Power system risk assessment method and device

Abstract

The invention belongs to the technical field of power systems, and particularly discloses a power system risk assessment method and device. According to the method, the load shedding amount of the load in the power system and the wind curtailment amount of the wind power plant are rapidly calculated according to the critical domain where the renewable energy such as different output values of the wind power plant is located and the linear mapping relation between the critical domain and the decision variables in the power system operation model. And calculating a risk assessment index based on the obtained load shedding amount and the wind curtailment amount, and performing risk assessment on the power system according to the risk assessment index. Compared with an existing power system risk assessment method, the method has the advantages that constraint conditions do not need to be redefined, an optimization model does not need to be established, and an optimization problem does not need to be solved, so that the calculation time of power system risk assessment is shortened, the risk assessment efficiency is effectively improved, response measures can be taken in time, and safe and stable operation of a power grid under high-proportion wind power access is guaranteed.

Description

Technical Field

[0001] The present application belongs to the technical field of power systems, and more specifically, to a method and device for risk assessment of power systems. Background Art

[0002] With the large-scale access of renewable energy to the power grid, its uncertainty has brought an impact on the economic and stable operation of the power system, and the challenges faced by power grid risk assessment are becoming increasingly serious. As an indispensable link in the power system, risk assessment can quantify the possible risk accidents, so that the staff can take relevant response plans in time to ensure the reliability and economy of power supply. The construction of new power systems has put forward higher requirements on the efficiency of risk assessment. With the continuous increase in the scale of wind power access to the power grid, the volatility and intermittency of wind power itself have increased the difficulty of power system risk assessment.

[0003] For different output scenarios of renewable energy, the existing risk assessment methods need to redefine constraints, establish optimization models and solve optimization problems when solving power system risk assessment problems. The whole process is time-consuming, which is not conducive to power workers to take timely response measures when risk events occur, nor is it conducive to the early prevention of risk events. Summary of the invention

[0004] In view of the defects of the prior art, the purpose of the present application is to provide a power system risk assessment method and device, aiming to solve the problem of low efficiency of the power system risk assessment method in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present application provides a power system risk assessment method, comprising:

[0006] Determine the load shedding amount in the power system and the wind farm abandonment amount according to the critical region where the wind farm output value in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model;

[0007] Determine the risk assessment index based on the load shedding and wind abandonment;

[0008] Conduct risk assessment on the power system based on risk assessment indicators.

[0009] In some embodiments, a method for obtaining a linear mapping relationship between a critical region where the output value of a wind farm in a power system is located and the output value and a decision variable in a power system operation model includes:

[0010] S1 randomly selects the initial output value of the wind farm from the undivided area in the parameter space;

[0011] S2 obtains the optimal solution of the decision variables in the power system operation model, and the power system operation model is determined according to the operation cost of the power system and the constraints followed by the decision variables in the operation cost;

[0012] S3 optimally divides the constraints according to the optimal solution to obtain valid constraints and invalid constraints;

[0013] S4 obtains the linear mapping relationship between the parameter variables determined by the initial output value and the decision variables and the critical domain where the parameter variables are located according to the valid constraint conditions and the invalid constraint conditions;

[0014] S5 marks the critical region where the parameter variable is located as the parameter space of the divided region, so as to update the undivided region in the parameter space;

[0015] S6 repeats S1 to S5 until all undivided areas in the parameter space are marked as divided areas;

[0016] S7 determines the critical region where the output value is located according to the critical region where the parameter variable determined by each initial output value of the wind farm is located;

[0017] S8 determines the linear mapping relationship between the output value and the decision variable according to the linear mapping relationship between the parameter variable determined by each initial output value and the decision variable.

[0018] In some embodiments, obtaining an optimal solution for a decision variable in a power system operation model includes:

[0019] Determine operating costs and constraints;

[0020] According to the constraints, the decision variable that minimizes the operating cost is taken as the optimal solution of the decision variable.

[0021] In some embodiments, optimally dividing the constraints according to the optimal solution to obtain valid constraints and invalid constraints includes:

[0022] According to the optimal solution, the constraints are optimally divided, the constraint parts with equal signs in the constraints are divided into valid constraints, and the constraint parts with unequal signs in the constraints are divided into invalid constraints.

[0023] In some embodiments, according to the valid constraint condition and the invalid constraint condition, obtaining the linear mapping relationship between the parameter variable determined by the initial output value and the decision variable and the critical region where the parameter variable is located includes:

[0024] According to the effective constraints, determine the linear function relationship between the decision variables and the parameter variables;

[0025] According to the linear function relationship and invalid constraints, the critical region of the parameter variable is determined.

[0026] In some embodiments, the risk assessment index is determined based on the load shedding amount and the wind abandonment amount, including:

[0027] Calculate the load shedding risk index according to the load shedding amount;

[0028] Calculate the risk index of wind abandonment based on the amount of wind abandonment;

[0029] Calculate the comprehensive risk index based on the load shedding risk index and wind abandonment risk index;

[0030] The risk assessment indicators are determined based on the load shedding risk indicators, wind abandonment risk indicators and comprehensive risk indicators.

[0031] In a second aspect, the present application provides a power system risk assessment device, comprising:

[0032] The first acquisition module is used to determine the load shedding amount in the power system and the wind farm abandonment amount according to the critical domain where the output value of the wind farm in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model;

[0033] The second acquisition module is used to determine the risk assessment index according to the load shedding amount and the wind abandonment amount;

[0034] The assessment module is used to conduct risk assessment on the power system based on risk assessment indicators.

[0035] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the power system risk assessment method described in the first aspect or any embodiments of the first aspect.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the power system risk assessment method described in the first aspect or any embodiments of the first aspect.

[0037] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the power system risk assessment method described in the first aspect or any embodiments of the first aspect.

[0038] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0039] The present application provides a method and device for risk assessment of a power system. For different output values ​​of renewable energy sources such as wind farms, the load shedding amount in the power system and the wind abandonment amount of the wind farm are quickly calculated based on the critical domain in which they are located and the linear mapping relationship between the decision variables in the power system operation model. The risk assessment index is calculated based on the obtained load shedding amount and wind abandonment amount, and the power system is risk assessed based on the risk assessment index. Compared with the existing power system risk assessment methods, there is no need to redefine constraints, establish optimization models, and solve optimization problems, which speeds up the calculation time of power system risk assessment and effectively improves the efficiency of risk assessment, so that timely countermeasures can be taken to ensure the safe and stable operation of the power grid with a high proportion of wind power access. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is one of the flow charts of the power system risk assessment method provided in the embodiment of the present application;

[0041] Figure 2 This is the second flow chart of the power system risk assessment method provided in the embodiment of the present application;

[0042] Figure 3 is a topological diagram of a 6-node power system provided in an embodiment of the present application;

[0043] Figure 4 It is a schematic diagram of parameter space division of a 6-node power system provided in an embodiment of the present application;

[0044] Figure 5 This is a schematic diagram comparing line flow values ​​of a 6-node power system provided in an embodiment of the present application;

[0045] Figure 6 is a schematic diagram of the structure of a power system risk assessment device provided in an embodiment of the present application;

[0046] Figure 7 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0049] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first acquisition module and a second acquisition module are used to distinguish different acquisition modules rather than to describe a specific order of the acquisition modules.

[0050] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0051] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0052] See also Figure 1 , an embodiment of the present application provides a power system risk assessment method, which may include: step 110, step 120 and step 130.

[0053] Step 110 determines the load shedding amount in the power system and the wind farm abandonment amount according to the critical region where the wind farm output value in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model;

[0054] Step 120 determines a risk assessment index according to the load shedding amount and the wind abandonment amount;

[0055] Step 130 performs risk assessment on the power system according to the risk assessment indicators.

[0056] In the embodiment of the present application, in step 110, the output value of the wind farm can be specifically the power generation of the wind farm. The power generation of the wind farm can be specifically any value in the parameter space, which is composed of all the power generation of the wind farm in the constraint conditions followed by the decision variables in the power system operation model. The decision variable can specifically include the output value PG of the i-th thermal power unit in the power system in time period t i,t , the load shedding amount Dd of the kth load in the power system in time period t k,t And the wind abandonment volume DW of the jth wind farm in the power system in time period t j,t .

[0057] In the specific implementation, based on the critical domain of the output value of the wind farm in the power system and the linear mapping relationship between the output value and the decision variable, the decision variable corresponding to the output value can be directly calculated, and combined with the specific formula, the load shedding amount in the power system and the wind farm abandonment amount can be calculated.

[0058] The load shedding amount of the load specifically refers to the sum of the load shedding amounts of each load in the power system in each time period. The wind abandonment amount of the wind farm specifically refers to the sum of the wind abandonment amounts of each wind farm in the power system in each time period.

[0059] In step 120, the sum of the load shedding of each load in the power system in each time period and the sum of the wind abandonment of each wind farm in each time period obtained above are substituted into a specific formula to obtain a risk assessment index of the power system.

[0060] In step 130, risk assessment is performed on the power system based on the risk assessment indicators obtained above.

[0061] It should be noted that in the embodiment of the present application, by replacing the above-mentioned wind farm with other renewable energy sources (such as solar energy, hydropower, etc.), risk assessment of the power system can also be achieved for different output values ​​of other renewable energy sources.

[0062] The embodiment of the present application provides a power system risk assessment method, which, for different output values ​​of renewable energy sources such as wind farms, quickly calculates the load shedding amount in the power system and the wind curtailment amount of the wind farm according to the critical domain in which it is located and the linear mapping relationship between it and the decision variables in the power system operation model. A risk assessment index is calculated based on the obtained load shedding amount and wind curtailment amount, and a risk assessment is performed on the power system based on the risk assessment index. Compared with the existing power system risk assessment methods, there is no need to redefine constraints, establish optimization models, and solve optimization problems, which speeds up the calculation time of power system risk assessment and effectively improves the efficiency of risk assessment so that timely countermeasures can be taken to ensure the safe and stable operation of the power grid with a high proportion of wind power access.

[0063] Further, in some embodiments, in step 120, determining the risk assessment index according to the load shedding amount and the wind abandonment amount may include:

[0064] Calculate the load shedding risk index according to the load shedding amount;

[0065] Calculate the risk index of wind abandonment based on the amount of wind abandonment;

[0066] Calculate the comprehensive risk index based on the load shedding risk index and wind abandonment risk index;

[0067] The risk assessment indicators are determined based on the load shedding risk indicators, wind abandonment risk indicators and comprehensive risk indicators.

[0068] In the embodiment of the present application, risk assessment indicators including load shedding risk indicator, wind abandonment risk indicator and comprehensive risk indicator are calculated according to the following formulas (1) to (5).

[0069] R ls =P(E ls )·S ev (E ls ) (1)

[0070]

[0071] R ct =P(E ct )·S ev (E ct ) (3)

[0072]

[0073] R com =c ls R ls +c ct R ct (5)

[0074] In the formula, R ls is the load shedding risk index, which can be used to indicate the risk of the power system being unable to meet the load supply. ls is a load shedding event, P(E ls ) is the probability of load shedding event, S ev (E ls ) is the severity of load shedding, P d is the load value in the power system, D d is the load shedding amount, specifically the sum of the load shedding amounts of each load in the power system in each period, R ct E is the wind abandonment risk index, which can be used to indicate the risk of partial shutdown of wind farms caused by insufficient wind power consumption in the power system. ct is the wind curtailment event, P(E ct ) is the probability of wind curtailment event, S ev (E ct ) is the severity of wind curtailment event, P w is the output value of the wind farm, D w is the wind abandonment volume, specifically the sum of wind abandonment volumes of each wind farm in the power system at each time period, R com It is a comprehensive risk index in the risk assessment index, which can be used to represent the system risk index after comprehensive consideration of various risk factors. It can be obtained by calculating the weighted average of the load shedding risk index and the wind abandonment risk index. ls and c ct are the weights of load shedding risk index and wind abandonment risk index, respectively, and c ls +c ct =1.

[0075] Furthermore, in some embodiments, in the above steps, the method of obtaining the critical region where the output value of the wind farm in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model may include:

[0076] S1 randomly selects the initial output value of the wind farm from the undivided area in the parameter space;

[0077] S2 obtains the optimal solution of the decision variables in the power system operation model, and the power system operation model is determined according to the operation cost of the power system and the constraints followed by the decision variables in the operation cost;

[0078] S3 optimally divides the constraints according to the optimal solution to obtain valid constraints and invalid constraints;

[0079] S4 obtains the linear mapping relationship between the parameter variables determined by the initial output value and the decision variables and the critical domain where the parameter variables are located according to the valid constraint conditions and the invalid constraint conditions;

[0080] S5 marks the critical region where the parameter variable is located as the parameter space of the divided region, so as to update the undivided region in the parameter space;

[0081] S6 repeats S1 to S5 until all undivided areas in the parameter space are marked as divided areas;

[0082] S7 determines the critical region where the output value is located according to the critical region where the parameter variable determined by each initial output value of the wind farm is located;

[0083] S8 determines the linear mapping relationship between the output value and the decision variable according to the linear mapping relationship between the parameter variable determined by each initial output value and the decision variable.

[0084] Please see further Figure 2 , the critical region where the output value of the wind farm in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model can be obtained through steps S1 to S5.

[0085] In a specific implementation, the initial output value of the wind farm is randomly selected from the above parameter space. In the initial state, the value range of the initial output value of the wind farm in the parameter space all belongs to the undivided area. In the embodiment of the present application, through steps S2 to S8, the undivided areas in the parameter space are marked as divided areas, and the divided areas are the critical domains where the initial output value of the wind farm is located. In the embodiment of the present application, different divided areas can be distinguished by adding identifiers, for example, adding identifiers such as critical domain 1, critical domain 2...

[0086] Solve the decision variable PG in the constructed power system operation modeli,t ,Dd k,t and DW j,t The power system operation model is specifically based on the operation cost of the power system and the decision variable PG in the operation cost. i,t ,Dd k,t and DW j,t The constraints followed are constructed.

[0087] According to the above optimal solution, the constraints are optimally divided into valid constraints and invalid constraints.

[0088] According to the valid constraints and invalid constraints obtained above, the linear mapping relationship between the parameter variable determined by the initial output value of the wind farm and the above decision variable and the critical region where the parameter variable is located are obtained. The linear mapping relationship can be represented by a linear function.

[0089] The critical region where the parameter variable is located is marked as the parameter space of the divided region, and the critical region where the parameter variable is located determined by different initial output values ​​of the wind farm can also be distinguished by adding a mark to the divided region. The undivided region in the parameter space is updated according to the divided region.

[0090] The above process is repeated until all undivided areas in the parameter space have been marked as divided areas. At this point, since all undivided areas have been marked as divided areas, it is impossible to randomly select the initial output value of the wind farm from the undivided areas of the parameter space again. Therefore, in the embodiment of the present application, whether the initial output value of the wind farm randomly selected from the undivided area in the parameter space is empty can also be used as a condition for stopping the above repetitive process. When the initial output value of the wind farm randomly selected from the undivided area in the parameter space is empty, the above repetitive process is stopped.

[0091] According to the critical regions where the parameter variables corresponding to the initial output values ​​of the wind farm obtained in the above process are located, the critical regions where the different output values ​​of the wind farm are located can be found.

[0092] According to the linear mapping relationship between the parameter variables corresponding to each initial output value of the wind farm and the above decision variables obtained in the above process, the linear mapping relationship between different output values ​​of the wind farm and the above decision variables can be determined.

[0093] Furthermore, in some embodiments, in the above steps, obtaining the optimal solution of the decision variables in the power system operation model may include:

[0094] Determine operating costs and constraints;

[0095] According to the constraints, the decision variable that minimizes the operating cost is taken as the optimal solution of the decision variable.

[0096] In the embodiment of the present application, the operating cost of the power system is calculated based on formula (6);

[0097]

[0098] Based on formulas (7)-(12), determine the constraints followed by the above decision variables;

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105] Among them, z is the operating cost, n1 is the number of thermal power units in the power system, n2 is the number of loads in the power system, n3 is the number of wind farms in the power system, and a i , b i and c i are the first cost coefficient, the second cost coefficient and the third cost coefficient of the output value of the i-th thermal power unit in the power system, PG i,t is the output value of the i-th thermal power unit in period t, c w is the wind abandonment penalty factor, c d is the load shedding penalty factor, Dd k,t is the load shedding amount of the kth load in time period t, DW j,t is the wind curtailment of the j-th wind farm in period t, and formula (7) represents the output value constraint of the i-th thermal power unit in period t. PG i are the upper and lower limits of the output value of the i-th thermal power unit, respectively. Formula (8) represents the capacity constraint of line l in the power system. is the maximum capacity of line l in the power system. Formula (9) represents the node power balance constraint in the power system. The inflow power and outflow power of any node b in the power system are equal. PW j,t is the initial output value of the jth wind farm in period t, LD k,t is the load value of the kth load in time period t, F l,t represents the power flow value of line l in time period t, KG b,iis the coefficient matrix between node b and the i-th thermal power unit in the power system, KW b,j is the coefficient matrix between node b and the jth wind farm, KD b,k is the coefficient value between node b and the kth load, KL b,l is the coefficient matrix between node b and line l. Formula (10) represents the line power flow constraint under the DC power flow model. l is the reactance value of line l, φ is the node phase angle in the power system, b l is the starting node of line l, e l is the terminal node of line l, formula (11) represents the wind abandonment constraint, and formula (12) represents the load shedding constraint.

[0106] With the goal of minimizing operating costs, the power system operation model constructed is as follows:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] By solving the power system operation model under the initial output value of the wind farm, the optimal solution of the decision variables is obtained.

[0115] Furthermore, in some embodiments, in the above step S3, optimally dividing the constraints according to the optimal solution to obtain valid constraints and invalid constraints may include:

[0116] According to the optimal solution, the constraints are optimally divided, the constraint parts with equal signs in the constraints are divided into valid constraints, and the constraint parts with unequal signs in the constraints are divided into invalid constraints.

[0117] In the embodiment of the present application, the constraints are optimally divided according to the optimal solution, and the above-mentioned power system operation model is expressed as a multi-parameter planning model as follows:

[0118] Minimizing the operating cost is expressed as min z = f(x);

[0119] The constraint condition is expressed as formula (13);

[0120] stGx≤w+Fθ (13)

[0121] Among them, G is the coefficient matrix corresponding to the decision variable, w is the target constant coefficient matrix, F is the coefficient matrix corresponding to the parameter variable, x is the decision variable, θ is the parameter variable, which is equivalent to a known quantity with a certain value range, which is determined by the initial output value of the wind farm. After the corresponding formulas (7) to (12) of the constraint conditions are determined, G, w, and F can be uniquely determined.

[0122] By performing optimal segmentation on the above formula (13), we can obtain the equation (14) corresponding to the valid constraint and the inequality (15) corresponding to the invalid constraint.

[0123] G1x * =w1+F1θ0 (14)

[0124] G2x * <w2+F2θ0 (15)

[0125] Among them, x * is the optimal solution of the decision variable corresponding to the parameter variable when taking a specific value, G1 is the coefficient matrix corresponding to the optimal solution of the decision variable in the valid constraint condition, w1 is the constant coefficient matrix in the valid constraint condition, F1 is the coefficient matrix corresponding to the parameter variable when taking a specific value in the valid constraint condition, θ0 is the parameter variable when taking a specific value, G2 is the coefficient matrix corresponding to the optimal solution of the decision variable in the invalid constraint condition, w2 is the constant coefficient matrix in the invalid constraint condition, and F2 is the coefficient matrix corresponding to the parameter variable when taking a specific value in the invalid constraint condition.

[0126] An electric power system risk assessment method provided in an embodiment of the present application expresses the electric power system operation model in the form of a multi-parameter planning model, can accurately calculate the value of the risk assessment index, and the calculated result is the same as the calculation result of directly solving the original optimization problem, thereby speeding up the efficiency of risk assessment.

[0127] Further, in some embodiments, in the above step S4, obtaining the linear mapping relationship between the parameter variable determined by the initial output value and the decision variable and the critical region where the parameter variable is located according to the valid constraint condition and the invalid constraint condition may include:

[0128] According to the effective constraints, determine the linear function relationship between the decision variables and the parameter variables;

[0129] According to the linear function relationship and invalid constraints, the critical region of the parameter variable is determined.

[0130] In the embodiment of the present application, the valid constraint condition and the invalid constraint condition obtained above are respectively expressed as formula (16) and formula (17);

[0131]

[0132]

[0133] Where c is the coefficient of the decision variable, λ1 is the dual multiplier of the valid constraint, and λ2 is the dual multiplier of the invalid constraint. The stationary condition.

[0134] By transforming formula (16), we can get formula (18) corresponding to the linear function between decision variables and parameter variables;

[0135]

[0136] Among them, x(θ) is a linear function, a x , b x They are the coefficient matrices corresponding to the linear mapping relationship between parameter variables and decision variables.

[0137] Substituting the above linear function into the inequality part of formula (17) yields Pick Will Simplified to formula (19);

[0138] a θ θ θ (19)

[0139] Among them, a θ , b θ The coefficient matrices corresponding to the linear mapping relationship between the parameter variables and the decision variables when they take specific values ​​are stored in the critical region where the parameter variables are located when they take specific values.

[0140] According to formula (19), the critical region of different wind farm output values ​​can be determined. Let θ be different wind farm output values, and set a θ , b θ Substitute them into formula (19). If formula (19) holds, it can be determined that any output value and its corresponding parameter variable are in the same critical region. θ , b θ That is, the coefficient matrix corresponding to the linear mapping relationship between the output value and the decision variable. According to the coefficient matrix and output value corresponding to the linear mapping relationship between the output value stored in the critical domain, the corresponding decision variable can be directly calculated, and then the risk assessment index can be calculated according to the calculation formulas (1) to (5), including the load shedding risk index value, the wind abandonment risk index value, and the comprehensive risk index value, to complete the risk assessment of the power system.​

[0141] An electric power system risk assessment method provided in an embodiment of the present application can represent the parameter space where the output value of a wind farm is located as a set of a series of critical domains, and convert the original risk assessment problem containing constraints into a linear mapping relationship of wind power output, thereby avoiding remodeling and solving the optimization problem, and can effectively speed up the risk assessment process.

[0142] Exemplarily, a power system risk assessment method provided by an embodiment of the present application was tested in a 6-node power system and a 30-node power system.

[0143] Please see further Figure 3 , the 6-node power system topology diagram is as follows Figure 3 As shown in Figure 1, the power system contains 6 nodes (Bus1~Bus6), 11 lines (1~11), 2 thermal power units (w1, w2), 2 wind farms, and 3 electric loads (G1~G3). The wind power output range of the two wind farms is set to [5, 120] MW, which form a 2D parameter space. The 30-node power system contains 30 nodes, 41 lines, 6 thermal power units, 2 wind farms, and 20 electric loads.

[0144] Please see further Figure 4 , we can see that after processing, the parameter space is divided into 23 critical domains, and the coefficient matrix corresponding to the linear mapping relationship of each critical domain is stored.

[0145] In a 6-node power system, under different wind power output scenarios, the first case (i.e., ignoring the uncertainty of wind power, establishing an optimization problem to calculate risk assessment indicators through existing methods) requires establishing constraints and solving the optimization problem to complete the power system risk assessment, while based on the second case (the power system risk assessment method provided in the embodiment of the present application), through the above steps, the embodiment of the present application directly locates the output values ​​of different wind farms to their corresponding critical domains, and calculates the risk indicators according to the coefficient matrix corresponding to the stored linear mapping relationship, including load shedding risk indicators, wind abandonment risk indicators and comprehensive risk indicators, to complete the risk assessment.

[0146] Please see further Figure 5 , which is a comparison of the line flow values ​​under the optimal solution calculated in the two cases. It can be seen that the line flow values ​​calculated in the two cases are completely equal, which shows that the power system risk assessment method provided in the embodiment of the present application can be effectively applied to power system risk assessment.

[0147] In a 30-node power system, under different wind power output scenarios, the first case requires the establishment of constraints and the solution of optimization problems to complete the power system risk assessment, while in the second case, through the above steps, the embodiment of the present application directly locates the output values ​​of different wind farms to their critical domains, and calculates the risk index values ​​according to the coefficient matrix corresponding to the stored linear mapping relationship, including the load shedding risk index, the wind abandonment risk index and the comprehensive risk index, to complete the risk assessment. Table 1 shows the calculation time comparison under three different wind power output scenarios. It can be seen from Table 1 that under these three wind power output scenarios, the time spent on calculating the risk index in the second case is much less than that in the first case. It can be concluded that the proposed rapid risk assessment method taking into account wind power uncertainty can effectively shorten the calculation time and improve the calculation efficiency of risk assessment.

[0148] Table 1

[0149]

[0150] Based on the above analysis, it can be seen that the present application can shorten the calculation time and improve the calculation efficiency while ensuring the accuracy of the solution.

[0151] A power system risk assessment device provided in the present application is described below. The power system risk assessment device described below and the power system risk assessment method described above can refer to each other.

[0152] See also Figure 6 An embodiment of the present application provides a power system risk assessment device, including: a first acquisition module 610, a second acquisition module 620 and an assessment module 630.

[0153] A first acquisition module 610 is used to determine the load shedding amount in the power system and the wind farm abandonment amount according to the critical domain where the output value of the wind farm in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model;

[0154] A second acquisition module 620 is used to determine a risk assessment index according to the load shedding amount and the wind abandonment amount;

[0155] The evaluation module 630 is used to perform risk evaluation on the power system according to the risk evaluation index.

[0156] An embodiment of the present application provides a power system risk assessment device, which, for different output values ​​of renewable energy sources such as wind farms, quickly calculates the load shedding amount in the power system and the wind curtailment amount of the wind farm according to the critical domain in which it is located and the linear mapping relationship between it and the decision variables in the power system operation model. A risk assessment index is calculated based on the obtained load shedding amount and wind curtailment amount, and a risk assessment is performed on the power system based on the risk assessment index. Compared with existing power system risk assessment methods, there is no need to redefine constraints, establish optimization models, and solve optimization problems, which speeds up the calculation time of power system risk assessment and effectively improves the efficiency of risk assessment, so that timely countermeasures can be taken to ensure the safe and stable operation of the power grid under a high proportion of wind power access.

[0157] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0158] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0159] Based on the method in the above embodiment, the present application embodiment provides an electronic device, see Figure 7 The electronic device may include: a processor (Processor) 710, a communication interface (Communications Interface) 720, a memory (Memory) 730 and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the method in the above embodiment.

[0160] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0161] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0162] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0163] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0164] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0165] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0166] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0167] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A power system risk assessment method, characterized in that: include: Determine the load shedding amount in the power system and the wind farm abandonment amount according to the critical region where the output value of the wind farm in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model; Determining a risk assessment index according to the load shedding amount and the wind abandonment amount; A risk assessment is performed on the power system according to the risk assessment indicator.

2. The power system risk assessment method according to claim 1, characterized in that: The method for obtaining the critical region where the output value of the wind farm in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model includes: S1 randomly selects the initial output value of the wind farm from the undivided area in the parameter space; S2 obtains the optimal solution of the decision variables in the power system operation model, wherein the power system operation model is determined according to the operation cost of the power system and the constraints followed by the decision variables in the operation cost; S3, optimally dividing the constraint conditions according to the optimal solution to obtain valid constraint conditions and invalid constraint conditions; S4, according to the valid constraint condition and the invalid constraint condition, obtaining a linear mapping relationship between the parameter variable determined by the initial output value and the decision variable and a critical region where the parameter variable is located; S5: marking the critical region where the parameter variable is located as a parameter space with a divided region, so as to update the undivided region in the parameter space; S6 repeats S1 to S5 until all undivided areas in the parameter space are marked as divided areas; S7, determining the critical region where the output value is located according to the critical region where the parameter variable determined by each initial output value of the wind farm is located; S8 determines the linear mapping relationship between the output value and the decision variable according to the linear mapping relationship between the parameter variable determined by the initial output values ​​and the decision variable.

3. The power system risk assessment method according to claim 2, characterized in that: The obtaining of the optimal solution of the decision variables in the power system operation model comprises: determining the operating cost and the constraint conditions; According to the constraint conditions, the decision variable that makes the running cost obtain the minimum value is taken as the optimal solution of the decision variable.

4. The power system risk assessment method according to claim 2, characterized in that: The optimally dividing the constraint conditions according to the optimal solution to obtain valid constraint conditions and invalid constraint conditions includes: According to the optimal solution, the constraint conditions are optimally divided, the constraint parts with equal signs in the constraint conditions are divided into valid constraint conditions, and the parts with unequal signs in the constraint conditions are divided into invalid constraint conditions.

5. The power system risk assessment method according to claim 4, characterized in that: The step of obtaining, according to the valid constraint condition and the invalid constraint condition, a linear mapping relationship between the parameter variable determined by the initial output value and the decision variable and a critical region where the parameter variable is located comprises: Determining a linear function relationship between the decision variable and the parameter variable according to the effective constraint condition; According to the linear function relationship and the invalid constraint condition, the critical region where the parameter variable is located is determined.

6. The power system risk assessment method according to claim 1, characterized in that: Determining the risk assessment index according to the load shedding amount and the wind abandonment amount includes: Calculating a load shedding risk index according to the load shedding amount; Calculating the wind abandonment risk index according to the wind abandonment amount; Calculating the comprehensive risk index according to the load shedding risk index and the wind abandonment risk index; The risk assessment index is determined according to the load shedding risk index, the wind abandonment risk index and the comprehensive risk index.

7. A power system risk assessment device, characterized in that: include: A first acquisition module is used to determine the load shedding amount of the load in the power system and the wind abandonment amount of the wind farm according to the critical domain where the output value of the wind farm in the power system is located and the linear mapping relationship between the output value and the decision variables in the power system operation model; A second acquisition module is used to determine a risk assessment index according to the load shedding amount and the wind abandonment amount; An assessment module is used to perform risk assessment on the power system according to the risk assessment index.

8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the power system risk assessment method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor executes the power system risk assessment method according to any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product runs on a processor, the processor is enabled to execute the power system risk assessment method according to any one of claims 1 to 6.

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