A distribution network reactive power optimization control method, system, terminal and medium

Abstract

The present invention discloses a method for optimizing reactive power control of a distribution network, and discloses a system and a storage medium having the method for optimizing reactive power control of a distribution network, wherein the method for optimizing reactive power control of a distribution network quantifies the voltage stability level of the distribution network by establishing an evaluation index for the voltage support capability of the distribution network, thereby providing a decision-making basis for reactive power control. At the same time, the optimization model realizes the dynamic adjustment of the output of new energy access and traditional generator sets, improves the ability of the power grid to adapt to fluctuations in new energy, maximizes the efficiency of new energy utilization, and promotes the green transformation of the power system. The present invention also provides a flexible control strategy to meet the reactive power control needs of distribution networks in different regions, scales, and operating conditions, and verifies the effectiveness of the method and the feasibility of engineering application through simulation experiments, which has important theoretical significance and application value.

Description

Technical Field

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

[0002] With the increasing global emphasis on sustainable energy and environmental protection, the widespread use of renewable energy sources such as wind and solar power in power systems has become key to driving energy transition and reducing greenhouse gas emissions. However, the volatility and uncertainty of renewable energy, particularly when influenced by environmental factors such as weather changes, pose unprecedented challenges to power system stability.

[0003] Against this backdrop, reactive power control has become increasingly important. It not only affects grid voltage stability but also directly impacts power system stability and power quality. Traditional reactive power control methods are no longer sufficient to address the irregularities of renewable energy output, necessitating innovative control strategies and optimization models. Summary of the Invention

[0004] The present invention aims to address at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method for optimizing reactive power control in a distribution network. This method, by establishing an evaluation index for the distribution network's voltage support capability, quantifies the grid's voltage stability level and provides a decision-making basis for reactive power control. Furthermore, the optimization model enables dynamic adjustment of renewable energy integration and the output of traditional generators, improving the grid's ability to adapt to fluctuations in renewable energy, maximizing renewable energy utilization efficiency, and promoting the green transformation of the power system.

[0005] The present invention also provides a system and a storage medium having the above-mentioned distribution network reactive power optimization control method.

[0006] The reactive power optimization control method for a distribution network according to the first embodiment of the present invention is characterized by comprising the following steps:

[0007] Evaluate the voltage support capability index of the power grid; wherein the voltage support capability index includes a node voltage deviation index, a voltage margin support index, and a reactive power balance margin index;

[0008] Constructing an objective function of a distribution network reactive power optimization model based on the voltage support capability indicator;

[0009] Based on the physical characteristics, operating conditions and safety standards of the power grid, the first constraint condition of the power grid operation is established;

[0010] Based on the characteristics of reactive power compensation equipment in the power system, the second constraint condition for power grid operation is established;

[0011] Based on the power output cap of renewable energy, the third constraint condition for grid operation is established;

[0012] The example information of the power system is used as input of the reactive power optimization model, and at least one of the first constraint condition, the second constraint condition, and the third constraint condition is used as a constraint condition of the reactive power optimization model to solve the reactive power compensation strategy.

[0013] The reactive power optimization control method for the distribution network according to the embodiment of the present invention has at least the following beneficial effects: This application quantifies the voltage stability level of the power grid by establishing an evaluation index for the voltage support capability of the distribution network, thereby providing a decision-making basis for reactive power control. At the same time, the reactive power optimization model realizes the dynamic adjustment of the access of new energy and the output of traditional generator sets, improves the ability of the power grid to adapt to the fluctuations of new energy, maximizes the efficiency of new energy utilization, and promotes the green transformation of the power system. The present invention also provides a flexible control strategy to meet the reactive power control needs of distribution networks in different regions, scales and operating conditions, and verifies the effectiveness of the method and the feasibility of engineering application through simulation experiments, which has important theoretical significance and application value.

[0014] According to some embodiments of the present invention, the node voltage deviation indicator L U Calculated using the following formula:

[0015]

[0016] Among them, U i is the voltage at node i; is the rated voltage of node i.

[0017] According to some embodiments of the present invention, the voltage margin support indicator L ΔU Calculated using the following formula:

[0018]

[0019] Where N is the number of nodes in the partition; E i is a measurement variable, U i,min 、U i,max are the upper and lower limits of the voltage at the i-th node; τ is the margin factor, which aims to set a virtual critical safety voltage before the voltage reaches the critical value.

[0020] According to some embodiments of the present invention, the reactive balance margin indicator L ΔQ Calculated using the following formula:

[0021]

[0022] Among them, Q L Indicates the reactive power demand of the load, Q i,GIt represents the maximum reactive power that can be adjusted in normal operation or phase leading operation of the i-th power plant unit or DG, Q i,r Indicates the capacity of each reactive compensation device, Q i,l is the maximum reactive power that each interconnected partition can provide, N g 、N r 、N l They are the number of power sources, the number of reactive compensation devices and the number of interconnected partitions.

[0023] According to some embodiments of the present invention, the objective function of the grid reactive power optimization model includes:

[0024] minF=γ1F1+γ2F2+γ3F3

[0025]

[0026] Among them, F is the overall objective function, F1, F2, F3 are sub-objective functions, λ1, λ2, λ3 are weights of sub-objective functions, t is a certain period in the time series, T is the set of time periods, Q L Indicates the reactive power demand of the load, Q i,G It represents the maximum reactive power that can be adjusted in normal operation or phase leading operation of the i-th power plant unit or DG, Q i,r Indicates the capacity of each reactive compensation device, Q i,l is the maximum reactive power that each interconnected partition can provide, N g 、N r 、N l They are the number of power sources, the number of reactive compensation devices and the number of interconnected partitions, Indicates the node voltage deviation index at time t, U i is the voltage at node i at time t; is the rated voltage of node i, represents the voltage margin support index at time t, is the measurement variable at time t, Represents the reactive power balance margin index at time t.

[0027] According to some embodiments of the present invention, the characteristics of the reactive power compensation equipment include a number constraint of mechanically switched capacitors and upper and lower reactive power limit constraints of a static VAR compensator.

[0028] According to some embodiments of the present invention, in the step of using the example information of the power system as the input of the reactive power optimization model, and using at least one of the first constraint, the second constraint, and the third constraint as the constraint of the reactive power optimization model, and solving the reactive power compensation strategy, the JuMP optimization toolkit is used to mathematically model the problem, and then the reactive power optimization model is solved using the CPLEX optimization solver to obtain the optimal reactive power compensation strategy.

[0029] The reactive power optimization control system for a distribution network according to the second embodiment of the present invention is characterized by comprising:

[0030] An indicator formulation module capable of evaluating the voltage support capability indicators of the power grid; wherein the voltage support capability indicators include a node voltage deviation indicator, a voltage margin support indicator, and a reactive balance margin indicator;

[0031] An objective function construction module is capable of constructing a distribution network reactive power optimization model based on the voltage support capability indicator;

[0032] A first constraint module is capable of constructing a first constraint condition for grid operation based on the physical characteristics, operating conditions and safety standards of the grid;

[0033] The second constraint module is capable of constructing a second constraint condition for grid operation based on the characteristics of reactive compensation equipment in the power system;

[0034] The third constraint module can construct the third constraint condition for grid operation based on the power output upper limit of renewable energy;

[0035] The strategy formulation module can use the calculation example information of the power system as the input of the reactive power optimization model, and use at least one of the first constraint condition, the second constraint condition, and the third constraint condition as the constraint condition of the reactive power optimization model to solve the reactive power compensation strategy.

[0036] According to the terminal of the third embodiment of the present application, the 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, the above-mentioned distribution network reactive power optimization control method is implemented.

[0037] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium stores computer-executable instructions, which are used to execute the above-mentioned distribution network reactive power optimization control method.

[0038] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0040] Figure 1 A schematic diagram of the steps of a method for optimizing reactive power control in a distribution network according to an embodiment of the present invention;

[0041] Figure 2This is a structural block diagram of a distribution network reactive power optimization control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0043] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0044] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0045] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0046] With the large-scale integration of renewable energy sources such as wind and solar, power systems face challenges brought by volatility and uncertainty. Traditional control methods are no longer sufficient to maintain system stability, especially at high penetration rates. To address the issues of distribution network stability and insufficient voltage support capacity under conditions of high renewable energy penetration, this paper proposes a reactive power control method for distribution networks based on multi-objective optimization.

[0047] Example 1

[0048] Reference Figure 1 , an embodiment of the present invention provides a method for optimizing reactive power control in a distribution network, the method comprising the following steps:

[0049] Step S100: Evaluate the voltage support capability index of the power grid; wherein the voltage support capability index includes a node voltage deviation index, a voltage margin support index, and a reactive balance margin index.

[0050] Step S200: constructing an objective function of a distribution network reactive power optimization model based on the voltage support capability indicator.

[0051] Step S300: Constructing a first constraint condition for grid operation based on the physical characteristics, operating conditions, and safety standards of the grid.

[0052] Step S400: Construct a second constraint condition for power grid operation based on the characteristics of reactive compensation equipment in the power system.

[0053] Step S500: constructing a third constraint condition for grid operation based on the power output upper limit of renewable energy;

[0054] Step S600: Using the calculation example information of the power system as the input of the reactive power optimization model, and using at least one of the first constraint condition, the second constraint condition, and the third constraint condition as the constraint condition of the reactive power optimization model to solve the reactive power compensation strategy.

[0055] Furthermore, in order to describe the purpose and calculation process of the above embodiment in more detail, the above steps are described in more detail.

[0056] Step S100: Evaluate the voltage support capability index of the power grid; wherein the voltage support capability index includes a node voltage deviation index, a voltage margin support index, and a reactive balance margin index.

[0057] 1) Node voltage deviation index:

[0058]

[0059] Among them, U i is the voltage at node i; is the rated voltage of node i.

[0060] 2) Voltage margin support indicators:

[0061]

[0062]

[0063] Where N is the number of nodes in the partition; E i is a measurement variable, U i,min 、U i,max are the upper and lower limits of the voltage at the i-th node; τ is the margin factor, which aims to set a virtual critical safety voltage before the voltage reaches the critical value.

[0064] 3) Reactive power balance margin index:

[0065]

[0066] Among them, QL Indicates the reactive power demand of the load, Q i,G It represents the maximum reactive power that can be adjusted in normal operation or phase leading operation of the i-th power plant unit or DG, Q i,r Indicates the capacity of each reactive compensation device, Q i,l is the maximum reactive power that each interconnected partition can provide, N g 、N r 、N l They are the number of power sources, the number of reactive compensation devices and the number of interconnected partitions.

[0067] Step S200: constructing an objective function of a distribution network reactive power optimization model based on the voltage support capability indicator.

[0068] The model uses the above three voltage support capability evaluation indicators as the objective function. Specifically including:

[0069] minF=γ1F1+γ2F2+γ3F3 (5)

[0070]

[0071] Among them, F is the overall objective function, F1, F2, and F3 are sub-objective functions, λ1, λ2, and λ3 are the weights of the sub-objective functions, t is a period in the time series, and T is the set of time periods.

[0072] Step S300: Constructing a first constraint condition for grid operation based on the physical characteristics, operating conditions, and safety standards of the grid.

[0073] Grid operation constraints are the cornerstone of ensuring safe, stable, and efficient operation of power systems. The grid operation constraints in this paper cover power flow constraints, voltage security constraints, and topology constraints, which are specifically expressed through equations (9) to (16). Power flow constraints ensure the balance between power supply and demand in the power system, preventing overload or insufficient power supply; voltage security constraints ensure that the voltage of all nodes is maintained within a safe range, protecting grid equipment and preventing voltage collapse; and topology constraints involve the physical structure and connection mode of the grid, ensuring that the grid maintains the correct connection and operation mode under various operating conditions.

[0074]

[0075] Among them, i, j, k are the node numbers of the distribution network. is the active power at node j during period t, are the active power flowing from node i to node j and from node j to node k in period t, r ij is the resistance of the line between nodes i and j, is the square value of the line current between nodes i and j during period t.

[0076]

[0077] in, is the reactive power at node j during period t, are the reactive power flowing from node i to node j and from node j to node k during period t, respectively, ij is the reactance of the line between nodes i and j.

[0078]

[0079] in, is the active power of DG at node j in period t, is the active power of the load at node j during period t.

[0080]

[0081] in, is the reactive power of DG at node j during period t, is the reactive power of the load at node j during period t.

[0082]

[0083] in, is the reactive power of the mechanically switched capacitor at node j during period t, is the reactive power of SVC at node j during period t, N C It is a collection of nodes equipped with reactive power compensation equipment.

[0084]

[0085] Among them, U j,max 、U j,min are the upper and lower limits of the voltage at node j.

[0086]

[0087] in, is the square of the voltage at node i.

[0088]

[0089] Among them, l ij,max It is the upper limit of the square value of the line current between nodes i and j.

[0090] These constraints, which comprehensively consider the physical characteristics, operating conditions, and safety standards of the power grid, provide the necessary boundary conditions for the reactive power optimization model of the distribution network. During the optimization process, they are considered along with other factors, such as reactive compensation equipment constraints and renewable energy constraints, to achieve optimal control of the reactive power in the distribution network. This approach not only ensures voltage stability and power balance in the power grid, but also improves its operational efficiency and power supply quality. This helps the grid maintain stability and reliability in the face of challenges such as high renewable energy penetration and load fluctuations, while also supporting the intelligent and green development of the power system.

[0091] Step S400: Construct a second constraint condition for power grid operation based on the characteristics of reactive compensation equipment in the power system.

[0092] In the power system, reactive power compensation equipment plays a vital role. They maintain the voltage stability of the power grid and improve the power transmission efficiency by regulating reactive power.

[0093] The reactive power compensation equipment constraint in the present invention is a key component of the reactive power optimization model of the distribution network, which includes the number constraint of mechanical switching capacitors and the upper and lower reactive power constraints of the static VAR compensator (SVC). Mechanical switching capacitors quickly respond to changes in the reactive power demand of the power grid through switching operations, and the number constraint ensures that the number of capacitors used does not exceed the maximum capacity of the system design, avoiding over- or under-compensation. As a dynamic reactive power compensation device, SVC can continuously adjust the reactive power output within a certain range. Its upper and lower limit constraints ensure that the reactive power output does not exceed the designed maximum and minimum values, ensuring that the equipment operates within a safe and effective range.

[0094] These constraints are specifically expressed in the model through equations (17) and (18). They are considered in combination with grid operation constraints and renewable energy constraints to achieve optimal control of the reactive power of the distribution network. Through this comprehensive optimization, the present invention not only ensures the voltage stability of the grid, but also improves the efficiency of reactive compensation equipment, reduces operating costs, and provides flexible and reliable support for the integration of renewable energy sources. This is of great significance to the efficient, economical, and environmentally friendly operation of the power system.

[0095]

[0096] Among them, Q j,svc,max , Q j,svc,min are the upper and lower limits of the SVC reactive power at node j.

[0097]

[0098] in, is the number of mechanically switched capacitors at node j during period t, N C,maxThe upper limit of the number of mechanical switching capacitors, Q C,step is the reactive power of a single mechanically switched capacitor.

[0099] Step S500: Constructing a third constraint condition for grid operation based on the power output upper limit of renewable energy.

[0100] In modern power systems, with the increasing integration of renewable energy sources such as wind and solar energy, they provide a clean and sustainable source of electricity for the power grid. However, the natural volatility and unpredictability of these energy sources also pose challenges to the stability and reliability of the power grid. To address these challenges, the distribution network reactive power optimization model of the present invention specifically considers the power output upper limit constraints of renewable energy sources, ensuring that the system can maintain efficient and stable operation when integrating these energy sources. Specifically, the power output upper limit constraint ensures that the output of renewable energy at any given point in time does not exceed its technical or physical maximum capacity, preventing equipment damage or grid instability caused by exceeding equipment capabilities.

[0101] These constraints are specifically expressed through equations (19) to (20) in the model, which comprehensively consider the current state of renewable energy, weather conditions, equipment capacity and other relevant factors, and define the maximum safe output limit of renewable energy under different operating conditions. By incorporating these renewable energy constraints into the reactive power optimization model, the present invention improves the adaptability of the power grid to the volatility of renewable energy and optimizes the reactive power balance of the power grid. This method not only maximizes the utilization efficiency of renewable energy, but also ensures the stability and voltage quality of the power grid, providing a flexible and sustainable reactive power management strategy for the power system. Ultimately, this reactive power optimization method that comprehensively considers the characteristics of renewable energy is of great significance for promoting the development of power systems in a more green, efficient and intelligent direction, and provides strong technical support for achieving energy transformation and responding to climate change.

[0102]

[0103] Among them, P j,pv,max 、P j,pv,min are the upper and lower limits of the PV active power at node j.

[0104]

[0105] Among them, Q j,pv,max , Q j,pv,min are the upper and lower limits of the photovoltaic reactive power at node j.

[0106]

[0107] Among them, is the decimal positive integer Convert to binary number, let the total number of digits be The value of the nth bit is The new variables introduced are

[0108]

[0109] in, The new variables introduced are M is a sufficiently large number in the Big-M method.

[0110]

[0111] Wherein, M is a sufficiently large number in the Big-M method.

[0112]

[0113] in, The number of transformer tap positions.

[0114] Step S600: Using the calculation example information of the power system as the input of the reactive power optimization model, and using at least one of the first constraint condition, the second constraint condition, and the third constraint condition as the constraint condition of the reactive power optimization model to solve the reactive power compensation strategy.

[0115] Ensuring stable operation and voltage quality in distribution networks is a core task of power system management. In this invention, the distribution network connects to the upper-level power grid via a key node 1. The location of this node has a decisive influence on the voltage stability of the entire power grid. To enhance the voltage support capability of this key node, the invention employs transformer tap voltage regulation technology. By adjusting the transformer tap position, the voltage level at the node can be effectively adjusted, thereby improving the voltage stability of the entire distribution network.

[0116] The solution process of this model is the key step to achieve the above goals. It specifically includes:

[0117] S601. Taking the calculation information of the distribution network, including detailed data of nodes, branches, loads, and power sources, as input, these data provide the necessary system parameters and operating conditions for the model.

[0118] S602. Use the JuMP optimization toolkit to mathematically model the problem. This step converts the actual power system operation problem into a solvable mathematical model, including the setting of the objective function and the expression of the constraints.

[0119] S603. The model is solved using the CPLEX optimization solver. CPLEX is a highly efficient mathematical programming solver capable of handling large-scale linear, integer, and mixed-integer programming problems. In the present invention, CPLEX is used to solve the established reactive power optimization model and find the optimal reactive power compensation strategy. This strategy aims to meet the voltage and reactive power margin requirements of the distribution network under different operating conditions, while minimizing voltage deviation and ensuring voltage quality and system stability.

[0120] S604: Output reactive power compensation strategy.

[0121] This calculation process not only effectively addresses the challenges posed by the high penetration of renewable energy, but also improves the distribution network's adaptability to renewable energy fluctuations and its voltage support capabilities while ensuring system stability. The implementation of this strategy will further promote the development of a more intelligent, efficient, and sustainable power system.

[0122] Example 2

[0123] Another embodiment of the present application provides a distribution network reactive power optimization control system, the system 20 comprising:

[0124] The indicator formulation module 201 is capable of evaluating the voltage support capability indicators of the power grid; wherein the voltage support capability indicators include a node voltage deviation indicator, a voltage margin support indicator, and a reactive balance margin indicator;

[0125] An objective function construction module 202 is capable of constructing an objective function of a distribution network reactive power optimization model based on the voltage support capability indicator;

[0126] The first constraint module 203 is capable of constructing a first constraint condition for the operation of the power grid based on the physical characteristics, operating conditions and safety standards of the power grid;

[0127] The second constraint module 204 can construct a second constraint condition for grid operation based on the characteristics of reactive compensation equipment in the power system;

[0128] The third constraint module 205 can construct a third constraint condition for grid operation based on the power output upper limit of renewable energy;

[0129] The strategy formulation module 206 can use the calculation example information of the power system as the input of the reactive power optimization model, and use at least one of the first constraint condition, the second constraint condition, and the third constraint condition as the constraint condition of the reactive power optimization model to solve the reactive power compensation strategy.

[0130] Another embodiment of the present application provides a terminal, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned distribution network reactive power optimization control method.

[0131] Specifically, a processor may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. A processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0132] Specifically, the processor is connected to the memory via a bus. The bus may include a path for transmitting information. The bus may be a PCI bus or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc.

[0133] The memory may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an EEPROM, CD-ROM or other optical disk storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0134] Optionally, the memory is used to store the code of the computer program for executing the solution of the present application, and the execution is controlled by the processor. The processor is used to execute the application code stored in the memory to implement Figure 2 The illustrated embodiment provides the functions of a reactive power optimization control system for a distribution network.

[0135] Another aspect of the present application provides a computer-readable storage medium storing computer-executable instructions for executing the above-mentioned Figure 1 The reactive power optimization control method of the distribution network is shown.

[0136] The embodiment of the present application establishes an evaluation index for the voltage support capacity of the distribution network, quantifies the level of grid voltage stability, and provides a decision-making basis for reactive power control. At the same time, the optimization model realizes the dynamic adjustment of new energy access and the output of traditional generator sets, improves the ability of the grid to adapt to the fluctuations of new energy, maximizes the efficiency of new energy utilization, and promotes the green transformation of the power system. The present invention also provides a flexible control strategy to meet the reactive power control needs of distribution networks in different regions, scales, and operating conditions, and verifies the effectiveness of the method and the feasibility of engineering application through simulation experiments, which has important theoretical significance and application value.

[0137] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0138] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, CD-ROM, digital versatile disks (DVD), or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage, or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0139] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A method for optimizing reactive power control in a distribution network, characterized in that: The following steps are involved: Evaluate the voltage support capability index of the power grid; wherein the voltage support capability index includes a node voltage deviation index, a voltage margin support index, and a reactive power balance margin index; Constructing a distribution network reactive power optimization model based on the voltage support capability index; Based on the physical characteristics, operating conditions and safety standards of the power grid, a first constraint condition for power grid operation is established; the first constraint condition includes a power flow constraint, a voltage safety constraint and a topology constraint; Based on the characteristics of reactive power compensation equipment in the power system, a second constraint condition for grid operation is established; the second constraint condition includes a constraint on the number of mechanically switched capacitors and upper and lower limits on the reactive power of the static VAR compensator; Based on the power output cap of renewable energy, the third constraint condition for grid operation is established; The example information of the power system is used as input of the reactive power optimization model, and at least one of the first constraint condition, the second constraint condition, and the third constraint condition is used as a constraint condition of the reactive power optimization model to solve the reactive power compensation strategy. The model also includes the following constraints: Among them, is the decimal positive integer Convert to binary number, let the total number of digits be The value of the nth bit is The new variables introduced are in, The new variables introduced are M is a sufficiently large number in the Big-M method. Wherein, M is a sufficiently large number in the Big-M method. in, The number of transformer tap positions.

2. The method according to claim 1, characterized in that The node voltage deviation index L U Calculated using the following formula: Among them, U i is the voltage at node i; is the rated voltage of node i.

3. The method according to claim 1, characterized in that The voltage margin support index L ΔU Calculated using the following formula: Where N is the number of nodes in the partition; E i is a measurement variable, U i,min 、U i,max are the upper and lower limits of the voltage at the i-th node; τ is the margin factor, which aims to set a virtual critical safety voltage before the voltage reaches the critical value.

4. The method according to claim 1, wherein The reactive balance margin index L ΔQ Calculated using the following formula: Among them, Q L Indicates the reactive power demand of the load, Q i,G It represents the maximum reactive power that can be adjusted in normal operation or phase leading operation of the i-th power plant unit or DG, Q i,r Indicates the capacity of each reactive compensation device, Q i,l is the maximum reactive power that each interconnected partition can provide, N g 、N r 、N l They are the number of power sources, the number of reactive compensation devices and the number of interconnected partitions.

5. The method according to claim 1, wherein The objective function of the grid reactive power optimization model includes: minF=γ1F1+γ2F2+γ3F3 Among them, F is the overall objective function, F1, F2, F3 are sub-objective functions, λ1, λ2, λ3 are weights of sub-objective functions, t is a certain period in the time series, T is the set of time periods, Q L Indicates the reactive power demand of the load, Q i,G It represents the maximum reactive power that can be adjusted in normal operation or phase leading operation of the i-th power plant unit or DG, Q i,r Indicates the capacity of each reactive compensation device, Q i,l is the maximum reactive power that each interconnected partition can provide, N g 、N r 、N l They are the number of power sources, the number of reactive compensation devices and the number of interconnected partitions, Indicates the node voltage deviation index at time t, U i is the voltage at node i at time t; is the rated voltage of node i, represents the voltage margin support index at time t, is the measurement variable at time t, Represents the reactive balance margin index at time t.

6. The method according to claim 1, characterized in that The characteristics of the reactive power compensation equipment include the number constraint of mechanical switching capacitors and the upper and lower reactive power limit constraints of the static VAR compensator.

7. The method according to claim 1, characterized in that In the step of using the example information of the power system as the input of the reactive power optimization model, and using at least one of the first constraint condition, the second constraint condition, and the third constraint condition as the constraint condition of the reactive power optimization model, and solving the reactive power compensation strategy, the JuMP optimization toolkit is used to mathematically model the problem, and then the reactive power optimization model is solved using the CPLEX optimization solver to obtain the optimal reactive power compensation strategy.

8. A reactive power optimization control system for a distribution network, characterized in that: include: An indicator formulation module capable of evaluating the voltage support capability indicators of the power grid; wherein the voltage support capability indicators include a node voltage deviation indicator, a voltage margin support indicator, and a reactive balance margin indicator; An objective function construction module is capable of constructing a distribution network reactive power optimization model based on the voltage support capability indicator; A first constraint module is capable of constructing first constraints for grid operation based on the physical characteristics, operating conditions, and safety standards of the grid; the first constraints include power flow constraints, voltage safety constraints, and topology constraints; A second constraint module is capable of constructing a second constraint condition for grid operation based on the characteristics of reactive compensation equipment in the power system; the second constraint condition includes a constraint on the number of mechanically switched capacitors and upper and lower reactive limits of the static VAR compensator; The third constraint module can construct the third constraint condition for grid operation based on the power output upper limit of renewable energy; The strategy formulation module can use the calculation case information of the power system as the input of the reactive power optimization model, and use at least one of the first constraint condition, the second constraint condition, and the third constraint condition as the constraint condition of the reactive power optimization model to solve the reactive power compensation strategy; the model also includes the following constraints: Among them, is the decimal positive integer Convert to binary number, let the total number of digits be The value of the nth bit is The new variables introduced are in, The new variables introduced are M is a sufficiently large number in the Big-M method. Wherein, M is a sufficiently large number in the Big-M method. in, The number of transformer tap positions.

9. A terminal, characterized in that: Said include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7. 10 . A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method according to claim 1 .

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