Method for efficiently evaluating adequacy of power generation and transmission system based on state analysis

Abstract

The invention relates to the technical field of power system evaluation, discloses a power generation and transmission system adequacy efficient evaluation method based on state analysis, and provides a novel adequacy evaluation index with definite geometric significance and an analytical calculation method thereof based on adequacy operation boundary analysis and scale factors of a power generation and transmission system. The abundance level of any system state under the influence of unplanned outage of equipment and load uncertainty factors can be accurately evaluated in an analysis mode; and then, the method is embedded into a Monte Carlo simulation process, so that the overall adequacy level of the power generation and transmission system in an evaluation period can be quickly quantified. The method can achieve the efficient evaluation of the power generation and transmission system in different states, is higher in evaluation accuracy, is reliable in evaluation result, and has an actual reference value.

Description

Technical Field

[0001] The present invention relates to the technical field of power system evaluation, and in particular to a method for efficiently evaluating the adequacy of a power generation and transmission system based on state analytical analysis. Background Art

[0002] The power generation and transmission system is the core component of the power system, and its safe and stable operation is crucial to ensuring power supply. In the power generation and transmission system, the power generation link produces electricity through the generator sets of the power plant and sends the electricity to the transmission network through the step-up transformer; the transmission link is responsible for transmitting the electricity to various load centers through high-voltage transmission lines and interconnecting transformers.

[0003] With the continuous growth of society's demand for power load and the increasing complexity of the topological structure of power generation and transmission systems, the problem of system adequacy has become more prominent. The adequacy of the power generation and transmission system (also known as static reliability) refers to the ability of the power system to continuously and stably supply power and meet the total load demand of users when considering the shutdown of various types of equipment in the system. Adequacy is directly related to the stable supply of power load. If the system adequacy is insufficient, the load may not be fully supplied during peak load or unplanned equipment shutdown, and may even cause power outages. Therefore, whether in the planning stage of new plans for generator sets, interconnection lines or grids, or in the formulation stage of equipment maintenance plans, adequacy assessments are required to determine whether the relevant plans or maintenance plans can meet the system's adequacy requirements.

[0004] At present, existing adequacy assessment methods usually rely on traditional reliability indicators such as expected short supply (EENS) to measure the adequacy level of the system. However, due to factors such as unplanned equipment outages and load uncertainty, the assessment process requires the analysis of a large number of possible system states one by one. Traditional methods usually calculate the load shedding amount under each system state through the optimal power flow model, which makes adequacy assessment a high-dimensional, nonlinear and extremely time-consuming NP-hard problem.

[0005] Therefore, it is an urgent need for current power companies to develop a new method for evaluating the adequacy of power generation and transmission systems that can analyze the system status, and to quickly quantify the adequacy level of the system using more efficient indicators within a limited decision-making time, thereby providing a scientific basis for planning and operation decisions. Summary of the invention

[0006] The present invention aims to provide an efficient evaluation method for the adequacy of a power generation and transmission system based on state analytical analysis, which can achieve efficient evaluation of the power generation and transmission system under different states, and has a high evaluation accuracy, and the evaluation results are reliable and have practical reference value.

[0007] The basic scheme provided by the present invention is: an efficient evaluation method for the adequacy of power generation and transmission systems based on state analytical analysis, comprising the following steps:

[0008] Step 1: Analyze the sufficient operating boundary of the power generation and transmission system from a geometric perspective, and quantitatively model the load variables of the power generation and transmission system, and calculate the variable interval and the load uncertainty domain based on the interval model based on the load variables;

[0009] A load loss risk metric index based on a proportional factor is set; the load loss risk metric index is the minimum multiple of the sufficient operating boundary and the load uncertainty domain remaining tangent when the load uncertainty domain expands outward or contracts inward for a given system state;

[0010] Based on the load loss risk measurement index, the risk of insufficient adequacy of the power generation and transmission system is described, and the load loss risk index is obtained;

[0011] Step 2: According to the differentiated unplanned outage reasons of power generation and transmission equipment, the exponential distribution, Weibull distribution and ARMIA algorithm are used to calculate the unplanned outage rates of three types of power generation and transmission equipment in the power generation and transmission system: generator sets, power transformers and transmission lines;

[0012] Step 3: Based on Step 1 and Step 2, the load loss risk index of any system state is analyzed and calculated, and the adequacy level of the power generation and transmission system under the system state is quantified;

[0013] Step 4: Based on step 3 and combined with Monte Carlo sampling technology, multiple system states that may occur during the planning period are randomly generated through parallel calculations, and analyzed one by one to calculate the adequacy index of the power generation and transmission system during the planning period.

[0014] The working principle and advantages of the present invention are:

[0015] The efficient evaluation method for the adequacy of power generation and transmission systems based on state analysis of the present invention can realize efficient evaluation of power generation and transmission systems under different states, and has a high evaluation accuracy, and the evaluation results are reliable and have practical reference value. The key points are:

[0016] First, this solution can handle a huge amount of analysis and fully evaluate the power generation and transmission system under different conditions. In actual operation, a power generation and transmission system with N devices has 2 N system states (equipment has two states: running and stopped); if we consider load uncertainty, assuming that 10 points are sampled evenly, there are also 10×2 NThe number of system states that need to be analyzed for one evaluation is extremely large. To address this, this solution specifically designs an adequacy evaluation index based on state analysis and geometric analysis, which can quickly and accurately quantify the reliability level of the power generation and transmission system under any system state, taking into account the impact of unplanned equipment outages and load uncertainty. In addition, there is no need to sample the load distribution or perform a large number of optimal load reduction calculations, so the calculation cost is low and the calculation efficiency is high.

[0017] Second, this scheme has a high evaluation accuracy and can accurately quantify the system's adequacy state. In the prior art, in each system state, it is usually necessary to calculate the corresponding load shedding amount through the optimal power flow model. Although some studies have proposed using models such as neural networks to accelerate the calculation of load shedding, such methods often have large errors and poor engineering applicability due to the difficulty in achieving sample balance in the training set. In this scheme, a new calculation method is designed to construct the system's ample operating boundary, analyze the relative position relationship between the ample operating boundary and the load boundary of the power generation and transmission system, and use the interval model to model the load uncertainty. A sufficiency evaluation index with a clear geometric meaning is defined, which can accurately quantify the sufficiency level of any system state under given equipment operating conditions and uncertain load conditions, and is not prone to errors.

[0018] Third, the evaluation indicators designed in this scheme have high practical application value. Traditional adequacy evaluation indicators (such as expected power shortage-EENS) cannot intuitively show the relationship between system load demand and supply capacity from a geometric perspective, so it is difficult to provide operators with a clear system adequacy optimization direction and reference basis, which limits the application of traditional adequacy evaluation methods in engineering practice. In this scheme, a new adequacy evaluation indicator with clear geometric meaning and its analytical calculation method are proposed, which can accurately evaluate the adequacy level of any system state under the influence of unplanned equipment outages and load uncertainty factors in an analytical way; then, by embedding it into the Monte Carlo simulation process, it can achieve rapid quantification of the overall adequacy level of the power generation and transmission system during the evaluation period, which can provide a scientific basis for power companies in planning expansion and formulating equipment maintenance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the overall architecture of an embodiment of a method for efficiently evaluating the adequacy of a power generation and transmission system based on state analytical analysis according to the present invention;

[0020] Figure 2 A schematic diagram of a load loss risk situation of a power generation and transmission system according to an embodiment of a method for efficiently evaluating the adequacy of a power generation and transmission system based on state analytical analysis of the present invention;

[0021] Figure 3A schematic diagram of geometric description of load variables of an embodiment of a method for efficiently evaluating the adequacy of a power generation and transmission system based on state analytical analysis of the present invention;

[0022] Figure 4 A schematic diagram of load uncertainty domain modeling of an interval model of an embodiment of a method for efficiently evaluating the adequacy of a power generation and transmission system based on state analytical analysis of the present invention;

[0023] Figure 5 A schematic diagram of proportional factor modeling of an embodiment of a method for efficiently evaluating the adequacy of a power generation and transmission system based on state analytical analysis according to the present invention;

[0024] Figure 6 It is a schematic diagram of a method flow of an embodiment of a method for efficiently evaluating the adequacy of a power generation and transmission system based on state analytical analysis according to the present invention;

[0025] Figure 7 It is a schematic diagram of the structure of the MRTS-79 test system including three partitions of an embodiment of the method for efficiently evaluating the adequacy of a power generation and transmission system based on state analysis of the present invention;

[0026] Figure 8 It is a visualization display diagram of the power generation and transmission system adequacy indicators corresponding to the three scenarios of the embodiment of the power generation and transmission system adequacy efficient evaluation method based on state analytical analysis of the present invention. DETAILED DESCRIPTION

[0027] The following is a further detailed description through specific implementation methods:

[0028] The embodiment is basically as shown in the attached Figure 1 As shown: An efficient evaluation method for the adequacy of power generation and transmission systems based on state analytical analysis includes the following steps:

[0029] Step 1: Analyze the sufficient operating boundary of the power generation and transmission system from a geometric perspective, and quantitatively model the load variables of the power generation and transmission system. Based on the load variables, calculate the variable interval and the load uncertainty domain based on the interval model, such as Figure 3 and Figure 4 shown.

[0030] Among them, the sufficient operation boundary of the power generation and transmission system is set to g(M)=0, and the basic load space of the power generation and transmission system can be divided into two parts based on the sufficient operation boundary: a reliable domain g(M)>0 and a failure domain g(M)<0.

[0031] After determining the load uncertainty domain, the load loss risk of the system can be described based on the geometric position relationship between the load uncertainty domain and the reliability domain and failure domain. Figure 2 As shown, take the power generation and transmission system with two load nodes as an example:

[0032] Case 1: If ΩM Completely in the region of g(M) ≥ 0 (e.g. Figure 2 -(a)), indicating that all possible values ​​of the load at each node can be satisfied in this state. In this case, the system power supply capacity is sufficient (the system refers to the power generation and transmission system), and there is no risk of insufficient redundancy.

[0033] Case 2: If Ω M There is an intersection with both the reliable domain g(M)>0 and the failure domain g(M)<0 (such as Figure 2 -(b)), then under certain load values, the system needs to reduce the load to meet the requirements of safe operation. At this time, the system power supply capacity is no longer fully sufficient, and there is a certain risk of insufficient redundancy.

[0034] Case 3: If Ω M Completely in the failure region g(M)<0 (e.g. Figure 2 -(c)), which means that the system power supply capacity is completely insufficient in this state. In other words, no matter what the specific load value is, load reduction needs to be implemented. The risk of insufficient system redundancy in this case will be further increased than in case 2.

[0035] The quantitative modeling includes: collecting historical load data of each load node in the power generation and transmission system at each hour from historical operation records, and using an interval model to quantitatively model the uncertainty of the load of each node in the power generation and transmission system at each hour.

[0036] Specifically, suppose the load variable M corresponding to the i-th node of the system is i The value varies within a certain range, and the interval value is expressed as follows:

[0037]

[0038] Where: Represents the load variable M i The value range of and Respectively represent the interval The upper and lower bounds of can be determined based on the historical load data of the power generation and transmission system.

[0039] Then based on the load variable M corresponding to the i-th node i The corresponding interval Calculate the mean of the load variable Mi and interval radius

[0040]

[0041] Therefore, the variable interval It can be expressed as in, is the load variable M i The mean of is the load variable M i The radius of the interval.

[0042] Furthermore, for the load variable M i The geometric description of Figure 3 As shown in Figure 2, the load uncertainty domain based on the interval model is as follows: Figure 4 shown.

[0043] A load loss risk metric based on a proportional factor is set; the load loss risk metric is the minimum multiple of the sufficient operating boundary remaining tangent to the load uncertainty domain when the load uncertainty domain expands outward or contracts inward for a given system state.

[0044] Based on the load loss risk measurement index, the risk of insufficient adequacy of the power generation and transmission system is described, and the load loss risk index is obtained.

[0045] Specifically, this load loss risk metric can be used to quantitatively describe the reliability of the system under different events.

[0046] The method of describing the risk of insufficient adequacy of the power generation and transmission system based on the load loss risk measurement index includes the following sub-steps:

[0047] By solving the optimization problem, the load loss risk index is obtained;

[0048] The optimization problem is defined as:

[0049] Among them, S f represents the set of all points in the failure domain g(M)<0; M(ρ,M r ,M c ) represents the set of all points in the load uncertainty domain after being scaled by ρ times; ρ is a non-negative proportional factor; R LLR It is an indicator of load loss risk.

[0050] like Figure 5 As shown in the figure, it is a schematic diagram of the proportional factor modeling under the above-mentioned cases 1 to 3. In the schematic diagram, the area marked in the blue dotted box represents the load uncertainty domain after scaling by ρ times. Here, the proportional factor under case 2 needs to be divided into two cases, corresponding to case 2.1 and case 2.2. Figure 5 It can be seen that in case 1 (such as Figure 5 -(a)), Ω M At least ρ times of magnification is required to reach g(M) = 0. At this time, sgn(g(M c ))·ρ≥1; Similarly, in case 2.1 (such as Figure 5-(b)) and Scenario 2.2 (as shown in Figure 5 -(c)), Ω M It can be reduced by a maximum of ρ times, at which time sgn(g(M c ))·ρ respectively satisfy 0≤sgn(g(M c ))·ρ<1 and -1 <sgn(g(M c ))·ρ<0; in case 3 (such as Figure 5 -(d)), Ω M At least ρ times of magnification is required to reach g(M) = 0. At this time, sgn(g(M c ))·ρ≤-1.

[0051] Therefore, from case 1 to case 3, R LLR The indicator value will gradually decrease from positive to negative, which is consistent with the trend of gradually decreasing system adequacy.

[0052] Furthermore, the obtained load loss risk index is corrected, and the corrected load loss risk index is as follows:

[0053]

[0054] Among them, ρ represents a non-negative proportional factor; represents the overall mean of all node load intervals; sgn(·) represents the sign function; δ represents the duration of the system event.

[0055] Here, through the correction in this step, the load loss risk index R LLR It has a non-negative characteristic. When the power generation and transmission system is fully sufficient, R LLR The value of R is equal to 0; as the system power supply capacity decreases, the risk of insufficient redundancy gradually increases. LLR The value of will increase accordingly, which has a more intuitive meaning.

[0056] Moreover, in the modified load loss risk index, the value of ρ satisfies the following conditions:

[0057] ρ=|min{sgn(g 1 (M c ))·ρ 1 ,sgn(g 2 (M c ))·ρ 2 ,…,sgn(g W (M c ))·ρ W}|;

[0058] Among them, ρ w By solving Ω MWith the wth abundant running boundary g w (M) = 0 to obtain the proportional factor value of the optimization problem; Ω M is the load uncertainty domain.

[0059] Here, considering that the power generation and transmission system usually contains multiple operating constraints, its sufficient operating boundary g(M) = 0 is usually composed of multiple independent boundaries g w (M)=0(w=1,2,…,W). If the load uncertainty domain Ω M Exceeding any sufficient operating boundary (i.e. violating any operating constraint) will result in load reduction in the system and create a risk of insufficient sufficient capacity. Therefore, the proportional factor is further limited to ensure accurate identification of risks.

[0060] In this step, a special adequacy evaluation index is set to quickly and accurately quantify the reliability level of the power generation and transmission system under any system state, taking into account the impact of unplanned equipment outages and load uncertainty. In addition, there is no need to sample the load distribution and perform a large number of optimal load reduction calculations, so the calculation cost is low and the calculation efficiency is high.

[0061] Step 2: According to the differentiated unplanned outage reasons of power generation and transmission equipment, the exponential distribution, Weibull distribution and ARMIA algorithm are used to calculate the unplanned outage rates of three types of power generation and transmission equipment in the power generation and transmission system: generator sets, power transformers and transmission lines.

[0062] In the power generation and transmission equipment, the unplanned outage rate of the generator set mainly includes two parts: the random outage rate P caused by unexpected events such as human error and accidental outage of auxiliary equipment; gen,r , and the aging outage rate P caused by failure events such as turbine blade breakage due to aging of the unit itself gen,a .

[0063] The unplanned outage rate of transmission lines mainly includes two parts: the random outage rate P caused by natural external events such as thunderstorms, wildfires, and branches accidentally hanging line,r And the aging outage rate P caused by failure events such as line insulation aging corrosion, long-term stress stretching and fracture line,a .

[0064] The unplanned outage rate of power transformers mainly includes two parts: the random outage rate P due to external severe weather trans,r And the aging outage rate P caused by failure events such as line insulation aging corrosion, long-term stress stretching and fracture trans,r .

[0065] It includes the following sub-steps:

[0066] Step 2.1, model the random outage rate of generator sets based on historical statistical data.

[0067] Among them, the probability of a random unplanned outage of a generator set during the evaluation period (i.e., the random outage rate) is:

[0068]

[0069] Where t is time, λ is gen is the incidence rate of accidents such as human error and auxiliary machine outage, which is calculated through historical statistical records in this embodiment:

[0070]

[0071] Where Y gen and N gen (i) represents the total number of years and the total number of outages in each year in the historical random unplanned outage records of the generator sets.

[0072] Step 2.2, model the aging outage rate of the generator sets.

[0073] Since conventional state monitoring quantities of generator sets (such as vibration offset) cannot effectively reflect the aging of generator sets in power plants, in this embodiment, a three-parameter Weibull distribution with operating time as a variable is used to calculate the aging outage rate (P gen,a ) is modeled, which is specifically expressed as follows:

[0074]

[0075] Where: γ gen is the location parameter, which is determined by the earliest time of the generator set aging failure counted in the historical records; η gen is the scale parameter; β gen is a shape parameter, which can be obtained by estimating the parameters of historical aging unplanned outage record data of generator sets.

[0076] Step 2.3, calculate the comprehensive unplanned outage rate P of the generator equipment taking into account the random factors and aging factors gen,all :P gen,all (t) = P gen,r (t)+P gen,a (t).

[0077] Step 2.4, random outage rate P of transmission lines line,r Modeling.

[0078] Specifically,

[0079] Where t is time, λ is line (t) is the incidence rate of line outages caused by natural external events such as thunderstorms, wildfires, and branches accidentally hanging. In this embodiment, it is calculated by combining historical statistical records and growth factors:

[0080]

[0081] Where Y line and N line (i) represents the total number of years and the total number of outages in each year in the random unplanned outage records of transmission lines; C cf (t) is the occurrence growth rate. In this embodiment, the annual growth rate of the outage rate in the past few years is analyzed by the ARMIA algorithm to calculate:

[0082]

[0083] In the formula, the time series value predicted by the ARIMA model, the actual time series data and the autoregressive parameter φ i and the sliding average parameter θ j are the parameters of the fitted ARIMA model; p is the order of the autoregressive part; q is the order of the moving average (MA) part; t is the error term.

[0084] Step 2.5, model the aging outage rate of transmission lines.

[0085] Since the state monitoring variables of the transmission line can effectively reflect the aging state of the transmission line, in this embodiment, the aging outage rate P of the transmission line growing over time is calculated by combining the state monitoring variables and the two-parameter Weibull distribution. line,a Modeling is performed, which is specifically expressed as follows:

[0086]

[0087] Where η line is the scale parameter; β line is a shape parameter, which can be obtained by estimating the parameters of historical aging and unplanned outage record data of transmission line equipment;

[0088] HI line (t) is the aging index of the transmission line at the evaluation time, and the specific calculation formula is:

[0089]

[0090] In the formula, H mis the normalized value of the mth state monitoring quantity of the transmission line, and M is the total number of monitoring quantities. In this embodiment, the monitoring items include seven items, namely, towers, ground wires, insulator strings, hardware, grounding devices, lightning protection facilities, and bird protection facilities. The weight of each item is w m Can be determined by expert experience.

[0091] Step 2.6, calculate the comprehensive unplanned outage rate P of transmission line equipment considering random factors and aging factors linae,all :P line,all (t) = P line,r (t)+P line,a (t).

[0092] Step 2.7, comprehensively model the power transformer based on the modeling methods of the generator equipment and the transmission line, such as Step 2.2 and Step 2.4, and obtain the comprehensive unplanned outage rate P of the power transformer equipment considering the random factors and aging factors. trans,all :

[0093] P trans,all (t) = P trans,r (t)+P trans,a (t);

[0094] Where P trans,r (t) and P trans,a (t) are the random outage rate and aging outage rate of power transformers respectively.

[0095] Step 3: Based on Step 1 and Step 2, the load loss risk index of any system state is analyzed and calculated, and the adequacy level of the power generation and transmission system under the system state is quantified, such as Figure 6 shown.

[0096] It includes the following sub-steps:

[0097] Step 3.1, assuming that there is Z in the power generation and transmission system n devices, using a Monte Carlo random number generator to generate Z n Random numbers:

[0098] R=[R 1 ,R 2 ,R 3 ,…,R r ,…,R Zn ]r=1,2,……,Z n ;

[0099] In the formula, R r is the random number corresponding to the rth device.

[0100] Step 3.2, determine the operating status of the device based on the random number.

[0101] The equipment in the power generation and transmission system usually has two states: normal operation and unplanned outage. In this embodiment, the equipment state is determined by comparing with the unplanned outage rate of the equipment calculated in step 2 according to the following rules:

[0102]

[0103] Step 3.3, based on the uncertainty range of each node load in each hour, randomly select the load value range in any hour as the load uncertainty domain corresponding to the system state.

[0104] Step 3.4, solve the system's sufficient operating boundary corresponding to the system state.

[0105] Specifically, boundary analysis is performed first.

[0106] It should be noted that for any system event, the risk of load loss is usually caused by the following two reasons: 1) branch currents such as transformers or transmission lines exceed the limit; 2) insufficient power generation capacity in certain areas of the system.

[0107] Therefore, the sufficient operation boundary of the power generation and transmission system includes two types: the first type of boundary is used to characterize the maximum load that the system can supply under the condition of satisfying the flow constraints of each branch; the second type of boundary is used to describe the maximum load that can be reliably supplied in different system areas. These two types of boundaries are independent of each other and together constitute the complete system sufficient operation boundary g(M)=0.

[0108] Step 3.5, based on the power flow analysis of each branch of the power generation and transmission system and the regional power generation capacity analysis, the system's sufficient operation boundary function is modeled.

[0109] Assume that the system state is s. If the system load value is in the region of g(M) ≥ 0, the load reduction amount of each node is equal to 0.

[0110] Therefore, in this embodiment, the DC power flow of each branch of the power generation and transmission system is calculated by the following formula:

[0111]

[0112] In the formula, θ s,i represents the voltage phase angle of the ith node in the system; K represents the total number of nodes in the system; d s,ik represents the element in the i-th row and k-th column of the inverse matrix B-1 of the node admittance matrix B; P s,k,in represents the injected power of node k; M krepresents the load value of node k. i and j represent the first node and the last node of branch r respectively; PL s,r represents the power flow of branch r; x s,r Represents the reactance of branch r.

[0113] Step 3.6, according to the branch power flow constraints of the power transmission system and the formula described in Step 3.5, use the ReLU function Γ(·) to convert the output variables P of each unit in the power transmission system s,g Expand to its maximum possible value The following two margin-of-operation functions can be established:

[0114]

[0115]

[0116] In the formula, the state identification variable υ s,r It is used to describe the operating status of a power transformer or a transmission line branch r. When the branch r is in an unplanned outage, υ s,r The value is 1, otherwise it is 0; variable υ s,g Following the same definition; the mathematical definition of the ReLU function is Γ(z) = max(z,0).

[0117] Step 3.7, for a r Repeat Step 3.5 to Step 3.6 to model the sufficient operating boundary corresponding to each branch of the power generation and transmission system to form a 2×N r A separate sufficiency run boundary function.

[0118] Step 3.8, model the sufficient operating boundary function of the power generation and transmission system based on regional power generation capacity analysis.

[0119] For a given system state s, if we want to ensure that the power generation capacity in each system area is sufficient, we need to ensure that the load demand in each system area is not greater than the total available power generation capacity of the area.

[0120] Therefore, for each island formed due to unplanned equipment outage, the total power generation capacity in the island must be greater than the load demand.

[0121] Therefore, a sufficient operating boundary function can be established for each island:

[0122]

[0123] In the formula, and They represent the generator node set and load node set in the island respectively.

[0124] Step 3.9, if the system has formed N island If there are N islands, then according to Step 3.8, establish N island A sufficient run boundary function as described in Step 3.8.

[0125] Step 3.10, model the regional surplus operation boundary function with only one external tie line in the power transmission system after considering unplanned equipment outage.

[0126] Under this condition, the total power generation capacity plus the interconnection line capacity in the region must be greater than the load demand. Therefore, the following sufficient operation boundary function can be established:

[0127]

[0128] Step 3.11, if the system has formed N cont If there is only one external connection line in the area, then N cont A sufficient run boundary function as described in Step 3.10.

[0129] Step 3.12, since the system margin operation boundary functions established from Step 3.5 to Step 3.11 are all linear functions, when the load uncertainty domain Ω M After scaling, the tangent point with the sufficient operating boundary g(M)=0 of the power generation and transmission system is located at the top corner or bottom corner of the hypercube box formed by the scaled load uncertainty domain.

[0130] Therefore, the values ​​of these top corner points or bottom corner points can be substituted into the sufficient running boundary g w (M) = 0 (w = 1, 2, ..., W), the value of the proportional factor ρ in each case can be obtained by calculating the equation.

[0131] After obtaining the ρ value corresponding to each sufficient operating boundary, it is directly substituted into the corrected load loss risk index formula in step 1 to realize the analytical calculation of the load loss risk index without solving the optimization problem shown in step 1 one by one.

[0132] Step 4: Based on step 3, combined with Monte Carlo sampling technology, multiple system states that may appear during the planning period are randomly generated through parallel calculations, and analyzed one by one, and then the adequacy index of the power generation and transmission system during the planning period is calculated; Figure 6 shown.

[0133] In this step, when randomly generating the system state, N representative system states are generated through multiple Monte Carlo random sampling of the system equipment operating states and various load intervals; then, using parallel computing technology, the N system state samples are assigned to the corresponding computing units for independent analysis, and the load loss risk index corresponding to each system state sample is calculated.

[0134] Specifically, when calculating the adequacy index of the power generation and transmission system during the planning period, the following sub-steps are included:

[0135] By performing statistical analysis on the evaluation results of all N system states obtained by sampling, it is determined whether the preset convergence requirement is met; the preset convergence requirement is: χ≤χ max ;

[0136] in,

[0137] χ is the convergence criterion for adequacy assessment, N sum The number of system states evaluated for the total generation; LLR aver and LLR n N sum The average adequacy risk index of the nth system state and the adequacy risk index of the nth system state;

[0138] If the preset convergence requirement is not met, N system states are generated again and analyzed to determine whether the preset convergence requirement is met;

[0139] If the preset convergence requirements are met, the adequacy index SR of the power generation and transmission system during the planning period is calculated according to the following formula:

[0140]

[0141] Among them, SR is the adequacy level of the power generation and transmission system.

[0142] This embodiment provides an efficient evaluation method for the adequacy of the power generation and transmission system based on state analytical analysis. By analyzing the relative position relationship between the sufficient operation boundary and the load boundary of the power generation and transmission system, and modeling the load uncertainty by using an interval model, a adequacy evaluation index with a clear geometric meaning is defined, which is used to quantitatively evaluate the adequacy level of any system state under given equipment operating conditions and uncertain load conditions. Subsequently, the component reliability parameters required for the evaluation are modeled, and different models such as exponential distribution and three-parameter Weibull distribution are used to calculate the unplanned outage rate of different types of equipment. On this basis, a method for constructing the sufficient operation boundary of the power generation and transmission system based on the optimal power flow model is proposed, and an analytical calculation method for the adequacy evaluation index under any system state is developed. Finally, combined with the Monte Carlo sampling technology, a large number of system states that may occur during the planning period are randomly generated, and analyzed one by one, and the adequacy index of the power generation and transmission system during the planning period is quickly calculated, thereby realizing an efficient and accurate quantitative evaluation of the system adequacy.

[0143] In addition, in order to illustrate the effectiveness of this solution, the MRTS-79 test system containing three partitions is taken as an example to demonstrate the operating effect of this solution.

[0144] like Figure 7 As shown in Figure 1, three MRTS-79 system scenarios with different equipment operating times are constructed to analyze the adequacy of the power generation and transmission system. The operating time of all equipment in the same area is the same in each scenario. The corresponding scenario descriptions are shown in Table 1.

[0145] Table 1 System scenario description

[0146] Scene Number System aging area Equipment operation time (years) No.1 / 1 No.2 R1 37 No.3 R1,R2 37

[0147] In all case analyses, the research period for adequacy assessment is set to one year. In the load uncertainty interval model, the mean load of each load node is its peak load, the interval radius is set to 5% of the peak load, and the weights of each monitoring quantity of the transmission line are assigned as shown in Table 2.

[0148] Table 2 Weight assignment of each monitoring quantity of transmission line

[0149]

[0150] Furthermore, the visualization of the corresponding power generation and transmission system adequacy indicators under the three scenarios is as follows: Figure 8 As shown. Figure 8It can be seen that as the operating time of system equipment increases, the system adequacy risk index will gradually increase; in scenarios 2 and 3, the system adequacy risk index values ​​increase by 25% and more than 90% respectively compared with scenario 1 which only considers the random failure mode of components, which is consistent with the trend that the reliability level of the power generation and transmission system gradually decreases with the increase of equipment operating time (equipment aging).

[0151] This result shows that the adequacy risk index proposed in this scheme can effectively measure the adequacy risk of the power generation and transmission system and has strong pertinence.

[0152] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement the scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for the ordinary technicians in the relevant field to implement this application. It should be pointed out that for the technicians in this field, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. An efficient evaluation method for the adequacy of power generation and transmission systems based on state analytical analysis, characterized in that: The following steps are involved: Step 1: Analyze the sufficient operating boundary of the power generation and transmission system from a geometric perspective, and quantitatively model the load variables of the power generation and transmission system, and calculate the variable interval and the load uncertainty domain based on the interval model based on the load variables; A load loss risk metric index based on a proportional factor is set; the load loss risk metric index is the minimum multiple of the sufficient operating boundary and the load uncertainty domain remaining tangent when the load uncertainty domain expands outward or contracts inward for a given system state; Based on the load loss risk measurement index, the risk of insufficient adequacy of the power generation and transmission system is described; And obtain the load loss risk index; Step 2: According to the differentiated unplanned outage reasons of power generation and transmission equipment, the exponential distribution, Weibull distribution and ARMIA algorithm are used to calculate the unplanned outage rates of three types of power generation and transmission equipment in the power generation and transmission system: generator sets, power transformers and transmission lines; Step 3: Based on Step 1 and Step 2, the load loss risk index of any system state is analyzed and calculated, and the adequacy level of the power generation and transmission system under the system state is quantified; Step 4: Based on step 3 and combined with Monte Carlo sampling technology, multiple system states that may occur during the planning period are randomly generated through parallel calculations, and analyzed one by one to calculate the adequacy index of the power generation and transmission system during the planning period.

2. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analysis according to claim 1 is characterized in that: In step 1, the quantitative modeling includes: collecting historical load data of each load node in the power generation and transmission system at each hour from historical operation records, and using an interval model to quantitatively model the uncertainty of the load of each node in the power generation and transmission system at each hour.

3. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 1 is characterized in that: In step 1, the load variable is defined as M i , the variable interval is in, is the load variable M i The mean of is the load variable M i The radius of the interval.

4. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 3 is characterized in that: In step 1, the risk of insufficient adequacy of the power generation and transmission system is described based on the load loss risk measurement index, including the following sub-steps: By solving the optimization problem, the load loss risk index is obtained; The optimization problem is defined as: The sufficient operation boundary of the power generation and transmission system is set to g(M)=0, and the basic load space of the power generation and transmission system is divided into two parts: a reliable domain g(M)>0 and a failure domain g(M)<0; S f represents the set of all points in the failure domain g(M)<0; M(ρ,M r ,M c ) represents the set of all points in the load uncertainty domain after being scaled by ρ times; ρ is a non-negative proportional factor; R LLR It is an indicator of load loss risk.

5. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 4 is characterized in that: The obtained load loss risk index is also corrected, and the corrected load loss risk index is as follows: Among them, ρ represents a non-negative proportional factor; represents the overall mean of all node load intervals; sgn(·) represents the sign function; δ represents the duration of the system event.

6. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 5 is characterized in that: In the modified load loss risk index, the value of ρ satisfies the following conditions: ρ=|min{sgn(g1(M c ))·ρ1,sgn(g2(M c ))·ρ2,…,sgn(g W (M c ))·r W }|; Among them, ρ w By solving Ω M With the wth abundant running boundary g w The proportional factor value obtained by the optimization problem composed of (M) = 0; sgn(·) represents the sign function; Ω M is the load uncertainty domain.

7. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 1 is characterized in that: In step 2, the unplanned outage rate includes a random outage rate caused by external random events and an aging outage rate caused by failure events.

8. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 1 is characterized in that: In step 3, the following sub-steps are included: Step 3.1, assuming that there is Z in the power generation and transmission system n devices, using a Monte Carlo random number generator to generate Z n A random number; Step 3.2, determine the operating status of the device based on the random number; Step 3.3, based on the uncertainty range of each node load in each hour, randomly select the load value range in any hour as the load uncertainty domain corresponding to the system state; Step 3.4, solve the sufficient operation boundary of the power generation and transmission system corresponding to the system state; Step 3.5, based on the power flow analysis of each branch of the power generation and transmission system and the regional power generation capacity analysis, the sufficient operation boundary function of the power generation and transmission system is modeled; Based on the ample operating boundary function, when the load uncertainty domain is scaled, the tangent point with the ample operating boundary of the power generation and transmission system is located at the top corner or bottom corner of the hypercube box formed by the scaled load uncertainty domain; the value of the top corner or bottom corner is substituted into each ample operating boundary to calculate the value of the proportional factor in each case; Based on the proportionality factor, the load loss risk index is calculated.

9. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 1, characterized in that: In step 4, when randomly generating the system state, N representative system states are generated by multiple Monte Carlo random sampling of the system equipment operating states and various load intervals; then, using parallel computing technology, the N system state samples are distributed to the corresponding computing units for independent analysis, and the load loss risk index corresponding to each system state sample is calculated.

10. The method for efficiently evaluating the adequacy of power generation and transmission systems based on state analytical analysis according to claim 9, characterized in that: In step 4, when calculating the adequacy index of the power generation and transmission system during the planning period, the following sub-steps are included: By performing statistical analysis on the evaluation results of all N system states obtained by sampling, it is determined whether the preset convergence requirement is met; the preset convergence requirement is: χ≤χ max ; in, χ is the convergence criterion for adequacy assessment, N sum The number of system states evaluated for the total generation; LLR aver and LLR n N sum The average adequacy risk index of the nth system state and the adequacy risk index of the nth system state; If the preset convergence requirement is not met, N system states are generated again and analyzed to determine whether the preset convergence requirement is met; If the preset convergence requirements are met, the adequacy index SR of the power generation and transmission system during the planning period is calculated according to the following formula: Among them, SR is the adequacy level of the power generation and transmission system.

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