CN118100200A - Partition control method and system for real-time voltage risk of active power distribution network - Google Patents
Partition control method and system for real-time voltage risk of active power distribution network Download PDFInfo
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Abstract
The invention discloses a partition control method and a partition control system for real-time voltage risks of an active power distribution network, relates to the technical field of real-time voltage control, provides a real-time voltage risk control framework with different time scales, selects a risk control strategy combining dynamic network area division and electric vehicle dispatching, selects a voltage risk control strategy considering adjustable resource power sensitivity sequencing in a short time scale, and provides a partition of the power distribution network based on a real-time voltage out-of-limit risk matrix and a K-means clustering method.
Description
Technical Field
The invention relates to the technical field of real-time voltage control, in particular to a partition control method and a partition control system for real-time voltage risk of an active power distribution network.
Background
With the increase of the permeability of the distributed power supply, more flexibility and controllability are brought to the active power distribution network, but serious voltage out-of-limit problems are caused by the uncertainty and fluctuation of the output of the distributed power supply. In particular in networks where the number of Photovoltaic (PV) and Electric Vehicles (EV) is increasing, the voltage problem becomes more serious.
Disclosure of Invention
In order to solve the defects in the background art, the invention aims to provide a partition control method and a partition control system for real-time voltage risk of an active power distribution network, which can guide electric vehicles to charge rapidly and orderly based on voltage risk region division and improve the safety characteristics of power grid operation.
In a first aspect, the object of the present invention can be achieved by the following technical solutions: a partition control method for real-time voltage risk of an active power distribution network comprises the following steps:
Receiving power distribution network prediction data, performing node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data, and outputting to obtain node voltage out-of-limit risk data, wherein the power distribution network prediction data comprises power distribution network photovoltaic real-time output and power distribution network load real-time output;
calculating through Euclidean distance by using node voltage out-of-limit risk data to obtain a voltage out-of-limit risk matrix, and performing cluster analysis on the nodes of the power distribution network by using a K-means method and the voltage out-of-limit risk matrix to obtain a clustering result;
Based on the node voltage out-of-limit risk data and the clustering result, setting a charging price, inputting the charging price into a pre-established electric vehicle charging and discharging model, and outputting to obtain charging optimization required power of the charging station;
and respectively controlling the voltages in different scene modes according to the charging optimization required power of the charging station, wherein the different scene modes comprise a normal scene mode and an emergency scene mode.
With reference to the first aspect, in certain implementations of the first aspect, the method further includes: the prediction range of the power distribution network prediction data is one hour, the prediction resolution is one minute, and the prediction process adopts Monte Carlo sampling.
With reference to the first aspect, in certain implementations of the first aspect, the method further includes: the calculation formula for node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data is as follows:
Ki＝Nvio,i·max{Vdi,s} (3)
wherein s represents the s-th scene, N vio,i is the total out-of-limit number of the node i, N s is the total scene number generated, and V i,s、Vmax、Vmin is the actual voltage value of the node i under the scene s, the maximum value and the minimum value of the voltage allowable out-of-limit respectively; ρ vio,i and V vio,i represent the probability and severity of the voltage violation, respectively, and γ represents the threshold for node i voltage violation; VVOR denotes the risk of system out-of-limit, N a denotes the total number of nodes in the system, V di,s is the voltage deviation value under scene s, and K i denotes the voltage out-of-limit risk of i nodes.
With reference to the first aspect, in certain implementations of the first aspect, the method further includes: the normalization process of the total out-of-limit times N vio,i and the severity V vio,i of the voltage out-of-limit of the node i is as follows:
Wherein N' vio,i、V′vio,i is the voltage out-of-limit times and the voltage out-of-limit severity of the normalized node i, respectively.
With reference to the first aspect, in certain implementations of the first aspect, the method further includes: the step of obtaining a voltage threshold risk matrix through Euclidean distance calculation by using node voltage threshold risk data comprises the following steps:
Where R v (i, j) is an element in R v, characterizing the degree of similarity of nodes i, j, a small R v (i, j) means that nodes i and j have similar risk of voltage violation, placed in the same control region.
With reference to the first aspect, in certain implementations of the first aspect, the method further includes: the clustering analysis is carried out on the nodes of the power distribution network by using a K-means method and a voltage out-of-limit risk matrix to obtain a clustering result:
randomly selecting k nodes from the power distribution network as the mass center of each region; calculating all R v (i, j) in the power distribution network, and dividing each node into areas which are closest to the nearest centroid according to R v (i, j); and calculating a mean value for each region according to the aggregated nodes in each region to obtain new k centroid points as a clustering result, and then continuously performing iterative calculation R v (i, j) until the clustering result is not changed.
With reference to the first aspect, in certain implementations of the first aspect, the method further includes: the pre-established electric automobile charging and discharging model is as follows:
Wherein cp j is the charging price of the electric vehicle charged at the jth charging station; Δfc j is the total charge of the electric vehicle at the jth charging station; ct is the journey time cost; ΔT j is the travel time of the electric vehicle to the jth charging station, and can be estimated by normal distribution, and μ j、σj is the average value and standard value of normal distribution respectively; SOC max、SOCt is the maximum value and the actual value of the battery electric quantity of the electric automobile at the moment t respectively; SOC a is the amount of electric vehicle charge to the charging station, v a is the average speed of the electric vehicle.
The charging demand at time t of the jth charging station is available as follows:
P EVi,t represents the charging power of the ith electric vehicle at the time t, and P FCS,j,t represents the charging demand power at the time t of the jth charging station;
The electric automobile charge-discharge model constraint includes: upper and lower limit constraints of state of charge, upper and lower limit constraints of electric automobile output and reactive power constraints.
With reference to the first aspect, in certain implementations of the first aspect, the method further includes: real-time voltage control based on reactive power-voltage sensitivity sequencing is adopted in the normal mode, and real-time voltage control of reactive power increase and active power reduction is adopted in the emergency mode;
real-time voltage control process in normal mode:
Wherein P, Q, θ, V are respectively the active injection power, reactive injection power, voltage phase angle and amplitude of the node; j Pθ、JPV、JQθ、JQV is the deviation of the active power and the reactive power to the phase angle and the voltage amplitude; s VQ、SVP is the reactive-voltage sensitivity, active-voltage sensitivity of the node respectively;
the formula of the photovoltaic reactive power output is as follows:
Wherein: q' PV,t is the photovoltaic reactive power output at time t, Q PV,t-1 is the photovoltaic reactive power output at time t-1, and V c,t is the node voltage at time t under consideration of the PV normal fluctuation and EV partition control; v cmin、Vc,max is the minimum and maximum values of the node voltage allowed operating range; s VQ (f, d) is the influence degree of the reactive power of the node d on the voltage of the node f; q res,t is the reactive margin of the photovoltaic at time t in normal mode;
when the light Fu Mogong margin is adjusted, node voltage is still out of limit, and the adjustment by using the reactive power of the EV is needed to be considered:
Wherein: q EV,t、QEV,t-1 is reactive power output by EV at time t and time t-1 respectively; s VQ (f, c) is the influence degree of the reactive power of the node c on the voltage of the node f;
In emergency control mode:
when the active power of the PV drops suddenly, the PV and EV will provide reactive power to support the node voltage; as the PV output decreases, when the voltage exceeds the threshold V e, the emergency control mode is started for a second period of time, the PV reactive power margin reserved in the normal control mode will be used to support the voltage
Further, after implementing (16) - (19), when V c,t still violates the lower voltage limit V min, the vehicle-to-network interaction (V2G) mode of the electric vehicle is activated:
Wherein: p EV,t is the active output power of the electric vehicle at time t, S VP (f, c) is the influence degree of the active power of the node c on the voltage of the node f, and at this time, the reactive power of the electric vehicle is also regulated to the maximum value, so that the risk of voltage out-of-limit is reduced.
In order to achieve the above object, the present invention discloses a partition control system for real-time voltage risk of an active power distribution network, comprising:
The risk calculation module is used for receiving power distribution network prediction data, carrying out node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data, and outputting the node voltage out-of-limit risk data to obtain the power distribution network prediction data, wherein the power distribution network prediction data comprises power distribution network photovoltaic real-time output and power distribution network load real-time output;
the clustering analysis module is used for obtaining a voltage out-of-limit risk matrix by using node voltage out-of-limit risk data through Euclidean distance calculation, and performing clustering analysis on the nodes of the power distribution network by using a K-means method and the voltage out-of-limit risk matrix to obtain a clustering result;
the charging calculation module is used for setting a charging price based on the node voltage out-of-limit risk data and the clustering result, inputting the charging price into a pre-established electric vehicle charging and discharging model, and outputting the charging optimization required power of the charging station;
And the voltage control module is used for respectively controlling the voltages in different scene modes according to the charging optimization demand power of the charging station, wherein the different scene modes comprise a normal scene mode and an emergency scene mode.
With reference to the second aspect, in certain implementations of the second aspect, the system further includes: the prediction range of the power distribution network prediction data in the risk calculation module is one hour, the prediction resolution is one minute, and the prediction process adopts Monte Carlo sampling;
Or a calculation formula for performing node voltage out-of-limit risk assessment calculation by using power distribution network prediction data in the risk calculation module is as follows:
Ki＝Nvio,i·max{Vdi,s} (3)
wherein s represents the s-th scene, N vio,i is the total out-of-limit number of the node i, N s is the total scene number generated, and V i,s、Vmax、Vmin is the actual voltage value of the node i under the scene s, the maximum value and the minimum value of the voltage allowable out-of-limit respectively; ρ vio,i and V vio,i represent the probability and severity of the voltage violation, respectively, and γ represents the threshold for node i voltage violation; VVOR denotes the risk of system out-of-limit, N a denotes the total number of nodes in the system, V di,s is the voltage deviation value under scene s, and K i denotes the voltage out-of-limit risk of i nodes.
Or the normalization process of the total out-of-limit times N vio,i of the node i and the severity degree V vio,i of the voltage out-of-limit in the risk calculation module is as follows:
wherein, N' vio,i、V′vio,i is the voltage out-of-limit times and the voltage out-of-limit severity of the normalized node i respectively;
Preferably, the process of obtaining the voltage out-of-limit risk matrix by using node voltage out-of-limit risk data in the cluster analysis module through Euclidean distance calculation comprises the following steps:
Where R v (i, j) is an element in R v, characterizing the degree of similarity of nodes i, j, a small R v (i, j) means that nodes i and j have similar risk of voltage violation, placed in the same control region;
Or a clustering analysis module performs clustering analysis on the power distribution network nodes by using a K-means method and a voltage out-of-limit risk matrix to obtain a clustering result:
Randomly selecting k nodes from the power distribution network as the mass center of each region; calculating all R v (i, j) in the power distribution network, and dividing each node into areas which are closest to the nearest centroid according to R v (i, j); calculating a mean value for each region according to the aggregated nodes in each region to obtain new k centroid points as a clustering result, and then continuously performing iterative calculation R v (i, j) until the clustering result is not changed;
preferably, an electric vehicle charging and discharging model pre-established in the charging calculation module is as follows:
Wherein cp j is the charging price of the electric vehicle charged at the jth charging station; Δfc j is the total charge of the electric vehicle at the jth charging station; ct is the journey time cost; ΔT j is the travel time of the electric vehicle to the jth charging station, and can be estimated by normal distribution, and μ j、σj is the average value and standard value of normal distribution respectively; SOC max、SOCt is the maximum value and the actual value of the battery electric quantity of the electric automobile at the moment t respectively; SOC a is the amount of electric vehicle charge to the charging station, v a is the average speed of the electric vehicle.
The charging demand at time t of the jth charging station is available as follows:
P EVi,t represents the charging power of the ith electric vehicle at the time t, and P FCS,j,t represents the charging demand power at the time t of the jth charging station;
The electric automobile charge-discharge model constraint includes: upper and lower limit constraints of state of charge, upper and lower limit constraints of electric automobile output and reactive power constraints.
Preferably, real-time voltage control based on reactive power-voltage sensitivity sequencing is adopted in a normal mode in the voltage control module, and real-time voltage control of reactive power increase and active power reduction is adopted in an emergency mode;
real-time voltage control process in normal mode:
Wherein P, Q, θ, V are respectively the active injection power, reactive injection power, voltage phase angle and amplitude of the node; j Pθ、JPV、JQθ、JQV is the deviation of the active power and the reactive power to the phase angle and the voltage amplitude; s VQ、SVP is the reactive-voltage sensitivity, active-voltage sensitivity of the node respectively;
the formula of the photovoltaic reactive power output is as follows:
Wherein: q' PV,t is the photovoltaic reactive power output at time t, Q PV,t-1 is the photovoltaic reactive power output at time t-1, and V c,t is the node voltage at time t under consideration of the PV normal fluctuation and EV partition control; v cmin、Vc,max is the minimum and maximum values of the node voltage allowed operating range; s VQ (f, d) is the influence degree of the reactive power of the node d on the voltage of the node f; q res,t is the reactive margin of the photovoltaic at time t in normal mode;
when the light Fu Mogong margin is adjusted, node voltage is still out of limit, and the adjustment by using the reactive power of the EV is needed to be considered:
Wherein: q EV,t、QEV,t-1 is reactive power output by EV at time t and time t-1 respectively; s VQ (f, c) is the influence degree of the reactive power of the node c on the voltage of the node f;
In emergency control mode:
when the active power of the PV drops suddenly, the PV and EV will provide reactive power to support the node voltage; as the PV output decreases, when the voltage exceeds the threshold V e, the emergency control mode is started for a second period of time, the PV reactive power margin reserved in the normal control mode will be used to support the voltage
Further, after implementing (16) - (19), when V c,t still violates the lower voltage limit V min, the vehicle-to-network interaction (V2G) mode of the electric vehicle is activated:
Wherein: p EV,t is the active output power of the electric vehicle at time t, S VP (f, c) is the influence degree of the active power of the node c on the voltage of the node f, and at this time, the reactive power of the electric vehicle is also regulated to the maximum value, so that the risk of voltage out-of-limit is reduced.
The invention has the beneficial effects that:
The invention provides a real-time voltage risk control framework with different time scales, wherein a long time scale selects a risk control strategy combining dynamic network area division and electric vehicle dispatching, and a short time scale selects a voltage risk control strategy considering adjustable resource power sensitivity sequencing.
The invention provides a method for dividing a power distribution network based on a real-time voltage out-of-limit risk matrix and a K-means clustering method. The invention evaluates the voltage out-of-limit risk of each node in consideration of the uncertainty of the distributed power supply and the load, and provides a novel regional risk coordination strategy which is helpful for guiding the charging behavior of the electric automobile driver, thereby reducing the potential voltage out-of-limit risk of different control areas.
The invention considers the regulation sequence problem of adjustable resources in normal scenes and emergency scenes in short time scale, and can be used for solving the problem of rapid voltage out-of-limit under unexpected emergency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort;
FIG. 1 is a schematic flow chart of the method of the present invention;
Fig. 2 is a block diagram of an electric vehicle partition scheduling strategy based on voltage risk and electricity price according to the invention;
FIG. 3 is a schematic view of the region division results based on real-time risk assessment of the present invention;
FIG. 4 is a schematic diagram of the charging demand results of the instant invention fast charging stations (FCS 1, FCS 2);
FIG. 5 is a schematic diagram of the voltage at the node before and after the regulation according to the present invention;
Fig. 6 is a schematic diagram of the system structure of the present invention.
FIG. 7 is a schematic diagram of 33 node system voltage before control.
Fig. 8 is a graph of the reactive power removal and active power increase cases of the distributed photovoltaic node and the active power removal cases of the EV node when the voltage is beyond the limit at the time of 12:00.
Fig. 9 is a schematic diagram showing the voltage amplitude of the node voltage 18 after photovoltaic and EV regulation as a function of the number of regulation.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
the following description is made of the relevant terms related to the embodiments of the present application:
Active distribution network: the active power distribution network refers to a power distribution network with a large amount of access to distributed power sources and power flowing bidirectionally, and is also called an active power distribution network. The active power distribution network is a network for energy exchange and distribution, current flows in two directions with fault current, and current flow and fault analysis, voltage reactive power control, relay protection methods and operation management measures of the traditional power distribution network are not adapted any more, so that corresponding adjustment and improvement are required. The distributed power supply is called an active power distribution network, and aims to emphasize that the distributed power supply actively adjusts reactive power and active power output of the distributed power supply and coordinate and control the power distribution network by applying a modern communication means so as to fully play the role of the distributed power supply and realize the optimal operation of the power distribution network.
Monte Carlo sampling: the monte carlo method is a method that uses random numbers (or more commonly pseudo random numbers) to solve many computational problems. The solved problem is associated with a certain probability model, and statistical simulation or sampling is realized by an electronic computer so as to obtain an approximate solution of the problem. To symbolically represent the probabilistic statistics of this approach, gambling city Monte Carlo nomenclature is borrowed.
K-means method: the method is also called as K-means clustering algorithm (K-means clustering algorithm), which is a clustering analysis algorithm for iterative solution, and comprises the steps of dividing data into K groups, randomly selecting K objects as initial clustering centers, calculating the distance between each object and each seed clustering center, and distributing each object to the closest clustering center. The cluster centers and the objects assigned to them represent a cluster. For each sample assigned, the cluster center of the cluster is recalculated based on the existing objects in the cluster. This process will repeat until a certain termination condition is met. The termination condition may be that no (or a minimum number of) objects are reassigned to different clusters, no (or a minimum number of) cluster centers are changed again, and the sum of squares of errors is locally minimum.
As shown in fig. 1, a method for controlling real-time voltage risk of an active power distribution network in a partitioning manner includes the following steps:
Receiving power distribution network prediction data, performing node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data, and outputting to obtain node voltage out-of-limit risk data, wherein the power distribution network prediction data comprises power distribution network photovoltaic real-time output and power distribution network load real-time output;
The prediction range of the power distribution network prediction data is one hour, the prediction resolution is one minute, and the prediction process adopts Monte Carlo sampling; in order to coincide with the control step in the real-time voltage control, the resolution of the predicted data is converted from 1 minute to 10 seconds based on the linear interpolation method. Thus, the prediction process generates 360 scenarios, where each scenario represents a possible PV and load output situation, and then generates a node voltage value for each scenario by load flow calculation.
The calculation formula for carrying out node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data is as follows:
Ki＝Nvio,i·max{Vdi,s} (3)
wherein s represents the s-th scene, N vio,i is the total out-of-limit number of the node i, N s is the total scene number generated, and V i,s、Vmax、Vmin is the actual voltage value of the node i under the scene s, the maximum value and the minimum value of the voltage allowable out-of-limit respectively; ρ vio,i and V vio,i represent the probability and severity of the voltage violation, respectively, and γ represents the threshold for node i voltage violation; VVOR denotes the risk of system out-of-limit, N a denotes the total number of nodes in the system, V di,s is the voltage deviation value under scene s, and K i denotes the voltage out-of-limit risk of i nodes.
Further, the normalization process of the total out-of-limit times N vio,i and the severity of voltage out-of-limit V vio,i of the node i is as follows:
Wherein N' vio,i、V′vio,i is the voltage out-of-limit times and the voltage out-of-limit severity of the normalized node i, respectively.
Calculating through Euclidean distance by using node voltage out-of-limit risk data to obtain a voltage out-of-limit risk matrix, and performing cluster analysis on the nodes of the power distribution network by using a K-means method and the voltage out-of-limit risk matrix to obtain a clustering result; based on voltage risk clustering result divide into high risk, well risk, low risk area with voltage node, to the area that load night peak is in well high risk level, set up higher price of charging, to the lower area of risk level, set up lower price of charging, through the orderly electric automobile behavior of charging of guiding of price of electricity, and then reduce the electric wire netting voltage risk of crossing limit, the concrete form is:
In the day-ahead stage, based on node voltage out-of-limit risk data, setting a charging price, inputting the charging price into a pre-established electric vehicle charging and discharging model, and outputting day-ahead charging optimization required power of a charging station;
And in a real-time stage, taking the adjustable active power and reactive power of the charging station as a resource to participate in voltage control under different scenes of the power grid, wherein the different scene modes comprise a normal scene mode and an emergency scene mode.
And (3) calculating through Euclidean distance by using node voltage out-of-limit risk data to obtain a voltage out-of-limit risk matrix:
Where R v (i, j) is an element in R v, characterizing the degree of similarity of nodes i, j, a small R v (i, j) means that nodes i and j have similar risk of voltage violation, placed in the same control region.
And (3) performing cluster analysis on the nodes of the power distribution network by using a K-means method and a voltage out-of-limit risk matrix to obtain a clustering result:
randomly selecting k nodes from the power distribution network as the mass center of each region; calculating all R v (i, j) in the power distribution network, and dividing each node into areas which are closest to the nearest centroid according to R v (i, j); and calculating a mean value for each region according to the aggregated nodes in each region to obtain new k centroid points as a clustering result, and then continuously performing iterative calculation R v (i, j) until the clustering result is not changed.
Based on node voltage out-of-limit risk data, setting a charging price, inputting the charging price into a pre-established electric vehicle charging and discharging model, and outputting to obtain charging optimization required power of a charging station;
The network partition is used for selecting the electric automobile to charge based on VVOR in the daytime, so that potential voltage out-of-limit risks caused by overlarge charging requirements of the charging station are reduced.
To reduce the adverse effect of unordered charging of an electric vehicle on voltage distribution, the system operator will make a charge selection of the electric vehicle based on the charge price incentive and VVOR calculated in each region.
Since the distribution system operators are mainly concerned about the safety of operation, a charging station with a lower VVOR will set a lower charging price to attract more charged electric vehicles, while a charging station with a higher VVOR will set a higher charging price to reduce the risk of voltage violations caused by unordered charging of electric vehicles. Upon receiving the price signal generated by the charging station, the electric vehicle driver will make a charging selection in consideration of his own interests.
The pre-established electric vehicle charge and discharge model is herein as follows by optimizing the charging station selection of the electric vehicle driver to minimize the total cost, including the charging cost of the first phase and the travel time cost of the second phase:
Wherein cp j is the charging price of the electric vehicle charged at the jth charging station; Δfc j is the total charge of the electric vehicle at the jth charging station; ct is the journey time cost; ΔT j is the travel time of the electric vehicle to the jth charging station, and can be estimated by normal distribution, and μ j、σj is the average value and standard value of normal distribution respectively; SOC max、SOCt is the maximum value and the actual value of the battery electric quantity of the electric automobile at the moment t respectively; SOC a is the amount of electric vehicle charge to the charging station, v a is the average speed of the electric vehicle.
The charging demand at time t of the jth charging station is available as follows:
P EVi,t represents the charging power of the ith electric vehicle at the time t, and P FCS,j,t represents the charging demand power at the time t of the jth charging station;
electric automobile charge-discharge model constraint
SOCmin≤SOCt-1+PEVi,tηΔt/EEV≤SOCmax
PEVmin≤PEVi,t≤PEVmax (11)
The SOC min、SOCmax respectively represents the minimum value and the maximum value of the battery state of the ith electric automobile; η represents a charging efficiency; Δt represents a charging duration of the electric vehicle; s EV represents the maximum capacity of the ith electric automobile; p EVmin、PEVmax represents the minimum value and the maximum value of the charging power of the ith electric automobile respectively; q EVi,t represents reactive power that can be provided by the ith electric car at time t.
Obviously, the active power and the reactive power of the electric automobile can be adjusted under the condition of considering the interaction mode of the automobile network. Although active power is more efficient in voltage control, the cost of active power is more expensive than reactive power. Thus, in this context, the vehicle network interaction mode is only activated when all reactive power resources in the distribution network are exhausted.
And respectively controlling the voltages in different scene modes according to the charging optimization required power of the charging station, wherein the different scene modes comprise a normal scene mode and an emergency scene mode.
Specifically, the following examples are provided to further illustrate the present invention:
real-time voltage control based on reactive power-voltage sensitivity sequencing is adopted in a normal mode, and real-time voltage control of reactive power increase and active power reduction is adopted in an emergency mode;
real-time voltage control process in normal mode: the purpose of real-time voltage control is to keep the voltage values of the critical nodes within a low risk range, while centralized control is relatively time-consuming in computation and iteration processes, so regulation methods that account for power-voltage sensitivity ordering are contemplated herein. In order to establish the relationship between the reactive power of the control device and the critical bus voltage value, the power-voltage sensitivity is calculated once per hour:
Wherein P, Q, θ, V are respectively the active injection power, reactive injection power, voltage phase angle and amplitude of the node; j Pθ、JPV、JQθ、JQV is the deviation of the active power and the reactive power to the phase angle and the voltage amplitude; s VQ、SVP is the reactive-voltage sensitivity, active-voltage sensitivity of the node respectively;
the formula of the photovoltaic reactive power output is as follows:
Wherein: q' PV,t is the photovoltaic reactive power output at time t, Q PV,t-1 is the photovoltaic reactive power output at time t-1, and V c,t is the node voltage at time t under consideration of the PV normal fluctuation and EV partition control; v cmin、Vc,max is the minimum and maximum values of the node voltage allowed operating range; s VQ (f, d) is the influence degree of the reactive power of the node d on the voltage of the node f; q res,t is the reactive margin of the photovoltaic at time t in normal mode;
when the light Fu Mogong margin is adjusted, node voltage is still out of limit, and the adjustment by using the reactive power of the EV is needed to be considered:
Wherein: q EV,t、QEV,t-1 is reactive power output by EV at time t and time t-1 respectively; s VQ (f, c) is the influence degree of the reactive power of the node c on the voltage of the node f;
In emergency control mode:
When the active power of the PV drops suddenly, the PV and EV will provide reactive power to support the node voltage; as the PV output decreases, when the voltage exceeds a threshold V e (slightly greater than Vmin), an emergency control mode is initiated for a second period of time, the PV reactive power margin reserved in the normal control mode will be used to support the voltage, and the goal in the emergency control mode is to further increase the PV reactive power margin to prevent the voltage from continuing to drop or to continue to rise beyond the allowable range of power distribution network operation.
Further, after implementing (16) - (19), when V c,t still violates the lower voltage limit V min, the vehicle-to-network interaction (V2G) mode of the electric vehicle is activated:
Wherein: p EV,t is the active output power of the electric vehicle at time t, S VP (f, c) is the influence degree of the active power of the node c on the voltage of the node f, and at this time, the reactive power of the electric vehicle is also regulated to the maximum value, so that the risk of voltage out-of-limit is reduced.
Table 1 distribution network 33 node parameters
| Apparatus and method for controlling the operation of a device | Capacity of | Node location |
| PV | 400kVA | 3,7,11,13,15,17,18,19,23,25,27,29 |
| FCS (Rapid charging station) | 24Kw/perEV | 4,14 |
Embodiment two: in a second aspect, as shown in fig. 6, to achieve the above object, the present invention discloses a partition control system for real-time voltage risk of an active power distribution network, including:
The risk calculation module is used for receiving power distribution network prediction data, carrying out node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data, and outputting the node voltage out-of-limit risk data to obtain the power distribution network prediction data, wherein the power distribution network prediction data comprises power distribution network photovoltaic real-time output and power distribution network load real-time output;
the clustering analysis module is used for obtaining a voltage out-of-limit risk matrix by using node voltage out-of-limit risk data through Euclidean distance calculation, and performing clustering analysis on the nodes of the power distribution network by using a K-means method and the voltage out-of-limit risk matrix to obtain a clustering result;
the charging calculation module is used for setting a charging price based on the node voltage out-of-limit risk data and the clustering result, inputting the charging price into a pre-established electric vehicle charging and discharging model, and outputting the charging optimization required power of the charging station;
And the voltage control module is used for respectively controlling the voltages in different scene modes according to the charging optimization demand power of the charging station, wherein the different scene modes comprise a normal scene mode and an emergency scene mode.
With reference to the second aspect, in certain implementations of the second aspect, the system further includes: the prediction range of the power distribution network prediction data in the risk calculation module is one hour, the prediction resolution is one minute, and the prediction process adopts Monte Carlo sampling;
Or a calculation formula for performing node voltage out-of-limit risk assessment calculation by using power distribution network prediction data in the risk calculation module is as follows:
Ki＝Nvio,i·max{Vdi,s} (3)
wherein s represents the s-th scene, N vio,i is the total out-of-limit number of the node i, N s is the total scene number generated, and V i,s、Vmax、Vmin is the actual voltage value of the node i under the scene s, the maximum value and the minimum value of the voltage allowable out-of-limit respectively; ρ vio,i and V vio,i represent the probability and severity of the voltage violation, respectively, and γ represents the threshold for node i voltage violation; VVOR denotes the risk of system out-of-limit, N a denotes the total number of nodes in the system, V di,s is the voltage deviation value under scene s, and K i denotes the voltage out-of-limit risk of i nodes.
Or the normalization process of the total out-of-limit times N vio,i of the node i and the severity degree V vio,i of the voltage out-of-limit in the risk calculation module is as follows:
wherein, N' vio,i、V′vio,i is the voltage out-of-limit times and the voltage out-of-limit severity of the normalized node i respectively;
Preferably, the process of obtaining the voltage out-of-limit risk matrix by using node voltage out-of-limit risk data in the cluster analysis module through Euclidean distance calculation comprises the following steps:
Where R v (i, j) is an element in R v, characterizing the degree of similarity of nodes i, j, a small R v (i, j) means that nodes i and j have similar risk of voltage violation, placed in the same control region;
Or a clustering analysis module performs clustering analysis on the power distribution network nodes by using a K-means method and a voltage out-of-limit risk matrix to obtain a clustering result:
Randomly selecting k nodes from the power distribution network as the mass center of each region; calculating all R v (i, j) in the power distribution network, and dividing each node into areas which are closest to the nearest centroid according to R v (i, j); calculating a mean value for each region according to the aggregated nodes in each region to obtain new k centroid points as a clustering result, and then continuously performing iterative calculation R v (i, j) until the clustering result is not changed;
preferably, an electric vehicle charging and discharging model pre-established in the charging calculation module is as follows:
/>
Wherein cp j is the charging price of the electric vehicle charged at the jth charging station; Δfc j is the total charge of the electric vehicle at the jth charging station; ct is the journey time cost; ΔT j is the travel time of the electric vehicle to the jth charging station, and can be estimated by normal distribution, and μ j、σj is the average value and standard value of normal distribution respectively; SOC max、SOCt is the maximum value and the actual value of the battery electric quantity of the electric automobile at the moment t respectively; SOC a is the amount of electric vehicle charge to the charging station, v a is the average speed of the electric vehicle.
The charging demand at time t of the jth charging station is available as follows:
P EVi,t represents the charging power of the ith electric vehicle at the time t, and P FCS,j,t represents the charging demand power at the time t of the jth charging station;
The electric automobile charge-discharge model constraint includes:
Wherein, P EV,i,t and Q EVi,t are respectively the active/reactive power of the ith EV at time t; SOC t is the state of charge at time t, and SOC min and SOC max are the upper and lower limits of the state of charge, respectively; η is the charging efficiency of the electric automobile; p EVmin and P EVmax are respectively the upper limit and the lower limit of the charging power of the electric automobile; s EV is the electric vehicle inverter capacity.
Preferably, real-time voltage control based on reactive power-voltage sensitivity sequencing is adopted in a normal mode in the voltage control module, and real-time voltage control of reactive power increase and active power reduction is adopted in an emergency mode;
real-time voltage control process in normal mode:
Wherein P, Q, θ, V are respectively the active injection power, reactive injection power, voltage phase angle and amplitude of the node; j Pθ、JPV、JQθ、JQV is the deviation of the active power and the reactive power to the phase angle and the voltage amplitude; s VQ、SVP is the reactive-voltage sensitivity, active-voltage sensitivity of the node respectively;
the formula of the photovoltaic reactive power output is as follows:
/>
Wherein: q' PV,t is the photovoltaic reactive power output at time t, Q PV,t-1 is the photovoltaic reactive power output at time t-1, and V c,t is the node voltage at time t under consideration of the PV normal fluctuation and EV partition control; v cmin、Vc,max is the minimum and maximum values of the node voltage allowed operating range; s VQ (f, d) is the influence degree of the reactive power of the node d on the voltage of the node f; q res,t is the reactive margin of the photovoltaic at time t in normal mode;
when the light Fu Mogong margin is adjusted, node voltage is still out of limit, and the adjustment by using the reactive power of the EV is needed to be considered:
Wherein: q EV,t、QEV,t-1 is reactive power output by EV at time t and time t-1 respectively; s VQ (f, c) is the influence degree of the reactive power of the node c on the voltage of the node f;
In emergency control mode:
when the active power of the PV drops suddenly, the PV and EV will provide reactive power to support the node voltage; as the PV output decreases, when the voltage exceeds the threshold V e, the emergency control mode is started for a second period of time, the PV reactive power margin reserved in the normal control mode will be used to support the voltage
Further, after implementing (16) - (19), when V c,t still violates the lower voltage limit V min, the vehicle-to-network interaction (V2G) mode of the electric vehicle is activated:
Wherein: p EV,t is the active output power of the electric vehicle at time t, S VP (f, c) is the influence degree of the active power of the node c on the voltage of the node f, and at this time, the reactive power of the electric vehicle is also regulated to the maximum value, so that the risk of voltage out-of-limit is reduced.
As shown in FIG. 7, the system voltage of 33 nodes before control is the maximum risk of out-of-limit voltage of 12:00 PM, the maximum node voltage node is 29, and the voltage amplitude is 1.0582.
Fig. 8 shows the case where the distributed photovoltaic node cuts off reactive power and increases active power and the case where the EV node cuts off active power when the voltage is beyond the limit at the time 12:00.
Fig. 9 shows the voltage amplitude of the node voltage 18 after photovoltaic and EV regulation and the change process of the voltage amplitude with the regulation times, and the voltage tends to be stable after the 6 th regulation, the amplitude is kept at 1.0457, and the voltage is in a low risk state. Further demonstrating the effectiveness herein.
Based on the same inventive concept, the present invention also provides a computer apparatus comprising: one or more processors, and memory for storing one or more computer programs; the program includes program instructions and the processor is configured to execute the program instructions stored in the memory. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (DIGITAL SIGNAL Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), field-Programmable gate array (Field-Programmable GATEARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., that are the computational core and control core of the terminal for implementing one or more instructions, particularly for loading and executing one or more instructions within a computer storage medium to implement the methods described above.
It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which when executed by a processor performs the above method. The storage media may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing has shown and described the basic principles, principal features, and advantages of the present disclosure. It will be understood by those skilled in the art that the present disclosure is not limited to the embodiments described above, which have been described in the foregoing and description merely illustrates the principles of the disclosure, and that various changes and modifications may be made therein without departing from the spirit and scope of the disclosure, which is defined in the appended claims.
Claims (10)
1. The partition control method for the real-time voltage risk of the active power distribution network is characterized by comprising the following steps of:
Receiving power distribution network prediction data, performing node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data, and outputting to obtain node voltage out-of-limit risk data, wherein the power distribution network prediction data comprises power distribution network photovoltaic real-time output and power distribution network load real-time output;
calculating through Euclidean distance by using node voltage out-of-limit risk data to obtain a voltage out-of-limit risk matrix, and performing cluster analysis on the nodes of the power distribution network by using a K-means method and the voltage out-of-limit risk matrix to obtain a clustering result;
Based on the node voltage out-of-limit risk data and the clustering result, setting a charging price, inputting the charging price into a pre-established electric vehicle charging and discharging model, and outputting to obtain charging optimization required power of the charging station;
and respectively controlling the voltages in different scene modes according to the charging optimization demand power of the charging station, wherein the different scene modes comprise a normal scene mode and an emergency scene mode.
2. The method for partitioning control of real-time voltage risk of an active power distribution network according to claim 1, wherein the prediction range of the power distribution network prediction data is one hour, the prediction resolution is one minute, and the prediction process adopts monte carlo sampling.
3. The method for controlling the real-time voltage risk of the active power distribution network according to claim 1, wherein the calculation formula for performing node voltage out-of-limit risk assessment calculation by using power distribution network prediction data is as follows:
Ki＝Nvio,i·max{Vdi,s} (3)
wherein s represents the s-th scene, N vio,i is the total out-of-limit number of the node i, N s is the total scene number generated, and V i,s、Vmax、Vmin is the actual voltage value of the node i under the scene s, the maximum value and the minimum value of the voltage allowable out-of-limit respectively; ρ vio,i and V vio,i represent the probability and severity of the voltage violation, respectively, and γ represents the threshold for node i voltage violation; VVOR denotes the risk of system out-of-limit, N a denotes the total number of nodes in the system, V di,s is the voltage deviation value under scene s, and K i denotes the voltage out-of-limit risk of i nodes.
4. A method for controlling real-time voltage risk of an active power distribution network according to claim 3, wherein the normalization process of the total out-of-limit number N vio,i of the node i and the severity V vio,i of the voltage out-of-limit is as follows:
Wherein, N v'io,i、Vv'io,i is the voltage out-of-limit times and the voltage out-of-limit severity of the normalized node i respectively.
5. The method for controlling the real-time voltage risk of the active power distribution network according to claim 1, wherein the step of obtaining the voltage out-of-limit risk matrix by calculating the euclidean distance by using the node voltage out-of-limit risk data is characterized in that:
Where R v (i, j) is an element in R v, characterizing the degree of similarity of nodes i, j, a small R v (i, j) means that nodes i and j have similar risk of voltage violation, placed in the same control region.
6. The method for controlling the real-time voltage risk partition of the active power distribution network according to claim 1, wherein the clustering analysis is performed on the power distribution network nodes by using a K-means method and a voltage out-of-limit risk matrix to obtain a clustering result:
randomly selecting k nodes from the power distribution network as the mass center of each region; calculating all R v (i, j) in the power distribution network, and dividing each node into areas which are closest to the nearest centroid according to R v (i, j); and calculating a mean value for each region according to the aggregated nodes in each region to obtain new k centroid points as a clustering result, and then continuously performing iterative calculation R v (i, j) until the clustering result is not changed.
7. The method for controlling real-time voltage risk zone of active power distribution network according to claim 1, wherein the pre-established electric vehicle charge-discharge model is as follows:
Wherein cp j is the charging price of the electric vehicle charged at the jth charging station; Δfc j is the total charge of the electric vehicle at the jth charging station; ct is the journey time cost; ΔT j is the travel time of the electric vehicle to the jth charging station, and can be estimated by normal distribution, and μ j、σj is the average value and standard value of normal distribution respectively; SOC max、SOCt is the maximum value and the actual value of the battery electric quantity of the electric automobile at the moment t respectively; SOC a is the amount of electric vehicle charge to the charging station, v a is the average speed of the electric vehicle.
The charging demand at time t of the jth charging station is available as follows:
P EVi,t represents the charging power of the ith electric vehicle at the time t, and P FCS,j,t represents the charging demand power at the time t of the jth charging station;
The electric automobile charge-discharge model constraint includes: upper and lower limit constraints of state of charge, upper and lower limit constraints of electric automobile output and reactive power constraints.
8. The method for controlling the real-time voltage risk of the active power distribution network according to claim 1, wherein the normal mode adopts real-time voltage control based on reactive power-voltage sensitivity sequencing, and the emergency mode adopts real-time voltage control of reactive power increase and active power reduction;
real-time voltage control process in normal mode:
Wherein P, Q, θ, V are respectively the active injection power, reactive injection power, voltage phase angle and amplitude of the node; j Pθ、JPV、JQθ、JQV is the deviation of the active power and the reactive power to the phase angle and the voltage amplitude; s VQ、SVP is the reactive-voltage sensitivity, active-voltage sensitivity of the node respectively;
the formula of the photovoltaic reactive power output is as follows:
Wherein: q' PV,t is the photovoltaic reactive power output at time t, Q PV,t-1 is the photovoltaic reactive power output at time t-1, and V c,t is the node voltage at time t under consideration of the PV normal fluctuation and EV partition control; v cmin、Vc,max is the minimum and maximum values of the node voltage allowed operating range; s VQ (f, d) is the influence degree of the reactive power of the node d on the voltage of the node f; q res,t is the reactive margin of the photovoltaic at time t in normal mode;
when the light Fu Mogong margin is adjusted, node voltage is still out of limit, and the adjustment by using the reactive power of the EV is needed to be considered:
Wherein: q EV,t、QEV,t-1 is reactive power output by EV at time t and time t-1 respectively; s VQ (f, c) is the influence degree of the reactive power of the node c on the voltage of the node f;
In emergency control mode:
when the active power of the PV drops suddenly, the PV and EV will provide reactive power to support the node voltage; as the PV output decreases, when the voltage exceeds the threshold V e, the emergency control mode is started for a second period of time, the PV reactive power margin reserved in the normal control mode will be used to support the voltage
Further, after implementing (16) - (19), when V c,t still violates the lower voltage limit V min, the vehicle-to-network interaction (V2G) mode of the electric vehicle is activated:
Wherein: p EV,t is the active output power of the electric vehicle at time t, S VP (f, c) is the influence degree of the active power of the node c on the voltage of the node f, and at this time, the reactive power of the electric vehicle is also regulated to the maximum value, so that the risk of voltage out-of-limit is reduced.
9. A zone control system for real-time voltage risk of an active power distribution network, comprising:
The risk calculation module is used for receiving power distribution network prediction data, carrying out node voltage out-of-limit risk assessment calculation by using the power distribution network prediction data, and outputting the node voltage out-of-limit risk data to obtain the power distribution network prediction data, wherein the power distribution network prediction data comprises power distribution network photovoltaic real-time output and power distribution network load real-time output;
the clustering analysis module is used for obtaining a voltage out-of-limit risk matrix by using node voltage out-of-limit risk data through Euclidean distance calculation, and performing clustering analysis on the nodes of the power distribution network by using a K-means method and the voltage out-of-limit risk matrix to obtain a clustering result;
the charging calculation module is used for setting a charging price based on the node voltage out-of-limit risk data and the clustering result, inputting the charging price into a pre-established electric vehicle charging and discharging model, and outputting the charging optimization required power of the charging station;
And the voltage control module is used for respectively controlling the voltages in different scene modes according to the charging optimization demand power of the charging station, wherein the different scene modes comprise a normal scene mode and an emergency scene mode.
10. The system for partitioning control of real-time voltage risk of an active power distribution network according to claim 9, wherein the prediction range of the power distribution network prediction data in the risk calculation module is one hour, the prediction resolution is one minute, and the prediction process adopts monte carlo sampling;
Or a calculation formula for performing node voltage out-of-limit risk assessment calculation by using power distribution network prediction data in the risk calculation module is as follows:
Ki＝Nvio,i·max{Vdi,s} (3)
Wherein s represents the s-th scene, N vio,i is the total out-of-limit number of the node i, N s is the total scene number generated, and V i,s、Vmax、Vmin is the actual voltage value of the node i under the scene s, the maximum value and the minimum value of the voltage allowable out-of-limit respectively; ρ vio,i and V vio,i represent the probability and severity of the voltage violation, respectively, and γ represents the threshold for node i voltage violation; VVOR represents the risk of system out-of-limit, N a represents the total node number in the system, V di,s is the voltage deviation value under scene s, and K i represents the voltage out-of-limit risk of i nodes;
Or the normalization process of the total out-of-limit times N vio,i of the node i and the severity degree V vio,i of the voltage out-of-limit in the risk calculation module is as follows:
wherein, N' vio,i、V′vio,i is the voltage out-of-limit times and the voltage out-of-limit severity of the normalized node i respectively;
Preferably, the process of obtaining the voltage out-of-limit risk matrix by using node voltage out-of-limit risk data in the cluster analysis module through Euclidean distance calculation comprises the following steps:
Where R v (i, j) is an element in R v, characterizing the degree of similarity of nodes i, j, a small R v (i, j) means that nodes i and j have similar risk of voltage violation, placed in the same control region;
Or a clustering analysis module performs clustering analysis on the power distribution network nodes by using a K-means method and a voltage out-of-limit risk matrix to obtain a clustering result:
Randomly selecting k nodes from the power distribution network as the mass center of each region; calculating all R v (i, j) in the power distribution network, and dividing each node into areas which are closest to the nearest centroid according to R v (i, j); calculating a mean value for each region according to the aggregated nodes in each region to obtain new k centroid points as a clustering result, and then continuously performing iterative calculation R v (i, j) until the clustering result is not changed;
preferably, an electric vehicle charging and discharging model pre-established in the charging calculation module is as follows:
Wherein cp j is the charging price of the electric vehicle charged at the jth charging station; Δfc j is the total charge of the electric vehicle at the jth charging station; ct is the journey time cost; ΔT j is the travel time of the electric vehicle to the jth charging station, and can be estimated by normal distribution, and μ j、σj is the average value and standard value of normal distribution respectively; SOC max、SOCt is the maximum value and the actual value of the battery electric quantity of the electric automobile at the moment t respectively; SOC a is the amount of electric vehicle charge to the charging station, v a is the average speed of the electric vehicle.
The charging demand at time t of the jth charging station is available as follows:
P EVi,t represents the charging power of the ith electric vehicle at the time t, and P FCS,j,t represents the charging demand power at the time t of the jth charging station;
The electric automobile charge-discharge model constraint includes: upper and lower limit constraints of state of charge, upper and lower limit constraints of electric automobile output and reactive power constraints;
preferably, real-time voltage control based on reactive power-voltage sensitivity sequencing is adopted in a normal mode in the voltage control module, and real-time voltage control of reactive power increase and active power reduction is adopted in an emergency mode;
real-time voltage control process in normal mode:
Wherein P, Q, θ, V are respectively the active injection power, reactive injection power, voltage phase angle and amplitude of the node; j Pθ、JPV、JQθ、JQV is the deviation of the active power and the reactive power to the phase angle and the voltage amplitude; s VQ、SVP is the reactive-voltage sensitivity, active-voltage sensitivity of the node respectively;
the formula of the photovoltaic reactive power output is as follows:
Wherein: q' PV,t is the photovoltaic reactive power output at time t, Q PV,t-1 is the photovoltaic reactive power output at time t-1, and V c,t is the node voltage at time t under consideration of the PV normal fluctuation and EV partition control; v cmin、Vc,max is the minimum and maximum values of the node voltage allowed operating range; s VQ (f, d) is the influence degree of the reactive power of the node d on the voltage of the node f; q res,t is the reactive margin of the photovoltaic at time t in normal mode;
when the light Fu Mogong margin is adjusted, node voltage is still out of limit, and the adjustment by using the reactive power of the EV is needed to be considered:
Wherein: q EV,t、QEV,t-1 is reactive power output by EV at time t and time t-1 respectively; s VQ (f, c) is the influence degree of the reactive power of the node c on the voltage of the node f;
In emergency control mode:
when the active power of the PV drops suddenly, the PV and EV will provide reactive power to support the node voltage; as the PV output decreases, when the voltage exceeds the threshold V e, the emergency control mode is started for a second period of time, the PV reactive power margin reserved in the normal control mode will be used to support the voltage
Further, after implementing (16) - (19), when V c,t still violates the lower voltage limit V min, the vehicle-to-network interaction (V2G) mode of the electric vehicle is activated:
Wherein: p EV,t is the active output power of the electric vehicle at time t, S VP (f, c) is the influence degree of the active power of the node c on the voltage of the node f, and at this time, the reactive power of the electric vehicle is also regulated to the maximum value, so that the risk of voltage out-of-limit is reduced.
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