An evaluation method for the imbalance degree between power generation and load in a regional power grid

Abstract

The present invention discloses an evaluation method for the source-load imbalance degree of a regional power grid, including: 1. Constructing a probability scenario set reflecting the random characteristics of wind power, photovoltaic power output, and load changes; 2. Enumerating the operating states of a multi-region interconnected power grid; 3. Calculating the power flow to determine whether there are any violations in the overall power grid. If there are violations, step 4 is executed; otherwise, step 5 is executed; 4. Establishing two optimization models for power adjustment and solving them using the particle swarm optimization algorithm; 5. Calculating the probability of the current state and the source-load imbalance degree of the regional power grid; 6. Whether all the operating states have been enumerated. If so, step 7 is executed; otherwise, return to step 2; 7. Calculating the source-load imbalance degree of a single regional power grid using the probability weighting method; 8. Analyzing the source-load balance ability of a single regional power grid. The present invention takes into account the uncertainties of the source, grid, and load, evaluates the source-load imbalance degree of a single region in a multi-region interconnected power grid, and can provide a basis for power grid operation and planning.

Description

Technical Field

[0001] The present invention relates to the field of safe operation of power systems, and particularly relates to a method for evaluating the imbalance degree of source and load in a regional power grid. Background Art

[0002] New energy power generation such as wind power and photovoltaic power is an effective way to alleviate the fossil energy crisis. To improve the new energy consumption capacity, cross-regional interconnection can make full use of regional complementary characteristics, optimize resource allocation, and achieve the maximum utilization of new energy. However, environmental factors such as wind speed and light intensity change randomly, resulting in random fluctuations in the output of wind power and photovoltaic power generation, with poor matching with the load, thus having a great impact on the safe operation of the power grid. In the scenario of new energy grid connection, quantitatively evaluating the active power balance ability between the source and load in the regional power grid can provide a basis for power grid operation and planning.

[0003] Currently, the research on the source and load balance ability of the power grid mostly focuses on the overall network power balance and the tie-line exchange power, ignoring the active power balance ability of the regional power grid. If a certain regional power grid overly relies on the power exchange with other power grids, the failure of the external power grid or tie-line will have a greater impact on the safety of this power grid. Therefore, it is necessary to evaluate the active power imbalance degree of the regional power grid. Summary of the Invention

[0004] The present invention is to solve the above-mentioned deficiencies existing in the prior art, and proposes a method for evaluating the imbalance degree of source and load in a regional power grid, in order to be able to evaluate and analyze the source and load balance ability of a single region in a multi-region interconnected power grid, so as to provide a basis for power grid planning and safe operation.

[0005] The present invention solves the technical problems by adopting the following technical solutions:

[0006] A method for evaluating the imbalance degree of source and load in a regional power grid according to the present invention is applied to a multi-region interconnected power grid formed by the exchange of power through tie-lines among various regional power grids. The multi-region interconnected power grid includes power generation equipment, transmission line equipment, and power consumption equipment. The power generation equipment includes: conventional generators, wind turbines, and photovoltaic power generation equipment. Its characteristics are that the evaluation method is carried out according to the following steps:

[0007] Step 1: Construct a probability scenario set reflecting the random characteristics of wind power and photovoltaic power output and load changes;

[0008] Obtain the historical data of wind speed and light intensity from the regional power grid, and respectively fit the distribution of wind speed and light intensity, so as to obtain the wind speed probability density function and the light intensity probability density function;

[0009] Use the K-means clustering method to divide the historical data of wind speed and light intensity respectively, and correspondingly obtain n wind wind speed scenarios and npv a light intensity scenario;

[0010] Calculate the probability of each wind speed scenario using the wind speed probability density function; then calculate the active power output of the wind power under each wind speed according to the output formula, thereby constituting n wind wind turbine output scenarios;

[0011] Calculate the probability of each light intensity scenario using the light intensity probability density function; then calculate the active power output of the photovoltaic under each light intensity according to the output formula, thereby constituting n pv photovoltaic output scenarios;

[0012] Obtain the historical data of the load from the multi - area interconnected power grid, and use the K - means clustering method to divide the load data into n load load scenarios;

[0013] Step 2: Randomly combine the wind turbine output scenarios, photovoltaic output scenarios, and load scenarios, and set one AC device in the multi - area interconnected power grid to fail one by one. Among them, the AC devices include N G conventional generators and N line transmission lines, thereby obtaining each operating state of the multi - area interconnected power grid; denote the i - th operating state as y i ; and initialize the variable i = 1;

[0014] Step 3: Perform a power flow calculation on the multi - area interconnected power grid in the i - th operating state y i , and determine whether there is a situation of line overload, or node voltage violation, or conventional generator output power violation. If so, execute Step 4; otherwise, execute Step 5;

[0015] Step 4: Use two optimization models to adjust the power of the multi - area interconnected power grid:

[0016] Establish a first optimization model with the minimum load shedding power as the objective function; establish a second optimization model with the comprehensive minimum of the tie - line exchange power and the load shedding power as the objective function; and use the particle swarm algorithm to solve the two optimization models respectively, and obtain the minimum load shedding power ΔP L1 (y i ), ΔP L2 (y i );

[0017] Step 5: Calculate the probability p(y i ) of the i - th operating state y i in the multi - area interconnected power grid and the source - load imbalance degree δ 1_Ω (y i ) of a single regional power grid Ω after load shedding for the power system using the two optimization models respectively, δ2_Ω (y i );

[0018] Step 6. Determine whether i = n wind ×n pv ×n load ×(N G +N line ) holds. If it holds, execute Step 7; otherwise, assign i + 1 to i and return to Step 2;

[0019] Step 7. Use the probability weighted summation method to calculate the comprehensive source-load imbalance degree of a single regional power grid Ω under two optimization models

[0020] Step 8. Judge the source-load balance ability of a single regional power grid Ω by the degree of closeness to 0;

[0021] When Adopt the second optimization model to adjust the power of the multi-regional interconnected power grid to control the imbalance degree of the regional source-load within a single region.

[0022] The characteristics of the evaluation method for the regional power grid imbalance degree of the present invention also lie in that the two optimization models in Step 4 are established according to the following process:

[0023] Step 4.1. Use Equation (1) to construct the first objective function f1:

[0024]

[0025] In Equation (1), N node represents the total number of nodes in the multi-regional interconnected power grid; ΔP Lj represents the load shedding amount of the jth node; ε Pj is the weight factor of the shortest distance from the jth node to the enumerated faulty device, obtained from Equation (2):

[0026]

[0027] In Equation (2), B represents a constant greater than 1; φ j represents the length of the shortest path from the jth node to the enumerated faulty device;

[0028] Step 4.2. Use Equation (3) to construct the second objective function f2:

[0029]

[0030] In Equation (3), N tie_line represents the number of tie lines between regional power grids; P lqIt represents the exchange power on the qth tie line; ρ1 and ρ2 are the weighting factors of the tie line exchange power and the load shedding amount respectively;

[0031] Step 4.3: Construct the node power balance constraint, the output constraint of the conventional generator, the node voltage constraint, the line load constraint, and use Equation (4) to construct the load shedding power constraint, so as to be used as the constraint conditions for the first objective function f1 and the second objective function f2, so as to form two optimization models;

[0032] 0 ≤ ΔP Lj ≤ P Lj (4)

[0033] In Equation (4), P Lj represents the load of the jth node before load shedding.

[0034] In the said Step 5, it is set that N W wind turbines in the multi-area interconnected power grid have the same output in the ith operating state y i , and N S photovoltaic devices have the same output in the ith operating state y i , so as to calculate the probability p(y i ) of the multi-area interconnected power grid in the ith operating state y i by using Equation (5):

[0035]

[0036] In Equation (5), p L (y i ) represents the load scenario probability of the multi-area interconnected power grid in the ith operating state y i ; p W (y i ) represents the output scenario probability of the wind turbine in the ith operating state y i ; p S (y i ) represents the output scenario probability of the photovoltaic power generation equipment in the ith operating state y i of the power system; p z (y i ) represents the state probability of the zth AC device in the ith operating state y i , and is obtained by using Equation (6):

[0037]

[0038] In Equation (6), μ z and λ z represent the repair rate and failure rate of the zth AC device respectively.

[0039] In step 5, the source-load imbalance degree δ of a single regional power grid Ω under the w-th optimization model is calculated using Equation (7) when calculating the i-th operating state y i of the regional power grid Ω w_Ω (y i ), where w = 1, 2

[0040]

[0041] In Equation (7), P Gj , P Wj , and P Sj respectively represent the active power provided by the conventional generator, wind turbine generator, and photovoltaic power generation equipment at the j-th node

[0042] In step 7, the comprehensive source-load imbalance degree of a single regional power grid Ω under the w-th optimization model is calculated using Equation (8).

[0043]

[0044] In Equation (8), Φ represents the set of operating states of the interconnected power grid of the entire region

[0045] An electronic device according to the present invention includes a memory and a processor, characterized in that the memory is used to store a program for supporting the processor to execute any one of the evaluation methods, and the processor is configured to execute the program stored in the memory

[0046] A computer-readable storage medium according to the present invention, characterized in that a computer program stored on the computer-readable storage medium is run by a processor to perform the steps of any one of the evaluation methods

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows

[0048] 1. The evaluation method for the imbalance degree of the regional power grid provided by the present invention takes into account the new energy output and the random fluctuation of the load, reduces a large amount of data into a small number of typical scenarios, and at the same time considers the random faults of the power grid, so as to comprehensively consider the uncertainty factors of the source, grid, and load, making the evaluation result closer to the actual operation situation

[0049] 2. After the over-limit state appears in the power flow calculation of the multi-region interconnected power grid, the present invention adds the tie-line exchange power to the objective function during the power optimization process, thereby controlling the source-load imbalance degree of a single regional power grid, which is beneficial to reducing the dependence of the regional power grid on the external power grid and avoiding large-scale faults

[0050] 3. The present invention proposes an unbalance index for regional power grids, which is defined as the absolute value of the ratio of the active power of all loads and the active power output of power sources in a single region to 1. Thus, the source-load balance ability of a single region can be judged through the calculation of the unbalance index, and the power balance level of the regional power grid is quantified. Description of the Drawings

[0051] Figure 1 It is a structural diagram of a regional power grid in an embodiment of the present invention;

[0052] Figure 2 It is a flowchart of the method of the present invention;

[0053] Figure 3 It is a flowchart of step 1 for dividing load scenarios in the present invention;

[0054] Figure 4 It is a flowchart of the establishment and solution of the optimization model in step 4 of the present invention. Detailed Embodiment

[0055] In this embodiment, as Figure 1 shown, the multi-region interconnected power grid is composed of three regional power grids A, B, and C, and power is exchanged between regions through 3 tie lines. The entire interconnected power grid includes N W wind turbines, N S photovoltaic units, N G conventional units, and N line transmission lines.

[0056] For Figure 1 the power grid structure shown, in this embodiment, a method for evaluating the unbalance of a regional power grid is to consider the random fluctuations of wind power, photovoltaic output, and load, take into account the random failures of AC equipment, evaluate the source-load unbalance of the regional power grid, and incorporate the control of the exchanged power between regional power grids into the objective function in the optimization model, so as to control the dependence of the regional power grid on the external power grid. Specifically, as Figure 2 shown, it is carried out according to the following steps:

[0057] Step 1: Construct a probability scenario set reflecting the random characteristics of wind power, photovoltaic output, and load changes;

[0058] Obtain the historical data of wind speed and light intensity from the regional power grid, and respectively fit the distribution of wind speed and light intensity, so as to obtain the wind speed probability density function and the light intensity probability density function;

[0059] Use the K-means clustering method to divide the historical data of wind speed and light intensity respectively, and correspondingly obtain n wind wind speed scenarios and n pv light intensity scenarios;

[0060] Calculate the probability of each wind speed scenario using the wind speed probability density function; then calculate the active power output of the wind power under each wind speed according to the output formula, thus constituting n wind wind turbine output scenarios;

[0061] Calculate the probability of each light intensity scenario using the light intensity probability density function; then calculate the active power output of the photovoltaic under each light intensity according to the output formula, thus constituting n pv photovoltaic output scenarios;

[0062] Obtain the historical data of the load from the multi-region power grid, and use the K-means clustering method to divide the load data into n load load scenarios;

[0063] Step 1.1: Use the probability density function f(v) of the Weibull distribution in Equation (1) to fit the wind speed distribution:

[0064]

[0065] In Equation (1), v represents the wind speed, with the unit of m / s; k represents the shape parameter, and c represents the scale parameter. The shape parameter k and the scale parameter c are obtained using Equations (2) and (3):

[0066]

[0067]

[0068] In Equations (2) and (3), μ v represents the average value of the wind speed; σ v represents the standard deviation of the wind speed; Г is the Gama function;

[0069] Obtain the active power output P W of the wind turbine when the wind speed is v using Equation (4):

[0070]

[0071] In Equation (4), v in represents the cut-in wind speed; v out represents the cut-out wind speed; v rated represents the rated wind speed; P rated represents the rated output power of the wind turbine;

[0072] Divide the wind speed v in the interval v in < v ≤ v rated into n v wind speed scenarios with a step size of h wind The divided wind speed scenarios are For wind speeds in 0 ≤ v ≤ v in and v ≥ v outThe intervals are all incorporated into v1, in v rated <v ≤ v out The interval is incorporated into v nwind . Calculate the probability p of each wind speed scenario according to Equation (1) W , and calculate the active power output P at each wind speed according to Equation (4) W ;

[0073] Step 1.2: Use the probability density function f(r) of the Beta distribution in Equation (5) to fit the light intensity distribution:

[0074]

[0075] In Equation (5), r and r max represent the actual light intensity and the maximum light intensity respectively, with the unit of W / m 2 ; α and β represent the shape parameters, which are obtained using Equations (6) and (7):

[0076]

[0077]

[0078] Obtain the active power output P of the photovoltaic at the light intensity of r using Equation (8) S :

[0079] P S = rA r η (8)

[0080] In Equation (8), A r represents the installation area, with the unit of m 2 ; η represents the conversion efficiency of the photovoltaic module;

[0081] Divide the light intensity r directly into n r light intensity scenarios with a step size of h pv . The divided light scenarios are Calculate the probability p of each light scenario according to Equation (5) S , and calculate the active power output P at each light intensity according to Equation (8) S ;

[0082] Step 1.3: Denote the load at the N node th node of the regional power grid in the t-th hour as Use K-means clustering to cluster the load, and the flow chart is as shown in Figure 3 , and the steps are as follows:

[0083] Step 1.3.1: Obtain the total load L in the t-th hour of the whole year using Equation (9) t . Randomly divide the loads of 8760 hours into n loadThe average value of the k-th type of load in the class is denoted as A k (k = 1, 2,..., n load )

[0084]

[0085] Step 1.3.2, the distance d t from the total load L k at the t-th hour to the average value A t of the k-th type of load is calculated, and the total load L t at the t-th hour is classified into the category closest to A k :

[0086]

[0087] Step 1.3.3, the average value A k of the loads in each category is recalculated using Equation (11);

[0088]

[0089] In Equation (11), e k represents the number of loads classified into the k-th category; Ψ k represents the set of loads in the k-th category;

[0090] Step 1.3.4, Steps 1.3.2 and 1.3.3 are repeated until the average value A k no longer changes;

[0091] Step 1.3.5, the proportion of the load quantity in each category to the total load points is statistically calculated, and thus the probability p L of each load occurrence is obtained;

[0092] Step 2, the fan output scenarios, photovoltaic output scenarios, and load scenarios are randomly combined, and one AC device in the multi-area interconnected power grid is set to fail one by one. The AC devices include N G conventional generators and N line transmission lines, thereby obtaining each operating state of the multi-area interconnected power grid; let the i-th operating state be denoted as y i ; and initialize the variable i = 1;

[0093] Step 3, perform a power flow calculation on the multi-area interconnected power grid in the i-th operating state y i , and determine whether there is a situation of line overload, or node voltage violation, or conventional generator output power violation. If so, execute Step 4; otherwise, execute Step 5;

[0094] Step 4, two optimization models are used to adjust the power of the multi-area interconnected power grid:

[0095] Without controlling the source-load imbalance of a single regional power grid, a first optimization model is established with the minimum load shedding power as the objective function; controlling the source-load balance ability of a single regional power grid, a second optimization model is established with the comprehensive minimum of the tie-line exchange power and the load shedding power as the objective function; and the particle swarm algorithm is used to solve the two optimization models respectively, and the minimum load shedding power ΔP of the power system is obtained accordingly L1 (y i )、ΔP L2 (y i ), and the flow chart is as Figure 4 follows;

[0096] Step 4.1: Use Equation (12) to construct the first objective function f1, where the load shedding amount is weighted by distance. The closer the distance to the set faulty AC equipment, the greater the load shedding; conversely, the smaller the load shedding:

[0097]

[0098] In Equation (12), N node represents the total number of nodes in the multi-region interconnected power grid; ΔP Lj represents the load shedding amount of the jth node; ε Pj is the weight factor of the shortest distance from the jth node to the enumerated faulty equipment, obtained from Equation (13):

[0099]

[0100] In Equation (13), B represents a constant greater than 1; φ j represents the length of the shortest path from the jth node to the enumerated faulty equipment;

[0101] Step 4.2: Use Equation (14) to construct the second objective function f2:

[0102]

[0103] In Equation (14), N tie_line represents the number of tie-lines between regional power grids; P lq represents the exchange power on the qth tie-line; ρ1 and ρ2 are the weighting factors of the tie-line exchange power and the load shedding amount, and are taken as 0.3 and 0.7 respectively according to experience;

[0104] Step 4.3: Construct node power balance constraints, output constraints of conventional generators, node voltage constraints, line load constraints, and use Equation (4) to construct load shedding power constraints, so as to be the constraint conditions of the first objective function f1 and the second objective function f2, thus constituting two optimization models;

[0105] 0 ≤ ΔP Lj ≤ PLj (15)

[0106] In formula (15), P Lj represents the load of the j-th node before load shedding;

[0107] Step 4.4: Use the particle swarm optimization algorithm to solve the two optimization models to obtain the minimum load shedding power ΔP L1 (y i ) and ΔP L2 (y i ), the steps are as follows:

[0108] Step 4.4.1: Set the maximum number of iterations of the particle to T max , in this implementation case, take T max = 200. When the number of loops T = 1, construct a population X = {x1, x2,..., x n} with n particles. Generally, n is 20 - 200. In this implementation case, take n = 40. Each particle contains m-dimensional information, and m is the number of control variables in the optimization model. In this implementation case, m = 2*N G . Initialize the particles within the constraints of Step 4.3. The a-th particle randomly generates a corresponding position x a (t) and velocity v a (t). Substitute the position of the particle into formulas (12) and (14) to calculate the objective function f. At this time, the optimal value f(x a ) of the a-th particle is denoted as P a.best , and the optimal value f among all particles is denoted as G a.best .

[0109] Step 4.4.2: Update the velocity and position of the particle using formulas (16) and (17):

[0110] v a (T + 1) = ωv a (T) + c1o1(T)[P a.best (T) - x a (T)] + c2o2(T)[G a.best (T) - x a (T)] (16)

[0111] x a (T + 1) = x a (T) + v a (T + 1) (17)

[0112] In formulas (16) and (17), c1 and c2 represent learning factors, which are usually 2 according to experience; o1 and o2 represent random numbers between 0 and 1; ω represents the inertia weight, which is obtained by using the linear decreasing weight strategy and formula (18).

[0113]

[0114] In formula (18), ω max and ω min represent the maximum and minimum values of ω respectively, which are generally taken as 0.9 and 0.4 according to experience.

[0115] Step 4.4.3: Select the objective function as the fitness value of each particle, that is, calculate the fitness value using formula (12) or formula (14). Compare the fitness value of each particle with its individual optimal P a.best . If the fitness value is better than P a.best , then the fitness value is used as the new P a.best , otherwise it remains unchanged. Then compare the current fitness value of each particle with the global optimal value G a.best . If the current fitness value is better than G a.best , then update G a.best , otherwise it remains unchanged.

[0116] Step 4.4.4: Judge whether the maximum number of iterations T max is reached. If it is reached, terminate and output the global optimal value and the corresponding particle position information; otherwise return to Step 4.4.2, and T = T + 1;

[0117] Step 5: Calculate the probability p(y i ) of the i-th operating state y of the multi-region interconnected power grid, and the source-load imbalance degrees δ i (y 1_Ω ), δ i (y 2_Ω ) of a single regional power grid Ω after load shedding for the power system using two optimization models respectively; i ;

[0118] Set that the outputs of N W wind turbines in the multi-region interconnected power grid are the same in the i-th operating state y i , and the outputs of N S photovoltaic devices are the same in the i-th operating state y i . Thus, use formula (5) to calculate the probability p(y i ) of the i-th operating state y of the multi-region interconnected power grid: i

[0119]

[0120] ​In Equation (19), p L (y i ) represents the probability of the load scenario when the multi - area interconnected power grid is in the i - th operating state y i ; p W (y i ) represents the probability of the output scenario of the wind turbine when the multi - area interconnected power grid is in the i - th operating state y i ; p S (y i ) represents the probability of the output scenario of the photovoltaic power generation equipment when the power system is in the i - th operating state y i ; p z (y i ) represents the probability of the state of the z - th AC device when the multi - area interconnected power grid is in the i - th operating state y i , and it is obtained by using Equation (20):

[0121]

[0122] In Equation (20), μ z and λ z represent the repair rate and failure rate of the z - th AC device respectively;

[0123] Calculate the source - load imbalance degree δ i of a single - area power grid Ω under the w - th optimization model when the multi - area interconnected power grid is in the i - th operating state y w_Ω (y i ), w = 1, 2:

[0124]

[0125] In Equation (21), P Gj , P Wj , P Sj represent the active power provided by the conventional generator, wind turbine, and photovoltaic power generation equipment at the j - th node respectively;

[0126] Step 6. Judge whether i = n wind ×n pv ×n load ×(N G +N line ) holds. If it holds, execute Step 7; otherwise, assign i + 1 to i and return to Step 2;

[0127] Step 7. Adopt the probability - weighted summation method to calculate the comprehensive source - load imbalance degree of a single - area power grid Ω under the two optimization models

[0128] Calculate the comprehensive source - load imbalance degree of a single - area power grid Ω under the w - th optimization model by using Equation (22)

[0129]

[0130] In formula (22), Φ represents the set of operating states of the fully interconnected power grid.

[0131] Step 8: Analyze the source-load balancing ability of a single regional power grid Ω;

[0132] The source-load balancing ability of a single regional power grid Ω is judged by the degree of closeness to 0: The closer it is to 0, the better the source-load balancing ability within the single regional power grid Ω; The larger it is, the worse the source-load balancing ability of the single regional power grid Ω.

[0133] If the second optimization model is adopted to adjust the power of the multi-regional interconnected power grid to control the unbalance degree of the regional source and load within a single region; otherwise, it means that the second optimization model cannot control the unbalance degree of the single regional power grid.

[0134] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0135] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is run by a processor, it executes the steps of the above method.

Claims

1. A method for evaluating the imbalance degree of power generation and load in a regional power grid, which is applied to a multi-region interconnected power grid formed by the exchange of power between various regional power grids through tie lines. The multi-region interconnected power grid includes power generation equipment, transmission line equipment, and power consumption equipment. The power generation equipment includes: Conventional generators, wind turbines, and photovoltaic power generation equipment; characterized in that the evaluation method is carried out according to the following steps: Step 1: Construct a probability scenario set that reflects the random characteristics of wind power, photovoltaic power output, and load changes; Obtain historical data of wind speed and light intensity from the regional power grid, and respectively fit the distribution of wind speed and light intensity to obtain the wind speed probability density function and the light intensity probability density function; The historical data of wind speed and light intensity are respectively divided by the K-means clustering method, and n wind wind speed scenarios and n pv light intensity scenarios are obtained accordingly; Calculate the probability of each wind speed scenario using the wind speed probability density function; then calculate the active power output of the wind power at each wind speed according to the output formula, thus constituting n wind fan output scenarios; Calculate the probabilities of each light intensity scenario using the probability density function of light intensity; then calculate the active power output of the photovoltaic under each light intensity according to the output formula, thus constituting n pv photovoltaic output scenarios; Obtain historical data of the load from the multi-region interconnected power grid, and use the K-means clustering method to divide the load data into n load load scenarios; Step 2: Randomly combine the fan output scenarios, photovoltaic output scenarios, and load scenarios, and set one AC device in the multi-region interconnected power grid to fail one by one. Among them, the AC devices include N G conventional generators and N line transmission lines, so as to obtain the various operating states of the multi-region interconnected power grid; Denote the i-th operating state as y i ; And initialize the variable i = 1; Step 3: Perform power flow calculation on the multi-region interconnected power grid in the i-th operating state y i , and determine whether there is a situation of line overload, or node voltage violation, or conventional generator output power violation. If so, execute Step 4; otherwise, execute Step 5; Step 4: Use two optimization models to adjust the power of the multi-region interconnected power grid; Establish the first optimization model with the minimum load shedding power as the objective function; establish the second optimization model with the comprehensive minimum of the tie-line exchange power and the load shedding power as the objective function; and use the particle swarm algorithm to solve the two optimization models respectively, and correspondingly obtain the minimum load shedding power ΔP L1 (y i )、ΔP L2 (y i ); Step 5: Calculate the probability \(p(y)\) of the \(i\)-th operating state \(y\) in the multi-region interconnected power grid and the source-load imbalance degrees \(\delta^{(1)}(y)\) and \(\delta^{(2)}(y)\) of a single regional power grid \(\Omega\) after load shedding is carried out on the power system using two optimization models respectively; i of i and the source-load imbalance degrees \(\delta\) of a single regional power grid \(\Omega\) after load shedding is carried out on the power system using two optimization models respectively; 1_Ω (y i ), \(\delta\) 2_Ω (y i ); Step 6, determine whether i = n wind ×n pv ×n load ×(N G +N line ) holds. If it holds, execute Step 7. Otherwise, assign i + 1 to i and return to Step 2; Step 7: Use the probability weighted summation method to calculate the comprehensive source-load imbalance degree of a single regional power grid Ω under the two optimization models Step 8. Determine the source-load balance capacity of a single regional power grid Ω by the degree of proximity to 0; When The second optimization model is used to adjust the power of the multi - area interconnected power grid to achieve the control of the imbalance degree of regional sources and loads within a single area.

2. The evaluation method for the unbalance degree of the regional power grid according to claim 1, characterized in that, The two optimization models in Step 4 are established according to the following process: Step 4.1: Use Equation (1) to construct the first objective function f1: In formula (1), N node represents the total number of nodes in the multi - area interconnected power grid; ΔP Lj represents the load shedding amount of the j - th node; ε Pj is the weight factor of the shortest distance from the j - th node to the enumerated faulty equipment, which is obtained by formula (2): In formula (2), B represents a constant greater than 1; φ j represents the length of the shortest path from the j-th node to the enumerated faulty device; Step 4.2: Use Equation (3) to construct the second objective function f2: In formula (3), N tie_line represents the number of tie lines between regional power grids; P lq represents the exchange power on the q-th tie line; ρ1 and ρ2 are the weighting factors of the tie line exchange power and the load shedding amount respectively; Step 4.3: Construct node power balance constraints, output constraints of conventional generators, node voltage constraints, line load constraints, and use Equation (4) to construct load shedding power constraints, thereby serving as the constraint conditions for the first objective function f1 and the second objective function f2 to form two optimization models; 0 ≤ ΔP Lj ≤ P Lj (4) In formula (4), P Lj represents the load of the j-th node before load shedding.

3. The evaluation method for the unbalance degree of the regional power grid according to claim 1, characterized in that In step 5, it is assumed that the outputs of N wind turbines in the multi - area interconnected power grid are the same in the i - th operating state y W , and the outputs of N photovoltaic devices are the same in the i - th operating state y i . Then, the probability p(y S ) of the multi - area interconnected power grid in the i - th operating state y i is calculated using equation (5): i i )​ In formula (5), p L (y i ) represents the load scenario probability of the multi-region interconnected power grid in the i-th operating state y i ; p W (y i ) represents the output scenario probability of the wind turbine in the i-th operating state y i ; p S (y i ) represents the output scenario probability of the photovoltaic power generation equipment in the i-th operating state y i ; p z (y i ) represents the state probability of the z-th AC device in the i-th operating state y i , and is obtained using formula (6): In Equation (6), μ z , λ z respectively represent the repair rate and failure rate of the z-th AC device.

4. The evaluation method for the unbalance degree of the regional power grid according to claim 1, wherein In the said step 5, the source-load imbalance degree δ of a single regional power grid Ω under the w-th optimization model is calculated using Equation (7) when the i-th operating state is y i (y w_Ω ), where w = 1, 2 i ​ In formula (7), P Gj , P Wj , P Sj respectively represent the active power provided by the conventional generator, wind turbine, and photovoltaic power generation device of the j-th node.

5. The evaluation method for the unbalance degree of the regional power grid according to claim 1, characterized in that In step 7, the comprehensive source-load imbalance degree of a single regional power grid Ω under the w-th optimization model is calculated using Equation (8). In Equation (8), Φ represents the set of operating states of the entire-region interconnected power grid.

6. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor to execute any one of the evaluation methods recited in claims 1-5, and the processor is configured to execute the program stored in the memory.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it executes the steps of any one of the evaluation methods recited in claims 1-5.

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