Power grid load dynamic balancing method and system

Abstract

The invention relates to the technical field of power grid load control, in particular to a power grid load dynamic balancing method and system, and the method comprises the steps: obtaining all electric vehicles which are accessed to a smart power grid at all adjustment moments, and the predicted residual charging duration and charging power of each electric vehicle, acquiring power load data of each power grid node at each adjustment moment; determining a first electric energy influence coefficient, a second electric energy influence coefficient and a comprehensive electric energy influence coefficient of each power grid node at each adjustment moment; acquiring a model for simulating the change of the power grid quality of the line where each power grid node is located along with the load; and based on the model and a preset target, determining a target function in a particle swarm optimization algorithm, and based on an optimal solution of the target function, dynamically balancing the charging load of the smart power grid. The invention aims to improve the stability and reliability of the optimized smart power grid in each region, so as to better realize the dynamic balance control of the power grid load.

Description

Technical Field

[0001] The present application relates to the technical field of power grid load control, and in particular to a method and system for dynamic balancing of power grid load. Background Art

[0002] Smart grid load balancing technology is widely used in smart grids. It can improve the stability and reliability of the grid, help reduce the operating costs of the grid and improve the utilization rate of power resources. Vehicle-to-grid interaction (V2G) technology, as an important branch of smart grid load balancing technology, connects large-scale electric vehicles to the smart grid. According to the grid load conditions and the charging needs of electric vehicles, optimization algorithms such as particle swarm optimization algorithm are used to optimize the charging load of electric vehicles during the charging process, so as to achieve dynamic balanced control of the grid load and better respond to the peak shaving and valley filling and peak regulation needs of the grid.

[0003] Existing electric vehicle charging load optimization methods usually take the minimization of the power load curve fluctuation of the power grid as the objective function when ensuring the stability and reliability of the power grid. However, this objective function ignores the impact of the temporal and spatial distribution of electric vehicle charging load on the power quality of the power grid in each area of ​​the smart grid, which may cause the optimized power grid to be prone to voltage instability, harmonic pollution, increased power loss and overload of power equipment, thereby affecting the balance and stability of the power system. Summary of the invention

[0004] In view of the above, it is necessary to provide a power grid load dynamic balancing method and system, which can improve the stability and reliability of the optimized smart grid in various regions compared with the traditional power grid load dynamic balancing method, and thus better realize the dynamic balancing control of the power grid load:

[0005] In a first aspect, an embodiment of the present application provides a method for dynamic balancing of power grid loads, the method comprising the following steps:

[0006] Obtain all electric vehicles that have been connected to the smart grid at each adjustment time, as well as the estimated remaining charging time and charging power of each electric vehicle, and obtain the power load data of each grid node in the smart grid at each adjustment time;

[0007] Based on the power load data and the charging power of multiple electric vehicles that are adjacent to each grid node at each adjustment time, the total power load of each grid node at each adjustment time is obtained;

[0008] Determine the first electric energy influence coefficient of each grid node at each adjustment time based on the change in total power load between each adjustment time and the adjacent adjustment time, and the change in charging power of the plurality of electric vehicles in between, and in combination with the change in power load data in between;

[0009] Determine the second electric energy influence coefficient of each grid node at each adjustment time based on the distance from each grid node to the corresponding multiple electric vehicles and the estimated remaining charging time of the multiple electric vehicles at each adjustment time;

[0010] Based on the first electric energy influence coefficient and the second electric energy influence coefficient, obtaining a comprehensive electric energy influence coefficient of each grid node at each adjustment time;

[0011] Based on the degree of distortion of voltage signals and current signals of each grid node at multiple historical adjustment moments and the comprehensive electric energy influence coefficient, a model for simulating the change of grid quality of the line where each grid node is located with load is obtained;

[0012] Based on the model and the preset target, the objective function in the particle swarm optimization algorithm is determined, and based on the optimal solution of the objective function, the charging power of each electric vehicle connected to the smart grid at the current adjustment moment is obtained to dynamically balance the charging load of the smart grid.

[0013] In one embodiment, the total power load is the sum of the charging power of the multiple electric vehicles at each adjustment time and the power load data.

[0014] In one embodiment, the process of determining the first electric energy influence coefficient is:

[0015] The sum of the charging powers of the multiple electric vehicles at each adjustment time is used as the total electric vehicle charging load of each grid node at each adjustment time; the calculation formula of the first electric energy influence coefficient is:

[0016] Wherein, S1(i,t) represents the first electric energy influence coefficient of the grid node U(i) at the tth adjustment moment; H(i,t) represents the difference in total power load between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; H1(i,t) represents the difference in total vehicle charging load between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; H2(i,t) represents the difference in power load data between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; α is a preset value greater than 0; norm() is a normalization function.

[0017] In one embodiment, the process of determining the second electric energy influence coefficient is:

[0018] At each adjustment time, the straight-line distance between each grid node and each of the plurality of electric vehicles is recorded as the spatial distance;

[0019] The second electric energy influence coefficient is positively correlated with the spatial distance of each grid node at each adjustment time, and is negatively correlated with the estimated remaining charging time of each electric vehicle among the multiple electric vehicles.

[0020] In one embodiment, the expression of the second electric energy influence coefficient is:

[0021] Wherein, S2(i,t) represents the second electric energy influence coefficient of the grid node U(i) at the t-th adjustment moment; the electric vehicle set A(t) consisting of all electric vehicles that have been connected to the smart grid at the t-th adjustment moment is obtained, and the distance between the grid node U(i) and each electric vehicle in the electric vehicle set A(t) is calculated respectively, and the set consisting of all electric vehicles whose distance is less than the preset distance is taken as the neighboring electric vehicle set UR(i,t) of the grid node U(i) at the t-th adjustment moment, and M represents the number of electric vehicles in the neighboring electric vehicle set UR(i,t); d1(m) represents the distance between the m-th electric vehicle in the neighboring electric vehicle set UR(i,t) and the grid node U(i); the ratio of the estimated remaining charging time of the m-th electric vehicle in the neighboring electric vehicle set UR(i,t) at the t-th adjustment moment to the time interval between two adjacent adjustment moments is calculated, and d2(m) represents the minimum value between the ratio and the value 1; β is a parameter preset to be greater than 0; norm() is a normalization function.

[0022] In one embodiment, the comprehensive electric energy impact coefficient is the product of the first electric energy impact coefficient and the second electric energy impact coefficient.

[0023] In one embodiment, the process of acquiring the model for simulating the grid quality of the line where each grid node is located changes with the load is:

[0024] Calculating the total harmonic distortion rate of the voltage signal and the total harmonic distortion rate of the current signal at each adjustment moment in the multiple historical adjustment moments; calculating the average of the total harmonic distortion rate of the voltage signal and the total harmonic distortion rate of the current signal of each grid node at each adjustment moment;

[0025] The reciprocal of the sum of the mean value and a preset value greater than 0 is used as the power quality coefficient of each grid node at each adjustment time;

[0026] Arrange the comprehensive power impact coefficient and power quality coefficient of each power grid node at the multiple historical adjustment moments in time sequence to form comprehensive power impact sequences and power quality sequences;

[0027] The data at the same position in the first-order difference sequence of the comprehensive power influence sequence of each power grid node and the first-order difference sequence of the power quality sequence are combined into two-dimensional data points, and curve fitting is performed on all the two-dimensional data points corresponding to each power grid node to obtain the model.

[0028] In one embodiment, the objective function is determined as follows:

[0029] Calculate the difference in comprehensive electric energy impact coefficient between the current moment and the previous adjustment moment, and record it as the impact difference;

[0030] Based on the model and the impact difference, the power quality change of each grid node at the current moment is obtained; the sum of the power quality changes of all grid nodes at the current moment is taken as the comprehensive power quality change of the smart grid;

[0031] The expression of the objective function is:

[0032] F(t)=Q(t) max +f(t) min , f(t)=P(t)-P(t-1), P(t)=P1(t)+P2(t); where F(t) represents

[0033] represents the objective function in the particle swarm optimization algorithm; Q(t) max represents the comprehensive change of power quality Q(t) of the maximized smart grid at the tth regulation moment; f(t) min represents the minimized power load fluctuation f(t) of the smart grid at the tth adjustment moment; f(t) represents the power load fluctuation of the smart grid at the tth adjustment moment; P(t) and P(t-1) represent the total power load of the smart grid at the tth and (t-1)th adjustment moments respectively; P1(t) represents the total charging power of all electric vehicles that have been connected to the smart grid at the tth adjustment moment; P2(t) represents the sum of the power load data of all grid nodes in the smart grid at the tth adjustment moment, where the total power load of the smart grid at each adjustment moment is the sum of the total power loads of all grid nodes at each adjustment moment.

[0034] In one embodiment, the maximum power of each electric vehicle is used as the maximum constraint value of the charging power of each electric vehicle in the objective function, and 0 is used as the minimum constraint value of the charging power of each electric vehicle in the objective function.

[0035] In a second aspect, an embodiment of the present application further provides a power grid load dynamic balancing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned power grid load dynamic balancing methods when executing the computer program.

[0036] This application has at least the following beneficial effects:

[0037] The present application determines the first electric energy impact coefficient of each grid node at each adjustment time based on the change in the total electric power load between each adjustment time and the adjacent adjustment time, and the change in the charging power of the plurality of electric vehicles in between, and in combination with the change in the electric power load data in between. The beneficial effect is that the time distribution difference of the charging load of the electric vehicle and other types of electric loads is taken into account, and the power quality change caused by the other types of electric loads is avoided as part of the power quality change caused by the charging load of the electric vehicle.

[0038] The present application analyzes the spatial distribution of the charging load of electric vehicles in the area where each grid node is located, and the impact of the charging status of electric vehicles on the power quality of the line where the grid node is located, and determines the second power influence coefficient of each grid node at each adjustment time based on the distance between each grid node and the multiple electric vehicles and the estimated remaining charging time at each adjustment time, and obtains the comprehensive power influence coefficient of each grid node at each adjustment time based on the first power influence coefficient and the second power influence coefficient. The beneficial effect is that the influence of the charging load of electric vehicles and other types of power loads in time and space on the power quality change of the line where the grid node is located is considered, and the degree of differentiation of the degree of influence of the charging load of electric vehicles on the power quality change of the line where the grid node is located in the area where different grid nodes are located is improved;

[0039] The present application obtains a model for simulating the change of grid quality of the line where each grid node is located with load based on the degree of distortion of the voltage signal and the current signal of each grid node at multiple historical adjustment moments, and the comprehensive electric energy influence coefficient, and then obtains the comprehensive change of power quality according to the fitting model and the change of the comprehensive electric energy influence coefficient. The beneficial effect is that the time and space distribution of the charging load of all electric vehicles at the time to be adjusted, and the increase or decrease of the power quality of the line where each grid node is located caused by the change relative to the previous adjustment moment, are used as the evaluation standard of the stability and reliability of the power quality of the smart grid. Compared with the evaluation standard based on the fluctuation of the power load curve in the traditional method, it can more fully capture the subtle changes in the time and space distribution of the electric vehicle charging load, and the changes in power quality caused by it, and can provide more comprehensive and accurate information on the operation status of the power grid, thereby improving the stability and reliability of the optimized smart grid in various regions, and thus better realizing the dynamic balance control of the power grid load. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A flowchart of a method for dynamic load balancing of a power grid provided in one embodiment of the present application;

[0042] Figure 2 Schematic diagram of the model determination process. DETAILED DESCRIPTION

[0043] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. It should be understood that, unless otherwise specified, " / " means or.

[0045] It should also be noted that the terms "first" and "second" in the present application are used to distinguish similar objects rather than to describe a specific order or sequence.

[0046] The following is a detailed description of a specific scheme of a power grid load dynamic balancing method and system provided by the present application in conjunction with the accompanying drawings.

[0047] See also Figure 1 , which shows a flowchart of a method for dynamic load balancing of a power grid provided by an embodiment of the present application, the method comprising the following steps:

[0048] Step 1: Obtain all electric vehicles that have been connected to the smart grid at each adjustment time, as well as the estimated remaining charging time and charging power of each electric vehicle, and obtain the power load data of each grid node in the smart grid at each adjustment time.

[0049] Obtain a set of electric vehicles consisting of all electric vehicles that have been connected to the smart grid at each adjustment time, wherein the adjustment time refers to the time when the charging power of the electric vehicle is adjusted.

[0050] In this embodiment, the time interval between any two adjacent adjustment moments is 15 minutes. The value of the time interval is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0051] The on-board navigation system of the electric vehicle is used to obtain the longitude and latitude coordinates of each electric vehicle in each tram set; the battery management system of the electric vehicle is used to collect the estimated remaining charging time, charging power and maximum charging power of each electric vehicle in each tram set.

[0052] Through the smart meters installed at each grid node in the smart grid, the power load data of each grid node at each adjustment time is obtained.

[0053] Step 2, determine the total power load of each grid node at each adjustment time, determine the first power influence coefficient and the second power influence coefficient of each grid node at each adjustment time, and then obtain the comprehensive power influence coefficient of each grid node at each adjustment time, and combine the degree of distortion of the voltage signal and the current signal of each grid node at multiple historical adjustment times to obtain a model for simulating the change of grid quality of the lines where each grid node is located with load.

[0054] This application is based on the temporal and spatial distribution of the charging load of all electric vehicles connected to the smart grid in the area where each grid node is located within the jurisdiction of the smart grid, evaluates the impact on the power quality of the lines where all grid nodes are located in the smart grid, and constructs an objective function based on the evaluation results to optimize the charging power of electric vehicles connected to the smart grid, thereby reducing the impact of the charging load of electric vehicles on the power quality of the lines where each grid node is located.

[0055] Step 2.1, based on the power load data and the charging power of multiple electric vehicles that are located adjacent to each grid node at each adjustment time, obtain the total power load of each grid node at each adjustment time.

[0056] The greater the load change of the total power load in the power grid within a certain period of time, the more likely it is to cause changes in the power quality of the power grid. If the charging load of electric vehicles connected to the power grid has greater variability than other types of power loads, the change in the charging load of electric vehicles will have a greater impact on the change in the power quality of the power grid.

[0057] Taking the i-th grid node U(i) in the smart grid and the tram set A(t) at the t-th adjustment time as examples, according to the longitude and latitude coordinates of the grid node U(i) and the longitude and latitude coordinates of all electric vehicles in the tram set A(t), the distance between the grid node U(i) and each electric vehicle in the tram set A(t) is calculated respectively, and each electric vehicle with a distance less than a preset distance is regarded as each neighboring electric vehicle of the grid node U(i) at the t-th adjustment time, and the set consisting of all electric vehicles with a distance less than the preset distance is regarded as the neighboring tram set UR(i,t) of the grid node U(i) at the t-th adjustment time, which is used to represent the set consisting of all electric vehicles connected to the smart grid at the t-th adjustment time in the area where the grid node U(i) is located.

[0058] In this embodiment, the value of the preset distance is 1 kilometer. The value of the preset distance is preset manually and the implementer can set it according to the actual situation. This application does not impose any special restrictions.

[0059] The sum of the charging powers of all electric vehicles in the neighboring tram set UR(i,t) is recorded as the total tram charging load of the grid node U(i) at the tth adjustment time, and the sum of the power load data of the grid node U(i) at the tth adjustment time and the total tram charging load is recorded as the total power load of the grid node U(i) at the tth adjustment time.

[0060] According to the same calculation method as the total vehicle charging load and the total power load of the grid node U(i) at the tth adjustment moment, the total vehicle charging load and the total power load of the grid node U(i) at each adjustment moment are calculated respectively.

[0061] Step 2.2, based on the change in total power load of each grid node between each adjustment moment and the adjacent adjustment moment, as well as the change in charging power of the multiple electric vehicles in between, and combined with the change in power load data in between, determine the first electric energy impact coefficient of each grid node at each adjustment moment.

[0062] Based on the difference in the total electric vehicle charging load between the grid node U(i) at each adjustment time and its adjacent adjustment time, as well as the difference in the power load data between them, and combined with the difference in the total power load between them, the first electric energy influence coefficient of the grid node U(i) at each adjustment time is determined, and the expression is:

[0063] Wherein, S1(i,t) represents the first electric energy influence coefficient of the grid node U(i) at the tth adjustment moment; H(i,t) represents the difference in total power load between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; H1(i,t) represents the difference in total vehicle charging load between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; H2(i,t) represents the difference in power load data between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; α is a preset value greater than 0, the purpose is to prevent the denominator from being 0, the value of α is preset manually, and the implementer can set it by himself. In this embodiment, the value of α is 0.01; norm() is a normalization function.

[0064] In this embodiment, the difference in total power load between the tth adjustment moment and its previous adjacent adjustment moment, the difference in total vehicle charging load, and the difference in power load data are calculated. As another implementation method, the implementer may also calculate the difference in total power load between the tth adjustment moment and its next adjacent adjustment moment, the difference in total vehicle charging load, and the difference in power load data.

[0065] In this embodiment, when calculating the first electric energy influence coefficient at the tth adjustment moment, all the differences involved are the absolute values ​​of the differences. As other implementation methods, based on the measurable differences between the total power loads, the differences between the total charging loads of trams, and the differences between the power load data, the implementers may use other calculation methods for measurement, such as ratios, squares of differences, etc. This application does not impose any special restrictions.

[0066] In this embodiment, the Min-Max normalization method is used to Normalization is performed, as another implementation method, in which On the basis of normalization processing, the implementer may adopt other existing technologies, such as Z-Score normalization method, decimal calibration normalization method, etc., and this application does not impose any special restrictions.

[0067] It should be noted that: in the time period between the tth adjustment moment and its adjacent adjustment moment, the greater the change in the total power load in the grid node U(i), that is, the greater H(i,t), and the greater the change in the charging load generated by all electric vehicles connected to the smart grid in the area where the grid node U(i) is located, compared with the change in the other types of power loads, that is, The larger it is, the greater the impact of the charging load generated by the electric vehicles in the neighboring tram set UR(i,t) on the changes in the grid power quality of the line where the grid node U(i) is located will be compared with the changes in other types of power loads over time, that is, the larger S1(i,t) is.

[0068] Step 2.3, based on the distance from each grid node to the corresponding multiple electric vehicles at each adjustment moment, and the estimated remaining charging time of the multiple electric vehicles, determine the second electric energy influence coefficient of each grid node at each adjustment moment.

[0069] For different grid nodes, even if all electric vehicles connected to the smart grid in the areas where these grid nodes are located have the same charging load change and the same other types of electric load changes in a certain time period, the change in the power quality of the line where the grid node is located will vary due to the distance between the electric vehicle and the grid node and the charging time of the electric vehicle. The reason is that the closer the location of the electric vehicle's charging pile is to the grid node, the lower the transmission energy consumption of the grid, the higher the stability of the grid, and the smaller its impact on the change of the grid's power quality. The shorter the estimated remaining charging time of the electric vehicle in the said time period, the easier it is to cause instantaneous shocks to the grid, and the greater its impact on the change of the grid's power quality.

[0070] Based on the above analysis, based on the distance between the grid node U(i) and each of its neighboring electric vehicles at each adjustment time, and the estimated remaining charging time of each neighboring electric vehicle at each adjustment time, the second electric energy influence coefficient of the grid node U(i) at each adjustment time is determined, and the expression is:

[0071] Wherein, S2(i,t) represents the second electric energy influence coefficient of the grid node U(i) at the tth adjustment moment; M represents the number of electric vehicles in the neighboring tram set UR(i,t); d1(m) represents the distance between the mth electric vehicle in the neighboring tram set UR(i,t) and the grid node U(i); the ratio of the estimated remaining charging time of the mth electric vehicle in the neighboring tram set UR(i,t) at the tth adjustment moment to the time interval is calculated, and d2(m) represents the minimum value between the ratio and the value 1. The reason is that when the estimated remaining charging time is greater than the time interval, it will not cause an instantaneous impact on the grid in the time period between the tth adjustment moment and its adjacent adjustment moments. Therefore, when the estimated remaining charging time is greater than the time interval, the value of d2(m) is 1; β is a parameter preset to be greater than 0, the purpose of which is to prevent the denominator from being 0. The value of β is preset by humans and can be set by the implementer. In this embodiment, the value of β is 0.01; norm() is a normalization function.

[0072] In this embodiment, the Min-Max normalization method is used to Normalization is performed, as another implementation method, in which On the basis of normalization processing, the implementer may adopt other existing technologies, such as Z-Score normalization method, decimal calibration normalization method, etc., and this application does not impose any special restrictions.

[0073] It should be noted that the closer the location of the electric vehicles in the neighboring tram set UR(i,t) is to the grid node U(i) when charging, and the longer the estimated remaining charging time of the electric vehicles in the neighboring tram set UR(i,t) at the tth adjustment time, that is, The smaller it is, the smaller the impact of the electric vehicles in the neighboring tram set UR(i,t) on the power quality of the line where the grid node U(i) is located is, that is, The smaller it is, the smaller the spatial distribution of the charging load generated by the electric vehicles in the neighboring tram set UR(i,t) will be, and the smaller the impact of the change in the power quality of the line where the grid node U(i) is located, that is, the smaller S2(i,t) is.

[0074] Step 2.4, based on the first electric energy influence coefficient and the second electric energy influence coefficient, obtain the comprehensive electric energy influence coefficient of each grid node at each adjustment time.

[0075] The fusion result of the first power influence coefficient and the second power influence coefficient of the grid node U(i) at the t-th adjustment moment is used as the comprehensive power influence coefficient of the grid node U(i) at the t-th adjustment moment, which is used to characterize the distribution of the charging load of electric vehicles connected to the smart grid in the area where the grid node U(i) is located at the t-th adjustment moment, and the comprehensive influence degree on the power quality change of the line where the grid node U(i) is located. The greater the comprehensive influence degree, the greater the calculated comprehensive power influence coefficient.

[0076] It should be understood that fusion refers to combining multiple independent variables in a way that enhances the overall effect, such as additive relationship, multiplicative relationship, etc., and implementers can limit it according to actual conditions.

[0077] In this embodiment, the sum of the first power influence coefficient and the second power influence coefficient of the grid node U(i) at the tth adjustment moment is used as the comprehensive power influence coefficient of the grid node U(i) at the tth adjustment moment.

[0078] In another embodiment, the product of the first power influence coefficient and the second power influence coefficient of the grid node U(i) at the tth adjustment moment is used as the comprehensive power influence coefficient of the grid node U(i) at the tth adjustment moment.

[0079] Step 2.5, based on the degree of distortion of the voltage signal and the current signal of each grid node at multiple historical adjustment moments and the comprehensive electric energy impact coefficient, obtain a model for simulating the change of grid quality of the line where each grid node is located with load.

[0080] Furthermore, a model is constructed based on the historical data of the smart grid to estimate the increase or decrease in the power quality of the lines at each grid node in the area governed by the smart grid when the charging load of all electric vehicles connected to the smart grid changes between two adjacent adjustment moments.

[0081] Specifically, a time interval T is selected from the log of the power load management system of the smart grid, and the comprehensive power influence coefficient of each grid node in the smart grid at each adjustment moment in the time interval T is calculated based on all historical data of the smart grid in the time interval T according to the same calculation method as the comprehensive power influence coefficient of the grid node U(i) at the tth adjustment moment.

[0082] In this embodiment, the length of the time interval T is 1 day. The length of the time interval T is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0083] Still taking the grid node U(i) as an example, the comprehensive power influence coefficients of the grid node U(i) at all adjustment moments within the time interval T are arranged in time sequence to form a comprehensive power influence sequence of the grid node U(i).

[0084] Furthermore, in order to evaluate the degree of impact on the power quality of the lines where all grid nodes in the smart grid are located, the voltage signal and current signal of each grid node in the smart grid at each adjustment moment within the time interval T are obtained from the log of the power load management system of the smart grid, and the sampling time of the voltage signal and the current signal are kept consistent with the time interval.

[0085] In this embodiment, when collecting voltage signals and current signals, the collection frequency is 100 Hz. The value of the collection frequency is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0086] The total harmonic distortion rate of the voltage signal and the total harmonic distortion rate of the current signal at each adjustment time in the time interval T are calculated respectively, and the mean of the total harmonic distortion rate of the voltage signal and the total harmonic distortion rate of the current signal at each adjustment time is calculated. The reciprocal of the sum of the mean and ε is used as the power quality coefficient of the grid node U(i) at each adjustment time in the time interval T, which is used to evaluate the power quality of the line where the grid node U(i) is located at each adjustment time. The larger the mean, the more the voltage or current waveform of the line where the grid node U(i) is located deviates from the ideal sinusoidal wave shape, the greater the degree of waveform distortion, the lower the power quality of the line where the grid node U(i) is located, that is, the smaller the power quality coefficient, wherein ε is a preset value greater than 0, the purpose is to prevent the denominator from being 0, the value of ε is preset by humans, and the implementer can set it by himself. In this embodiment, the value of ε is 0.01; the calculation of the total harmonic distortion rate is a well-known technology and will not be repeated in this application.

[0087] The power quality coefficients of the grid node U(i) at all adjustment moments within the time interval T are arranged in time sequence to form a power quality sequence of the grid node U(i), which is used to characterize the change of the power quality of the line where the grid node U(i) is located within the time interval T over time.

[0088] The first-order difference processing is performed on the comprehensive power influence sequence and the power quality sequence respectively to obtain the first-order difference sequence of the comprehensive power influence sequence and the first-order difference sequence of the power quality sequence, so as to obtain the increase or decrease of the first power influence coefficient, the increase or decrease of the second power influence coefficient, and the increase or decrease of the power quality coefficient at each adjustment moment in the time interval T compared with the previous adjustment moment. Among them, the first-order difference processing is a well-known technology and will not be repeated in this application.

[0089] The data at the same position in the first-order difference sequence of the comprehensive power impact sequence and the first-order difference sequence of the power quality sequence are combined into two-dimensional data points, and curve fitting is performed on all two-dimensional data points to obtain a fitting model, which is used to estimate the increase or decrease in the power quality of the lines where each grid node is located in the area governed by the smart grid when the charging load of all electric vehicles connected to the smart grid changes at two adjacent adjustment moments. The schematic diagram of the model determination process is shown in Figure 2 shown.

[0090] In this embodiment, the least squares method is used to perform curve fitting on all two-dimensional data points. As other implementation methods, on the basis of curve fitting, the implementer can use other existing technologies for curve fitting, such as local weighted regression, K-nearest neighbor regression, etc., and this application does not impose any special restrictions.

[0091] Step 3: Based on the model and the preset target, determine the objective function in the particle swarm optimization algorithm, obtain the charging power of each electric vehicle connected to the smart grid at the current adjustment time based on the optimal solution of the objective function, and dynamically balance the charging load of the smart grid.

[0092] The result of calculating the comprehensive power influence coefficient of the grid node U(i) at the tth adjustment moment minus the comprehensive power influence coefficient at the adjacent previous adjustment moment is substituted into the fitting model to obtain the change in power quality of the grid node U(i) at the tth adjustment moment, which is used to characterize the increase or decrease in power quality of the line where the grid node U(i) is located when the charging load of all electric vehicles in the neighboring tram set of the grid node U(i) at the tth adjustment moment changes compared with the distribution of the charging load in time and space at the previous adjustment moment.

[0093] The sum of the power quality changes of all grid nodes in the smart grid at the tth adjustment moment is taken as the comprehensive power quality change of the smart grid at the tth adjustment moment.

[0094] Maximizing the comprehensive change in power quality Q(t), minimizing user costs, and minimizing the overall power load fluctuation of the power grid are used as the optimization goals of the smart grid when optimizing the charging power of all electric vehicles in the electric vehicle set A(t). The objective function of the particle swarm optimization algorithm corresponding to the optimization goal is obtained, and the expression is:

[0095] F(t)=Q(t) max +f(t) min , f(t)=P(t)-P(t-1), P(t)=P1(t)+P2(t); where F(t) represents

[0096] represents the objective function in the particle swarm optimization algorithm; Q(t) max represents the comprehensive change of power quality Q(t) of the maximized smart grid at the tth regulation moment; f(t) min represents the minimization of the power load fluctuation f(t) of the smart grid at the tth adjustment time; f(t) represents the power load fluctuation of the smart grid at the tth adjustment time; P(t) and P(t-1) represent the total power load of the smart grid at the tth and (t-1)th adjustment time, respectively; P1(t) represents the total charging power of all electric vehicles that have been connected to the smart grid at the tth adjustment time; P2(t) represents the sum of the power load data of all grid nodes in the smart grid at the tth adjustment time, where the total power load of the smart grid at each adjustment time is the sum of the total power loads of all grid nodes at each adjustment time;

[0097] The maximum power of each electric vehicle in the electric vehicle set A(t) is used as the maximum constraint value of the charging power of each electric vehicle in the objective function, and 0 is used as the minimum constraint value of the charging power of each electric vehicle in the objective function, and the particle swarm optimization algorithm is used to obtain the optimal solution of the objective function. The particle swarm optimization algorithm is a well-known technology and will not be described in detail in this application.

[0098] In this embodiment, the number of particles in the particle swarm optimization algorithm is 50, the inertia weight is 0.4, the individual learning factor and the group learning factor are both 2, and the maximum number of iterations is 100. The values ​​of the number of particles, inertia weight, individual learning factor, group learning factor and maximum number of iterations are all preset by humans, and the implementer can limit them according to actual conditions. This application does not impose any special restrictions.

[0099] Based on the optimal solution of the objective function, the charging power of each electric vehicle in the electric vehicle set A(t) is obtained respectively, and each electric vehicle is charged as the charging power at the tth adjustment moment to achieve dynamic balancing of the charging load of electric vehicles in the smart grid.

[0100] Based on the same inventive concept as the above method, an embodiment of the present application also provides a power grid load dynamic balancing system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned power grid load dynamic balancing methods are implemented.

[0101] In summary, the present application determines the first electric energy impact coefficient of each grid node at each adjustment time based on the change in the total electric power load between each adjustment time and the adjacent adjustment time, and the change in the charging power of the plurality of electric vehicles in between, and in combination with the change in the electric power load data in between. The beneficial effect is that the time distribution difference of the charging load of the electric vehicle and other types of electric loads is taken into account, and the power quality change caused by the other types of electric loads is avoided as part of the power quality change caused by the charging load of the electric vehicle.

[0102] Furthermore, the present application analyzes the spatial distribution of the charging load of electric vehicles in the area where each grid node is located, and the impact of the charging status of electric vehicles on the power quality of the line where the grid node is located, and determines the second power influence coefficient of each grid node at each adjustment time based on the distance between each grid node and the multiple electric vehicles and the estimated remaining charging time at each adjustment time, and obtains the comprehensive power influence coefficient of each grid node at each adjustment time based on the first power influence coefficient and the second power influence coefficient. The beneficial effect is that the influence of the charging load of electric vehicles and other types of power loads in time and space on the power quality changes of the line where the grid node is located is taken into account, and the degree of differentiation of the degree of influence of the charging load of electric vehicles on the power quality changes of the line where the grid node is located in the area where different grid nodes are located is improved;

[0103] Furthermore, the present application obtains a model for simulating the change of grid quality with load in the lines where each grid node is located based on the degree of distortion of the voltage signal and the current signal of each grid node at multiple historical adjustment moments, and the comprehensive power influence coefficient, and then obtains the comprehensive change of power quality according to the fitting model and the change of the comprehensive power influence coefficient. The beneficial effect is that the time and spatial distribution of the charging load of all electric vehicles at the time to be adjusted, and the increase or decrease in the power quality of the lines where each grid node is located caused by the change relative to the previous adjustment moment, are used as the evaluation criteria for the stability and reliability of the power quality of the smart grid. Compared with the evaluation criteria based on the fluctuation of the power load curve in the traditional method, it can more fully capture the subtle changes in the time and space distribution of the electric vehicle charging load, and the changes in power quality caused by it, and can provide more comprehensive and accurate information on the operation status of the power grid, thereby improving the stability and reliability of the optimized smart grid in various regions, and thus better realizing the dynamic balanced control of the power grid load.

[0104] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0105] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. A method for dynamic balancing of power grid loads, characterized in that: The method comprises the following steps: Obtain all electric vehicles that have been connected to the smart grid at each adjustment time, as well as the estimated remaining charging time and charging power of each electric vehicle, and obtain the power load data of each grid node in the smart grid at each adjustment time; Based on the power load data and the charging power of multiple electric vehicles that are adjacent to each grid node at each adjustment time, the total power load of each grid node at each adjustment time is obtained; Determine the first electric energy influence coefficient of each grid node at each adjustment time based on the change in total power load between each adjustment time and the adjacent adjustment time, and the change in charging power of the plurality of electric vehicles in between, and in combination with the change in power load data in between; Determine the second electric energy influence coefficient of each grid node at each adjustment time based on the distance from each grid node to the corresponding multiple electric vehicles and the estimated remaining charging time of the multiple electric vehicles at each adjustment time; Based on the first electric energy influence coefficient and the second electric energy influence coefficient, obtaining a comprehensive electric energy influence coefficient of each grid node at each adjustment time; Based on the degree of distortion of voltage signals and current signals of each grid node at multiple historical adjustment moments and the comprehensive electric energy influence coefficient, a model for simulating the change of grid quality of the line where each grid node is located with load is obtained; Based on the model and the preset target, the objective function in the particle swarm optimization algorithm is determined, and based on the optimal solution of the objective function, the charging power of each electric vehicle connected to the smart grid at the current adjustment moment is obtained to dynamically balance the charging load of the smart grid.

2. A method for dynamic balancing of power grid loads according to claim 1, characterized in that: The total power load is the sum of the charging power of the multiple electric vehicles at each adjustment time and the power load data.

3. A method for dynamic load balancing of a power grid as claimed in claim 1, characterized in that: The process of determining the first electric energy influence coefficient is as follows: The sum of the charging powers of the multiple electric vehicles at each adjustment time is used as the total electric vehicle charging load of each grid node at each adjustment time; the calculation formula of the first electric energy influence coefficient is: Where S1(i,t) represents the first electric energy influence coefficient of the grid node U(i) at the tth adjustment moment; H(i,t) represents the difference in total power load between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; H1(i,t) represents the difference in total vehicle charging load between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; H2(i,t) represents the difference in power load data between the grid node U(i) at the tth adjustment moment and its adjacent adjustment moment; α is a preset value greater than 0; norm() is a normalization function.

4. A method for dynamic balancing of power grid loads as claimed in claim 2, characterized in that: The determination process of the second electric energy influence coefficient is: At each adjustment time, the straight-line distance between each grid node and each of the plurality of electric vehicles is recorded as the spatial distance; The second electric energy influence coefficient is positively correlated with the spatial distance of each grid node at each adjustment time, and is negatively correlated with the estimated remaining charging time of each electric vehicle among the multiple electric vehicles.

5. A method for dynamic balancing of power grid loads as claimed in claim 1, characterized in that: The expression of the second electric energy influence coefficient is: Wherein, S2(i,t) represents the second electric energy influence coefficient of the grid node U(i) at the t-th adjustment moment; obtain the electric vehicle set A(t) consisting of all electric vehicles that have been connected to the smart grid at the t-th adjustment moment, calculate the distance between the grid node U(i) and each electric vehicle in the electric vehicle set A(t), and take the set consisting of all electric vehicles whose distance is less than the preset distance as the neighboring electric vehicle set UR(i,t) of the grid node U(i) at the t-th adjustment moment, M represents the number of electric vehicles in the neighboring electric vehicle set UR(i,t); d1(m) represents the distance between the m-th electric vehicle in the neighboring electric vehicle set UR(i,t) and the grid node U(i); calculate the ratio of the estimated remaining charging time of the m-th electric vehicle in the neighboring electric vehicle set UR(i,t) at the t-th adjustment moment to the time interval between two adjacent adjustment moments, and d2(m) represents the minimum value between the ratio and the value 1; β is a parameter preset to be greater than 0; norm() is a normalization function.

6. A method for dynamic balancing of power grid loads according to claim 1, characterized in that: The comprehensive electric energy impact coefficient is the product of the first electric energy impact coefficient and the second electric energy impact coefficient.

7. A method for dynamic balancing of power grid loads as claimed in claim 1, characterized in that: The acquisition process of the model for simulating the grid quality of the line where each grid node is located changes with the load is as follows: Calculating the total harmonic distortion rate of the voltage signal and the total harmonic distortion rate of the current signal at each adjustment moment in the multiple historical adjustment moments; calculating the average of the total harmonic distortion rate of the voltage signal and the total harmonic distortion rate of the current signal of each grid node at each adjustment moment; The reciprocal of the sum of the mean value and a preset value greater than 0 is used as the power quality coefficient of each grid node at each adjustment time; Arrange the comprehensive power impact coefficient and power quality coefficient of each power grid node at the multiple historical adjustment moments in time sequence to form comprehensive power impact sequences and power quality sequences; The data at the same position in the first-order difference sequence of the comprehensive power influence sequence of each power grid node and the first-order difference sequence of the power quality sequence are combined into two-dimensional data points, and curve fitting is performed on all the two-dimensional data points corresponding to each power grid node to obtain the model.

8. A method for dynamic load balancing of a power grid as claimed in claim 1, characterized in that: The determination process of the objective function is: Calculate the difference in comprehensive electric energy impact coefficient between the current moment and the previous adjustment moment, and record it as the impact difference; Based on the model and the impact difference, the power quality change of each grid node at the current moment is obtained; the sum of the power quality changes of all grid nodes at the current moment is taken as the comprehensive power quality change of the smart grid; The expression of the objective function is: F(t) = Q(t) max + f(t) min , f(t) = P(t) - P(t - 1), P(t) = P1(t) + P2(t); where, F(t) represents represents the objective function in the particle swarm optimization algorithm; Q(t) max represents the comprehensive change of power quality Q(t) of the maximized smart grid at the tth regulation moment; f(t) min It represents minimizing the power load fluctuation f(t) of the smart grid at the tth regulation moment; f(t) represents the power load fluctuation of the smart grid at the tth adjustment moment; P(t) and P(t-1) represent the total power load of the smart grid at the tth and (t-1)th adjustment moments respectively; P1(t) represents the total charging power of all electric vehicles that have been connected to the smart grid at the tth adjustment moment; P2(t) represents the sum of the power load data of all grid nodes in the smart grid at the tth adjustment moment, where the total power load of the smart grid at each adjustment moment is the sum of the total power loads of all grid nodes at each adjustment moment.

9. A method for dynamic load balancing of a power grid as claimed in claim 8, characterized in that: The maximum power of each electric vehicle is used as the maximum constraint value of the charging power of each electric vehicle in the objective function, and 0 is used as the minimum constraint value of the charging power of each electric vehicle in the objective function.

10. A power grid load dynamic balancing system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for dynamic balancing of power grid loads as described in any one of claims 1 to 9 are implemented.

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