A Distributed Resource Local Control Method Considering Voltage Safety and User Response Capability

Abstract

This invention provides a distributed resource local control method that considers voltage safety and user responsiveness. The method includes collecting distributed renewable energy generation data, load demand data, and historical electricity consumption data of local users for each time period of the day; classifying users into different types based on their historical electricity consumption data and calculating the corresponding renewable energy responsiveness for each type of user; substituting the distributed renewable energy generation data and load demand data for each time period of the day into a pre-established renewable energy output closed-loop rolling control model to obtain the node voltage for each time period after control by the model; determining the time periods requiring control based on the node voltages for each time period; and utilizing the renewable energy responsiveness of different types of users to control renewable energy output and load absorption in real time. This invention can fully utilize user responsiveness potential, reduce voltage deviations caused by distributed resource absorption, and ensure the safe and economical operation of distributed resource local control.

Description

Technical Field

[0001] This invention belongs to the field of distributed resource local absorption and regulation technology, specifically relating to a distributed resource local regulation method that considers voltage safety and user response capability. Background Technology

[0002] With the construction of a new power system based on new energy sources and the steady achievement of "dual carbon" goals in my country, distributed resources such as distributed photovoltaic, micro wind turbines, and electric vehicle charging piles have developed rapidly. However, the local access of massive distributed resources has brought significant challenges to the implementation of power energy security. Distributed resources have high "green and environmentally friendly" performance and low assembly costs, making them an important component of the new power system. Therefore, it is of great significance to do a good job in the regulation and control of the local access of distributed resources.

[0003] Currently, the local integration of distributed resources faces significant challenges due to the uncertainty of renewable energy output and the volatility of local loads, greatly increasing the complexity of ensuring transmission and distribution voltage security. When local renewable energy output is excessive and difficult to absorb, higher economic costs are required to equip sufficient capacity energy storage devices or additional reactive power compensators to store excess resources or ensure energy supply to the main grid. Conversely, insufficient local renewable energy output coupled with high load demand leads to a decline in grid connection voltage quality and affects power supply reliability, resulting in negative impacts on the grid's economic efficiency, safety performance, and public perception. Existing solutions for the local integration of distributed resources can be broadly categorized into two types: redundant energy charging and discharging, and power electronic control.

[0004] However, neither of the above two methods has completely solved the problems caused by the local regulation of distributed resources, and when the load side faces the reduction or overflow of new energy output, the new energy response potential of users of different load types has not been fully explored. Summary of the Invention

[0005] In view of this, the present invention aims to provide a control method for local control of distributed resources that considers voltage safety and user responsiveness, so as to solve the problems caused by the existing local control process of distributed resources and fully tap the potential of users' new energy response.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a distributed resource local control method that considers voltage safety and user responsiveness, comprising the following steps:

[0008] Collect distributed renewable energy generation data, load demand data, and local user historical electricity consumption data for each time period of the day;

[0009] Based on local users' historical electricity consumption data, different types of users are divided, and the corresponding renewable energy response capacity is calculated for each type of user. The renewable energy response capacity is the ability of renewable energy to undertake load reduction.

[0010] Substitute the distributed renewable energy generation data and load demand data for each time period of the day into the pre-established renewable energy output closed-loop rolling control model to obtain the node voltage for each time period after the control model is controlled. The renewable energy output closed-loop rolling control model takes into account the user response capability, takes the distributed renewable energy generation data and load demand data as input, and uses the closed-loop rolling method to obtain the node voltage for each time period of the day.

[0011] Considering the safety of node voltage, the time periods that need to be regulated are determined based on the node voltage in each time period;

[0012] During periods when regulation is required, the output of new energy sources and the absorption of loads are regulated in real time by utilizing the new energy response capabilities of different types of users.

[0013] Furthermore, in the closed-loop rolling control model for new energy output, user response capability is taken into account in the objective function, which is as follows:

[0014]

[0015] In the formula, Let represent the objective function, t represent each time period of the day, T be the total number of time periods per day, not represent each local renewable energy grid connection point, N_not be the total number of local renewable energy grid connection points, line represent each transmission line of the local distribution network, N_line be the total number of local distribution network transmission lines, neg represent local distributed resources, N_neg be the total number of local distributed resources, h represent each local user load type, and H be the total number of local user load types. The duration of the time interval. This refers to the voltage deviation penalty coefficient at the grid connection point of new energy sources. c is the transmission line loss coefficient. g,down To reduce the cost coefficient of distributed resource active power, c l,down To reduce the cost of active power response to load conditions, For the voltage deviation at the grid connection point of new energy sources, I line,t R is the current flowing through line during time period t. line P is the resistance of the line. neg,t P represents the active power reduction of distributed resource neg within time period t. h,t This represents the active power reduction in load response for user type h during time period t.

[0016] Furthermore, considering node voltage security, the time periods requiring regulation are determined based on the node voltage at each time period, specifically including:

[0017] Determine the allowable deviation range of real-time node voltage under safe operation of the distribution network based on node voltage safety requirements;

[0018] If the voltage during a given period after control by the regulation model is within the allowable deviation range of the real-time node voltage, the distribution network is considered to be operating stably, and no regulation is required for the corresponding period; otherwise, regulation is considered to be required for the corresponding period.

[0019] Furthermore, user types are categorized into peak-load, high-load, and off-peak types, specifically including:

[0020] An initial array for user clustering is constructed using local users' historical electricity consumption data. The initial array for user clustering includes the daily load curves of users within a set time range.

[0021] Set cluster centers for three user types and determine the corresponding typical daily load curves for the three user types;

[0022] Calculate the average distance from the daily load curve to the centroid of each cluster in the initial array of user clusters;

[0023] Choose the cluster category corresponding to the minimum distance from the three average distances as the type of the corresponding user.

[0024] Furthermore, the corresponding new energy response capabilities are calculated for different types of users, specifically including:

[0025] The analytic hierarchy process (AHP) was used to calculate the renewable energy response capabilities of three types of load users in different time periods, and the response capability evaluation matrix A for time period t was determined. t :

[0026]

[0027] In the matrix, both rows and columns represent three user types, and matrix element a 11 To a 33 Indicates the responsiveness level of the row type relative to the column type;

[0028] The weights for new energy response capabilities for the three types of users are determined as follows:

[0029]

[0030] In the formula, w c A weighted matrix for user type response to new energy capabilities, where the matrix elements represent the response capabilities corresponding to the three user types;

[0031] Calculate the largest eigenvalue of the response capability evaluation matrix, and verify the reliability of the evaluation results using the following formula:

[0032]

[0033]

[0034] In the formula, To evaluate the eigenvalue column vectors of a matrix, CI is the maximum value among the eigenvalues, CR is the consistency index, RI is the benchmark consistency index;

[0035] Determine whether the consistency ratio meets the set conditions. If not, redetermine the response capability evaluation matrix until the set conditions are met. At this point, use the new energy response capability weights of the three types of users to represent the new energy response capability corresponding to different types of users.

[0036] Furthermore, leveraging the renewable energy response capabilities of different types of users to achieve real-time control of renewable energy output and load absorption specifically includes:

[0037] The load reduction required for regulation during the time period is calculated based on the voltage deviation. The voltage deviation is the difference between the node voltage after control by the regulation model and the local real-time node voltage during the time period that needs to be regulated.

[0038] Based on the renewable energy response capabilities of different types of users, the required load reduction amount is allocated to the corresponding types of users for load reduction, thereby achieving real-time voltage regulation.

[0039] Furthermore, the required load reduction amount is allocated according to the following formula:

[0040]

[0041] In the formula, P c_i For the active power reduction amount of user c_i, w c_i Let h represent the load type of each local user, H represent the total number of local user load types, and P represent the new energy response capability weight for user c_i. l,down U represents the active power reduction required to respond to voltage deviation at the grid connection point of new energy sources on the load side. ld X is the load-side voltage bus voltage. l For the line reactance from the grid connection point to the load bus, For the load-side power factor angle, This represents the voltage deviation at the grid connection point.

[0042] Furthermore, the constraints of the closed-loop rolling control model for new energy output specifically include:

[0043] The specific constraints for safe operation are as follows:

[0044]

[0045] In the formula, V not,t Let V be the voltage at node not during time period t. not,min The minimum safe operating voltage for node not is V. not,max I represents the maximum safe operating voltage for node not. line I is the current in the line. line,min I is the minimum safe operating current of the line. line,man P is the maximum safe operating current of the line. 0,t Q 0,t P represents the active and reactive power transmission between the upstream main grid and the local distribution network during time period t. 0,min P represents the lower limit of active power transmission between the upstream main grid and the local distribution network. 0,max Q represents the upper limit of active power transmission between the upstream main grid and the local distribution network. 0,min Q represents the lower limit of reactive power transmission between the upstream main grid and the local distribution network. 0,max This is the upper limit for reactive power transmission between the upstream main grid and the local distribution network;

[0046] The power balance constraints are as follows:

[0047]

[0048] In the formula, P in,not,t Q in,not,t Let P be the active and reactive power injected into node not during time period t. G,not,t Q G,not,t P represents the active and reactive power of node not injected into the main network during time period t. s,not,t P represents the net active power of the energy storage device charging and discharging at node not during time period t. f,not,t Q f,not,t The active and reactive load demands at node not within time period t;

[0049] The power flow constraints of the distribution network are as follows:

[0050]

[0051] In the formula, sp represents each starting node in the local distribution network line. It is the set of starting nodes with dp as the tail node. Let P be the set of tail nodes starting from dp, and kp be the tail node on each line starting from dp. sp,dp,t Q sp,dp,t Let I be the active and reactive power transmitted from node sp to node dp during time period t. sp,dp,t Rsp,dp X sp,dp For time period t, P represents the line current, line resistance, and line reactance from node sp to node dp. kp,dp P kp,dp P represents the active and reactive power transmitted from node kp. in,dp,t Q in,dp,t The active and reactive power of node dp are injected for time period t.

[0052] Furthermore, it also includes collecting local node voltage data and performing anomaly cleaning. Anomaly cleaning specifically includes:

[0053] Arrange the voltage data of each node at the same voltage level in descending order to obtain a voltage array;

[0054] Identify short circuits or open circuits from voltage data;

[0055] Calculate the interquartile range of the voltage array;

[0056] The inner limit range of normal voltage values ​​is determined based on the interquartile range, and abnormal voltage data is identified from the voltage array according to the inner limit range;

[0057] Calculate the regression equation for the voltage array after excluding abnormal voltage data and short-circuit or open-circuit voltage data;

[0058] Substitute the abnormal voltage data and the short-circuit or open-circuit voltage data into the voltage array index in the voltage array regression equation to obtain the voltage fill value.

[0059] Furthermore, the voltage array regression equation is as follows:

[0060]

[0061] In the formula, The slope of the regression equation. The intercept of the regression equation is n_cor, which is the size of the voltage array after excluding outliers, and x is the value of x. cor,i Let u be the index of the i-th item in the voltage array. cor,i Let cor,i be the voltage value of the voltage array.

[0062] Secondly, the present invention provides a distributed resource local control device that considers voltage safety and user responsiveness, comprising:

[0063] The data acquisition module collects distributed renewable energy generation data, load demand data, and local user historical electricity consumption data for each time period of the day.

[0064] The user type classification module is used to classify different types of users based on local users' historical electricity consumption data, and to calculate the corresponding renewable energy response capability for different types of users. The renewable energy response capability is the ability of renewable energy to undertake load reduction.

[0065] The node voltage calculation module is used to input the distributed renewable energy generation data and load demand data of each time period of the day into the pre-established renewable energy output closed-loop rolling control model to obtain the node voltage of each time period after the control model is controlled. The renewable energy output closed-loop rolling control model takes into account the user response capability, takes the distributed renewable energy generation data and load demand data as input, and uses the closed-loop rolling method to obtain the node voltage of each time period of the day.

[0066] The control period determination module is used to determine the control period based on the node voltage in each period, taking into account the node voltage safety.

[0067] The control module is used to control the output of new energy and the absorption of load in real time by utilizing the new energy response capabilities of different types of users during the period when control is required.

[0068] Accordingly, the present invention also provides a computer device, the device including a processor and a memory:

[0069] The memory is used to store computer programs and send the instructions of the computer programs to the processor;

[0070] The processor executes, according to the instructions of the computer program, a distributed resource local control method that considers voltage safety and user responsiveness, as described in the first aspect.

[0071] Accordingly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a distributed resource local control method considering voltage safety and user responsiveness as described in the first aspect.

[0072] In summary, this invention provides a distributed resource local control method that considers voltage safety and user responsiveness. The method includes collecting distributed renewable energy generation data, load demand data, and historical electricity consumption data of local users for each time period of the day; classifying users into different types based on their historical electricity consumption data and calculating the corresponding renewable energy responsiveness for each type of user; substituting the distributed renewable energy generation data and load demand data for each time period of the day into a pre-established renewable energy output closed-loop rolling control model to obtain the node voltage for each time period after control by the model; considering node voltage safety, determining the time periods requiring control based on the node voltage for each time period; and utilizing the renewable energy responsiveness of different types of users to control renewable energy output and load absorption in real time during the control periods. This invention can fully utilize user responsiveness potential, reduce voltage deviations caused by distributed resource absorption, and ensure the safe and economical operation of distributed resource local control. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 A flowchart illustrating a distributed resource local control method considering voltage safety and user responsiveness, provided in an embodiment of the present invention;

[0075] Figure 2 Voltage data of 10kV renewable energy grid connection points for the 100 time periods prior to cleaning, provided in this embodiment of the invention;

[0076] Figure 3 This is the voltage data of 10kV new energy grid connection points for 100 time periods after cleaning provided in this embodiment of the invention;

[0077] Figure 4 Typical daily load curves for three types of users provided in embodiments of the present invention;

[0078] Figure 5 This invention provides a comparison of the voltage at a new energy grid connection point before and after implementation. Detailed Implementation

[0079] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0080] Please see Figure 1 This invention provides a distributed resource local control method that considers voltage safety and user responsiveness, comprising the following steps:

[0081] S1: Collect distributed renewable energy generation data, load demand data, and local user historical electricity consumption data for each time period of the day;

[0082] S2: Based on local users' historical electricity consumption data, different types of users are divided, and the corresponding renewable energy response capacity is calculated for different types of users. The renewable energy response capacity is the ability of renewable energy to undertake load reduction.

[0083] S3: Substitute the distributed renewable energy generation data and load demand data for each time period of the day into the pre-established renewable energy output closed-loop rolling control model to obtain the node voltage for each time period after the control model is controlled. The renewable energy output closed-loop rolling control model takes into account the user response capability, and uses the distributed renewable energy generation data and load demand data as input to obtain the node voltage for each time period of the day in a closed-loop rolling manner.

[0084] S4: Considering the safety of node voltage, determine the time periods that need to be regulated based on the node voltage in each time period;

[0085] S5: During periods when regulation is required, utilize the renewable energy response capabilities of different types of users to regulate renewable energy output and load consumption in real time.

[0086] It should be noted that the uncertainty of new energy output and the volatility of local load need to be offset by the deep regulation capabilities of flexible resources. However, the existing regulation capabilities of the power system are limited. Exploring the flexible regulation potential of resources such as controllable loads, interruptible loads, electric vehicle charging networks, and energy storage on the demand side can provide ancillary services such as peak shaving and frequency regulation for the system.

[0087] For the problem of local access to distributed resources, existing solutions can be broadly divided into two categories: redundant energy charging and discharging and power electronic control.

[0088] Redundant energy charging and discharging, through the configuration of energy storage devices, enhances the resilience of the power system to uncertainties in renewable energy output and load demand, thereby maintaining the stable operation of the transmission and distribution network due to the integration of distributed renewable energy. Energy storage devices can reduce the impact of sudden changes in source and load on the grid. However, this method is highly dependent on energy storage capacity, and increasing energy storage capacity often comes with reduced economic efficiency and increased complexity in dispatch implementation. Power electronic control methods utilize intelligent sensors, automatic controllers, rectifiers, inverters, and other devices to micro-regulate distributed resources, enabling unitized and refined management of distributed resources and strengthening the flexible interaction between distributed resources and the main grid. However, this method has high requirements for the rated current, rated voltage, and capacity parameters of power electronic devices, and the converters are expensive, making it not very suitable for microgrids. Since neither of these two methods completely solves the problems caused by the local regulation of distributed resources, and the renewable energy response potential of different load types of users has not been fully explored when the load side faces reductions or overflows in renewable energy output.

[0089] In this embodiment, the user's renewable energy response capability is taken into account in the local regulation of distributed resources, and the renewable energy output and load absorption are regulated in real time according to the renewable energy response capability of different types of users.

[0090] When facing a reduction or overflow of renewable energy output on the load side, local regulation of distributed resources requires two types of data. The first is the user's renewable energy response capability, that is, the user's ability to withstand load reduction. By utilizing the different renewable energy response capabilities of users, precise regulation can be achieved during local regulation. The second is the amount to be regulated, that is, the amount required to regulate to the target level. Based on this data, the range of current regulation needs to be determined, and precise regulation can be achieved by coordinating with the renewable energy response capabilities of different users.

[0091] In this embodiment, regarding the renewable energy response capability, users are first categorized into different types based on their historical electricity consumption data. The corresponding renewable energy response capability is then calculated for each user type to reflect their ability to withstand load reduction. The specific calculation method is not limited in this embodiment.

[0092] Secondly, for the quantities that need to be regulated, this embodiment proposes a closed-loop rolling regulation model for renewable energy output. This model takes distributed renewable energy generation data and load demand data as inputs and uses a closed-loop rolling method to obtain the node voltage for each time period. That is, the node voltage of the previous time period, the renewable energy generation power and load demand forecast values ​​of the current time period are used as inputs for the next time period to obtain the node voltage of the next time period. The calculation is continuously rolled until the last time period is calculated, and finally the node voltage calculated for each time period is obtained. The quantity that needs to be regulated is determined based on the node voltage value.

[0093] This embodiment provides a distributed resource local control method that considers voltage safety and user responsiveness. This method can fully utilize user responsiveness potential, reduce voltage deviation caused by distributed resource absorption, and ensure the safe and economical operation of distributed resource local control.

[0094] In a preferred embodiment of the present invention, the collection of distributed renewable energy generation data, load demand data, and local user historical electricity consumption data for each time period of the day specifically includes: predicted values ​​of local distributed renewable energy generation power and local load demand for each time period of the day, real-time voltage data of local distributed renewable energy grid-connected nodes, and historical electricity consumption data of local users, i.e., the user's daily average load curve. The real-time voltage data of local distributed renewable energy grid-connected nodes can be used to compare with the node voltage output by the control model to determine the range requiring control.

[0095] In a preferred embodiment of the present invention, the method further includes cleaning local node voltage anomaly data by performing voltage diagnosis on the real-time voltage data of local distributed renewable energy grid-connected nodes. The cleaning process is as follows:

[0096] S11. Sort the voltage data of each node at the same voltage level in descending order to obtain a voltage array.

[0097] The voltage array is U={u1,u2,…,u n}, where n is the array size, u n This represents the minimum voltage value in the array.

[0098] S12. Identify short circuits or open circuits from voltage data.

[0099] Identify whether the voltage array contains a zero potential point or other voltage level values, and then determine whether a short circuit or open circuit fault has occurred at that point:

[0100]

[0101] Where u v_i Let v_i be the voltage of the i-th item in array U. For other voltage levels;

[0102] S13. Calculate the interquartile range of the voltage array.

[0103] Calculate the upper quartile, median, and lower quartile of the voltage array U, and then calculate the interquartile distance I. qr :

[0104] When n=4m, m=0,1,2…:

[0105]

[0106] Where u m Q1 represents the voltage of the m-th node in the voltage array after it is sorted in descending order, where m is any natural number, Q3 is the lower quartile of the array, and Q4 is the upper quartile of the array.

[0107] When n = 4m + 1, m = 0, 1, 2...:

[0108]

[0109] When n = 4m + 2, m = 0, 1, 2...:

[0110]

[0111] When n = 4m + 3, m = 0, 1, 2...:

[0112]

[0113] Where I qr Interquartile distance;

[0114] S14. Determine the inner limit range of normal voltage values ​​based on the interquartile range, and identify abnormal voltage data according to the inner limit range:

[0115]

[0116] Where F min F is the lower limit of the inner limit range. max This is the upper limit of the inner limit range. As a boundary coefficient, if the value in any voltage array exceeds the inner limit, the data is considered abnormal.

[0117] S15. Calculate the regression equation for the voltage array after excluding abnormal data.

[0118] In one embodiment, the voltage array regression equation can be as follows:

[0119]

[0120] in, The slope of the regression equation. The intercept of the regression equation is n_cor, which is the size of the voltage array after excluding outliers, and x is the value of x. cor,i Let u be the index of the i-th item in the voltage array. cor,i The voltage value of the cor,i item in the voltage array;

[0121] S16. Substitute the corresponding numbers of the outlier data into the regression equation to obtain the voltage compensation value:

[0122]

[0123] Where ufix,i The voltage magnitude after fixing (i) for abnormal data, x fix,i The number of the abnormal data fix,i.

[0124] In a preferred embodiment of the present invention, the classification of users into different types based on local users' historical electricity consumption data specifically employs an improved K-means method with three load types as central clusters. Different user types are clustered, with the central clusters for the three user types being peak-loading, high-load-rate, and peak-avoiding types, respectively. The specific classification process is as follows:

[0125] S21. Construct an initial array for user clustering using local users' historical electricity consumption data. The initial array for user clustering includes the daily load curves of users within a set time range.

[0126] In practical implementation, the daily load curves of each local user over the past 90 days can be selected to form the initial user cluster array B. The daily load curve is composed of 24 data points, each data point representing the average load rate of each hour during the day.

[0127]

[0128] Where b 1,h To b 90,h This is the daily load curve for user h from day 1 to day 90.

[0129] S22. Set cluster centers for the three types of users and determine the corresponding typical daily load curves for the three types of users.

[0130] In practical implementation, three types of user cluster centers can be set: peak-loading type, high-load-rate type, and peak-avoiding type, and typical daily load curves for these three types of users can be defined, such as... Figure 2 As shown.

[0131] S23. Calculate the average distance from the daily load curve to the center cluster of each cluster in the initial array of user clusters.

[0132] In practical implementation, the following formula is used for calculation:

[0133]

[0134] where dis h,c Let b be the average distance between the daily load curve of user h and the central cluster c. day,h For the daily load curve of users h on day, cen c Typical daily load curve for central cluster c

[0135] S24. Select the central cluster category corresponding to the minimum distance from the three average distances as the type of the corresponding user.

[0136] In practical implementation, compare B h Average distance from the daily load curve to the centroid of each cluster:

[0137]

[0138] where dis h,min c is the minimum of the three average distances of user h. min The cluster category corresponding to the minimum distance is the clustering result for this user.

[0139] In a preferred embodiment of the present invention, calculating the corresponding new energy response capability for different types of users specifically includes:

[0140] S25. Calculate the renewable energy response capability of three types of load users in different time periods using the analytic hierarchy process (AHP), and determine the response capability evaluation matrix A for time period t. t :

[0141]

[0142] In the matrix, both rows and columns represent three user types, and matrix element a 11 To a 33 Indicates the responsiveness level of the row type relative to the column type;

[0143] The weights for new energy response capabilities for the three types of users are determined as follows:

[0144]

[0145] In the formula, w c A weighted matrix for user type response to new energy capabilities, where the matrix elements represent the response capabilities corresponding to the three user types;

[0146] S26. Calculate the largest eigenvalue of the response capability evaluation matrix, and verify the reliability of the evaluation results using the following formula:

[0147]

[0148] In the formula, To evaluate the eigenvalue column vectors of a matrix, CI is the maximum value among the eigenvalues, CR is the consistency index, RI is the benchmark consistency index;

[0149] Determine if the consistency ratio meets the set conditions. If not, redetermine the response capability evaluation matrix until the conditions are met. At this point, use the weights of the three user types' new energy response capabilities to represent the new energy response capabilities corresponding to different user types. In practice, if CR < 0.1, the evaluation result is considered to have passed the test.

[0150] In a preferred embodiment of the present invention, the user response capability is incorporated into the objective function in the closed-loop rolling control model for new energy output. The specific objective function is as follows:

[0151]

[0152] In the formula, Let represent the objective function, t represent each time period of the day, T be the total number of time periods per day, not represent each local renewable energy grid connection point, N_not be the total number of local renewable energy grid connection points, line represent each transmission line of the local distribution network, N_line be the total number of local distribution network transmission lines, neg represent local distributed resources, N_neg be the total number of local distributed resources, h represent each local user load type, and H be the total number of local user load types. The duration of the time interval. This refers to the voltage deviation penalty coefficient at the grid connection point of new energy sources. c is the transmission line loss coefficient. g,down To reduce the cost coefficient of distributed resource active power, c l,down To reduce the cost of active power response to load conditions, For the voltage deviation at the grid connection point of new energy sources, I line,t R is the current flowing through line during time period t. line P is the resistance of the line. neg,t P represents the active power reduction of distributed resource neg within time period t. h,t H represents the active power reduction of user type h during time period t. When the user load type is the peak load type, high load rate type, or peak avoidance type mentioned in the previous embodiments, H=3.

[0153] In a preferred embodiment of the present invention, the constraints of the closed-loop rolling control model for new energy output specifically include:

[0154] The specific constraints for safe operation are as follows:

[0155]

[0156] In the formula, V not,t Let V be the voltage at node not during time period t. not,min The minimum safe operating voltage for node not is V. not,max I represents the maximum safe operating voltage for node not. line I is the current in the line. line,min I is the minimum safe operating current of the line. line,man P is the maximum safe operating current of the line. 0,t Q 0,tP represents the active and reactive power transmission between the upstream main grid and the local distribution network during time period t. 0,min P represents the lower limit of active power transmission between the upstream main grid and the local distribution network. 0,max Q represents the upper limit of active power transmission between the upstream main grid and the local distribution network. 0,min Q represents the lower limit of reactive power transmission between the upstream main grid and the local distribution network. 0,max This is the upper limit for reactive power transmission between the upstream main grid and the local distribution network;

[0157] The power balance constraints are as follows:

[0158]

[0159] In the formula, P in,not,t Q in,not,t Let P be the active and reactive power injected into node not during time period t. G,not,t Q G,not,t P represents the active and reactive power of node not injected into the main network during time period t. s,not,t P represents the net active power of the energy storage device charging and discharging at node not during time period t. f,not,t Q f,not,t The active and reactive load demands at node not within time period t;

[0160] The power flow constraints of the distribution network are as follows:

[0161]

[0162] In the formula, sp represents each starting node in the local distribution network line. It is the set of starting nodes with dp as the tail node. Let P be the set of tail nodes starting from dp, and kp be the tail node on each line starting from dp. sp,dp,t Q sp,dp,t Let I be the active and reactive power transmitted from node sp to node dp during time period t. sp,dp,t R sp,dp X sp,dp For time period t, P represents the line current, line resistance, and line reactance from node sp to node dp. kp,dp P kp,dp P represents the active and reactive power transmitted from node kp. in,dp,t Q in,dp,t The active and reactive power of node dp are injected for time period t.

[0163] In a preferred embodiment of the present invention, the distributed renewable energy generation data and load demand data for each time period of the day are substituted into a pre-established closed-loop rolling control model for renewable energy output to obtain the node voltage for each time period after control by the control model, specifically including:

[0164] S31. Input the node voltage of the distribution network in the previous period and the predicted values ​​of new energy power generation and load demand in the current period into the local control model;

[0165] S32. Obtain the node voltage magnitude for this time period after being controlled by the closed-loop rolling control model:

[0166]

[0167] in This is the voltage matrix of new energy grid-connected nodes after rolling regulation, where each element represents the voltage magnitude of each new energy grid-connected node, and N_not represents the total number of grid-connected nodes.

[0168] In a preferred embodiment of the present invention, considering node voltage safety, the time periods requiring regulation are determined based on the node voltage at each time period, specifically including:

[0169] S41. Determine the allowable deviation range of real-time node voltage under safe operation of the distribution network based on the node voltage safety requirements.

[0170] In practical implementation, the allowable deviation range of real-time node voltage under safe operation of the distribution network can be as follows:

[0171]

[0172] U g For the voltage at the grid connection point of new energy sources, U gN This is the rated voltage at the grid connection point for new energy sources.

[0173] S42. If the voltage during the time period controlled by the regulation model is within the allowable deviation range of the real-time node voltage, the distribution network is considered to be operating stably and no regulation is required for the corresponding time period; otherwise, regulation is considered to be required for the corresponding time period.

[0174] In practical implementation, if the output voltage of the time-period control model is within the allowable deviation range of the real-time voltage:

[0175]

[0176] This assumes the distribution network is operating stably and the control measures meet demand. This represents the voltage of the not_ith node in the new energy grid-connected node voltage matrix after rolling regulation;

[0177] Conversely, if the output voltage of the time-period control model exceeds the allowable deviation range of the real-time voltage:

[0178]

[0179] This necessitates real-time control of renewable energy output and load absorption based on the response capabilities of different types of users.

[0180] In a preferred embodiment of the present invention, the real-time control of renewable energy output and load absorption is achieved by utilizing the renewable energy response capabilities of different types of users, specifically including:

[0181] S51. Calculate the load reduction amount required for regulation during the time period that needs to be regulated based on the voltage deviation. The voltage deviation is the difference between the node voltage after control by the regulation model and the local real-time node voltage during the time period that needs to be regulated.

[0182] In practical implementation, this difference can be calculated using the following formula:

[0183]

[0184] Where P l,down U represents the active power reduction required to respond to voltage deviation at the grid connection point of new energy sources on the load side. ld X is the load-side voltage bus voltage. l For the line reactance from the grid connection point to the load bus, For the load-side power factor angle, This represents the voltage deviation at the grid connection point.

[0185] S52. Based on the renewable energy response capabilities of different types of users, the required load reduction amount is allocated to the corresponding types of users for load reduction, thereby completing real-time voltage regulation.

[0186] In practical implementation, the active power reduction on the load side can be calculated using the following formula:

[0187]

[0188] In the formula, P c_i For the active power reduction amount of user c_i, w c_i Let h represent the weight of the new energy response capability of user c_i, h represent the load type of each local user, and H represent the total number of local user load types.

[0189] The following example illustrates a distributed resource local control method of the present invention that considers voltage safety and user responsiveness. In this example, user behavior, grid connection point voltage, power, and load forecast data from the same park containing distributed resources of multiple capacity levels are used as the original dataset. In this embodiment, the rolling control model time interval is selected as 1 hour and T=24 hours (1 day), the evaluation index for user renewable energy responsiveness ends at level 3, the average consistency index RI=0.52, and the safe operating range of node voltage is [0.95, 1.05] pu. Voltage data of 10kV renewable energy grid connection points from 100 time periods within the park are selected, and abnormal data are cleaned and interpolated according to the method of the present invention. The results are shown in the appendix. Figure 3 As shown; select the electricity consumption data (load rate) of 100 households in the park and follow the attached... Figure 4 The typical daily load curves of the three types of users were clustered, and the weight of load reduction undertaken by the new energy response of the three types of users in different time periods was calculated by the analytic hierarchy process. The results are shown in Table 1.

[0190] Table 1. K-means clustering results of electricity consumption data from 100 households and load reduction weights for the three types of users.

[0191]

[0192] In this example, to verify the optimization effect of the proposed implementation method on voltage safety, a comparative analysis was conducted on the voltage situation of a new energy grid connection point before and after implementation over a 24-hour period, as shown in the attached figure. Figure 5 As shown; before the implementation of the method described in this invention, the voltage at this point exceeded the limit, such as at 11:00. 16:00 peak period for new energy power output and 3:00 During the off-peak period of new energy power output at 8:00, the safety of power transmission and distribution is difficult to guarantee. After using the proposed method, the above-mentioned voltage over-limit problem is effectively controlled, and the occurrence of voltage over-limit is curbed.

[0193] Based on the same inventive concept, this application also provides a distributed resource local control device that considers voltage safety and user responsiveness for implementing the distributed resource local control method that considers voltage safety and user responsiveness described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in the embodiments of the distributed resource local control device considering voltage safety and user responsiveness provided below can be found in the limitations of the distributed resource local control method considering voltage safety and user responsiveness described above, and will not be repeated here.

[0194] This embodiment provides a distributed resource local control device that considers voltage safety and user responsiveness, including:

[0195] The data acquisition module collects distributed renewable energy generation data, load demand data, and local user historical electricity consumption data for each time period of the day.

[0196] The user type classification module is used to classify different types of users based on local users' historical electricity consumption data, and to calculate the corresponding renewable energy response capability for different types of users. The renewable energy response capability is the ability of renewable energy to undertake load reduction.

[0197] The node voltage calculation module is used to input the distributed renewable energy generation data and load demand data of each time period of the day into the pre-established renewable energy output closed-loop rolling control model to obtain the node voltage of each time period after the control model is controlled. The renewable energy output closed-loop rolling control model takes into account the user response capability, takes the distributed renewable energy generation data and load demand data as input, and uses the closed-loop rolling method to obtain the node voltage of each time period of the day.

[0198] The control period determination module is used to determine the control period based on the node voltage in each period, taking into account the node voltage safety.

[0199] The control module is used to control the output of new energy and the absorption of load in real time by utilizing the new energy response capabilities of different types of users during the period when control is required.

[0200] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0201] This invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory, wherein when the computer program is executed on the processor, it implements a distributed resource local control method that considers voltage safety and user responsiveness as described in any of the above methods.

[0202] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory.

[0203] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0204] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device 3. The memory 302 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0205] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a distributed resource local control method that considers voltage safety and user responsiveness as described in any of the above methods.

[0206] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0207] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0208] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0209] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0210] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0211] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed resource local control method considering voltage safety and user responsiveness, characterized in that, Includes the following steps: Collect distributed renewable energy generation data, load demand data, and local user historical electricity consumption data for each time period of the day; Based on the local users' historical electricity consumption data, different types of users are divided, and the corresponding renewable energy response capacity is calculated for each type of user. The renewable energy response capacity is the ability of renewable energy to undertake load reduction. The distributed renewable energy generation data and load demand data for each time period of the day are substituted into the pre-established renewable energy output closed-loop rolling control model to obtain the node voltage for each time period after the control model is controlled. The renewable energy output closed-loop rolling control model takes into account the user response capability, takes the distributed renewable energy generation data and load demand data as input, and uses the closed-loop rolling method to obtain the node voltage for each time period of the day. Considering the safety of node voltage, the time periods that need to be regulated are determined based on the node voltage in each time period; During periods when regulation is required, the output of new energy and load absorption are regulated in real time by utilizing the new energy response capabilities of different types of users. In the aforementioned closed-loop rolling control model for new energy output, user response capability is incorporated into the objective function, which is specifically as follows: ； In the formula, Let represent the objective function, t represent each time period of the day, T be the total number of time periods per day, not represent each local renewable energy grid connection point, N_not be the total number of local renewable energy grid connection points, line represent each transmission line of the local distribution network, N_line be the total number of local distribution network transmission lines, neg represent local distributed resources, N_neg be the total number of local distributed resources, h represent each local user load type, and H be the total number of local user load types. The duration of the time interval. This refers to the voltage deviation penalty coefficient at the grid connection point of new energy sources. c is the transmission line loss coefficient. g,down To reduce the cost coefficient of distributed resource active power, c l,down To reduce the cost of active power response to load conditions, For the voltage deviation at the grid connection point of new energy sources, I line,t R is the current flowing through line during time period t. line P is the resistance of the line. neg,t P represents the active power reduction of distributed resource neg within time period t. h,t This represents the active power reduction in load response for user type h during time period t.

2. The distributed resource local control method considering voltage safety and user responsiveness according to claim 1, characterized in that, Considering node voltage safety, the time periods requiring regulation are determined based on the node voltages at each time period, specifically including: Determine the allowable deviation range of real-time node voltage under safe operation of the distribution network based on node voltage safety requirements; If the voltage during a given period after the control model is within the allowable deviation range of the real-time node voltage, the distribution network is considered to be operating stably, and no control is required for the corresponding period; otherwise, control is considered to be required for the corresponding period.

3. The distributed resource local control method considering voltage safety and user responsiveness according to claim 1, characterized in that, User types include peak-load, high-load, and off-peak types, which are further categorized into different types, specifically including: The local user historical electricity consumption data is used to construct an initial array for user clustering, which includes the daily load curves of users within a set time range; Define cluster centers for the three user types and determine the corresponding typical daily load curves for the three user types; Calculate the average distance from the daily load curve to each cluster center in the initial array of user clusters; Choose the cluster category corresponding to the minimum distance from the three average distances as the type of the corresponding user.

4. The distributed resource local control method considering voltage safety and user responsiveness according to claim 3, characterized in that, Calculating the corresponding new energy response capability for the different types of users specifically includes: The analytic hierarchy process (AHP) was used to calculate the renewable energy response capabilities of three types of load users in different time periods, and the response capability evaluation matrix A for time period t was determined. t : ； In the matrix, both rows and columns represent three user types, and matrix element a 11 To a 33 Indicates the responsiveness level of the row type relative to the column type; The weights for new energy response capabilities for the three types of users are determined as follows: ； In the formula, w c A weighted matrix for user type response to new energy capabilities, where the matrix elements represent the response capabilities corresponding to the three user types; Calculate the largest eigenvalue of the response capability evaluation matrix, and verify the reliability of the evaluation results using the following formula: ； ； ； In the formula, To evaluate the column vectors of matrix eigenvalues, CI is the maximum value among the eigenvalues, CR is the consistency index, RI is the benchmark consistency index; Determine whether the consistency ratio meets the set conditions. If not, redetermine the response capability evaluation matrix until the set conditions are met. At this time, use the new energy response capability weights of the three types of users to represent the new energy response capabilities corresponding to the different types of users.

5. The distributed resource local control method considering voltage safety and user responsiveness according to claim 1, characterized in that, Real-time regulation of renewable energy output and load absorption is achieved by utilizing the renewable energy response capabilities of different types of users, specifically including: The load reduction required for regulation during the time period that needs to be regulated is calculated based on the voltage deviation. The voltage deviation is the difference between the node voltage after control by the regulation model and the local real-time node voltage during the time period that needs to be regulated. Based on the new energy response capabilities of different types of users, the required load reduction amount is allocated to the corresponding types of users for load reduction, thereby completing real-time voltage regulation.

6. The distributed resource local control method considering voltage safety and user responsiveness according to claim 5, characterized in that, The required load reduction amount is allocated according to the following formula: ； ； In the formula, P c_i For the active power reduction amount of user c_i, w c_i Let h represent the load type of each local user, H represent the total number of local user load types, and P represent the new energy response capability weight for user c_i. l,down U represents the active power reduction required to respond to voltage deviation at the grid connection point of new energy sources on the load side. ld X is the load-side voltage bus voltage. l For the line reactance from the grid connection point to the load bus, For the load-side power factor angle, This represents the voltage deviation at the grid connection point.

7. The distributed resource local control method considering voltage safety and user responsiveness according to claim 1, characterized in that, The constraints of the closed-loop rolling control model for new energy output specifically include: The specific constraints for safe operation are as follows: ； ； In the formula, V not,t V is the voltage at node not during time period t. not,min The minimum safe operating voltage for node not is V. not,max I represents the maximum safe operating voltage for node not. line I is the current in the line. line,min I is the minimum safe operating current of the line. line,man P is the maximum safe operating current of the line. 0,t Q 0,t P represents the active and reactive power transmission between the upstream main grid and the local distribution network during time period t. 0,min P represents the lower limit of active power transmission between the upstream main grid and the local distribution network. 0,max Q represents the upper limit of active power transmission between the upstream main grid and the local distribution network. 0,min Q represents the lower limit of reactive power transmission between the upstream main grid and the local distribution network. 0,max This is the upper limit for reactive power transmission between the upstream main grid and the local distribution network; The power balance constraints are as follows: ； In the formula, P in,not,t Q in,not,t Let P be the active and reactive power injected into node not during time period t. G,not,t Q G,not,t P represents the active and reactive power of node not injected into the main network during time period t. s,not,t P represents the net active power of the energy storage device charging and discharging at node not during time period t. f,not,t Q f,not,t The active and reactive load demands at node not within time period t; The power flow constraints of the distribution network are as follows: ； In the formula, sp represents each starting node in the local distribution network line. It is the set of starting nodes with dp as the tail node. Let P be the set of tail nodes starting from dp, and kp be the tail node on each line starting from dp. sp,dp,t Q sp,dp,t Let I be the active and reactive power transmitted from node sp to node dp during time period t. sp,dp,t R sp,dp X sp,dp For time period t, P represents the line current, line resistance, and line reactance from node sp to node dp. kp,dp P kp,dp P represents the active and reactive power transmitted from node kp. in,dp,t Q in,dp,t The active and reactive power of node dp are injected for time period t.

8. The distributed resource local control method considering voltage safety and user responsiveness according to claim 1, characterized in that, It also includes collecting local node voltage data and performing abnormal data cleaning, wherein the abnormal data cleaning specifically includes: Arrange the voltage data of each node at the same voltage level in descending order to obtain a voltage array; Identify short circuits or open circuits from the voltage data; Calculate the interquartile range of the voltage array; The normal voltage value range is determined based on the interquartile range, and abnormal voltage data is identified from the voltage array according to the range. Calculate the voltage array regression equation after excluding the abnormal voltage data and short-circuit or open-circuit voltage data; Substitute the abnormal voltage data and the short-circuit or open-circuit point voltage data into the voltage array's regression equation to obtain the voltage fill value.

9. The distributed resource local control method considering voltage safety and user responsiveness according to claim 8, characterized in that, The voltage array regression equation is as follows: ； In the formula, The slope of the regression equation. The intercept of the regression equation is n_cor, which is the size of the voltage array after excluding outliers, and x is the value of x. cor,i Let u be the index of the i-th item in the voltage array. cor,i Let cor,i be the voltage value of the voltage array.

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