Droop gain optimization and droop control method based on intraday robust optimization model of microgrid droop gain

Abstract

The present invention discloses a droop gain optimization and droop control method based on a microgrid group droop gain intraday robust optimization model, wherein the droop gain optimization method based on the microgrid group droop gain intraday robust optimization model comprises: constructing a microgrid group day-ahead optimization scheduling model to obtain a day-ahead optimization scheduling result; constructing a microgrid group droop gain intraday robust optimization model; iteratively optimizing the microgrid group droop gain intraday robust optimization model to obtain an optimal droop gain; and a droop control method based on the microgrid group droop gain intraday robust optimization model comprises: real-time collection of real-time output data of each microgrid node, combining the real-time output data with the day-ahead scheduling optimization result and inputting it into the microgrid group droop gain intraday robust optimization model, and adjusting the control strategy according to the droop gain optimization result.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a droop gain optimization and droop control method based on a microgrid group droop gain intraday robust optimization model. Background Art

[0002] In today's power system, a serious imbalance exists between the rapid growth in demand for renewable energy access and the limited carrying capacity of distribution networks, leading to frequent problems such as voltage fluctuations, frequency instability, and line overloads. To effectively promote the integration and consumption of renewable energy, microgrid technology has rapidly developed. As a small power system capable of self-control, protection, and management, microgrids can effectively integrate distributed energy resources, energy storage systems, and loads, improving the reliability and flexibility of the grid.

[0003] However, the limited capacity and regulation capabilities of individual microgrids make it difficult to address the complexities of large-scale renewable energy integration. To address this, multiple microgrids are being flexibly interconnected to form microgrid clusters. By exchanging power across these clusters, they can optimize resource allocation and energy complementarity, further improving system stability and economic efficiency. However, existing scheduling methods are often based on deterministic models or simple robust optimization methods, which often lack flexibility and real-time responsiveness when dealing with fluctuations in renewable energy.

[0004] Therefore, how to introduce robust optimization strategies in optimized operation, cope with the uncertainty of new energy, and improve the operating efficiency and system stability of microgrid groups has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] In view of this, the present invention provides a droop gain optimization and droop control method based on a microgrid group droop gain intraday robust optimization model, so as to at least solve the problem of lack of flexibility and stability in the scheduling method of the microgrid group in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The droop gain optimization method based on the intraday robust optimization model of the droop gain of the microgrid group includes the following steps:

[0008] A day-ahead optimization dispatch model for the microgrid group is constructed, and the day-ahead optimization dispatch results are obtained:

[0009] With the goal of minimizing system operating costs and transmission losses, and with energy storage charging and discharging power and state of charge constraints and basic constraints as constraints, a day-ahead optimization scheduling model for the microgrid group is constructed; based on the new energy output forecast data, load forecast data and microgrid group system parameters, the day-ahead optimization scheduling model for the microgrid group is solved to obtain the day-ahead optimization scheduling result; among which, the basic constraints include: DC area linear branch flow constraints, voltage amplitude constraints, flexible interconnection device constraints, AC area constraints, DC area interconnection line capacity constraints; the day-ahead optimization scheduling result includes: energy storage charging and discharging strategy and reference operating point, among which the energy storage charging and discharging strategy includes energy storage charging and discharging power and state of charge, and the reference operating point includes the basic active output of the flexible interconnection device VSC. and the expected voltage of the system nodes ;

[0010] Constructing a robust optimization model for the intraday droop gain of a microgrid group:

[0011] With the goal of minimizing system transmission loss, the main model for optimizing droop gain of microgrid group is constructed with droop control constraint and basic constraint as the constraints.

[0012] With the goal of minimizing the over-limit value of steady-state safety constraints, and with steady-state safety relaxation constraints, droop control constraints, and basic constraints as constraints, a sub-model for optimizing the extreme output scenario of new energy in microgrid clusters is constructed.

[0013] Combining the main model for optimizing the droop gain of a microgrid group and the sub-model for optimizing the extreme output scenario of new energy in a microgrid group, a robust optimization model for the droop gain of a microgrid group is constructed.

[0014] The intraday robust optimization model of the microgrid droop gain is iteratively optimized to obtain the optimal droop gain:

[0015] S1. Based on the day-ahead optimization scheduling results, combined with the photovoltaic output scenario set , build the main model of robust optimization of droop gain of microgrid group, solve it and obtain the droop gain optimization result under the current number of iterations ,in A collection of expected photovoltaic output scenarios. For the A finite set of extreme scenarios in a round of iterations;

[0016] S2. Determine the current droop gain optimization result Is the objective function of the intraday robust optimization model of the droop gain of the microgrid group greater than 0? If so, proceed to S3. If not, output the current droop gain optimization result. As the optimal droop gain;

[0017] S3. Identify the current droop gain optimization result through the microgrid group new energy extreme output scenario optimization sub-model The new energy output scenario that causes the most system safety constraints to exceed the limit is updated as a new extreme scenario , and repeat S1-S2 until the objective function of the current microgrid group new energy extreme output scenario optimization sub-model is 0, then the current droop gain optimization result as the optimal droop gain.

[0018] Preferably, the objective function of the microgrid group day-ahead optimization dispatch model is:

[0019] (1)

[0020] Where, Indicates the scheduling time point, express Moment and The time interval between two scheduling times, express The system consists of N microgrids, each of which is considered as an independent node in the system, constituting the full set , where N is the total number of microgrids; Indicates the operation and maintenance cost coefficient of energy storage charging and discharging; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; Indicates the price of electricity purchased or sold from the upper-level power grid; express Moment Microgrid Power purchase and sale from the upper power grid; represents the penalty coefficient for transmission loss on the DC regional interconnection line of the microgrid; Represents microgrid Its adjacent microgrid Interconnection lines resistance; represents the set of lines of the microgrid group; express Constant interconnection lines The transmission power on represents the voltage amplitude of microgrid 1, which is constant at 1 pu.

[0021] Preferably, the objective function of the main model for optimizing the droop gain of the microgrid group is:

[0022] (2)

[0023] Where, Represents the interconnection lines between adjacent microgrids At the moment , scene The transmission power in A collection of photovoltaic output scenarios S Any scene in represents the penalty coefficient for transmission loss on the DC regional interconnection line of the microgrid; Represents microgrid Its adjacent microgrid Interconnection lines The resistance, represents the voltage amplitude of microgrid 1, which is constant at 1 pu.

[0024] Preferably, the specific contents of the objective function of constructing the microgrid group new energy extreme output scenario optimization sub-model include:

[0025] When included N The microgrid cluster system includes N If there are new energy devices, the new energy output set can be expressed as:

[0026] (3)

[0027] Where, It is an uncertain set of new energy sources; represents the actual photovoltaic output of microgrid i; uncertainty parameters for new energy output; Providing forecast data for new energy output;

[0028] With the goal of minimizing the over-limit value of the steady-state safety constraint, the objective function of the microgrid group new energy extreme output scenario optimization sub-model is expressed as:

[0029] (4)

[0030] Where, is a column vector; , are all non-negative safety slack vectors, represents the upper limit of safety slack, where and They are the non-negative safety relaxation upper limit of voltage and the non-negative safety relaxation upper limit of DC transmission line power respectively; represents the lower limit of safety relaxation, where and They are the non-negative safety relaxation lower limit of voltage and the non-negative safety relaxation lower limit of DC transmission line power respectively.

[0031] Preferably, the energy storage charging and discharging power and state of charge constraints are:

[0032] (5)

[0033] (6)

[0034] (7)

[0035] (8)

[0036] (9)

[0037] Where, and Respectively represent the minimum and maximum values of the charge and discharge states; Indicates that the microgrid i is Energy storage charge state at all times; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; and represent the charging and discharging efficiency of energy storage respectively; Represents microgrid Rated power of energy storage; Indicates the scheduling time point, express Moment and Time is the time interval between two dispatches; and Respectively The charging and discharging state variables are all 0-1 variables. When the value is 1, it means that the energy storage is in the charging and discharging state, and when the value is 0, it means that the charging and discharging state is stopped. and Microgrid The maximum charging power and maximum discharging power of the medium energy storage.

[0038] Preferably, the linear branch power flow constraint in the DC region is:

[0039] (10)

[0040] (11)

[0041] (12)

[0042] Where, express Moment Microgrid Its adjacent microgrid Interconnection lines The transmission power on Representation node 's child nodes; express Active power injection of microgrid i at time instant; Represents a collection of nodes; and Respectively The voltage amplitude of the DC bus parent node and child node at the moment; Represents microgrid Its adjacent microgrid Interconnection lines resistance; express Moment Microgrid DC load; express Moment Microgrid The predicted photovoltaic output; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; express Moment Microgrid The power at the DC side port of the VSC is positive when it flows out of the VSC in the DC region;

[0043] Voltage amplitude constraint:

[0044] (13)

[0045] Where, and They represent the minimum and maximum values allowed for the DC bus voltage of the microgrid respectively; express Moment Microgrid DC bus voltage;

[0046] Flexible interconnection device constraints:

[0047] (14)

[0048] (15)

[0049] Where, and They are Active power and reactive power of the VSC AC port at the moment; Indicates the rated capacity of the VSC;

[0050] AC area constraints:

[0051] (16)

[0052] (17)

[0053] (18)

[0054] Where, and Respectively AC power and DC power on the low-voltage side of the transformer at all times; and Respectively Active load and reactive load in the AC area of the microgrid at all times; Indicates the rated capacity of the transformer;

[0055] DC regional transmission line capacity constraints:

[0056] (19)

[0057] Where, for Constant interconnection lines The maximum power allowed to flow.

[0058] Preferably, the droop control constraint is:

[0059] (20)

[0060] (twenty one)

[0061] (twenty two)

[0062] (twenty three)

[0063] (twenty four)

[0064] Where, express Moment Microgrid i Active power at the VSC DC port; express Moment Microgrid The basic active power output of the flexible interconnection device VSC; Represents Moment Microgrid Changes in VSC active output corresponding to voltage changes; express The change of the voltage of microgrid i relative to the expected voltage at time instant; express The droop control gain of the VSC in the microgrid i at time instant; Indicates the upper limit of droop control gain; express Moment Microgrid DC bus voltage; express Moment Microgrid The expected system voltage under the photovoltaic forecast output scenario; Indicates the sensitivity of active power injection to voltage amplitude; represents the system correlation matrix; is a diagonal matrix whose diagonal elements are given by composition, Represents microgrid Its adjacent microgrid Interconnection lines resistance;

[0065] Seek The formula contains bilinear terms, After linearization:

[0066] (25)

[0067] In the formula, let the nonlinear term , by dividing the variable interval into K segments for convex relaxation, where and are the original variables of the DC bus voltage and droop gain of the microgrid, and their value ranges are ; In order to transform the nonlinear relationship into a linear solvable form, the variable interval is divided into K segments, each segment corresponds to a subinterval k, and the component variables are introduced and Respectively represent the values in the kth subinterval, and through the binary variable Ensure uniqueness constraint, i.e. , ensuring that only one subinterval is activated; and is the local boundary in the kth subinterval, which is calculated by uniformly dividing the global range.

[0068] Preferably, the steady-state safety relaxation constraint is:

[0069] (26)

[0070] (27)

[0071] Where, and They represent the upper limit of the safety relaxation variable and the lower limit of the safety relaxation variable of the DC bus voltage of microgrid i respectively; and They represent the upper limit and lower limit of the security slack variable of the interconnection lines between microgrids respectively; Interconnection lines between microgrids The maximum power allowed to flow; Represents the child nodes of node i.

[0072] The droop control method based on the intraday robust optimization model of the droop gain of the microgrid group includes the following steps:

[0073] The real-time output data of each microgrid node is collected in real time, and combined with the day-ahead dispatch optimization results, it is input into the intraday robust optimization model of the droop gain of the microgrid group. The control strategy is adjusted according to the droop gain optimization results: when the fluctuation amplitude does not exceed the threshold or the system reference operating point is close to the steady-state safety boundary, the VSC adaptively adjusts the active power distribution based on the optimization results; when it exceeds the threshold, the system cannot ensure safe operation of the system within the photovoltaic uncertainty set through droop gain optimization alone, and each VSC local controller obtains the preset optimization parameters to complete control.

[0074] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a droop gain optimization and droop control method based on a microgrid group droop gain intraday robust optimization model, which has the following beneficial effects:

[0075] ① The present invention proposes a droop gain adjustment method based on robust optimization. Combined with a day-ahead and intraday double-layer optimization framework, the method performs robustness-enhanced parameter tuning on each VSC in a flexible interconnected microgrid group. By optimizing the active power-voltage droop gain of each VSC, the method enables the VSC to have stronger disturbance self-adaptation capability. Therefore, when the photovoltaic output fluctuates violently, the node voltage can still be maintained within the allowable range, thereby improving voltage stability. This method can effectively address the voltage stability problem of the low-voltage distribution network caused by photovoltaic fluctuations with high urban penetration.

[0076] ② The present invention introduces a photovoltaic output uncertainty set in the sub-model, identifies extreme disturbance scenarios, and feeds them back to the main model for iterative optimization to implement a robust regulation strategy for extreme scenarios. This modeling method enables the system to rely on robust regulation capabilities to quickly stabilize system operation when faced with sudden changes in photovoltaic output (sudden shadow obstruction, rapid movement of clouds), and is more suitable for power mutation scenarios that occur in high-penetration photovoltaic areas.

[0077] ③ The present invention jointly controls VSCs and micropower sources, and considers comprehensive constraints such as interconnected line currents and voltages to allocate power to each node in an optimization model. Under the model of the present invention, VSCs can timely adjust the direction and amplitude of power flow based on output fluctuations and load demands of different nodes, thereby achieving dynamic balance among multiple microgrids, improving overall operational coordination, and effectively realizing dynamic power allocation among multiple microgrids. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0079] Figure 1 Flowchart of the droop control method based on the intra-day robust optimization model of droop gain of microgrid group in the present invention;

[0080] Figure 2 This is a flow chart of the algorithm for solving the intraday robust optimization model of the droop gain of a microgrid group according to the present invention;

[0081] Figure 3 A diagram of a flexible interconnected microgrid model provided by an embodiment of the present invention;

[0082] Figure 4 This is a schematic diagram of the system voltage over-limit situation provided by an embodiment of the present invention, wherein Figure 4 (a) and Figure 4 (b) Schematic diagram of the system voltage over-limit situation in three microgrid areas under the traditional constant power control and the control method proposed in this invention;

[0083] Figure 5 This is a comparison diagram of the DC bus voltage distribution of the microgrid under the upper limit of photovoltaic output. Figure 5 (a) is the three-dimensional diagram of voltage distribution under constant power control under the current working condition, Figure 5 (b) is the three-dimensional diagram of voltage distribution under robust droop control under the current working condition. Figure 5 (c) Figure 5 (a) Front view of the 3D image, Figure 5 (d) Figure 5 (b) Front view of the 3D image;

[0084] Figure 6 This is a comparison diagram of the DC voltage distribution of the microgrid DC bus under the lower limit of photovoltaic output. Figure 6 (a) is the three-dimensional diagram of voltage distribution under constant power control under the current working condition, Figure 6(b) is the three-dimensional diagram of voltage distribution under robust droop control under the current working condition. Figure 6 (c) Figure 6 (a) Front view of the 3D image, Figure 6 (d) Figure 6 (b) Front view of the 3D image;

[0085] Figure 7 This is a comparison diagram of the transmission power distribution between microgrids under the upper limit of photovoltaic output. Figure 7 (a) is the three-dimensional diagram of voltage distribution under constant power control under the current working condition, Figure 7 (b) is the three-dimensional diagram of voltage distribution under robust droop control under the current working condition. Figure 7 (c) Figure 7 (a) Front view of the 3D image, Figure 7 (d) Figure 7 (b) Front view of the 3D image;

[0086] Figure 8 This is a comparison diagram of the transmission power distribution between microgrids under the lower limit of photovoltaic output. Figure 8 (a) is the three-dimensional diagram of voltage distribution under constant power control under the current working condition, Figure 8 (b) is the three-dimensional diagram of voltage distribution under robust droop control under the current working condition. Figure 8 (c) Figure 8 (a) Front view of the 3D image, Figure 8 (d) Figure 8 (b) Front view of the 3D image. DETAILED DESCRIPTION

[0087] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0088] The present invention provides a droop gain optimization method based on a microgrid group droop gain intraday robust optimization model, such as Figure 1 As shown, the following steps are included:

[0089] A day-ahead optimization dispatch model for the microgrid group is constructed, and the day-ahead optimization dispatch results are obtained:

[0090] With the goal of minimizing system operating costs and transmission losses, and with energy storage charging and discharging power and state of charge constraints and basic constraints as constraints, a day-ahead optimization scheduling model for the microgrid group is constructed; based on the new energy output forecast data, load forecast data and microgrid group system parameters, the day-ahead optimization scheduling model for the microgrid group is solved to obtain the day-ahead optimization scheduling result; among which, the basic constraints include: DC area linear branch flow constraints, voltage amplitude constraints, flexible interconnection device constraints, AC area constraints, DC area interconnection line capacity constraints; the day-ahead optimization scheduling result includes: energy storage charging and discharging strategy and reference operating point, among which the energy storage charging and discharging strategy includes energy storage charging and discharging power and state of charge, and the reference operating point includes the basic active output of the flexible interconnection device VSC. and the expected voltage of the system nodes ;

[0091] Constructing a robust optimization model for the intraday droop gain of a microgrid group:

[0092] With the goal of minimizing system transmission loss, the main model for optimizing droop gain of microgrid group is constructed with droop control constraint and basic constraint as the constraints.

[0093] With the goal of minimizing the over-limit value of steady-state safety constraints, and with steady-state safety relaxation constraints, droop control constraints, and basic constraints as constraints, a sub-model for optimizing the extreme output scenario of new energy in microgrid clusters is constructed.

[0094] Combining the main model for optimizing the droop gain of a microgrid group and the sub-model for optimizing the extreme output scenario of new energy in a microgrid group, a robust optimization model for the droop gain of a microgrid group is constructed.

[0095] The intraday robust optimization model of the microgrid droop gain is iteratively optimized to obtain the optimal droop gain:

[0096] S1. Based on the day-ahead optimization scheduling results, combined with the photovoltaic output scenario set , build the main model of robust optimization of droop gain of microgrid group, solve it and obtain the droop gain optimization result under the current number of iterations ,in A collection of expected photovoltaic output scenarios. For the A finite set of extreme scenarios in a round of iterations;

[0097] S2. Determine the current droop gain optimization result Is the objective function of the intraday robust optimization model of the droop gain of the microgrid group greater than 0? If so, proceed to S3. If not, output the current droop gain optimization result. As the optimal droop gain;

[0098] S3. Identify the current droop gain optimization result through the microgrid group new energy extreme output scenario optimization sub-model The new energy output scenario that causes the most system safety constraints to exceed the limit is updated as a new extreme scenario , and repeat S1-S2 until the objective function of the current microgrid group new energy extreme output scenario optimization sub-model is 0, then the current droop gain optimization result as the optimal droop gain.

[0099] It should be noted that:

[0100] The main model and sub-model are combined to construct a microgrid group droop gain intraday robust optimization model. The main model and sub-model are iteratively optimized. The microgrid group droop gain intraday robust optimization model is as follows: Figure 2 As shown in the figure. In each iteration, the main model optimizes the droop gain based on multiple expected PV output scenarios and a limited number of extreme scenarios. The sub-model identifies the PV output scenario with the most severe over-limit under this droop gain and adds it to the extreme scenario set of the main model. Then, based on the updated scenario set, the droop gain is re-optimized. The objective function of the sub-model under this droop gain is checked to see if it is zero. If not, the main model is returned to optimize the droop gain of the VSC until the objective function of the sub-model is zero. At this point, the obtained droop gain is the optimal droop gain, ensuring stable operation of the system in all scenarios and realizing efficient linkage between new energy and flexible interconnected devices at multiple time scales under uncertain conditions.

[0101] In this embodiment, five scenarios are generated based on predicted data with a 10% random error. This covers the typical fluctuation range of photovoltaic output (±10% random error) while avoiding excessive computational complexity. A "limited" number of extreme scenarios is dynamically determined through an iterative optimization process between the main model and the sub-model. After each round of main model optimization to obtain the droop gain, the sub-model searches for the worst-case operating scenario. When an over-limit risk is detected, the sub-model adds the extreme scenario to the main model's optimized scenario set and triggers a new round of iteration. The termination condition is that the sub-model verifies that all scenarios meet the safety constraints (i.e., the sub-model objective function is zero). At this point, the total number of extreme scenarios is naturally determined. "Over-limit" refers to crossing the non-negative safety slack upper and lower limits of voltage and the non-negative safety slack upper and lower limits of DC transmission line power.

[0102] In this embodiment, the new energy output prediction scenario specifically refers to predicting the output capacity of the photovoltaic power generation unit in the microgrid within the future scheduling cycle (24 hours), corresponding to P pv , and serves as the basis for the expected scenario set; the load forecast data represents the load power forecast curve at each grid node, including the DC load P dand active load and reactive load in the AC area; the microgrid system parameters include: ① topology parameters, such as node number, line resistance r ij ; ② VSC parameters, such as capacity upper limit; ② Energy storage system parameters, including maximum charge and discharge power, charge and discharge efficiency, energy storage capacity, initial state and SOC upper and lower limits; ③ System operation parameters, such as node voltage range, maximum transmission capacity of interconnected lines Plinemax, unit price of electricity purchase and sale, etc.

[0103] The energy storage charging and discharging strategy refers to the time series plan of the energy storage system's charging and discharging power within the next 24 hours determined by the day-ahead optimization scheduling model. Its core control variables include the charging power , discharge power , state of charge (SOC).

[0104] In order to further implement the above technical solution, the objective function of the microgrid group day-ahead optimization dispatch model is:

[0105] (1)

[0106] Where, Indicates the scheduling time point, express Moment and The time interval between two scheduling times, express The system consists of N microgrids, each of which is considered as an independent node in the system, constituting the full set , where N is the total number of microgrids; Indicates the operation and maintenance cost coefficient of energy storage charging and discharging; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; Indicates the price of electricity purchased or sold from the upper-level power grid; express Moment Microgrid Power purchase and sale from the upper power grid; represents the penalty coefficient for transmission loss on the DC regional interconnection line of the microgrid; Represents microgrid Its adjacent microgrid Interconnection lines resistance; represents the set of lines of the microgrid group; express Constant interconnection lines The transmission power on represents the voltage amplitude of microgrid 1, which is constant at 1 pu.

[0107] In order to further implement the above technical solution, the objective function of the main model for optimizing the droop gain of the microgrid group is:

[0108] (2)

[0109] Where, Represents the interconnection lines between adjacent microgrids At the moment , scene The transmission power in A collection of photovoltaic output scenarios S Any scene in represents the penalty coefficient for transmission loss on the DC regional interconnection line of the microgrid; Represents microgrid Its adjacent microgrid Interconnection lines The resistance, represents the voltage amplitude of microgrid 1, which is constant at 1 pu.

[0110] It should be noted that:

[0111] The objective function of the main model for droop gain optimization of the microgrid group is to minimize the system transmission loss in the current scheduling period by optimizing the active output and droop control gain of the flexible interconnected device VSC.

[0112] In order to further implement the above technical solution, the specific contents of the objective function of the microgrid group new energy extreme output scenario optimization sub-model are as follows:

[0113] When included N The microgrid cluster system includes N If there are new energy devices, the new energy output set can be expressed as:

[0114] (3)

[0115] Where, It is an uncertain set of new energy sources; represents the actual photovoltaic output of microgrid i; uncertainty parameters for new energy output; Providing forecast data for new energy output;

[0116] With the goal of minimizing the over-limit value of the steady-state safety constraint, the objective function of the microgrid group new energy extreme output scenario optimization sub-model is expressed as:

[0117] (4)

[0118] Where, is a column vector; , are all non-negative safety slack vectors, represents the upper limit of safety slack, where and They are the non-negative safety relaxation upper limit of voltage and the non-negative safety relaxation upper limit of DC transmission line power respectively; represents the lower limit of safety relaxation, where and They are the non-negative safety relaxation lower limit of voltage and the non-negative safety relaxation lower limit of DC transmission line power respectively.

[0119] It should be noted that:

[0120] The droop-controlled microgrid cluster's sub-model for optimizing extreme renewable energy output scenarios within a day aims to minimize the magnitude of steady-state safety constraint violations. It identifies the renewable energy output scenarios that, given the gains obtained by the slope optimization main model, result in the most system safety constraint violations. These identified extreme scenarios are then incorporated into the main model's scenario set. When the sub-model's objective function is zero, the system safety constraints are satisfied within the uncertain renewable energy output set, indicating that the droop gain optimized by the main model is appropriate.

[0121] In order to further implement the above technical solution, the energy storage charging and discharging power and state of charge constraints are:

[0122] (5)

[0123] (6)

[0124] (7)

[0125] (8)

[0126] (9)

[0127] Where, and Respectively represent the minimum and maximum values of the charge and discharge states; Indicates that the microgrid i is Energy storage charge state at all times; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; and represent the charging and discharging efficiency of energy storage respectively; Represents microgrid Rated power of energy storage; Indicates the scheduling time point, express Moment and Time is the time interval between two dispatches; and Respectively The charging and discharging state variables are all 0-1 variables. When the value is 1, it means that the energy storage is in the charging and discharging state, and when the value is 0, it means that the charging and discharging state is stopped. and Microgrid The maximum charging power and maximum discharging power of the medium energy storage.

[0128] It should be noted that:

[0129] To ensure the effective operation of the energy storage system, the charging and discharging power and state of charge (SOC) of the energy storage system need to meet the constraints of (5)-(9), where Equations (5) and (6) are expressions of the energy storage state of charge and are limited to a certain range; Equations (7) and (8) indicate that the charging and discharging power of the energy storage should not exceed its maximum capacity, where, and They represent the charge and discharge state variables, and are both 0-1 variables. A value of 1 indicates that the energy storage is in the charge / discharge state, and a value of 0 indicates that the charge / discharge state stops. They are constrained by formula (9) because the energy storage can only be in one working state at time t.

[0130] In order to further implement the above technical solution, the linear branch power flow constraints in the DC region are as follows:

[0131] (10)

[0132] (11)

[0133] (12)

[0134] Where, express Moment Microgrid Its adjacent microgrid Interconnection lines The transmission power on Representation node 's child nodes; express Active power injection of microgrid i at time instant; Represents a collection of nodes; and Respectively The voltage amplitude of the DC bus parent node and child node at the moment; Represents microgrid Its adjacent microgrid Interconnection lines resistance; express Moment Microgrid DC load; express Moment Microgrid The predicted photovoltaic output; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; express Moment Microgrid The power at the DC side port of the VSC is positive when it flows out of the VSC in the DC region;

[0135] Voltage amplitude constraint:

[0136] (13)

[0137] Where, and They represent the minimum and maximum values allowed for the DC bus voltage of the microgrid respectively; express Moment Microgrid DC bus voltage;

[0138] Flexible interconnection device constraints:

[0139] (14)

[0140] (15)

[0141] Where, and They are Active power and reactive power of the VSC AC port at the moment; Indicates the rated capacity of the VSC;

[0142] AC area constraints:

[0143] (16)

[0144] (17)

[0145] (18)

[0146] Where, and Respectively AC power and DC power on the low-voltage side of the transformer at all times; and Respectively Active load and reactive load in the AC area of the microgrid at all times; Indicates the rated capacity of the transformer;

[0147] DC regional transmission line capacity constraints:

[0148] (19)

[0149] Where, for Constant interconnection lines The maximum power allowed to flow.

[0150] It should be noted that:

[0151] Equation (10) represents the active power balance of microgrid i after linear approximation; Equation (11) describes the voltage relationship between adjacent microgrids; Equation (12) represents the injection power expression of microgrid i.

[0152] Equations (14) and (15) respectively represent the capacity constraints of the flexible interconnection device VSC, which ensure that the transformer does not exceed the rated capacity of the equipment and stipulate that the internal loss of the VSC during the power exchange process is negligible so that the power on the AC side and DC side of the VSC is equal.

[0153] Equations (16) and (17) represent the AC area power balance constraints; Equation (18) represents the linearized transformer capacity constraint.

[0154] Equation (19) represents the transmission line capacity constraint, which is used to prevent equipment failure or system instability due to overload and ensure that the power passing through each transmission line does not exceed the maximum capacity.

[0155] In order to further implement the above technical solution, the droop control constraint is:

[0156] (20)

[0157] (twenty one)

[0158] (twenty two)

[0159] (twenty three)

[0160] (twenty four)

[0161] Where, express Moment Microgrid i Active power at the VSC DC port; express Moment Microgrid The basic active power output of the flexible interconnection device VSC; Represents Moment Microgrid Changes in VSC active output corresponding to voltage changes; express The change of the voltage of microgrid i relative to the expected voltage at time instant; express The droop control gain of the VSC in the microgrid i at time instant; Indicates the upper limit of droop control gain; express Moment Microgrid DC bus voltage; express Moment Microgrid The expected system voltage under the photovoltaic forecast output scenario; Indicates the sensitivity of active power injection to voltage amplitude; represents the system correlation matrix; is a diagonal matrix whose diagonal elements are given by composition, Represents microgrid Its adjacent microgrid Interconnection lines resistance;

[0162] Seek The formula contains bilinear terms, After linearization:

[0163] (25)

[0164] In the formula, let the nonlinear term , by dividing the variable interval into K segments for convex relaxation, where and are the original variables of the DC bus voltage and droop gain of the microgrid, and their value ranges are ; In order to transform the nonlinear relationship into a linear solvable form, the variable interval is divided into K segments, each segment corresponds to a subinterval k, and the component variables are introduced and Respectively represent the values in the kth subinterval, and through the binary variable Ensure uniqueness constraint, i.e. , ensuring that only one subinterval is activated; and is the local boundary in the k-th subinterval, which is calculated by uniformly dividing the global range.

[0165] It should be noted that:

[0166] Constraint (20) indicates that the real-time output of the VSC DC port of the flexible interconnected device is obtained by adding the basic active power output of the flexible interconnected device obtained by day-ahead optimization to the real-time output change of the VSC given by constraint (21); constraint (22) indicates that the droop control gain of the flexible interconnected device VSC is limited to a reasonable range to prevent over-regulation from causing system instability; constraint (23) gives the deviation between the real-time voltage of the system and the expected voltage. Since constraint (21) contains a bilinear term, it leads to nonlinearity of the equation. By using the piecewise McCormick method for linearization, the linearized equation is as shown in equation (25). Binary variable For each partition k, if The value of belongs to partition k, then ,otherwise . Another variable of the bilinear term Decompose into .

[0167] In order to further implement the above technical solution, the steady-state safety relaxation constraint is:

[0168] (26)

[0169] (27)

[0170] Where, and They represent the upper limit of the safety relaxation variable and the lower limit of the safety relaxation variable of the DC bus voltage of microgrid i respectively; and They represent the upper limit and lower limit of the security slack variable of the interconnection lines between microgrids respectively; Interconnection lines between microgrids The maximum power allowed to flow; Represents the child nodes of node i.

[0171] It should be noted that:

[0172] By adding safety slack variables for the DC region voltage and transmission power, the voltage and transmission power can take values within a wider range. While ensuring that the sub-model always has a solution, the over-limit amplitude of the voltage and transmission power safety constraints under the droop gain obtained by optimizing the main model is quantitatively obtained. The steady-state safety slack constraints are expressed using Equations (26)-(27). It should be noted that in the microgrid cluster's new energy extreme output scenario optimization sub-model, the droop gain is a known parameter obtained through optimization of the main model.

[0173] The sub-model for optimizing the extreme output scenario of new energy in the microgrid group in the present invention is essentially a max-min robust optimization problem. The outer layer (max): searching for extreme scenarios of new energy fluctuations, the inner layer (min): optimizing the system operation under a given scenario. If the sub-model is to be solved by a commercial solver, it is necessary to rewrite the original problem into a unified mathematical framework in a compact form (matrix expression), integrate the decision variables into x, and classify the constraints into linear constraints and inequality constraints. This form clarifies the structural characteristics of the optimization problem and lays the foundation for theoretical transformation. Based on the strong duality theory in Boyd's "Convex Optimization", by introducing Lagrange multipliers (dual variables), the inner layer min problem is transformed into a max problem, and finally a single-layer max problem of formula (32) is obtained, eliminating the double-layer nested structure, then:

[0174] The optimal sub-model for the microgrid group's new energy extreme output scenario is expressed as the following compact matrix form:

[0175] (28)

[0176] (29)

[0177] (30)

[0178] (31)

[0179] Where, It is the uncertain output variable of new energy; 、 、 、 is the corresponding parameter matrix; 、 、 、 represents the corresponding parameter matrix; Equation (29) is the uncertainty output constraint of new energy; Equation (30) is the equality constraint; and Equation (31) is the inequality constraint.

[0180] Based on strong duality theory, Boolean variables are introduced and continuous variables , transforming the above max-min two-layer sub-model into:

[0181] (32)

[0182] Where, 、 、 are the dual variables corresponding to constraints (30), (31), and (32), respectively; 、 are the maximum and minimum values of the uncertainty output of new energy respectively; For the Great A sufficiently large positive number introduced in the method.

[0183] The droop control method based on the intraday robust optimization model of the droop gain of the microgrid group includes the following steps:

[0184] The real-time output data of each microgrid node is collected in real time, and combined with the day-ahead dispatch optimization results, it is input into the intraday robust optimization model of the droop gain of the microgrid group. The control strategy is adjusted according to the droop gain optimization results: when the fluctuation amplitude does not exceed the threshold or the system reference operating point is close to the steady-state safety boundary, the VSC adaptively adjusts the active power distribution based on the optimization results; when it exceeds the threshold, the system cannot ensure safe operation of the system within the photovoltaic uncertainty set through droop gain optimization alone, and each VSC local controller obtains the preset optimization parameters to complete control.

[0185] It should be noted that:

[0186] In this embodiment, the central controller collects real-time data on voltage deviation, VSC port power, tie-line interaction power, and real-time PV output from each microgrid node. This data, combined with the day-ahead dispatch optimization results, is input into a robust droop gain optimization model. The control strategy is adjusted based on the optimization results. When the fluctuation amplitude is moderate, the central controller optimizes gain parameters within a reasonable range based on the model, enabling the VSC to adaptively adjust active power distribution. However, when the fluctuation amplitude exceeds a threshold or the system reference operating point approaches the steady-state safety margin, droop gain optimization alone cannot guarantee safe operation within the PV uncertainty set. The central controller activates a preset robust droop gain and distributes the optimized parameters to each VSC local controller for execution, ensuring that the VSC maintains a certain level of regulation capability. The optimized parameters include the reference operating point obtained from day-ahead optimization and the robust droop gain obtained from intraday optimization. Together, they determine the droop curve of the VSC in microgrid i at time t. Simultaneously, the central controller sends an alarm to the dispatch system, facilitating manual intervention to ensure stable system operation.

[0187] This method can effectively address voltage stability issues in low-voltage distribution networks caused by fluctuations in urban PV penetration rates, and is particularly suitable for power surges in high-PV penetration areas. By coordinating the control of flexible interconnected devices and micropower sources, it enables dynamic power allocation between multiple microgrids connected to high-PV penetration urban areas, enhancing the system's adaptability to large PV fluctuations and improving the operational reliability and stability of the low-voltage distribution network.

[0188] The present invention will be further described below through specific examples:

[0189] In order to further analyze and verify the effectiveness of the flexible interconnected microgrid group droop control method based on multi-scenario robust optimization proposed in this embodiment, a MATLAB-based Figure 3 The flexible interconnected microgrid model shown in the figure consists of four microgrid nodes (MG1-MG4). Each node is interconnected on the DC side through a flexible interconnection device. Each sub-microgrid is equipped with a photovoltaic power generation unit, a VSC, an energy storage unit, and AC and DC loads.

[0190] The specific parameters are set as follows: MG1 is used as the main station to maintain a constant 750V DC bus voltage, and the DC bus voltage limit of other microgrids is 1±10% (pu) and has an adjustable droop control strategy. The system power benchmark is 100kVA, the VSC rated capacity is set to 1500kVA, the interconnection line power capacity between microgrids is set to 1500kVA, the maximum charge and discharge power of the energy storage system is 900kVA, the charge and discharge efficiency is 0.98, the initial SOC is 0.3, and the upper and lower limits of the allowed SOC are 0.9 and 0.1 respectively. The photovoltaic power generation output of the day is set based on the forecast data. The expected scenario within the day is generated by applying a random error subject to the ±10% disturbance range on this basis to reflect the typical operating state under the expected scenario. In the robust optimization model, the subproblem constructs an uncertain set and searches for extreme scenarios by setting a ±30% photovoltaic output error limit. The day-ahead scheduling period is set to t =24, scheduling interval Δ t =1; the intraday scheduling interval is Δ t =1.

[0191] In order to compare and verify the advantages of the method proposed in this invention, two control strategies are designed for comparison:

[0192] Strategy 1 (method of the present invention): adopt a droop control strategy based on multi-scenario robust optimization, consider the photovoltaic output prediction error, and perform scheduling optimization for multiple typical scenarios simultaneously.

[0193] Strategy 2 (traditional constant power control method): A constant power control strategy is adopted, that is, no droop adjustment is performed, and the output power of the VSC is maintained constant, equal to the set value of the day-ahead scheduling plan.

[0194] The simulation process is as follows: Based on the day-ahead PV output forecast, random perturbations are introduced within a set ±30% error range. 1,000 sets of actual output scenarios are constructed. The two control strategies are then substituted into the power flow simulation to evaluate the system voltage over-limit probability and power flow distribution. Figure 4 shows the voltage over-limit results using a box plot. Figure 4 (a) is the traditional method, Figure 4(b) is the method of the present invention. Each box in the figure represents the distribution characteristics of the voltage of each microgrid node in the test scenario, including key statistical indicators such as maximum value, minimum value, upper quartile, median and lower quartile. Figure 4 (a) When the traditional constant power control strategy is adopted, the voltage distribution in the three microgrid areas is wide, some nodes have obvious upper and lower limit violations, and the system stability is poor; Figure 4 In (b), after using the control method of the present invention, the voltage distribution range is significantly narrowed, and all node voltages are controlled within the allowable range of 1±10%, showing better voltage regulation capability and system robustness.

[0195] In order to further demonstrate the system control performance of the two strategies under extreme working conditions, the photovoltaic output of each microgrid is set as the upper and lower bounds of the uncertainty set, and two sets of typical extreme scenarios are constructed. Figures 5 to 8 respectively show the comparative results of the traditional constant power control method and the method proposed in this invention in terms of system voltage distribution and power control under two sets of typical extreme scenarios. Figures 5 and 6 respectively show the DC bus voltage distribution of each level when the photovoltaic output is set as the upper and lower bounds of the uncertainty set. Figure 5 (a) Figure 6 (a) is the three-dimensional voltage distribution diagram under the constant power control method, Figure 5 (b) Figure 6 (b) is the three-dimensional voltage distribution diagram under the method of the present invention, corresponding to Figure 5 (c) Figure 6 (c) and Figure 5 (d) Figure 6 (d) are front views of the above figures. As can be seen from the figures, when using the traditional control strategy, the microgrid node voltage may exceed the allowable range, posing a significant steady-state safety risk to the system. However, after adopting the robust optimization method proposed in this invention, all node voltages are controlled within the safe range, and the overall distribution is closer to the per-unit value of 1, demonstrating stronger voltage steady-state control capability and robustness. Figure 7 and Figure 8 The power transmission between microgrids under the above two extreme output scenarios is further compared. Figure 7 (a) Figure 8 (a) is the three-dimensional power distribution diagram under the traditional method. Figure 7 (b) Figure 8 (b) is the power distribution diagram under this method, Figure 7 (c) Figure 8 (c) and Figure 7 (d) Figure 8(d) are the corresponding front views. The results in the figure show that conventional control methods often experience tie-line power over-limit under extreme operating conditions. However, the proposed method, by optimizing the VSC droop gain, effectively controls power distribution, ensuring that the transmission power of each tie-line remains within the rated capacity range. This significantly improves the system's operational safety and dynamic adaptability in high-penetration photovoltaic uncertainty scenarios.

[0196] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A droop gain optimization method based on a robust optimization model of droop gain of a microgrid group is characterized by: The following steps are involved: A day-ahead optimization dispatch model for the microgrid group is constructed, and the day-ahead optimization dispatch results are obtained: With the goal of minimizing system operating costs and transmission losses, and with energy storage charging and discharging power and state of charge constraints and basic constraints as constraints, a day-ahead optimization scheduling model for the microgrid group is constructed; based on the new energy output forecast data, load forecast data and microgrid group system parameters, the day-ahead optimization scheduling model for the microgrid group is solved to obtain the day-ahead optimization scheduling result; among which, the basic constraints include: DC area linear branch flow constraints, voltage amplitude constraints, flexible interconnection device constraints, AC area constraints, DC area interconnection line capacity constraints; the day-ahead optimization scheduling result includes: energy storage charging and discharging strategy and reference operating point, among which the energy storage charging and discharging strategy includes energy storage charging and discharging power and state of charge, and the reference operating point includes the basic active output of the flexible interconnection device VSC. and the expected voltage of the system nodes ; Constructing a robust optimization model for the intraday droop gain of a microgrid group: With the goal of minimizing system transmission loss, the main model for optimizing droop gain of microgrid group is constructed with droop control constraint and basic constraint as the constraints. With the goal of minimizing the over-limit value of steady-state safety constraints, and with steady-state safety relaxation constraints, droop control constraints, and basic constraints as constraints, a sub-model for optimizing the extreme output scenario of new energy in microgrid clusters is constructed. Combining the main model for optimizing the droop gain of a microgrid group and the sub-model for optimizing the extreme output scenario of new energy in a microgrid group, a robust optimization model for the droop gain of a microgrid group is constructed. The intraday robust optimization model of the microgrid droop gain is iteratively optimized to obtain the optimal droop gain: S1. Based on the day-ahead optimization scheduling results, combined with the photovoltaic output scenario set , build the main model of robust optimization of droop gain of microgrid group, solve it and obtain the droop gain optimization result under the current number of iterations ,in A collection of expected photovoltaic output scenarios. For the A finite set of extreme scenarios in a round of iterations; S2. Determine the current droop gain optimization result Is the objective function of the intraday robust optimization model of the droop gain of the microgrid group greater than 0? If so, proceed to S3. If not, output the current droop gain optimization result. As the optimal droop gain; S3. Identify the current droop gain optimization result through the microgrid group new energy extreme output scenario optimization sub-model The new energy output scenario that causes the most system safety constraints to exceed the limit is updated as a new extreme scenario , and repeat S1-S2 until the objective function of the current microgrid group new energy extreme output scenario optimization sub-model is 0, then the current droop gain optimization result as the optimal droop gain.

2. The droop gain optimization method based on the intraday robust optimization model of droop gain of microgrid group according to claim 1 is characterized in that: The objective function of the day-ahead optimization dispatch model of the microgrid group is: （1） Where, Indicates the scheduling time point, express Moment and The time interval between two scheduling times, express The system consists of N microgrids, each of which is considered as an independent node in the system, constituting the full set , where N is the total number of microgrids; Indicates the operation and maintenance cost coefficient of energy storage charging and discharging; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; Indicates the price of electricity purchased or sold from the upper-level power grid; express Moment Microgrid Power purchase and sale from the upper power grid; represents the penalty coefficient for transmission loss on the DC regional interconnection line of the microgrid; Represents microgrid Its adjacent microgrid Interconnection lines resistance; represents the set of lines of the microgrid group; express Constant interconnection lines The transmission power on represents the voltage amplitude of microgrid 1, which is constant at 1 pu.

3. The droop gain optimization method based on the intraday robust optimization model of droop gain of microgrid group according to claim 1 is characterized in that: The objective function of the main model for optimizing the droop gain of the microgrid group is: （2） Where, Represents the interconnection lines between adjacent microgrids At the moment , scene The transmission power in A collection of photovoltaic output scenarios S Any scene in represents the penalty coefficient for transmission loss on the DC regional interconnection line of the microgrid; Represents microgrid Its adjacent microgrid Interconnection lines The resistance, represents the voltage amplitude of microgrid 1, which is constant at 1 pu.

4. The droop gain optimization method based on the intraday robust optimization model of droop gain of microgrid group according to claim 1 is characterized in that: The specific contents of the objective function of constructing the optimization sub-model for microgrid cluster new energy extreme output scenarios include: When included N The microgrid cluster system includes N If there are new energy devices, the new energy output set can be expressed as: （3） Where, It is an uncertain set of new energy sources; represents the actual photovoltaic output of microgrid i; uncertainty parameters for new energy output; Providing forecast data for new energy output; With the goal of minimizing the over-limit value of the steady-state safety constraint, the objective function of the microgrid group new energy extreme output scenario optimization sub-model is expressed as: （4） Where, is a column vector; , are all non-negative safety slack vectors, represents the upper limit of safety slack, where and They are the non-negative safety relaxation upper limit of voltage and the non-negative safety relaxation upper limit of DC transmission line power respectively; represents the lower limit of safety relaxation, where and They are the non-negative safety relaxation lower limit of voltage and the non-negative safety relaxation lower limit of DC transmission line power respectively.

5. The droop gain optimization method based on the intra-day robust optimization model of droop gain of microgrid group according to claim 1 is characterized in that: The energy storage charging and discharging power and state of charge constraints are: （5） （6） （7） （8） （9） Where, and Respectively represent the minimum and maximum values of the charge and discharge states; Indicates that the microgrid i is Energy storage charge state at all times; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; and represent the charging and discharging efficiency of energy storage respectively; Represents microgrid Rated power of energy storage; Indicates the scheduling time point, express Moment and Time is the time interval between two dispatches; and Respectively The charging and discharging state variables are all 0-1 variables. When the value is 1, it means that the energy storage is in the charging and discharging state, and when the value is 0, it means that the charging and discharging state is stopped. and Microgrid The maximum charging power and maximum discharging power of the medium energy storage.

6. The droop gain optimization method based on the intraday robust optimization model of droop gain of microgrid group according to claim 1 is characterized in that: The linear branch power flow constraint in the DC region is: （10） （11） （12） Where, express Moment Microgrid Its adjacent microgrid Interconnection lines The transmission power on Representation node 's child nodes; express Active power injection of microgrid i at time instant; Represents a collection of nodes; and Respectively The voltage amplitude of the DC bus parent node and child node at the moment; Represents microgrid Its adjacent microgrid Interconnection lines resistance; express Moment Microgrid DC load; express Moment Microgrid The predicted photovoltaic output; and They are Moment Microgrid Energy storage charging power and energy storage discharging power; express Moment Microgrid The power at the DC side port of the VSC is positive when it flows out of the VSC in the DC region; Voltage amplitude constraint: （13） Where, and They represent the minimum and maximum values allowed for the DC bus voltage of the microgrid respectively; express Moment Microgrid DC bus voltage; Flexible interconnection device constraints: （14） （15） Where, and They are Active power and reactive power of the VSC AC port at the moment; Indicates the rated capacity of the VSC; AC area constraints: （16） （17） （18） Where, and Respectively AC power and DC power on the low-voltage side of the transformer at all times; and Respectively Active load and reactive load in the AC area of the microgrid at all times; Indicates the rated capacity of the transformer; DC regional transmission line capacity constraints: （19） Where, for Constant interconnection lines The maximum power allowed to flow.

7. The droop gain optimization method based on the intraday robust optimization model of droop gain of microgrid group according to claim 1 is characterized in that: The droop control constraint is: （20） （21） （22） （23） （24） Where, express Moment Microgrid i Active power at the VSC DC port; express Moment Microgrid The basic active power output of the flexible interconnection device VSC; Represents Moment Microgrid Changes in VSC active output corresponding to voltage changes; express The change of the voltage of microgrid i relative to the expected voltage at time instant; express The droop control gain of the VSC in the microgrid i at time instant; Indicates the upper limit of droop control gain; express Moment Microgrid DC bus voltage; express Moment Microgrid The expected system voltage under the photovoltaic output forecast scenario; Indicates the sensitivity of active power injection to voltage amplitude; represents the system correlation matrix; is a diagonal matrix whose diagonal elements are given by composition, Represents microgrid Its adjacent microgrid Interconnection lines resistance; Seek The formula contains bilinear terms, After linearization: （25） In the formula, let the nonlinear term , by dividing the variable interval into K segments for convex relaxation, where and are the original variables of the DC bus voltage and droop gain of the microgrid, and their value ranges are ; In order to transform the nonlinear relationship into a linear solvable form, the variable interval is divided into K segments, each segment corresponds to a subinterval k, and the component variables are introduced and Respectively represent the values in the kth subinterval, and through the binary variable Ensure uniqueness constraint, i.e. , ensuring that only one subinterval is activated; and is the local boundary in the kth subinterval, which is calculated by uniformly dividing the global range.

8. The droop gain optimization method based on the intra-day robust optimization model of droop gain of microgrid group according to claim 1 is characterized in that: Steady-state safety relaxation constraint: （26） （27） Where, and They represent the upper limit of the safety relaxation variable and the lower limit of the safety relaxation variable of the DC bus voltage of microgrid i respectively; and They represent the upper limit and lower limit of the security slack variable of the interconnection lines between microgrids respectively; Interconnection lines between microgrids The maximum power allowed to flow; Represents the child nodes of node i.

9. A droop control method based on a microgrid group droop gain intraday robust optimization model, based on the droop gain optimization method based on a microgrid group droop gain intraday robust optimization model according to any one of claims 1 to 8, characterized in that: The following steps are involved: The real-time output data of each microgrid node is collected in real time, and combined with the day-ahead dispatch optimization results, it is input into the intraday robust optimization model of the droop gain of the microgrid group. The control strategy is adjusted according to the droop gain optimization results: when the fluctuation amplitude does not exceed the threshold or the system reference operating point is close to the steady-state safety boundary, the VSC adaptively adjusts the active power distribution based on the optimization results; when it exceeds the threshold, the system cannot ensure safe operation of the system within the photovoltaic uncertainty set through droop gain optimization alone, and each VSC local controller obtains the preset optimization parameters to complete control.

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