Power system source network coordination performance comprehensive evaluation method based on fuzzy evaluation

Abstract

The invention provides a power system source network coordination performance comprehensive evaluation method based on fuzzy evaluation. The power system source network coordination performance comprehensive evaluation method comprises the following steps: step 1, extracting indexes and evaluation methods related to power system source network interaction performance evaluation from collected national standard line standards; 2, determining an index weight by using a fuzzy comprehensive evaluation method and an order relation analysis method, and calculating an evaluation value according to the weight and the index value; 3, performing index analysis on the real machine and the simulation model to obtain an evaluation value; 4, comparing the real machine parameters with the index parameters, and establishing a control strategy evaluation system; and 5, comparing the real machine index evaluation value with the simulation model index evaluation value, and establishing a simulation model evaluation system. The fuzzy comprehensive evaluation method and the order relation analysis method are combined and applied to the power system source network coordination performance evaluation, the weight of each evaluation index is accurately determined through the organic combination of the two methods, and the scientificity and the accuracy of the evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of comprehensive evaluation of electric power systems, and in particular to a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation. Background Art

[0002] The new power system has four important features: safety and efficiency, clean and low-carbon, flexibility and intelligent integration. Among them, safety and efficiency are the basic premise, clean and low-carbon are the core goals, flexibility is an important support, and intelligent integration is the basic guarantee. The new power system is an important component of the new energy system and a key carrier for achieving the "dual carbon" goal. The core of the new power system is to increase the proportion of new energy, such as wind and solar power generation.

[0003] The new power system has the risk of large voltage fluctuations, large frequency fluctuations and low-frequency oscillations in the coordination of sources and networks. In order to effectively identify these risk points, it is necessary to carry out refined modeling in the auxiliary links of the excitation system, the generator speed control system including the power system, and the generator grid-related protection, and expand the scope of traditional electromechanical transient simulation. Comprehensive evaluation of hydropower units is an important method to measure the advantages and disadvantages of simulation models and control strategies.

[0004] Comprehensive evaluation methods mainly include analytic hierarchy process (AHP) and fuzzy comprehensive evaluation. AHP is a quantitative evaluation method that uses expert judgment and mathematical models to determine the importance of hazard sources by constructing a hierarchical structure. It is used to deal with multiple indicators, fuzzy uncertainty and subjectivity of the evaluation object. Fuzzy comprehensive evaluation method applies the concepts and techniques of fuzzy mathematics to the evaluation process, and obtains comprehensive evaluation results through steps such as establishing a fuzzy evaluation model, determining the weights of evaluation indicators, and performing fuzzy evaluation calculations.

[0005] Although the current background technology solutions have designed evaluation systems to some extent, these solutions still have some obvious defects and shortcomings. Similar to the technology of the present invention is the comprehensive evaluation of the maintenance effect of hydropower units based on hierarchical analysis and improved fuzzy evaluation. This method mainly uses hierarchical analysis and fuzzy evaluation to establish a comprehensive evaluation model for the maintenance effect of hydropower units. However, the hierarchical analysis method needs to meet the matrix construction consistency test, otherwise it cannot be used. In the background technology, there is an existing invention patent "A method for evaluating the coordination cost of grid connection based on new energy power generation" that models and analyzes various factors that affect the coordinated operation cost of the power grid. It cannot accurately evaluate the advantages and disadvantages of the simulation model and control strategy from the perspective of economic indicators. Summary of the invention

[0006] In order to solve the current technical problems, the main purpose of the present invention is to provide a comprehensive evaluation method for the source-grid coordination performance of a power system based on fuzzy evaluation. This evaluation method uses a sequence relationship analysis method to determine the indicator weights on the basis of the hierarchical analysis method, does not require consistency verification, and is simple and intuitive. In addition, the present invention not only constructs a complete comprehensive evaluation system, but also selects evaluation indicators from multiple aspects, and has a more comprehensive understanding of simulation models and control strategies.

[0007] In order to realize the above technical features, the object of the present invention is achieved as follows: 1. A comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation, characterized in that it includes the following steps:

[0008] Step 1: extract indicators and evaluation methods related to the power system source-grid interaction performance evaluation from the collected national and industry standards;

[0009] Step 2: Use fuzzy comprehensive evaluation method and order relationship analysis method to determine the index weights, and calculate the evaluation value based on the weights and index values;

[0010] Step 3, perform index analysis on the real machine and the simulation model to obtain evaluation values;

[0011] Step 4: Compare the real machine parameters with the index parameters and establish a control strategy evaluation system;

[0012] Step 5: Compare the real machine index evaluation value with the simulation model index evaluation value to establish a simulation model evaluation system.

[0013] The evaluation indicators extracted in step 1 specifically include: hydraulic interference analysis indicators, excitation system PSS analysis indicators, primary frequency regulation performance analysis indicators, AGC performance analysis indicators, and AVC control function analysis indicators.

[0014] Among the hydraulic interference analysis indicators:

[0015] Maximum output of turbine P m The hydraulic disturbance is evaluated by the ratio of the rated output P1, and the hydraulic disturbance function R1 of the hydropower unit under the new power system is obtained:

[0016] R1=P m / P1;

[0017] Among them, the closer R1 is to 1, the smaller the impact of hydraulic interference on the generator and the power grid is. The real machine data and simulation data are substituted for evaluation;

[0018] Fluctuation rate of hydropower units:

[0019]

[0020] Where: n is the discrete time series {t i}, γ i The output of the hydropower unit is t i The change in time, FR is the volatility, the larger the value, the greater the oscillation, the worse the stability, and vice versa, the better the stability; P i For i The output of hydropower units at all times;

[0021] For a ramp event R(i,j), for any P e , e∈(i,j), and any P i-1 , P j+1 , both satisfy R(i-1,j)=0 and R(i,j+1)=0, and the climbing event is expressed as:

[0022]

[0023] Where: i is the event before climbing, j is the event after climbing, P e is the electromagnetic power;

[0024] Climbing rate of hydropower unit:

[0025] The ramp rate indicates the change in unit output per unit time and is expressed as RR:

[0026]

[0027] Where: m For a climb;

[0028] The number of climbs is represented by N:

[0029]

[0030] A climb is expressed as:

[0031]

[0032] Where: ΔP=P j -P i and Δt=t j -t i are the amplitude and duration of the mth climb respectively;

[0033] Head loss of hydropower unit:

[0034]

[0035] The head loss of a hydropower unit is the sum of all the head losses along the way and the local head losses;

[0036] Where: n is the number of equal cross-section segments, m is the local resistance number, h wis the head loss of the hydropower unit, h fi is the total head loss along the way, h jk is the local head loss.

[0037] Among the PSS analysis indicators of the excitation system, the implementation steps of PSS online evaluation based on WAMS data are as follows:

[0038] 1) Start recording when low-frequency oscillation occurs, and record the system response signal, PSS output signal and the system operating conditions at that time;

[0039] 2) Detect the dominant oscillation mode in the output signal, there may be multiple, and obtain the frequency f, amplitude A and phase θ of each mode ui ;

[0040] 3) Detect the amplitude A of each dominant oscillation mode component in the PSS output signal ui and phase θ ui ;

[0041] 4) Calculate or retrieve the amplitude-frequency characteristics A of the open-loop system in each dominant oscillation mode Gi and phase-frequency characteristics;

[0042] 5) For mode i, the quantitative calculation formula for judging the PSS control effect is:

[0043]

[0044]

[0045] Where: k is the amplitude ratio, which mainly indicates the observability and controllability of the PSS to be evaluated for the oscillation mode i. For a specific oscillation mode, if the observability is poor, the control quantity A output by the PSS ui and the impact of the unit on this mode A Gi will be smaller, thus reducing the control of the PSS to be evaluated on the mode. For the situation where multiple units’ PSS need to be evaluated, in order to make the indicators comparable, A Gi , A ui and A yi All use the same reference value per unit; -cos(θ ui +θ Gi -θ yi ) is the influence of PSS phase shift on control effect obtained by ∠H(jω)+∠H(jω)+∠H(jω); P i The greater the combined effect of PSS gain and phase shift, the better the PSS control effect, and vice versa.

[0046] The primary frequency regulation performance analysis index is specifically: each time the grid-connected main body responds to the AGC control instruction during operation, the action of the grid-connected main body after responding to the AGC instruction is evaluated and measured from three aspects: adjustment speed, adjustment accuracy, and response time, as follows:

[0047] 1) Adjustment speed performance index K1:

[0048] K1 refers to the rate of responding to AGC control instructions, and the calculation formula is as follows:

[0049]

[0050] Where: P is the actual output of the regulation process, unit MW; ΔP z is the final command of the regulation process minus the initial output, in MW; ΔzP is the regulation amplitude in the actual regulation process, in MW; Δzt is the regulation time in the actual regulation process, in s; P z is the instruction of any point in the adjustment process, P is the actual output corresponding to the point, If it is less than 0, the regulation process is negative regulation; if it is greater than 0, the regulation process is positive regulation;

[0051] The calculation formula of the adjustment process calculation parameter T0 is:

[0052]

[0053] Where, T1 is the adjustment compensation time, hydropower, including pumped storage: 0 to 5 seconds; V0 is the active power increase and decrease rate of the unit, unit: MW / min;

[0054] 2) Adjustment accuracy performance index K2:

[0055]

[0056] In the formula, e is the regulation accuracy of the regulation process. The regulation accuracy algorithm calculates the average value of the ratio between the difference between the N unit output points and the command and the rated capacity of the unit before and after the unit's active power first enters the regulation dead zone. If the regulation process cannot be maintained due to new commands, the average of the two points is taken accordingly. If it still cannot be obtained, the ratio of the first point entering the dead zone is taken.

[0057]

[0058] The unit instructions and unit active power are stored at intervals not exceeding 5 seconds;

[0059] 3) Response time performance indicator K3:

[0060]

[0061] After the dispatching master station system command is issued, the time taken by the AGC frequency modulation unit to reliably step out of the regulation dead zone consistent with the regulation direction based on the original output point, that is:

[0062] Where t = T E -T S , t is the actual response time of the grid-connected entity regulation; T N is the standard response time of the grid-connected entity, while the standard response time of hydropower is 10s; T S and T E They are the moment when the grid-connected main body starts regulating and steps out of the regulation dead zone consistent with the regulation direction, and the unit of response time is second;

[0063] 4) Primary frequency regulation stability of hydropower units:

[0064] The stability of primary frequency regulation is expressed by the output guarantee coefficient:

[0065]

[0066] Where: P0 is the initial output when the frequency is not stable in the range of 50±0.05Hz; P is the automatic sampling output 15s to 45s after the frequency is not stable in the range of 50±0.05Hz; P r is the rated output of the unit; f is the sampling frequency 15s to 45s after the frequency fails to stabilize in the range of 50±0.05Hz; f r is the rated frequency of the unit; Δf sq It is the dead zone of primary frequency modulation. When f<50Hz, it is positive, otherwise it is negative; e P It is the speed change rate, that is, the power permanent difference coefficient.

[0067] The AGC performance analysis indicators are specifically:

[0068] 1) Objective function:

[0069] The economic operation of a hydropower plant should first ensure that the target load given by the dispatching side is met under the condition of safe power generation equipment and good power quality, with the goal of minimizing water consumption for power generation in order to obtain the maximum economic benefits. The following mathematical model is established:

[0070]

[0071] Where: W is the water consumption of all units, i is the unit number, n is the number of units, and ΔT is the time length;

[0072] 2) Dealing with balance constraints:

[0073]

[0074] 3) Unit output constraints:

[0075] P imin <P i <P imax ;

[0076] Where P imin is the minimum active power value that the i-th unit can generate under the current water head; P imax is the maximum active power value that the i-th unit can generate under the current water head.

[0077] The AVC control function analysis indicators are specifically:

[0078] 1) Equal power factor method:

[0079] Use equal power factor to distribute reactive power. The specific method is as follows:

[0080]

[0081] Where, P is the total active power of the reactive joint control unit, P i is the active power of the reactive joint control unit, i is the unit serial number, and n is the number of reactive joint control units;

[0082]

[0083] In the formula, Q AVC is the total reactive power of the joint control units, Q is the total reactive power given, is the sum of reactive power of single-control units;

[0084]

[0085] P and Q AVC Substitute Q i , the reactive power allocated to each unit can be obtained.

[0086] When allocating reactive power, each unit needs to consider the unit PQ limit. The maximum / minimum value of reactive power can be generated under the current actual active power, and the reactive power allocation cannot exceed this range;

[0087] 2) Equal margin allocation:

[0088] The requirement of this principle is to distribute reactive power according to the reactive power margin of each generator participating in voltage control, that is, the unit with more residual reactive power provides more reactive power, and the unit with less residual reactive power provides less reactive power, so as to ensure that each unit always has the same amount of regulation capacity within its adjustable range; the dynamic reactive power margin based on the PQ curve needs to calculate the dynamic lagging margin and dynamic leading margin of the generator respectively as the basis for reactive power distribution when the trigger condition is met;

[0089] μ z =(Qmax -Q act ) / Q max ;

[0090] μ j =(Q min -Q act ) / Q min ;

[0091] In the formula, μ z is the hysteresis margin, μ j is the phase margin, Q max is the upper limit of dynamic reactive power, Q min is the lower limit of dynamic reactive power, Q act is the actual value of reactive power;

[0092] 3) Equal capacity allocation:

[0093] The requirement of this principle is to distribute reactive power according to the reactive power capacity of each generator participating in voltage control, that is, the unit with large capacity provides more reactive power, and the unit with small capacity provides less reactive power, so as to ensure that the distribution amount of each unit is linearly related to the reactive power capacity of each unit:

[0094]

[0095] In the formula, Q maxi is the maximum reactive power of the i-th unit;

[0096]

[0097] Will and Substituting into the above formula, we can get the reactive power value Q allocated to each unit: maxt .

[0098] The fuzzy comprehensive evaluation method in step 2 includes the following steps:

[0099] Step 2.1, construct the fuzzy matrix;

[0100] Step 2.2, use the ordinal relationship analysis method to evaluate the indicator set, select performance indicators, and determine the ordinal relationship of indicators;

[0101] Step 2.3, after determining the weight W of each indicator and the fuzzy matrix R, calculate its comprehensive evaluation matrix A.

[0102] 1) Construction of fuzzy matrix:

[0103] Construct a fuzzy matrix for the evaluation index, and record the first-level index as a={a1,a2,...,a n}, the total number is represented by n; the secondary index is B = {b1, b2, ..., bm}, with a total of m; represent the membership degree of the secondary index to the primary index as a fuzzy matrix R:

[0104]

[0105] Evaluate each index, and represent the evaluation results with the evaluation set V = {v1, v2,..., v n}, where n is the total number of evaluation results;

[0106] 2) Weight determination:

[0107] Use the order relation analysis method to evaluate the index set; select the volatility, ramp rate, shortest start-stop time, response time, maximum fluctuation amplitude, unit rated capacity, primary frequency regulation response speed, primary frequency regulation stability, overshoot, head loss, etc. of the hydropower unit as performance indicators, and determine the index order relation;

[0108] First, perform the consistency processing on the indexes. If x i is more important than x j relative to the unit, n ≥ i, j ≥ 1, then it can be defined that x i ≥ x j . For n indexes, the following relational expression can be obtained:

[0109] x1 ≥ x2 ≥ x3 ≥ … ≥ x n ;

[0110] Then x1 ≥ x2 ≥ x3 ≥ … ≥ x n is the selected index order relation. For this target layer, x1 is the most important, x2 is the second, and so on;

[0111] Determine the ratio of the importance degrees of adjacent indexes. Let x k-1 and x k be adjacent indexes after determining the index order relation in the index system, where 1 < k ≤ n; the decision maker makes a rational judgment on the importance degrees of indexes x k-1 and x k , and obtains that the ratio of the importance degrees of the two indexes is r k = W k-1 / W k , where W k-1 and W k are the importance degrees of indexes x k-1 and x k respectively;

[0112] Determine the weight coefficient, use the order relation analysis method for analysis, and define the weight coefficient of the nth index as W n , and its calculation method is:

[0113]

[0114] W k-1 =r k W k ;

[0115] Where: 1 <k≤n;

[0116] The indicators of each layer are compared pairwise to obtain the relative importance of the indicators. A judgment matrix is ​​formed according to the relative importance of the indicators. The value of the judgment matrix is ​​expressed by the 9-point scaling method:

[0117] Index x k-1 and x k The importance is the same, then r k is 1; index x k-1 Compared to x k Slightly important, then r k is 1.2; index x k-1 Compared to x k Obviously important, then r k is 1.4; index x k-1 Compared to x k Strongly important, then r k is 1.6; index c k-1 Compared to x k Extremely important, then r k is 1.8;

[0118] The regulation performance judgment matrix of hydropower units in the new power system is:

[0119]

[0120] After forming the judgment matrix of the indicator set, the weight of each indicator is calculated as follows:

[0121]

[0122] 3) Evaluation value determination:

[0123] After determining the weight W of each indicator and the fuzzy matrix R, calculate its comprehensive evaluation matrix A:

[0124] A = W × R;

[0125] Quantify the final evaluation of A:

[0126] F=A×V T ;

[0127] Where: F is the comprehensive evaluation value of the regulation performance of the hydropower unit under the new power system; V T is the evaluation set;

[0128] Acquire real data from the power plant and compare it with the simulation data. The comparison results are used to guide the setting of the next round of parameters of the simulation model to improve the accuracy of the model. Then, based on the simulation results and the actual operation of the units, evaluate the power system source-grid coordination strategy to ensure the stability and performance of the power system.

[0129] According to the evaluation results of real machine data and simulation model data, a comprehensive evaluation is conducted on the interactive performance of power system sources and grids. Based on the evaluation results, the control strategy and simulation model are optimized to improve the interactive performance of power system sources and grids.

[0130] The order relation analysis method in step 2 comprises the following steps:

[0131] Make the indicators consistent;

[0132] Determine the importance ratio of adjacent indicators;

[0133] Determine the weight factor.

[0134] The present invention has the following beneficial effects:

[0135] 1. The present invention combines the fuzzy comprehensive evaluation method and the ordinal relationship analysis method for the first time in the evaluation of the power system source-grid coordination performance. By organically combining the two methods, the weights of various evaluation indicators are accurately determined, thereby improving the scientificity and accuracy of the evaluation results.

[0136] 2. The present invention constructs a comprehensive and scientific power system source-grid coordination performance evaluation system through systematic evaluation steps, including indicator extraction, weight determination, real machine and simulation model analysis, and the establishment of control strategy and simulation model evaluation system.

[0137] 3. The present invention establishes a multi-level evaluation system, which not only independently evaluates the real machine and the simulation model, but also establishes a control strategy evaluation system and a simulation model evaluation system through comparative analysis, providing multi-angle support for the comprehensive evaluation of the source-grid coordination performance of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0138] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0139] Figure 1 A system flow chart is established for the comprehensive evaluation of the present invention.

[0140] Figure 2 It is the evaluation index of the present invention. DETAILED DESCRIPTION

[0141] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0142] Embodiment 1:

[0143] Process of the present invention:

[0144] The present invention provides a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation, comprising:

[0145] Step 1: Extract indicators and evaluation methods related to the power system source-grid interaction performance evaluation from the collected national and industry standards;

[0146] Step 2: Use fuzzy comprehensive evaluation method and order relationship analysis method to determine the index weight, and calculate the evaluation value based on the weight and index value;

[0147] Step 3: Analyze the indicators of the real machine and the simulation model to obtain the evaluation value;

[0148] Step 4: Compare the real machine parameters with the index parameters and establish a control strategy evaluation system;

[0149] Step 5: Compare the real machine index evaluation value with the simulation model index evaluation value to establish a simulation model evaluation system;

[0150] Furthermore, the fuzzy comprehensive evaluation method comprises the following steps:

[0151] (1) Construct fuzzy matrix;

[0152] (2) Use the ordinal relationship analysis method to evaluate the indicator set, select performance indicators, and determine the ordinal relationship of the indicators;

[0153] (3) After determining the weights W of each indicator and the fuzzy matrix R, calculate the comprehensive evaluation matrix A;

[0154] Furthermore, the sequence relationship analysis method comprises the following steps:

[0155] (1) Make the indicators consistent;

[0156] (2) Determine the importance ratio of adjacent indicators;

[0157] (3) Determine the weight coefficient.

[0158] Embodiment 2:

[0159] Specific implementation process:

[0160] First, collect national standards and industry standards related to the evaluation of power system source-grid interaction performance, including relevant indicators and methods for power system source-grid coordination performance evaluation, as well as requirements and parameters for actual operating performance; extract indicators and evaluation methods related to power system source-grid interaction performance evaluation from the collected national and industry standards. The following is the selection of evaluation indicators and specific parameter requirements.

[0161] (1) Hydraulic interference analysis indicators:

[0162] Hydraulic disturbance is not a control condition for regulating parameters, but it is more serious than small fluctuations. Therefore, we will evaluate the impact of hydraulic disturbance on the generator and the power grid through the output swing of the unit. The output of the generator is the product of voltage and current. Generally, when hydraulic disturbance occurs, the voltage changes quickly due to the rapid change of electromagnetic waves, so it stabilizes quickly. It can be considered that the change in output is the change in current. Therefore, the maximum output of the turbine P can be used m The hydraulic disturbance is evaluated by the ratio of the rated output P1, and the hydraulic disturbance function R1 of the hydropower unit under the new power system is obtained:

[0163] R1=P m / P1;

[0164] Among them, the closer R1 is to 1, the smaller the impact of hydraulic interference on the generator and the power grid is. The real machine data and simulation data are substituted for evaluation;

[0165] 1) Fluctuation rate of hydropower units:

[0166]

[0167] Where: n is the discrete time series {t i}, γ i The output of the hydropower unit is t i The change in time, FR is the volatility, the larger the value, the greater the oscillation, the worse the stability, and vice versa, the better the stability; P i For i The output of hydropower units at all times;

[0168] For a ramp event R(i,j), for any P e , e∈(i,j), and any P i-1 , P j+1 , both satisfy R(i-1,j)=0 and R(i,j+1)=0, and the climbing event is expressed as:

[0169]

[0170] Where: i is the event before climbing, j is the event after climbing, P e is the electromagnetic power;

[0171] 2) Climbing rate of hydropower unit:

[0172] The ramp rate indicates the change in unit output per unit time and is expressed as RR:

[0173]

[0174] Where: m For a climb;

[0175] The number of climbs is represented by N:

[0176]

[0177] A climb is expressed as:

[0178]

[0179] Where: ΔP=P j -P i and Δt = t j -t i are the amplitude and duration of the mth climb respectively;

[0180] 3) Head loss of hydropower unit:

[0181]

[0182] The head loss of a hydropower unit is the sum of all the head losses along the way and the local head losses;

[0183] Where: n is the number of equal cross-section segments, m is the local resistance number, h w is the head loss of the hydropower unit, h fi is the total head loss along the way, h jk is the local head loss.

[0184] (2) Excitation system PSS analysis indicators:

[0185] The steps to implement PSS online evaluation based on WAMS data are as follows:

[0186] 1) Start recording when low-frequency oscillation occurs, and record the system response signal, PSS output signal and the system operating conditions at that time;

[0187] 2) Detect the dominant oscillation mode in the output signal, there may be multiple, and obtain the frequency f, amplitude A and phase 0 of each mode ui ;

[0188] 3) Detect the amplitude A of each dominant oscillation mode component in the PSS output signal ui and phase θ ui ;

[0189] 4) Calculate or retrieve the amplitude-frequency characteristics A of the open-loop system in each dominant oscillation mode Gi and phase-frequency characteristics;

[0190] 5) For mode i, the quantitative calculation formula for judging the PSS control effect is:

[0191]

[0192] Where: k is the ratio of amplitudes, which mainly indicates the observability and controllability of the PSS to be evaluated for the oscillation mode i. For a specific oscillation mode, if the observability is poor, the control quantity A output by the PSS is ui and the impact of the unit on this mode A Gi will be smaller, thus reducing the control of the PSS to be evaluated on the mode. For the situation where multiple units’ PSS need to be evaluated, in order to make the indicators comparable, A Gi , A ui and A yi All use the same reference value per unit; -cos(θ ui +θ Gi -θ yi ) is the influence of PSS phase shift on control effect obtained by ∠H(jω)+∠H(jω)+∠H(jω); P i The greater the combined effect of PSS gain and phase shift, the better the PSS control effect, and vice versa.

[0193] (3) Primary frequency modulation performance analysis indicators:

[0194] Each time the grid-connected subject responds to the AGC control instruction during operation, the action of the grid-connected subject after responding to the AGC instruction is evaluated and measured from three aspects: adjustment speed, adjustment accuracy, and response time. The details are as follows:

[0195] 1) Adjustment speed performance index K1:

[0196] K1 refers to the rate of responding to AGC control instructions, and the calculation formula is as follows:

[0197]

[0198] Where: P is the actual output of the regulation process, unit MW; ΔP z is the final command of the regulation process minus the initial output, in MW; ΔP is the regulation amplitude in the actual regulation process, in MW; ΔT is the regulation time in the actual regulation process, in s; P z is the instruction at any point in the adjustment process, P is the actual output corresponding to that point, If it is less than 0, the regulation process is negative regulation; if it is greater than 0, the regulation process is positive regulation;

[0199] The calculation formula of the adjustment process calculation parameter T0 is:

[0200]

[0201] Where, T1 is the adjustment compensation time, hydropower, including pumped storage: 0 to 5 seconds; V0 is the active power increase and decrease rate of the unit, unit: MW / min;

[0202] 2) Adjustment accuracy performance index K2:

[0203]

[0204] In the formula, e is the regulation accuracy of the regulation process. The regulation accuracy algorithm calculates the average value of the ratio between the difference between the N unit output points and the command and the rated capacity of the unit before and after the unit's active power first enters the regulation dead zone. If the regulation process cannot be maintained due to new commands, the average of the two points is taken accordingly. If it still cannot be obtained, the ratio of the first point entering the dead zone is taken.

[0205]

[0206] The unit instructions and unit active power are stored at intervals not exceeding 5 seconds;

[0207] 3) Response time performance indicator K3:

[0208]

[0209] After the dispatching master station system command is issued, the time taken by the AGC frequency modulation unit to reliably step out of the regulation dead zone consistent with the regulation direction based on the original output point, that is:

[0210] Where t = T E -T S , t is the actual response time of the grid-connected entity regulation; T N is the standard response time of the grid-connected entity, while the standard response time of hydropower is 10s; T S and T E They are the moment when the grid-connected main body starts regulating and steps out of the regulation dead zone consistent with the regulation direction, and the unit of response time is second;

[0211] 4) Primary frequency regulation stability of hydropower units:

[0212] The stability of primary frequency regulation is expressed by the output guarantee coefficient:

[0213]

[0214] Where: P0 is the initial output when the frequency is not stable in the range of 50±0.05Hz; P is the automatic sampling output 15s to 45s after the frequency is not stable in the range of 50±0.05Hz; P r is the rated output of the unit; f is the sampling frequency 15s to 45s after the frequency fails to stabilize in the range of 50±0.05Hz; f r is the rated frequency of the unit; Δf sq It is the dead zone of primary frequency modulation. When f<50Hz, it is positive, otherwise it is negative; e P It is the speed change rate, that is, the power permanent difference coefficient.

[0215] (4) AGC performance analysis indicators:

[0216] 1) Objective function:

[0217] The economic operation of a hydropower plant should first ensure that the target load given by the dispatching side is met under the condition of safe power generation equipment and good power quality, with the goal of minimizing water consumption for power generation in order to obtain the maximum economic benefits. The following mathematical model is established:

[0218]

[0219] Where: W is the water consumption of all units, i is the unit number, n is the number of units, and ΔT is the time length;

[0220] 2) Dealing with balance constraints:

[0221]

[0222] 3) Unit output constraints:

[0223] P imin <P i <P imax ;

[0224] Where P imin is the minimum active power value that the i-th unit can generate under the current water head; P imax is the maximum active power value that the i-th unit can generate under the current water head.

[0225] (5) AVC control function analysis indicators:

[0226] 1) Equal power factor method:

[0227] Use equal power factor to distribute reactive power. The specific method is as follows:

[0228]

[0229] Where, P is the total active power of the reactive joint control unit, P i is the active power of the reactive joint control unit, i is the unit serial number, and n is the number of reactive joint control units;

[0230]

[0231] In the formula, Q AVC is the total reactive power of the joint control units, Q is the total reactive power given, is the sum of reactive power of single-control units;

[0232]

[0233] P and Q AVC Substitute Qi , the reactive power allocated to each unit can be obtained.

[0234] When allocating reactive power, each unit needs to consider the unit PQ limit. The maximum / minimum value of reactive power can be generated under the current actual active power, and the reactive power allocation cannot exceed this range;

[0235] 2) Equal margin allocation:

[0236] The requirement of this principle is to distribute reactive power according to the reactive power margin of each generator participating in voltage control, that is, the unit with more residual reactive power provides more reactive power, and the unit with less residual reactive power provides less reactive power, so as to ensure that each unit always has the same amount of regulation capacity within its adjustable range; the dynamic reactive power margin based on the PQ curve needs to calculate the dynamic lagging margin and dynamic leading margin of the generator respectively as the basis for reactive power distribution when the trigger condition is met;

[0237] μ z =(Q max -q act ) / Q max ;

[0238] μ j =(Q min -Q act ) / Q min ;

[0239] In the formula, μ z is the hysteresis margin, μ j is the phase margin, Q max is the upper limit of dynamic reactive power, Q min is the lower limit of dynamic reactive power, Q act is the actual value of reactive power;

[0240] 3) Equal capacity allocation:

[0241] The requirement of this principle is to distribute reactive power according to the reactive power capacity of each generator participating in voltage control, that is, the unit with large capacity provides more reactive power, and the unit with small capacity provides less reactive power, so as to ensure that the distribution amount of each unit is linearly related to the reactive power capacity of each unit:

[0242]

[0243] In the formula, Q maxi is the maximum reactive power of the i-th unit;

[0244]

[0245] Will and Substituting into the above formula, we can get the reactive power value Q allocated to each unit: maxt .

[0246] After completing the index extraction, the fuzzy comprehensive evaluation method and the ordinal relationship analysis method are used to determine the index weights, and the evaluation value is calculated based on the weights and index values.

[0247] The fuzzy comprehensive evaluation method determines the evaluation set based on key factors or indicators, and determines the value range of the attributes for each evaluation indicator, that is, the evaluation set; constructs a fuzzy evaluation matrix based on the evaluation set, in which each row represents an evaluation indicator, each column represents an evaluation object, and the matrix elements are the evaluation values ​​of the corresponding evaluation indicators to the evaluation objects; determines the fuzzy weight, and determines the fuzzy weight of each indicator according to the importance of the evaluation indicator, which can be obtained through expert consultation, fuzzy hierarchical analysis method and other methods; calculates the membership function, and converts the fuzzy weight into a membership function to describe the weight distribution corresponding to each evaluation indicator; solves the comprehensive evaluation, and performs fuzzy operations on each element of the fuzzy evaluation matrix according to the membership function to obtain the fuzzy results of the comprehensive evaluation: interprets and analyzes the results after fuzzification, explores the contribution of different indicators to the results, and proposes improvement measures or corresponding decisions based on the results.

[0248] 1) Construction of fuzzy matrix:

[0249] Construct a fuzzy matrix for the evaluation index, and record the first-level index as A = {a1, a2, ..., a n}, the total number is represented by n; the secondary index is B = {b1, b2, ..., b m}, the total number is m; the membership degree of the secondary index to the primary index is expressed as the fuzzy matrix R:

[0250]

[0251] Each indicator is evaluated and the evaluation results are expressed as an evaluation set V = {v1, v2, ..., v n} indicates that n is the total number of evaluation results;

[0252] 2) Weight determination:

[0253] The index set is evaluated by using the sequence relationship analysis method; the fluctuation rate, ramp rate, shortest start-stop time, response time, maximum fluctuation amplitude, rated capacity of the unit, primary frequency regulation response speed, primary frequency regulation stability, overshoot, head loss, etc. of the hydropower unit are selected as performance indicators to determine the sequence relationship of the indicators;

[0254] First, the indicators are unified. If x i The importance ratio of the unit is x j Strong, n≥i, j≥1, then by definition we can get x i ≥xj , the following relationship holds for n indicators:

[0255] x1 ≥ x2 ≥ x3 ≥ … ≥ x n ;

[0256] Then x1 ≥ x2 ≥ x3 ≥ … ≥ x n is the selected indicator order relationship. For this target layer, x1 is the most important, x2 is the second most important, and so on;

[0257] Determine the ratio of the importance degrees of adjacent indicators. Let x k-1 and x k be adjacent indicators after determining the indicator order relationship in the indicator system, where 1 < k ≤ n; The decision maker makes a rational judgment on the importance degrees of indicators x k-1 and x k , and obtains that the ratio of the importance degrees of the two indicators is r k = W k-1 / W k , where, W k-1 and W k are the importance degrees of indicators x k-1 and x k respectively;

[0258] Determine the weight coefficients. Use the order relation analysis method for analysis, and define the weight coefficient of the nth indicator as W n , and its calculation method is:

[0259]

[0260] W k-1 = r k W k ;

[0261] In the formula: 1 < k ≤ n;

[0262] Compare the indicators in each layer pairwise to obtain the relative importance between the indicators. Form a judgment matrix according to the relative importance between the indicators. The values of the judgment matrix are represented by the 9-point scale method:

[0263] If the indicators x k-1 and x k are of the same importance, then r k is 1; If the indicator x k-1 is slightly more important than x k , then r k is 1.2; If the indicator x k-1 is significantly more important than x k , then r k is 1.4; If the indicator x k-1 is strongly more important than x k , then r kis 1.6; index x k-1 Compared to x k Extremely important, then r k is 1.8;

[0264] The regulation performance judgment matrix of hydropower units in the new power system is:

[0265]

[0266] After forming the judgment matrix of the indicator set, the weight of each indicator is calculated as follows:

[0267]

[0268] 3) Evaluation value determination:

[0269] After determining the weights W of each indicator and the fuzzy matrix R, calculate the comprehensive evaluation matrix A:

[0270] A = W × R;

[0271] Quantify the final evaluation of a:

[0272] F=A×V T ;

[0273] Where: F is the comprehensive evaluation value of the regulation performance of the hydropower unit under the new power system; V T is the evaluation set;

[0274] Real data is obtained from the power plant and compared with the simulation data. The comparison results are used to guide the setting of the next round of parameters of the simulation model to improve the accuracy of the model. Based on the simulation results and the actual operation of the units, the power system source-grid coordination strategy is evaluated to ensure the stability and performance of the power system.

[0275] According to the evaluation results of real machine data and simulation model data, a comprehensive evaluation is conducted on the interactive performance of power system sources and grids. Based on the evaluation results, the control strategy and simulation model are optimized to improve the interactive performance of power system sources and grids.

Claims

1. A comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation, characterized in that: The following steps are involved: Step 1: extract indicators and evaluation methods related to the power system source-grid interaction performance evaluation from the collected national and industry standards; Step 2: Use fuzzy comprehensive evaluation method and order relationship analysis method to determine the index weights, and calculate the evaluation value based on the weights and index values; Step 3, perform index analysis on the real machine and the simulation model to obtain evaluation values; Step 4: Compare the real machine parameters with the index parameters and establish a control strategy evaluation system; Step 5: Compare the real machine index evaluation value with the simulation model index evaluation value to establish a simulation model evaluation system.

2. According to claim 1, a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation is characterized in that: The evaluation indicators extracted in step 1 specifically include: hydraulic interference analysis indicators, excitation system PSS analysis indicators, primary frequency regulation performance analysis indicators, AGC performance analysis indicators, and AVC control function analysis indicators.

3. According to claim 2, a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation is characterized in that: Among the hydraulic interference analysis indicators: Maximum output of turbine P m The hydraulic disturbance is evaluated by the ratio of the rated output P1, and the hydraulic disturbance function R1 of the hydropower unit under the new power system is obtained: R1=P m / P1; Among them, the closer R1 is to 1, the smaller the impact of hydraulic interference on the generator and the power grid is. The real machine data and simulation data are substituted for evaluation; Fluctuation rate of hydropower units: Where: n is the discrete time series {t i }, γ i The output of the hydropower unit is t i The change in time, FR is the volatility, the larger the value, the greater the oscillation, the worse the stability, and vice versa, the better the stability, P i For i The output of hydropower units at all times; For a ramp event R(i, j), for any P e , e∈(i, j), and any P i-1 , P j+1 , both satisfy R(i-1, j) = 0 and R(i, j+1) = 0, and the climbing event is expressed as: Where: i is the event before climbing, j is the event after climbing, P e is the electromagnetic power; Climbing rate of hydropower unit: The ramp rate indicates the change in unit output per unit time and is expressed as RR: Where: m For a climb; The number of climbs is represented by N: A climb is expressed as: Where: ΔP=P j -P i and Δt = t j -t i are the amplitude and duration of the mth climb respectively; Head loss of hydropower unit: The head loss of a hydropower unit is the sum of all the head losses along the way and the local head losses; Where: n is the number of equal cross-section segments, m is the local resistance number, h w is the head loss of the hydropower unit, h fi is the total head loss along the way, h jk is the local head loss.

4. According to claim 3, a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation is characterized in that: Among the PSS analysis indicators of the excitation system, the implementation steps of PSS online evaluation based on WAMS data are as follows: 1) Start recording when low-frequency oscillation occurs, and record the system response signal, PSS output signal and the system operating conditions at that time; 2) Detect the dominant oscillation mode in the output signal, there may be multiple, and obtain the frequency f, amplitude A and phase θ of each mode ui ; 3) Detect the amplitude A of each dominant oscillation mode component in the PSS output signal ui and phase θ ui ; 4) Calculate or retrieve the amplitude-frequency characteristics A of the open-loop system in each dominant oscillation mode Gi and phase-frequency characteristics; 5) For mode i, the quantitative calculation formula for judging the PSS control effect is: Where: k is the ratio of amplitudes, which mainly indicates the observability and controllability of the PSS to be evaluated for the oscillation mode i. For a specific oscillation mode, if the observability is poor, the control quantity A output by the PSS is ui and the impact of the unit on this mode A Gi will be smaller, thus reducing the control of the PSS to be evaluated on the mode. For the situation where multiple units’ PSS need to be evaluated, in order to make the indicators comparable, A Gi , A ui and A yi All use the per-unit value of the unified reference value; -cos(θ ui +θ Gi -θ yi ) is the influence of PSS phase shift on control effect obtained according to ∠H(jω)+∠H(jω)+∠H(jω); Pi combines the effects of PSS gain and phase shift. The greater the effect, the better the PSS control effect, and vice versa.

5. According to claim 4, a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation is characterized in that: The primary frequency regulation performance analysis index is specifically: each time the grid-connected main body responds to the AGC control instruction during operation, the action of the grid-connected main body after responding to the AGC instruction is evaluated and measured from three aspects: adjustment speed, adjustment accuracy, and response time, as follows: 1) Adjustment speed performance index K1: K1 refers to the rate of responding to AGC control instructions, and the calculation formula is as follows: Where: P is the actual output of the regulation process, unit MW; ΔP z The final command of the regulation process - the initial output, in MW; AP is the adjustment amplitude in the actual adjustment process, in MW; ΔT is the adjustment time in the actual adjustment process, in s; P z is the instruction of any point in the adjustment process, P is the actual output corresponding to the point, If it is less than 0, the regulation process is negative regulation; if it is greater than 0, the regulation process is positive regulation; The calculation formula of the adjustment process calculation parameter T0 is: Where, T1 is the adjustment compensation time, hydropower, including pumped storage: 0 to 5 seconds; V0 is the active power increase and decrease rate of the unit, unit: MW / min; 2) Adjustment accuracy performance index K2: In the formula, e is the regulation accuracy of the regulation process. The regulation accuracy algorithm calculates the average value of the ratio between the difference between the N unit output points and the command and the rated capacity of the unit before and after the unit's active power first enters the regulation dead zone. If the regulation process cannot be maintained due to new commands, the average of the two points is taken accordingly. If it still cannot be obtained, the ratio of the first point entering the dead zone is taken. The unit instructions and unit active power are stored at intervals not exceeding 5 seconds; 3) Response time performance indicator K3: After the dispatching master station system command is issued, the time taken by the AGC frequency modulation unit to reliably step out of the regulation dead zone consistent with the regulation direction based on the original output point, that is: Where t = T E -T S , t is the actual response time of the grid-connected subject regulation; T N is the standard response time of the grid-connected entity, while the standard response time of hydropower is 10s; T S and T E They are the moment when the grid-connected main body starts regulating and steps out of the regulation dead zone consistent with the regulation direction, and the unit of response time is second; 4) Primary frequency regulation stability of hydropower units: The stability of primary frequency regulation is expressed by the output guarantee coefficient: Where: P0 is the initial output when the frequency is not stable in the range of 50±0.05Hz; P is the automatic sampling output 15s to 45s after the frequency is not stable in the range of 50±0.05Hz; P r is the rated output of the unit; f is the sampling frequency 15s to 45s after the frequency fails to stabilize in the range of 50±0.05Hz; f r is the rated frequency of the unit; Δf sq It is the dead zone of primary frequency modulation. When f<50Hz, it is positive, otherwise it is negative. P It is the speed change rate, that is, the power permanent difference coefficient.

6. According to claim 5, a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation is characterized in that: The AGC performance analysis indicators are specifically: 1) Objective function: The economic operation of a hydropower plant should first ensure that the target load given by the dispatching side is met under the condition of safe power generation equipment and good power quality, with the goal of minimizing water consumption for power generation in order to obtain the maximum economic benefits. The following mathematical model is established: Where: W is the water consumption of all units, i is the unit number, n is the number of units, and ΔT is the time length; 2) Dealing with balance constraints: 3) Unit output constraints: P imin <P i <P imax ; Where P imin is the minimum active power value that the i-th unit can generate under the current water head; P imax is the maximum active power value that the i-th unit can generate under the current water head.

7. The method for comprehensive evaluation of power system source-grid coordination performance based on fuzzy evaluation according to claim 6, characterized in that: The AVC control function analysis indicators are specifically: 1) Equal power factor method: Use equal power factor to distribute reactive power. The specific method is as follows: Where, P is the total active power of the reactive joint control unit, P i is the active power of the reactive joint control unit, i is the unit serial number, and n is the number of reactive joint control units; In the formula, Q AVC is the total reactive power of the joint control units, Q is the total reactive power given, is the sum of reactive power of single-control units; P and Q AVC Substitute Q i , the reactive power allocated to each unit can be obtained. When allocating reactive power, each unit needs to consider the unit PQ limit. The maximum / minimum value of reactive power can be generated under the current actual active power, and the reactive power allocation cannot exceed this range; 2) Equal margin allocation: The requirement of this principle is to distribute reactive power according to the reactive power margin of each generator participating in voltage control, that is, the unit with more residual reactive power provides more reactive power, and the unit with less residual reactive power provides less reactive power, so as to ensure that each unit always has the same amount of regulation capacity within its adjustable range; the dynamic reactive power margin based on the PQ curve needs to calculate the dynamic lagging margin and dynamic leading margin of the generator respectively as the basis for reactive power distribution when the trigger condition is met; μ z =(Q max -Q act ) / Q max ; μ j =(Q min -Q act ) / Q min ; In the formula, μ z is the hysteresis margin, μ j is the phase margin, Q max is the upper limit of dynamic reactive power, Q min is the lower limit of dynamic reactive power, Q act is the actual value of reactive power; 3) Equal capacity allocation: The requirement of this principle is to distribute reactive power according to the reactive power capacity of each generator participating in voltage control, that is, the unit with large capacity provides more reactive power, and the unit with small capacity provides less reactive power, so as to ensure that the distribution amount of each unit is linearly related to the reactive power capacity of each unit: In the formula, Q maxi is the maximum reactive power of the i-th unit; Will and Substituting into the above formula, we can get the reactive power value Q allocated to each unit: maxt .

8. According to claim 7, a comprehensive evaluation method for power system source-grid coordination performance based on fuzzy evaluation is characterized in that: The fuzzy comprehensive evaluation method in step 2 includes the following steps: Step 2.1, construct the fuzzy matrix; Step 2.2, use the ordinal relationship analysis method to evaluate the indicator set, select performance indicators, and determine the ordinal relationship of indicators; Step 2.3, after determining the weight W of each indicator and the fuzzy matrix R, calculate its comprehensive evaluation matrix A.

9. The method for comprehensive evaluation of power system source-grid coordination performance based on fuzzy evaluation according to claim 8, characterized in that: 1) Construction of fuzzy matrix: Construct a fuzzy matrix for the evaluation index, and record the first-level index as A = {a1, a2, ..., a n }, the total number is represented by n; the secondary index is B = {b1, b2, ..., b m }, the total number is m; the membership degree of the secondary index to the primary index is expressed as the fuzzy matrix R: Each indicator is evaluated and the evaluation results are expressed as an evaluation set V = {v1, v2, ..., v n } indicates that n is the total number of evaluation results; 2) Weight determination: The index set is evaluated using the ordinal relation analysis method; Select the hydropower unit fluctuation rate, ramp rate, shortest start-stop time, response time, maximum fluctuation amplitude, unit rated capacity, primary frequency regulation response speed, primary frequency regulation stability, overshoot, head loss and other performance indicators to determine the indicator sequence relationship; First, the indicators are unified. If x i The importance ratio of the unit is x j Strong, n≥i, j≥1, then by definition we can get x i ≥x j , for n indicators, the following relationship is obtained: x1≥x2≥x3≥…≥x n ; Then x1≥x2≥x3≥…≥x n To select the index order relationship, for this target layer, x1 is the most important, followed by x2, and so on; Determine the ratio of the importance degrees of adjacent indicators, and let x k-1 and x k be adjacent indicators after determining the order relationship of indicators in the indicator system, where 1 < k ≤ n; the decision maker makes a rational judgment on the importance degrees of indicators x k-1 and x k , and obtains that the ratio of the importance degrees of the two indicators is r k = W k-1 / W k , where W k-1 and W k are the importance degrees of indicators x k-1 and x k respectively; Determine the weight coefficient, use the order relationship analysis method to analyze, and define the weight coefficient of the nth indicator as W n , which is calculated as: W k-1 =r k W k ; Where: 1<k≤n; The indicators of each layer are compared pairwise to obtain the relative importance of the indicators. A judgment matrix is ​​formed according to the relative importance of the indicators. The value of the judgment matrix is ​​expressed by the 9-point scaling method: Index x k-1 and x k The importance is the same, then r k is 1; index x k-1 Compared to x k Slightly important, then r k is 1.2; index x k-1 Compared to x k Obviously important, then r k is 1.4; index x k-1 Compared to x k Strongly important, then r k is 1.6; index x k-1 Compared to x k Extremely important, then r k is 1.8; The regulation performance judgment matrix of hydropower units in the new power system is: After forming the judgment matrix of the indicator set, the weight of each indicator is calculated as follows: 3) Evaluation value determination: After determining the weights W of each indicator and the fuzzy matrix R, calculate the comprehensive evaluation matrix A: A = W × R; Quantify the final evaluation of A: F=A×V T ; Where: F is the comprehensive evaluation value of the regulation performance of the hydropower unit under the new power system; V T is the evaluation set; Acquire real data from the power plant and compare it with the simulation data. The comparison results are used to guide the setting of the next round of parameters of the simulation model to improve the accuracy of the model. Then, based on the simulation results and the actual operation of the units, evaluate the power system source-grid coordination strategy to ensure the stability and performance of the power system. According to the evaluation results of real machine data and simulation model data, a comprehensive evaluation is conducted on the interactive performance of power system sources and grids. Based on the evaluation results, the control strategy and simulation model are optimized to improve the interactive performance of power system sources and grids.

10. The method for comprehensive evaluation of power system source-grid coordination performance based on fuzzy evaluation according to claim 9, characterized in that: The order relation analysis method in step 2 comprises the following steps: Make the indicators consistent; Determine the importance ratio of adjacent indicators; Determine the weight factor.

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