the cost recovery time in days, and k is the electricity price in kWh.

[0054] From the above description, it can be seen that the objective function is based on the cost of reactive power compensation minus the economic benefits of the active network loss saved after compensation plus the voltage constraint, and takes the minimum value to meet the minimum compensation cost, the maximum active network loss saved after compensation and the system voltage constraint.

[0055] Furthermore, the constraints include:

[0056]

[0057] Where P i , Q i represents the energy value collected at point i, m represents the sum of all points in the distribution network, δ ij , B ij , G ij Represents the phase angle difference, conductivity value and sodium value of i and j respectively, U i , U j represent the energy difference between points i and j respectively.

[0058] Furthermore, the constraints also include:

[0059]

[0060] In the formula, Q ci Indicates the power of the capacitor at the compensation node, Q cimin , Q cimax Respectively represent the minimum and maximum reactive capacity of the reactive compensation equipment, U imin , U imax They represent the upper and lower limits of the node voltage respectively, and U0 represents the rated voltage on the line.

[0061] From the above description, it can be seen that determining the constraints of the state variables facilitates the reasonable construction of the subsequent objective function.

[0062] Furthermore, the Lagrange multiplier method for finding the extreme value under the constraint condition is used to calculate the objective function with the constraint condition to find the optimal solution, which includes:

[0063] Find the partial derivative of the objective function:

[0064]

[0065] The optimal solution I can be obtained c :

[0066]

[0067] Substitute the optimal solution into formula Q c =U j I C , calculate the compensation capacity Q c,opt .

[0068] From the above description, it can be seen that by using the Lagrange multiplier method for finding the extreme value under the constraint conditions to calculate the objective function with constraints, the optimal solution can be obtained by finding the partial derivative of the objective function. There is no need to further verify the optimal solution, which saves time cost. The reactive compensation optimization result obtained by searching is more feasible, and the power supply quality of the power grid is improved.

[0069] Please refer to Figure 2 An embodiment of the present invention provides a reactive power optimization terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a reactive power optimization method of the above-mentioned scheme is implemented.

[0070] The reactive power optimization method and terminal of the present invention propose a novel reactive power optimization method with maximum economic benefits and integrated voltage constraints, which not only takes into account economic benefits but also reasonably integrates voltage constraints, greatly simplifies the calculation process, reduces the amount of calculation, and makes reactive power compensation faster and more efficient, thereby improving and enhancing the power supply quality of the power grid. The following is an explanation through specific implementation methods:

[0071] Embodiment 1

[0072] Please refer to Figure 1 , Figure 3 and Figure 4 , a reactive power optimization method, comprising the steps of:

[0073] S1. Calculate the system power flow, find out the losses of each branch of the system, node voltage and node current, and the active power and reactive power input to each node.

[0074] The system power flow is calculated using the Newton-Raphson method or PQ decomposition method to obtain the losses of each branch of the system, node voltage, node current, active power and reactive power input to each node and other parameters.

[0075] S2. Construct a mathematical model for reactive power optimization, theoretically calculate the impact of compensation of each node on network loss, take the node with the largest network loss reduction after the compensation equipment is put into use as the node to be compensated, and calculate the active network loss saved after the compensation of the node to be compensated.

[0076] S21. The total loss is expressed as the sum of the active losses of each branch in the network.

[0077] Please refer to Figure 3 , taking a transmission line in the power network as an example, assuming that the total loss of the network is Δp, the total loss can be expressed as the cumulative sum of the active losses of each branch in the network:

[0078]

[0079] In the formula, I i represents the total current flowing through branch i, R i represents the resistance of branch i, I ia represents the active current flowing through branch i, I ir Represents the reactive current flowing through branch i.

[0080] Therefore, the total active network loss consists of two parts: one part is caused by the flow of active current; the other part is caused by the flow of reactive current. There is only one source node in the power grid, and the active power of all loads in the power network is provided by this source node. The active current changes very little during the entire optimization process and can be ignored, while the reactive current is the main object for optimization.

[0081] S22. If the loss P on a branch is known loss , Q loss and branch parameters, the reactive current I in the line r It can be solved as follows:

[0082]

[0083] Q loss =I 2 X=ΔUI r ;

[0084]

[0085] therefore

[0086] From this we can get:

[0087] Therefore, in a certain branch of the power grid, the change of reactive load at a certain node only affects the reactive flow on the branches starting from the source node and reaching the node, that is, the magnitude of reactive current, while the reactive flow on other branches remains basically unchanged, except for slight changes caused by voltage changes.

[0088] S22. The active power loss caused by the reactive current of the line before compensation is:

[0089]

[0090] When a compensation capacitor is added to a node j, the compensation current generated by the compensation capacitor is I c , after compensation, the active network loss Δp' caused by reactive current flow from the source node to node j r for:

[0091]

[0092] Where m represents the number of preceding branches from the jth node to the power source.

[0093] The active network loss Δp' saved after compensation is:

[0094]

[0095] S3. Construct an objective function based on the constraints of minimizing compensation cost, maximizing active network loss saved after compensation, and meeting the system voltage, and use the Lagrange multiplier method for finding the extreme value under the constraints to calculate the objective function with constraints to find the optimal solution.

[0096] S31. Based on the saved active network loss after compensation, combined with the cost factor of the reactive compensator invested, and finally considering the voltage constraint conditions, the objective function is constructed:

[0097]

[0098] in:

[0099] Q c =U j I c ;

[0100]

[0101] Where λ represents the node voltage penalty function, B o Represents the cost coefficient of reactive power compensation, in yuan / Var, I ir Represents the reactive current flowing through the branch, Q c is the reactive power of the node compensation capacitor, I c is the compensation current generated by the compensation capacitor, R i represents the branch resistance, max and min represent the upper and lower limits of the variable, respectively, U j is the voltage of a node j after adding a compensation capacitor, T is the cost recovery time in days, k is the electricity price in kWh, and k is usually 0.4. This formula is based on the economic benefits of the cost of reactive power compensation minus the saved active network loss after compensation plus the voltage constraint condition, and takes the minimum value.

[0102] The voltage calculation of the substation node after reactive power compensation is as follows:

[0103]

[0104] U i-1 is the voltage of the i-1th node, I ja is the active current flowing through branch j, I jr is the original reactive current flowing through branch j.

[0105] S32. Calculate the constraint conditions.

[0106] Constraint 1:

[0107]

[0108] Among them, P i , Q i represents the energy value collected at point i, m represents the sum of all points in the distribution network, and parameter δ ij , B ij , G ij Represents the phase angle difference, conductivity value and sodium value of i and j respectively, U i , U j represent the energy difference between points i and j respectively.

[0109] Constraint 2, the constraints on the state variables are:

[0110]

[0111] Where Q ci To compensate for the capacitor power at the node, Q cimin , Q cimax Respectively represent the minimum and maximum reactive capacity of reactive compensation equipment. Generally, it is estimated according to the capacity of the transformer, and the conventional design is mainly 10%-30% of the transformer capacity. imin , U imax are the upper and lower limits of node voltage respectively. U0 is the rated voltage on the line.

[0112] S33, using the Lagrange multiplier method for finding the extreme value under the constraint conditions to calculate the objective function with the constraint conditions, and find the optimal solution.

[0113] Find the partial derivative of the objective function:

[0114]

[0115] You can get:

[0116]

[0117] The optimal solution I c , put it into the formula Q c =U j I C The compensation capacity Q can be obtained c,opt .

[0118] S4. If the optimal solution is within a preset range, compensation is performed according to the optimal solution; if the optimal solution is less than a minimum value within the preset range, compensation is performed according to the minimum value; if the optimal solution is greater than a maximum value within the preset range, compensation is performed according to the maximum value.

[0119] If Q c,opt ∈[Q cmin , Q cmax ], then Q c,opt is the final solution;

[0120] If Q c,opt cmin When cmin If Q c,opt >Q cmax When cmax Compared with other reactive power optimization schemes, the present invention integrates voltage constraints and economic benefits throughout the entire calculation process, and the reactive power Q that needs to be compensated by the system is obtained. c,opt All conditions are met directly, and there is no need to verify them one by one, which saves time and cost. The reactive power compensation optimization result obtained by searching is more feasible, and the power supply quality of the power grid is improved.

[0121] Therefore, a reactive power optimization method with maximum economic benefit and comprehensive voltage constraints is proposed in this embodiment. The compensated reactive power Q obtained by this method is c It can automatically meet the maximum economic benefit and comprehensive voltage constraint conditions. Compared with first considering the maximum economic benefit to obtain the result and then verifying it with the voltage constraint condition, the calculation speed is faster, easier to implement and the calculation result is more feasible.

[0122] And this embodiment proposes a method based on a new independent variable I c The reactive power optimization objective function constructed can organically unify the constraint conditions and the function of maximizing economic benefits, and use I c The constructed formula is more intuitive and specific.

[0123] Embodiment 2

[0124] Please refer to Figure 2 A reactive power optimization terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of a reactive power optimization method of embodiment 1 is implemented.

[0125] ​In summary, the reactive power optimization method and terminal provided by the present invention, combined with the characteristics of the actual operation of the power grid, takes the minimum loss in the power grid and the minimum cost of reactive power compensation and the voltage not exceeding the limit as the goal, calculates the impact of compensation of each node on the network loss, finds the nodes with the largest reduction in network loss and the lowest compensation cost after the compensation equipment is put into use, and the voltage does not exceed the limit after compensation, and uses these nodes as nodes to be compensated to construct a new objective function. The Lagrange multiplier method for finding the extreme value under the constraint condition is used to find the objective function with constraints and find the optimal solution. Finally, verify whether the compensation capacity is within the specified range. If it is within the range, the optimal solution is the optimal compensation capacity. If it is not within the specified range, compensation is performed according to the maximum and minimum values ​​that can be compensated within the range. Therefore, the reactive power optimization method proposed in the present invention with the maximum economic benefit and comprehensive voltage constraints, the compensated reactive power Q obtained under this method c It can automatically meet the maximum economic benefit and comprehensive voltage constraint conditions. Compared with first considering the maximum economic benefit to obtain the result and then verifying it with the voltage constraint condition, the calculation speed is faster, easier to implement and the calculation result is more feasible.

[0126] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A reactive power optimization method, characterized in that: Includes steps: Calculate the system power flow, find out the losses of each branch of the system, node voltage and node current, and the active power and reactive power input to each node; Construct a mathematical model for reactive power optimization, calculate the impact of compensation on network loss for each node, take the node with the largest network loss reduction after the compensation equipment is put into use as the node to be compensated, and calculate the active network loss saved after the compensation of the node to be compensated; The objective function is constructed according to the constraints of minimizing the compensation cost, maximizing the active network loss saved after compensation, and meeting the system voltage, and the objective function with the constraints is calculated by using the Lagrange multiplier method for finding the extreme value under the constraints to find the optimal solution; If the optimal solution is within the preset range, compensation is performed according to the optimal solution; if the optimal solution is less than the minimum value within the preset range, compensation is performed according to the minimum value; if the optimal solution is greater than the maximum value within the preset range, compensation is performed according to the maximum value; The objective function is constructed based on the constraints of minimizing compensation cost, maximizing active network loss saved after compensation, and meeting the system voltage, including: Based on the saved active network loss after compensation, combined with the cost factor of the reactive power compensator invested, and according to the voltage constraint condition, the objective function is constructed: in: Where λ represents the node voltage penalty function, B o Represents the cost coefficient of reactive power compensation, in yuan / Var, I ir represents the reactive current flowing through branch i, Q c =U j I c is the reactive power of the node compensation capacitor, I c is the compensation current generated by the compensation capacitor, R i represents the resistance of branch i, max and min represent the upper and lower limits of the variable, respectively, U j is the voltage of node j after adding compensation capacitor, T is the cost recovery time in days, k is the electricity price in kWh; The method of calculating the objective function with constraints by using the Lagrange multiplier method for finding the extreme value under the constraints to find the optimal solution includes: Find the partial derivative of the objective function: The optimal solution I can be obtained c : Substitute the optimal solution into formula Q c =U j I C , calculate the compensation capacity Q c,opt .

2. A reactive power optimization method according to claim 1, characterized in that: The mathematical model of reactive power optimization is constructed, the influence of compensation of each node on the network loss is calculated, and the node with the largest network loss reduction after the compensation equipment is put into use is taken as the node to be compensated, including: The total loss is expressed as the sum of the active losses of each branch in the network: In the formula, I i represents the total current flowing through branch i, R i represents the resistance of branch i, I ia represents the active current flowing through branch i, I ir represents the reactive current flowing through branch i; After the compensation equipment is put into use, the network loss caused by the reactive current flow is greater than the network loss caused by the active current flow. The node through which the network loss of the reactive current is reduced the most is taken as the node to be compensated.

3. A reactive power optimization method according to claim 2, characterized in that: The calculating of the active network loss saved after the compensation of the node to be compensated comprises: According to the active power loss P of the branch where the node to be compensated is located loss , reactive power loss Q loss and branch parameters, calculate the reactive current I in branch i ir : Q loss =I 2 X=ΔUI ir ; In the formula, R represents the branch resistance, and U represents the voltage; The active power loss caused by the reactive current of the line before compensation is: When a compensation capacitor is added to a node j, the compensation current generated by the compensation capacitor is I c After compensation, the active network loss Δp' caused by reactive current flow from the source node to node j is r for: In the formula, m represents the number of preceding branches from the jth node to the power source point; The active network loss Δp' saved after compensation is:

4. A reactive power optimization method according to claim 1, characterized in that: The constraints include: Where P i , Q i represents the energy value collected at point i, m represents the sum of all points in the distribution network, δ ij , B ij , G ij They represent the phase angle difference, conductance and susceptance of i and j respectively.

5. A reactive power optimization method according to claim 1, characterized in that: The constraints also include: In the formula, Q ci Indicates the power of the capacitor at the compensation node, Q cimin , Q cimax Respectively represent the minimum and maximum reactive capacity of the reactive compensation equipment, U imin , U imax They represent the lower and upper limits of the node voltage respectively, and U0 represents the rated voltage on the line.

6. A reactive power optimization terminal, characterized in that: It comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a reactive power optimization method as described in any one of claims 1 to 5 is implemented.

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