CN114219214A

CN114219214A - Power grid comprehensive risk assessment system considering new energy and electric automobile access

Info

Publication number: CN114219214A
Application number: CN202111351030.3A
Authority: CN
Inventors: 刘禹彤; 齐阳; 潘霄; 赵琳; 吉星; 侯依昕; 商文颖; 满林坤; 李纯正
Original assignee: STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE; State Grid Corp of China SGCC
Current assignee: STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE; State Grid Corp of China SGCC
Priority date: 2021-11-16
Filing date: 2021-11-16
Publication date: 2022-03-22

Abstract

The invention belongs to the technical field of power grids, and particularly relates to a comprehensive risk assessment system for a power grid considering new energy and electric automobile access. The invention comprises the following steps: step 1, constructing a wind-solar output and electric automobile time sequence probability distribution model, and replacing the subjectively set initial charge state of various electric automobile models with the daily driving mileage; step 2, providing short-term safety risk indexes based on a complex network theory, and establishing economic risk, long-term safety risk and power grid high-efficiency risk indexes according to the economic operation of the power distribution network; and 3, establishing a three-dimensional multi-angle risk index system, and comprehensively evaluating the operation risk of the power distribution network under different electric automobile capacities. The comprehensive risk assessment method for the grid-connected operation of the electric automobile of the active power distribution network based on principal component analysis can comprehensively assess the safety risk and the economic risk and has positive guiding significance for the capacity of the electric automobile in a certain area.

Description

Power grid comprehensive risk assessment system considering new energy and electric automobile access

Technical Field

The invention belongs to the technical field of power grids, and particularly relates to a comprehensive risk assessment system for a power grid considering new energy and electric automobile access.

Background

In recent years, with the rapid increase of new energy and Electric Vehicle (EV) access capacity, the structure, the trend, and the operation mode of the conventional power distribution network have been greatly changed. On the one hand, randomness and uncertainty of DG (Distributed Generation) output can cause adverse effects such as line overload, power quality reduction and system loss increase. On the other hand, the random charging behavior of the EV brings new risk challenges to the safe and economically stable operation of the power system, and therefore, the operation risk after the DG and the EV are connected to the power grid is a problem to be solved urgently by the power system.

For risk assessment of a DG and an EV which are simultaneously accessed into a power distribution network, a traditional risk assessment analysis method mainly inherits a reliability assessment method and can be generally divided into an analytic method and a simulation method. However, most of the indexes are determined by expert evaluation or semi-quantitative analysis, and the indexes are easily influenced by subjective factors, and the actual size of the risk indexes is not completely considered. The relevance influence and the data dimension among evaluation indexes are effectively reduced by applying a dimensionality reduction objective weight evaluation method, so that the obtained evaluation is more credible, the defect of subjective empowerment is avoided, and the information value contained in the risk index can be fully reflected.

On the other hand, uncertainty factors in the distribution network are increasing in the large environment of mass access of such new energy, the explosive development of EVs, and the reform of the electric power market. These uncertainties have themselves adversely affected the operation of the distribution network and, in addition, when the number and capacity of such source-containing load points connected to the distribution network reaches a certain level, new changes to the overall distribution network system may result. The traditional single-power radiation type network is transformed into a multi-power combined power supply bidirectional tidal current network, which brings certain risks to the operation and control of a power distribution network.

Due to the existing power distribution network risk assessment method and the consideration of a single safety or economic risk index, the method is difficult to continue to be applicable. Therefore, there is a continuing need for development and improvement by those skilled in the art.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a comprehensive risk assessment system for a power grid considering new energy and electric automobile access. The invention aims to realize the purpose of comprehensively evaluating the safety risk and the economic risk.

The technical scheme adopted by the invention for realizing the purpose is as follows:

consider new forms of energy and electric automobile access's electric wire netting comprehensive risk assessment system, characterized by: the method comprises the following steps:

step 1, constructing a wind-solar output and electric automobile time sequence probability distribution model, and replacing the subjectively set initial charge state of various electric automobile models with the daily driving mileage;

step 2, providing short-term safety risk indexes based on a complex network theory, and establishing economic risk, long-term safety risk and power grid high-efficiency risk indexes according to the economic operation of the power distribution network;

and 3, establishing a three-dimensional multi-angle risk index system, and comprehensively evaluating the operation risk of the power distribution network under different electric automobile capacities.

Further, the constructing of the wind-solar power output and electric vehicle time sequence probability distribution model in step 1 includes:

(1) wind power, photovoltaic and conventional load models;

(2) an EV probability model.

Further, the wind power, photovoltaic and conventional load model comprises:

the wind power output is determined by the wind speed, and the statistical characteristic of the wind speed follows the double-parameter Weibull distribution; p_rRated output power v of the fan_co、v_ciAnd v_crCut-out, cut-in and rated wind speed, k, respectively_wAnd c_wRespectively representing the dimension and the shape parameters; thus P_wThe distribution function expression of the active power of the fan is as follows:

the solar illumination intensity is different due to different geographic environments and positions, and through measurement of a large amount of data, the Beta distribution represents the solar illumination intensity distribution in one day, so that the probability density function of the active power output by the photovoltaic power generation is as follows:

wherein Γ (g) is a Gamma function; alpha and beta are two parameters of Bate distribution, and represent the shape of the function; p_solarAnd P_solarmaxActual output and maximum output of the photovoltaic cell array are respectively obtained; r represents the radiance of the sun; eta and A are respectively the electric energy conversion efficiencyAnd the total area of the cell array;

the base load in any moment adopts normal distribution to reflect the randomness and the uncertainty thereof, and the active power P thereof_LDAnd reactive Q_LDThe probability model is:

wherein, mu_LP,tAnd mu_LQ,tRespectively representing the expected values of the active power and the reactive power of the conventional load at the moment t; lambda [ alpha ]_LP,tAnd λ_LQ,tRespectively expressed as the variation coefficients of the active power and the reactive power of the conventional load at the moment t.

The EV probability model comprises the following steps:

the initial charging time, the daily driving mileage and the charging time period of the EV are closely related, and the daily driving mileage d of the EV is fitted to satisfy normal distribution shown in formula (6) based on a Monte Carlo simulation method MCS;

the EV initial charging time t satisfies the normal distribution shown in equation (7):

wherein d is the daily driving mileage, mu_dAnd σ_dRespectively is the mean value and standard deviation of the daily driving mileage distribution characteristic;

the charging time is calculated through the daily driving mileage in the formula (6), as shown in the formula (8), the daily driving mileage is subjected to scientific and accurate fitting of data of automobile driving characteristics to obtain probability distribution, and the method is more objective and reasonable;

wherein, W₁₀₀The unit of the power consumption for EV driving is (kWgh)/hundred kilometers; eta_carEV charging efficiency; p_car,jIs the active power of the jth car, d is the daily driving mileage;

selecting a charging mode according with the actual condition according to the driving characteristics of different types of EVs;

the driving mileage after the battery of the private car is fully charged is far larger than the average daily driving mileage, and the daily driving requirement of the private car can be met by charging the battery once a day; charging places of private cars are 09:00-12:00 in the morning and 14:00-17:00 in the afternoon in parking lots of work units and 19:00 to 07:00 in residential areas in the evening, and the charging probabilities of the three are respectively assumed to be 0.2, 0.1 and 0.7; if the unit parking lot is charged and the charging time does not exceed 3h, selecting a rapid charging mode with a large constant current; if the charging is carried out in the parking lot of the residential area, the charging can be continued all night, and a conventional charging mode with moderate constant current is selected; the charging time of the public service vehicle is 19:00 to the next day of 07:00, and a conventional charging mode with moderate constant current is selected;

the bus and the taxi adopt a two-time-one-day charging mode; the bus carries out a rapid charging mode with a large constant current in the noon break time period of 10:00-16:30 shift change at noon, and carries out a conventional charging mode with a moderate constant current in the time period of 23:00 to 05:30 of the next day at night; the taxi selects a fast charging mode with larger constant current in two time intervals of 02:00-05:00 and 11:30-14: 30.

Further, the short-term safety risk index based on the complex network theory is provided in the step 2, and an economic risk, a long-term safety risk and a power grid high-efficiency risk index are established according to the economic operation of the power distribution network; the method comprises the following steps: establishing safety and economic risk indexes of new energy and electric vehicle access power grid:

firstly, the power grid is used as a complex system, power grid nodes are not connected in an isolated manner and are an integral body which is mutually restricted and influenced, wherein the fragility of each element is closely related to the structural position in the power grid and is also related to the influence of the power grid on other element nodes during operation; when the risk of accessing DGs and EVs into the power grid is evaluated, the influence of various factors needs to be comprehensively considered, and a power grid short-term risk evaluation model combining network structure vulnerability and a risk theory is provided; the node importance comprehensively considers the node degree, the betweenness and the proportion of the conventional load connected with the node, and the branch importance is measured by the line degree and the betweenness:

ρ_v,i＝α₁D_v,j+α₂B_v,j+α₃N_Pj (9)

ρ_l,i＝β₁D_l,j+β₂B_l,j (10)

wherein D is_v,jAnd B_v,jRespectively, node degree and betweenness, N_PjRepresenting node injected power; d_l,jAnd B_l,jRespectively, the number of lines and the number of medians; rho_v,iAnd ρ_l,iRespectively the node importance of the node i and the branch importance of the branch i; alpha is alpha₁、α₂And alpha₃Is a weight coefficient, α₁+α₂+α₃＝1；β₁And beta₂Is a weight coefficient, beta₁+β ₂1 is ═ 1; the weight value of the invention is determined by an analytic hierarchy process;

secondly, the EV charging load can bring short-term safety risk to the power grid, and the influence index is R_i ^vVoltage out-of-limit risk and R_i ^lThe calculation method of the branch out-of-limit risk is as follows:

step 1), node voltage out-of-limit operation risk:

wherein n is_v,i(t) is the number of voltage states of the node i at the t-th time; p (S)_v,j) Probability of being the jth voltage state; s_v,j(t) is the severity of voltage loss of the jth voltage state of the node i at the time t, and the calculation formula is as follows:

wherein, V_maxAnd V_minRespectively, the voltage qualified range and abovePer unit value of lower limit;

node voltage greater than V_maxLess than V_minThe range is the allowed fluctuation range, no voltage problem causes the loss of the power distribution network, and therefore the severity is 0; when the node voltage is less than V_minOr greater than V_maxIn time, the power distribution network is considered to be at risk, and the greater the deviation from the reasonable range, the higher the severity.

Branch power overload operation risk:

wherein n is_l,i(t) is the number of the power flow states of the branch i at the t-th moment; p (S)_l,j) The probability of the jth tide state; s_l,j(t) is the severity of the voltage loss of the jth power flow state of the branch i at the time t, and the calculation formula is as follows:

wherein L is_iThe ratio of the actual active power of the line to the rated active power of the line;

with R_SRICharacterizing the short-term comprehensive safety risk coefficient of system operation to

Characterizing voltage risks due to voltage violations of the distribution grid system and distribution uncertainties thereof to

Characterizing the power flow risk caused by power flow violations and distribution uncertainties in power distribution network systems, i.e.

Wherein, γ₁And gamma₂For the safety risk weight coefficient, γ₁+γ₂＝1；

The economic risk index of the DG and EV charging loads accessed to the power distribution network consists of two parts: line loss risk ELLR and business profit and loss risk EPLR, expressed as:

wherein the content of the first and second substances,

the price is the electricity price of the power distribution network in the time period t;

the network loss power of the power distribution network in the time period t;

the environmental benefit of subsidy electricity price given by the government at the time period t of the power distribution network is obtained;

and

respectively representing the operation and maintenance cost of the ith 'DG in the time t and the electricity sale price of the ith' DG in the time t;

the calculation formula is as follows:

wherein the content of the first and second substances,

time-varying electricity price of DG unit power

The active output power of the ith' DG in the t period; n is the number of DGs; n is the number of DGs; mu.s_i′The maintenance cost divided into the ith' unit capacity of DG; p_DGimThe active output of the ith' DG of the node m is obtained; p_t ^WDGAnd P_t ^WODGObtaining power from a power grid before and after DG access in a time period t; m_jThe emission coefficient of the pollution gas is the unit generated energy of the power distribution network; c_jThe treatment cost for different pollution gases; m' is the total class of exhaust gases;

the power grid load variance influences the loss inside a power grid, and under a certain EV number, the power grid load variance expression is as follows:

wherein the content of the first and second substances,

normal load for the t-th time period;

the charging and discharging power of the nth EV in the t-th time period; p_AVGThe average value of the total load of the power grid in one day in the power grid is obtained;

EV charging affects the efficiency of a power grid, and the influence index has average load rate, and the calculation method is as follows; calculating the average load rate by dividing the average load used in a period of time by the bearing capacity of the power distribution network system, wherein the bearing capacity of the power distribution network system is calculated as the maximum system access load capacity when the voltage is maximum and lower; and taking 24h for calculation, wherein the average load rate is as follows:

P％＝ω/P_e (24)

wherein omega is the average load of the power distribution network in 24 hours; p_eAnd carrying capacity for the power distribution network system.

Further, step 3, establishing a risk index system of three-dimensional multi-angle, applying comprehensive evaluation to the operation risk of the power distribution network under different electric vehicle capacities, includes:

the method comprises the steps of defining the capacity of the electric automobile as the sum of all EV rated charging powers in a charging state and a non-charging state in an area, ensuring safe and stable operation of DGs and EVs in a power distribution network according to risk indexes, establishing a comprehensive risk assessment system for new energy and electric automobiles to be connected into the power distribution network, analyzing risks caused by EVCs with different sizes to the power distribution network by adopting PCA, replacing a large number of original risk variables with a small number of risk variables, and containing all contents of original input risk variables.

By x₁,x₂,…,x_pRepresenting p risk indicators, c₁,c₂,…,c_pIs the weight of each index; the weighted sum of which is s ═ c₁x₁+c₂x₂+L+c_px_pThe different EVCs accessed to the power grid correspond to a comprehensive evaluation result which is recorded as s₁,s₂,…,s_hH is the number of different EVCs; then the PCA application procedure is as follows:

standardizing the risk index data calculated in the previous section to eliminate dimension to obtain standardized data:

B＝[b_i″(m″)]_h×k＝[B₁,B₂,…,Β_k]；

wherein the content of the first and second substances,

is taken as the mean value of the average value,

s_i″the standard deviation of the index is calculated as the index,

the matrix B satisfies E (B) after normalization_i″) 0 and D (B)_i″)＝1(i″＝1,2,…,k)；b_i″Indicates the i' "node normalization, m" indicates the number of EVCs of that class;

calculating a correlation coefficient matrix R after Z-Score processing based on the normalized risk matrix BETA, and calculating a characteristic value λ of R since the correlation coefficient matrix is equal to the covariance matrix and R is a positive definite matrix₁≥λ₂≥…≥λ_m″Not less than 0, and corresponding feature vector u₁,u₂,…,u_m″Then, the principal component calculates the expression:

Y_i″′＝(B_i″′)^Tu_i″′ (26)

wherein, i 'is 1,2, L p, i' th main component Y_i″′The variance of (a) is the specific gravity v of all the variances_i″′I.e. the contribution rate, is used for reflecting how large the original p indexes have, the cumulative contribution rate gamma represents how large the first k main components have, v_i″′And γ is:

if the accumulated variance of the principal components reaches a certain ratio, the original index can be replaced by the corresponding principal component, and the comprehensive risk assessment can be obtained by linear superposition calculation of the s principal components, namely

F＝ν₁Y₁+ν₂Y₂+L+ν_m″Y_m″ (29)。

Further, the step 1 of constructing the wind-solar output and electric vehicle time sequence probability distribution model includes building a simulation model in an IEEE33 node, setting an IEEE33 system diagram, equating 18 points to wind power, equating 33 points to photovoltaic, and building the simulation model for verifying the effect of a risk assessment system, and includes:

(1) analyzing EV charging load; (2) analyzing safety and economic risk indexes; (3) and (4) comprehensive risk assessment analysis.

Further, the EV charging load analysis includes: the expected charging power values of four EV types are obtained based on MCS:

the method is characterized in that a double-peak load state is formed due to the fact that private cars are charged in a large constant-current quick charging mode in 9:00-12:00 and 14:00-17:00, and although private cars are charged conventionally in the period from 19:00 to 7:00 the next day, a load peak is also caused due to a large amount of accesses, wherein the load of the private cars and the conventional load reach the peak value to aggravate the operation risk of a power grid, and the charging load of the private cars is reduced due to the fact that the electric quantity of a battery of the private cars approaches saturation in the range from 0:00 to 7: 00; conventional charging of a utility vehicle creates a unimodal load during 19:00-24:00, exacerbating to some extent the total load over that time period; in the period of 13:00-16:00, the load reaches the peak value in the daytime by the quick charging mode of the bus, and the load climbs at night and brings impact on the operation risk of the power distribution network by the conventional charging of the bus from 23:00 to the next day of 1:00, but the load and other types of EV charging loads form mutual complementation, and the load peak-valley difference is reduced to a certain extent; in the period of 3:00-5:00, the taxi fast charging with larger constant current occupies the dominant position of EV charging load; the peak of the charging load in the daytime is aggravated by the quick charging of the taxi in the period of 12:00-14:00, but the fluctuation of the load is reduced to a certain extent, so that the fluctuation of the charging load in the day is severe, and the analysis of the operation risk of the power grid is necessary.

Further, the safety and economic risk indicator analysis includes:

firstly, a deterministic evaluation is compared with a proposed short-term safety risk index, an EVC charging load of 13MW is equivalently accessed at a node 8, the voltages of the nodes 1-18 are evaluated in a time period of 20:00 to 21:00, and the randomness of the output power of a DG, the EV charging power and a conventional load is ignored during the deterministic evaluation; calculating the node voltage by adopting the average equivalent access node power; nodes 9-18 are nodes with voltages exceeding 0.93p.u., and only 10 nodes have voltage out-of-limit under deterministic evaluation; the nodes 6-18 all have voltage out-of-limit risks, namely most nodes have voltage out-of-limit probabilities, and since the probability and uncertainty are ignored in deterministic evaluation, the evaluation result cannot reflect the actual operating state;

due to the fact that short-term safety risks of the DG and the EV which are connected into the power distribution network are different under different time sequences, the node voltage out-of-limit operation risks of all times are obtained by considering the time sequences, voltage out-of-limit operation risks are mainly concentrated on the nodes 12-18 and the nodes 29-33 in the spatial dimension and gradually rise, and the

nodes

18 and 33 are located at the tail end of the power distribution network system and are caused by the fact that the electrical distance between the

nodes

18 and 33 and the DG or the EV is short; the time sequence change also has great influence on the electric energy quality of the nodes 12-18 and the nodes 29-33, and the time dimension shows that the voltage of the nodes from 8:00 to 16:00 is out of limit because the EV charging load is small and the DG output power is overlarge; the mass taxi fast charge results in a charge ratio of 3: certain voltage out-of-limit occurs at 00-5: 00;

the branch with the risk of exceeding the limit of the power flow in one day is as follows: during the period from 20:00 to 21:00, branch risks are mainly concentrated at the head end of the distribution network, the maximum branch off-limit risk occurs in the branch 1-2, and the maximum branch off-limit risk is also the highest point of the superposition of the EV charging load and the conventional load in the period, so that the branch tide off-limit risk is maximum;

obtaining the power distribution network through time sequence economic risk assessmentEconomic risk of one day; at 18:00 to 7: time period of 00C_ERIPositive, the maximum occurs at 20:00 to 21: a time period of 00; the most dangerous period is 19:00 to 22:00, during which the economic and safety risks are high, due to the maximum value reached by the superposition of the conventional load and the EV load;

meanwhile, the operating state of the power distribution network can be divided into four types:

class 1: from 0:00 to 7:00, R_SRIIs almost zero, and C_ERIThe value of (A) is positive; indicating economic losses due to insufficient DG output power and increased network losses, which, although safe, is not economical;

class 2: from 7:00 to 9:00, R_SRIIs zero and C_ERIIs negative; because the demand fluctuations of the load and the EV charging load are not large at this time, the DG can also obtain profit from the fluctuation, and the running state is safe and economical;

class 3: from 9:00 to 18:00, C_ERIIs negative and R_SRIThe value of (A) is positive; indicating that the operating conditions are economical but not safe, measures should be taken to reduce R_SRIReducing the output power of the DG or increasing the charging power of the EV station;

class 4: from 18:00 to 24:00, C_ERIAnd R_SRIAll values of (A) are positive; the method shows that the running state of the power distribution network is neither safe nor economical due to huge load demand and fluctuation of wind power photovoltaic output, so measures should be taken to improve the running quality of the actual power distribution network.

Further, the integrated risk assessment analysis includes:

selecting 21: the risk value at the time of 00, EVC is increased from 8MW to 14MW, and 13 groups of EV access values are obtained at the interval of 0.5 MW; constructing a 6 x 13 risk matrix according to the definition of the risk index; carrying out Z-Score method processing on the risk matrix to obtain a risk value; the KMO test is 0.871 and the Batterit sphericity test is similar to the chi-square test is 177, which are obtained by simulation of SPSS software, and the results prove that strong correlation exists between risk indexes and factor analysis can be carried out;

risk matrix factorization in PCAReducing dimension to obtain risk index and y₁All risk indicators have higher correlation with the first principal component, namely y₁99.2% of load variance index information, 97.2% of power grid high-efficiency performance index information, 92.4% of ELLR information, 91.5% of EPLR information, 98.6% of branch overload operation risk information and 99.2% of voltage out-of-limit risk information are respectively reflected in the data; the cumulative variance contribution 96.347% is obtained according to equation (28), so y is used₁ Original 6 risk indexes are represented, and the purpose of reducing the dimension of the original risk indexes is achieved;

calculating the score value of each risk index by a formula, and knowing the important correlation degree of each index, wherein the index weight of voltage out-of-limit, branch overload risk and power grid high efficiency is the largest and is also an important basis for the reconstruction of the next stage of the power distribution network;

in order to better reflect the superiority of the PCA comprehensive risk analysis method, the method is contrastively analyzed with a comprehensive risk based on the weighted entropy and a traditional voltage and current out-of-limit comprehensive risk evaluation method;

when the DG and the EV are simultaneously accessed to the power distribution network, the comprehensive risk evaluation values of the EVCs with different sizes are different, the value of the safety comprehensive risk which is singly considered is continuously increased along with the continuous increase of the EVC, and the comprehensive risk value of the power distribution network is also gradually increased along with the increase of the number of the accessed EVCs in a certain range; when the EVC is within the interval of 12MW, the comprehensive risk value of the operation of the power distribution network is reduced on the contrary, and the comprehensive risk value of the operation of the power distribution network has a relieving effect on the operation of the power distribution network, so that the operation risk state of the power distribution network can be better grasped under the condition that risk factors such as safety, economy and the like are considered simultaneously, and a positive guiding effect on the number of EVs accessed to the power distribution network can be well achieved.

A computer storage medium having a computer program stored thereon, the computer program when executed by a processor implementing the steps of the method for quantifying non-residential building demand flexibility based on electrical thermal storage and comfort management.

The invention has the following beneficial effects and advantages: the invention relates to a power grid comprehensive risk assessment system considering new energy and electric automobile access, in particular to a comprehensive risk assessment method for grid-connected operation of an electric automobile with an active power distribution network based on principal component analysis, which can comprehensively assess safety risks and economic risks and has positive guiding significance for the capacity of the electric automobile in a certain area.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a flow chart of the operational risk calculation of the present invention;

FIG. 2 is a diagram of an improved IEEE33 node system of the present invention;

FIG. 3 is a graph of the average power of various types of loads according to the present invention;

FIG. 4 is a comparison of the operational risk indicators of the present invention;

FIG. 5 is a graph of the distribution network node voltage out-of-limit operational risk of the present invention;

FIG. 6 is a time sequence security economic risk distribution diagram for a power distribution network of the present invention;

FIG. 7 is a graph of risk assessment values for different electric vehicle capacities according to the present invention.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below. The solution of some embodiments of the invention is described below with reference to fig. 1-7.

Example 1

The invention relates to a comprehensive risk evaluation system of a power grid considering new energy and electric automobile access, in particular to a comprehensive risk evaluation method of grid-connected operation of an electric automobile of an active power distribution network based on Principal Component Analysis (PCA), which comprises the following steps:

step 1, constructing a wind-solar output and electric automobile time sequence probability distribution model, and replacing the subjectively set initial charge state of various electric automobile models with the daily driving mileage.

The invention analyzes the random characteristics of the new energy output and the charging load of the electric automobile and constructs a constant-voltage-constant-current variable-power multi-type electric automobile charging load time sequence model based on daily driving mileage.

the invention provides time sequence short-term safety indexes such as voltage out-of-limit risks, branch power overload operation risks and the like based on a complex network theory, and introduces economic risks including distributed power supply operation benefits, long-term safety risks and power grid high efficiency indexes.

The method constructs the grid-connected operation risk evaluation matrix of the electric automobile with different capacities, uses a Principal Component Analysis (PCA) to reduce the dimension risk matrix and calculate objective weight coefficients, and carries out comprehensive risk evaluation sequencing on the results.

As shown in fig. 1, fig. 1 is a flow chart of the operational risk calculation of the present invention.

The method for constructing the wind-solar power output and electric automobile time sequence probability distribution model in the step 1 specifically comprises the following steps:

(1) wind power, photovoltaic and conventional load models.

The wind power output is mainly determined by the wind speed, wherein the statistical characteristic of the wind speed follows the double-parameter Weibull distribution. P_rRated output power v of the fan_co、v_ciAnd v_crCut-out, cut-in and rated wind speed, k, respectively_wAnd c_wRespectively representing the scale and shape parameters. Thus P_wThe distribution function expression of the active power of the fan is

The solar illumination intensity is different due to different geographic environments and positions, the Beta distribution can be used for representing the solar illumination intensity distribution in one day through measurement of a large amount of data, and then the probability density function of the active power output by the photovoltaic power generation is

Wherein Γ (g) is a Gamma function; alpha and beta are two parameters of Bate distribution, and represent the shape of the function; p_solarAnd P_solar _maxActual output and maximum output of the photovoltaic cell array are respectively obtained; r represents the radiance of the sun; eta and A are the electric energy conversion efficiency and the total area of the battery array respectively;

(2) An EV probability model.

The initial charging time and the daily driving mileage are closely related to the charging period of the EV, so that the data of the invention are obtained from NHTS data of 2017 and 2018 published by the Federal Highway administration in the whole network, and the daily driving mileage d of the EV meets the normal distribution shown in the formula (6) based on Monte Carlo simulation MCS (Monte Carlo simulation).

wherein d is the daily driving mileage, mu_dAnd σ_dThe mean and standard deviation of the daily driving range distribution characteristics are respectively.

The charging time is calculated according to the daily driving mileage in the formula (6), and is shown in the formula (8). The daily driving mileage can obtain the probability distribution of the daily driving mileage through scientifically and accurately fitting the data of the driving characteristics of the automobile, and is more objective and reasonable.

Wherein, W₁₀₀The unit of the power consumption for EV driving is (kWgh)/hundred kilometers; eta_carEV charging efficiency; p_car,jIs the active power of the jth car, and d is the daily driving range.

The main charging modes of the EV on the market at present include 3 charging modes of slow charging, conventional charging and fast charging, wherein the charging modes are distinguished by different given constant current sizes. The present invention selects a charging mode that is in accordance with the actual driving characteristics of different types of EVs.

The driving range of the fully charged private car battery is far larger than the average daily driving range, so that the daily driving requirement of the private car can be met by charging the battery once a day. The charging places of the private cars can be 09:00-12:00 in the morning and 14:00-17:00 in the afternoon in parking lots of work units and 19: 00-07: 00 in the residential areas in the evening, and the charging probabilities of the three are respectively assumed to be 0.2, 0.1 and 0.7. If the charging is carried out in the parking lot of a unit, the charging time does not exceed 3h, so that a quick charging mode with a large constant current is selected, and if the charging is carried out in the parking lot of a residential area, the charging can be continued all night, so that a conventional charging mode with a moderate constant current is selected. The bus owner is mainly used for daily business trip of a government organization, long-distance trip is not considered, the driving characteristics of the bus are similar to those of a private car, the charging requirement can be met by charging once a day, the charging time is 19: 00-07: 00 of the next day, and a conventional charging mode with moderate constant current is selected.

The bus and the taxi are charged only once in one day, so that the actual working operation requirement is difficult to meet, and a two-day charging mode is generally adopted. The bus has the operation time of 06:00-22:00, the route is fixed, the bus can be charged in a centralized manner, the charging is not arranged in the daytime operation peak period, the bus is charged in a rapid charging mode with a large constant current in the afternoon rest period of 10:00-16:30 shift at noon, and the bus is charged in a normal charging mode with a moderate constant current in the period of 23: 00-05: 30 next day at night. Because the rest time of the taxi is limited, but the electric quantity needs to be supplemented in time, the taxi selects a fast charging mode with a large constant current in two time periods of 02:00-05:00 and 11:30-14: 30.

The method for establishing the short-term safety risk index based on the complex network theory in the step 2 and establishing the economic risk, the long-term safety risk and the power grid high-efficiency risk index according to the economic operation of the power distribution network specifically comprises the following steps:

and establishing safety and economic risk indexes of the new energy and the electric automobile connected to the power grid. The method comprises the following steps:

the grid is taken as a complex system, grid nodes are not connected in an isolated manner, but are an integral body which is mutually restricted and influenced, wherein the fragility of each element is closely related to the structural position in the grid and is also related to the influence of the grid on other element nodes during operation. Therefore, when the risk of accessing the DG and the EV into the power grid is evaluated, the influence of various factors needs to be comprehensively considered, and therefore the invention provides a power grid short-term risk evaluation model combining network structure vulnerability and a risk theory. The node importance comprehensively considers the node degree, the betweenness and the proportion of the conventional load connected with the node, and the branch importance is measured by the line degree and the betweenness:

ρ_v,i＝α₁D_v,j+α₂B_v,j+α₃N_Pj (9)

ρ_l,i＝β₁D_l,j+β₂B_l,j (10)

wherein D is_v,jAnd B_v,jRespectively, node degree and betweenness, N_PjRepresenting node injected power; d_l,jAnd B_l,jRespectively, the number of lines and the number of medians; rho_v,iAnd ρ_l,iRespectively the node importance of the node i and the branch importance of the branch i; alpha is alpha₁、α₂And alpha₃Is a weight coefficient, α₁+α₂+α₃＝1；β₁And beta₂Is a weight coefficient, beta₁+β ₂1 is ═ 1; the weight value of the invention is determined by an Analytic Hierarchy Process (AHP);

secondly, the EV charging load can bring short-term safety risk to the power grid, and influence indexes

Risk of voltage out-of-limit and

the calculation method of the branch out-of-limit risk is as follows:

step 1) node voltage out-of-limit operation risk

wherein, V_maxAnd V_minThe voltage qualified range and the per unit values of the upper limit and the lower limit of the voltage qualified range are respectively.

Branch power overload operation risk

wherein L is_iIs the ratio of the actual active power of the line to the rated active power.

In the invention, R is_SRICharacterizing the short-term comprehensive safety risk coefficient of system operation to

Wherein, γ₁And gamma₂For the safety risk weight coefficient, γ₁+γ₂＝1。

The economic risk index ERI (economic risk indicators) of the access of DG and EV charging loads to a power distribution network consists of two parts: line loss risk ELLR (ecological line-loss risk) and business profit risk EPLR (ecological operational benefit or loss risk) are expressed as:

wherein the content of the first and second substances,

the network loss power of the power distribution network in the time period t;

and

respectively representing the operation and maintenance costs of the ith 'DG at time t and the electricity selling price of the ith' DG at time t.

The calculation formula is as follows:

wherein the content of the first and second substances,

time-varying electricity price of DG unit power

the grid Load variance GLV (grid Load variance) influences the loss inside the power grid, and the Load variance calculation method is as follows. Under a certain number of EVs, the power grid load variance expression is as follows:

wherein the content of the first and second substances,

normal load for the t-th time period;

the charging and discharging power of the nth EV in the t-th time period; p_AVGThe average value of the total load of the power grid in one day in the power grid is obtained.

The EV charging influences the efficiency of a power grid, and an influence index is the average load rate, and the calculation method is as follows. The average load rate is calculated by dividing the average load used in a period of time by the system load capacity of the distribution network, and the system load capacity of the distribution network is calculated as the system access maximum load capacity when the voltage is maximum and lower. The invention takes 24h to calculate, and the average load rate is as follows:

P％＝ω/P_e (24)

The invention establishes a three-dimensional multi-angle risk index system in step 3, and comprehensively evaluates the operation risk of the power distribution network under different electric automobile capacities. The method comprises the following steps:

in order to fully account for EV charging time and charging site uncertainty and its impact on the power distribution system, an electric vehicle capacity, evc, (electric vehicle capacity) is defined as the sum of all EV rated charging powers in a charged state and a non-charged state within an area. According to the risk indexes mentioned above, in order to ensure safe and stable operation of DGs and EVs in the power distribution network and establish a comprehensive risk assessment system for new energy and electric vehicles accessing the power distribution network, PCA is adopted to analyze risks caused by EVCs with different sizes to the power distribution network, a small number of risk variables are used for replacing a large number of original risk variables, and all contents of original input risk variables can be contained.

By x₁,x₂,…,x_pRepresenting p risk indicators, c₁,c₂,…,c_pIs the weight of each index. Weighted by itAnd is s ═ c₁x₁+c₂x₂+L+c_px_pThe different EVCs accessed to the power grid correspond to a comprehensive evaluation result which is recorded as s₁,s₂,…,s_hAnd h is the number of different EVCs. Then the PCA application procedure is as follows:

firstly, standardizing the risk index data calculated in the previous section to eliminate dimension to obtain standardized data:

B＝[b_i″(m″)]_h×k＝[B₁,B₂,…,Β_k]

wherein the content of the first and second substances,

is taken as the mean value of the average value,

s_i″the standard deviation of the index is calculated as the index,

the matrix B satisfies E (B) after normalization_i″) 0 and D (B)_i″)＝1(i″＝1,2,…,k)。b_i″Indicating the i' "node normalization and m" indicating the number of EVCs of that class.

Y_i″′＝(B_i″′)^Tu_i″′ (26)

wherein, i 'is 1,2, L p, i' th main component Y_i″′The variance of (a) is the specific gravity v of all the variances_i″′I.e. the contribution rate, to reflect how large the original p indexes have the comprehensive ability. The cumulative contribution rate γ represents how large the first k principal components share the overall capacity. V is_i″′And γ is:

F＝ν₁Y₁+ν₂Y₂+L+ν_m″Y_m″ (29)

The PCA effectively reduces the correlation influence and data dimension among evaluation indexes on the premise of keeping the main information of the original data, so that the obtained evaluation is more reliable. Meanwhile, the comprehensive evaluation takes the contribution rate of each principal component as the weight, so that the defect of subjective weighting is avoided, and the information value contained in the risk index can be fully reflected.

Example 2

The invention relates to a power grid comprehensive risk assessment system considering new energy and electric automobile access, and in specific implementation, as shown in fig. 2, fig. 2 is an improved IEEE33 node system diagram of the invention, 18 points are equivalent to wind power, 33 points are equivalent to photovoltaic, and a simulation model is built by taking an IEEE33 node as an example, so as to verify the effect of the risk assessment system, and the method specifically comprises the following steps:

(1) and analyzing the EV charging load.

Charging power expected values of four EV types are obtained based on MCS, as shown in FIG. 3, FIG. 3 is a drawing of various types of load average power of the present invention. The results of fig. 3 show that a double-peak load state is formed due to the fact that private cars are charged in a large constant-current quick charging mode in 9:00-12:00 and 14:00-17:00, and the private cars are charged normally in the period from 19:00 to 7:00 the next day, but a load peak is caused due to a large amount of accesses, wherein the load of the private cars and the load of the normal cars reach the peak value to aggravate the operation risk of a power grid, and the charging load of the private cars is reduced due to the fact that most of batteries of the private cars of 0: 00-24: 00-7:00 are nearly saturated; conventional charging of a utility vehicle creates a unimodal load during 19:00-24:00, somewhat exacerbating the total load over that time period. In the period of 13:00-16:00, the load reaches the peak value in the daytime by the quick charging mode of the bus, and the load climbs at night and brings impact on the operation risk of the power distribution network by the conventional charging of the bus from 23:00 to the next day of 1:00, but the load and other types of EV charging loads form mutual complementation, and the load peak-valley difference is reduced to a certain extent; during the period from 3:00 to 5:00, the taxi fast charging with a large constant current occupies the dominant position of the EV charging load, and during the period from 12:00 to 14:00, the peak value of the charging load in the daytime is aggravated by the fast charging of the taxi, but the fluctuation of the load is reduced to a certain extent. It follows that the fluctuations in the charging load are severe during the day and therefore a grid operation risk analysis is necessary.

(2) And (4) analyzing safety and economic risk indexes.

To investigate the rationality and necessity of the short-term safety risk indicator, the present invention first evaluates the voltages of nodes 1-18 for a period of 20:00 to 21:00 with a deterministic evaluation compared to the proposed short-term safety risk indicator, i.e. equivalent access to 13MW EVC charging load at node 8, with the results shown in fig. 4. The randomness of DG output power, EV charge power, and regular load is ignored in the deterministic evaluation. And calculating the node voltage by adopting the average equivalent access node power. As shown in fig. 4, fig. 4 is a comparison graph of the operational risk indicators of the present invention. As seen in fig. 4(a), nodes 9-18 are nodes whose voltages exceed 0.93p.u., in other words, only 10 nodes have voltage violations under deterministic evaluation. However, as shown in FIG. 4(b), according to the risk indicator proposed in the present invention, the nodes 6-18 all have the voltage threshold risk, i.e., most of the nodes have the voltage threshold probability. Since deterministic evaluation ignores "probability" and "uncertainty", the evaluation result cannot reflect the actual operating state.

Because the short-term safety risks of the DG and the EV in the power distribution network have difference under different time sequences, the node voltage out-of-limit operation risk of each time is obtained by considering the time sequence on the basis of fig. 3, as shown in fig. 5, fig. 5 is a node voltage out-of-limit operation risk graph of the power distribution network. As can be seen from fig. 5, the voltage violations in the spatial dimension are mainly concentrated on the nodes 12-18 and the nodes 29-33 and tend to escalate, which is caused by the

nodes

18 and 33 being at the end of the power distribution network system and being at a short electrical distance from the DG or EV. The time sequence change also has great influence on the electric energy quality of the nodes 12-18 and the nodes 29-33, and the time dimension shows that the voltage of the nodes from 8:00 to 16:00 is out of limit because the EV charging load is small and the DG output power is overlarge; the mass taxi fast charge results in a charge ratio of 3: a certain voltage out-of-limit occurs at 00-5: 00.

In addition, table 1 shows the branches that have a risk of tidal current violation during a day. During the period from 20:00 to 21:00, branch risk is mainly concentrated at the head end of the distribution network, the maximum branch off-limit risk occurs in the branch 1-2, and the period is also the highest point of superposition of EV charging load and conventional load, so that the branch load flow off-limit risk is maximum.

The economic risk of the power distribution network for one day is obtained through time sequence economic risk assessment, as shown in fig. 6, and fig. 6 is a time sequence safety economic risk distribution diagram of the power distribution network. At 18:00 to 7: time period of 00C_ERIPositive, the maximum occurs at 20:00 to 21: the 00 time period. The most dangerous period is 19:00 to 22:00, during this period, both economic and safety risks are high due to the maximum achieved by the superposition of the regular and EV loads. Meanwhile, the operating state of the power distribution network can be divided into four types:

class 1: from 0:00 to 7:00, R_SRIIs almost zero, and C_ERIThe value of (b) is positive. This represents an economic loss due to insufficient DG output power and increased network losses, which is safe but uneconomical.

Class 2: from 7:00 to 9:00, R_SRIIs zero and C_ERIThe value of (d) is negative. This is because it is now negativeThe demand fluctuations of the vehicle and EV charging load are not large, and the DG can also profit therefrom, and this operating state is both safe and economical.

Class 3: from 9:00 to 18:00, C_ERIIs negative and R_SRIThe value of (b) is positive. Indicating that the operating conditions are economical but not safe, measures should be taken to reduce R_SRIFor example, the output power of the DG is reduced or the charging power of the EV station is increased.

Class 4: from 18:00 to 24:00, C_ERIAnd R_SRIThe values of (A) are all positive. The method shows that the running state of the power distribution network is neither safe nor economical due to huge load demand and fluctuation of wind power photovoltaic output, so measures should be taken to improve the running quality of the actual power distribution network.

(3) Comprehensive risk assessment analysis

Selecting 21: the risk value at time 00, EVC, increases from 8MW to 14MW at 0.5MW intervals, resulting in 13 sets of EV access values. A6 x 13 risk matrix is constructed according to the risk indicator definition in section 3. The risk matrix was processed by the Z-Score method to obtain the risk values shown in Table 2. The KMO test is 0.871 and the approximate chi-square test of the Ballitt sphericity is 177 according to simulation of SPSS software, and the results prove that strong correlation exists between risk indexes and factor analysis can be performed.

Reducing the dimension of the risk matrix through factor analysis in PCA to obtain a risk index and y₁The correlation matrix of (a) is shown in table 3. As can be seen from Table 3, all risk indicators have a high correlation with the first principal component, i.e., y₁99.2% of load variance index information, 97.2% of power grid high-efficiency performance index information, 92.4% of ELLR information, 91.5% of EPLR information, 98.6% of branch overload operation risk information and 99.2% of voltage out-of-limit risk information are respectively reflected in the method. The cumulative variance contribution 96.347% is obtained according to equation (28), so y is used₁ Original 6 risk indexes are represented, and the purpose of reducing the dimension of the original risk indexes is achieved.

The score value of each risk index can be obtained by formula calculation, and the result is shown in table 4. The important correlation degree of each index can be known from the table 4, wherein the indexes of voltage out-of-limit, branch overload risk and power grid high efficiency have the largest weight, and the indexes are also important bases for the transformation of the next stage of the power distribution network.

In order to better reflect the superiority of the PCA comprehensive risk analysis method, the method is compared and analyzed with a comprehensive risk based on the weighted entropy and a traditional voltage and current out-of-limit comprehensive risk evaluation method.

When the DG and the EV are simultaneously connected to the power distribution network, the comprehensive risk assessment values of the EVCs with different sizes are shown in FIG. 5. As shown in fig. 7, fig. 7 is a risk assessment value chart of different capacities of the electric vehicle according to the present invention. The results of fig. 7 show that as the EVC increases, the value of the safety comprehensive risk is considered singly, but it can be found that by using the comprehensive risk evaluation method of the present invention, the comprehensive risk value of the distribution network increases gradually as the number of the access EVs increases, but when the EVC is within the interval of 12MW, the comprehensive risk value of the distribution network operation decreases, which has a relieving effect on the distribution network operation. Therefore, the running risk state of the power distribution network can be better grasped under the condition that the risk factors such as safety, economy and the like are considered simultaneously, and the active guiding effect on the number of the EVs accessing the power distribution network can be well achieved.

Example 3

Based on the same inventive concept, the embodiment of the present invention further provides a computer storage medium, where a computer program is stored on the computer storage medium, and when the computer program is executed by a processor, the steps of the power grid comprehensive risk assessment system considering new energy and electric vehicle access described in embodiment 1 are implemented.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

TABLE 1 tidal current Risk Table of partial branches

TABLE 2 risk index standardization processing table under each electric automobile capacity

TABLE 3 factor load matrix table

TABLE 4 component score coefficients

Index (I)	Score of	Index (I)	Score of	Index (I)	Score of
						Risk of voltage out-of-limit	0.172	ELLR	0.166	Long term security risk	0.172
Branch overload risk	0.172	EPLR	0.165	High efficiency of the grid	0.171

Claims

1. Consider new forms of energy and electric automobile access's electric wire netting comprehensive risk assessment system, characterized by: the method comprises the following steps:

2. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 1, characterized in that: the step 1 of constructing the wind-solar power output and electric automobile time sequence probability distribution model comprises the following steps:

(1) wind power, photovoltaic and conventional load models;

(2) an EV probability model.

3. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 2, characterized in that: the wind power, photovoltaic and conventional load model comprises:

wherein Γ (g) is a Gamma function; alpha and beta are two parameters of Bate distribution, and represent the shape of the function; p_solarAnd P_solarmaxActual output and maximum output of the photovoltaic cell array are respectively obtained; r represents the radiance of the sun; eta and A are the electric energy conversion efficiency and the total area of the battery array respectively;

wherein, mu_LP,tAnd mu_LQ,tRespectively representing the expected values of the active power and the reactive power of the conventional load at the moment t; lambda [ alpha ]_LP,tAnd λ_LQ,tRespectively representing the variation coefficients of the active power and the reactive power of the conventional load at the moment t;

the EV probability model comprises the following steps:

4. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 1, characterized in that: step 2, providing short-term safety risk indexes based on a complex network theory, and establishing economic risk, long-term safety risk and power grid high-efficiency risk indexes according to the economic operation of the power distribution network; the method comprises the following steps: establishing safety and economic risk indexes of new energy and electric vehicle access power grid:

ρ_v,i＝α₁D_v,j+α₂B_v,j+α₃N_Pj (9)

ρ_l,i＝β₁D_l,j+β₂B_l,j (10)

wherein D is_v,jAnd B_v,jRespectively, node degree and betweenness, N_PjRepresenting node injected power; d_l,jAnd B_l,jRespectively, the number of lines and the number of medians; rho_v,iAnd ρ_l,iRespectively the node importance of the node i and the branch importance of the branch i; alpha is alpha₁、α₂And alpha₃Is a weight coefficient, α₁+α₂+α₃＝1；β₁And beta₂Is a weight coefficient, beta₁+β₂1 is ═ 1; the weight value of the invention is determined by an analytic hierarchy process;

Risk of voltage out-of-limit and

the calculation method of the branch out-of-limit risk is as follows:

step 1), node voltage out-of-limit operation risk:

wherein, V_maxAnd V_minThe voltage qualified range and the per unit values of the upper limit and the lower limit of the voltage qualified range are respectively;

node voltage greater than V_maxLess than V_minThe range is the allowed fluctuation range, no voltage problem causes the loss of the power distribution network, and therefore the severity is 0; when the node voltage is less than V_minOr greater than V_maxIn time, the risk is considered to be brought to the power distribution network, and the severity is higher if the deviation from the reasonable range is larger;

branch power overload operation risk:

Wherein, γ₁And gamma₂Weighting factor for security risk，γ₁+γ₂＝1；

wherein the content of the first and second substances,

the price is the electricity price of the power distribution network in the time period t; p_t ^lossThe network loss power of the power distribution network in the time period t;

and

the calculation formula is as follows:

wherein the content of the first and second substances,

time-varying electricity price of DG unit power

wherein the content of the first and second substances,

normal load for the t-th time period;

P％＝ω/P_e (24)

5. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 1, characterized in that: 3, establishing a risk index system of three-dimensional multi-angle, and applying comprehensive evaluation on the operation risk of the power distribution network under different electric automobile capacities, wherein the risk index system comprises the following steps:

defining the capacity of the electric automobile as the sum of all EV rated charging powers in a charging state and a non-charging state in an area, ensuring safe and stable operation of DGs and EVs in a power distribution network according to risk indexes, establishing a comprehensive risk assessment system for new energy and electric automobiles to be accessed into the power distribution network, analyzing risks caused by EVCs with different sizes to the power distribution network by adopting PCA, replacing a large number of original risk variables with a small number of risk variables, and containing all contents of the original input risk variables;

by x₁,x₂,…,x_pRepresenting p risk indicators, c₁,c₂,…,c_pIs the weight of each index; the weighted sum of which is s ═ c₁x₁+c₂x₂+L+c_px_pThe different EVCs accessed to the power grid correspond to a comprehensive evaluationThe valence result, marked as s₁,s₂,…,s_hH is the number of different EVCs; then the PCA application procedure is as follows:

B＝[b_i″(m″)]_h×k＝[B₁,B₂,...,Β_k]；

wherein the content of the first and second substances,

is taken as the mean value of the average value,

s_i″the standard deviation of the index is calculated as the index,

the matrix B satisfies E (B) after normalization_i″) 0 and D (B)_i″)＝1(i″＝1,2,...,k)；b_i″Indicates the i' "node normalization, m" indicates the number of EVCs of that class;

Y_i″′＝(B_i″′)^Tu_i″′ (26)

wherein, i 'is 1,2, L p, i' th main component Y_i″′The variance of (a) is the specific gravity v of all the variances_i″′Namely the contribution rate, which is used for reflecting how large the original p indexes have comprehensive capacity and accumulating the tributesThe contribution ratio gamma represents the total ability of the first k main components, v_i″′And γ is:

F＝ν₁Y₁+ν₂Y₂+L+ν_m″Y_m″ (29)。

6. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 1, characterized in that: the step 1 of constructing the wind-solar output and electric vehicle time sequence probability distribution model is to build a simulation model in an IEEE33 node, set an IEEE33 system diagram, equate 18 points to wind power, equate 33 points to photovoltaic, and build the simulation model for verifying the effect of a risk assessment system, and the method comprises the following steps:

(1) analyzing EV charging load;

(2) analyzing safety and economic risk indexes;

(3) and (4) comprehensive risk assessment analysis.

7. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 6, characterized in that: the EV charging load analysis comprises the following steps:

the expected charging power values of four EV types are obtained based on MCS:

8. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 6, characterized in that: the safety and economic risk indicator analysis comprises:

due to the fact that short-term safety risks of the DG and the EV which are connected into the power distribution network are different under different time sequences, the node voltage out-of-limit operation risks of all times are obtained by considering the time sequences, voltage out-of-limit operation risks are mainly concentrated on the nodes 12-18 and the nodes 29-33 in the spatial dimension and gradually rise, and the nodes 18 and 33 are located at the tail end of the power distribution network system and are caused by the fact that the electrical distance between the nodes 18 and 33 and the DG or the EV is short; the time sequence change also has great influence on the electric energy quality of the nodes 12-18 and the nodes 29-33, and the time dimension shows that the voltage of the nodes from 8:00 to 16:00 is out of limit because the EV charging load is small and the DG output power is overlarge; the mass taxi fast charge results in a charge ratio of 3: certain voltage out-of-limit occurs at 00-5: 00;

obtaining the one-day economic risk of the power distribution network through time sequence economic risk assessment; at 18:00 to 7: time period of 00C_ERIPositive, the maximum occurs at 20:00 to 21: a time period of 00; the most dangerous period is 19:00 to 22:00, during which the economic and safety risks are high, due to the maximum value reached by the superposition of the conventional load and the EV load;

class 3: from 9:00 to 18:00, C_ERIIs negative and R_SRIThe value of (A) is positive; indicating that the operating conditions are economical but not safe, measures should be taken to reduce R_SRIReducing or increasing DG output powerCharging power of the EV station;

9. The comprehensive risk assessment system for the power grid considering the new energy and the electric automobile access according to claim 1, characterized in that: the integrated risk assessment analysis includes:

reducing the dimension of the risk matrix through factor analysis in PCA to obtain a risk index and y₁All risk indicators have higher correlation with the first principal component, namely y₁99.2% of load variance index information, 97.2% of power grid high-efficiency performance index information, 92.4% of ELLR information, 91.5% of EPLR information, 98.6% of branch overload operation risk information and 99.2% of voltage out-of-limit risk information are respectively reflected in the data; the cumulative variance contribution 96.347% is obtained according to equation (28), so y is used₁Original 6 risk indexes are represented, and the purpose of reducing the dimension of the original risk indexes is achieved;

10. A computer storage medium, characterized by: the computer storage medium having stored thereon a computer program that, when executed by a processor, performs the steps of a method for quantifying non-residential building demand flexibility based on electrical thermal storage and comfort management of claims 1-9.