JP2019030065A - Power system reliability evaluation system - Google Patents

Abstract

To appropriately evaluate the reliability of supply at occurrence of an expected accident in a power system including a renewable energy power source.SOLUTION: A reliability evaluation system 1 for occurrence of an expected accident in a power system including a renewable energy power source, includes: a system state estimation unit 112 that estimates a future system state on the basis of measurement information including at least any one of voltages, phases, loads, power generator outputs, and power flows at multiple points in a power system 21, renewable energy output prediction information including an output prediction value related to the renewable energy power source, a value greater than the prediction value by a predetermined amount, and a value less than the prediction value by a predetermined amount, demand prediction information of power in the power system, and operation plan information about power supply in the power system; and a reliability evaluation unit 113 that evaluates a supply reliability on the basis of the future system state estimated by the system state estimation unit, expected accident information including multiple expected accident forms, and the renewable energy output prediction information.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to a power system reliability evaluation system.

  In a power system, the degree of ability to supply power to consumers is called supply reliability. It is preferable that the supply reliability not only at the time of steady operation but also when an assumed accident occurs (assuming that an assumed accident has occurred) can be appropriately evaluated. By doing so, it is possible to implement advance measures such as adjustment of the generator output, phase adjustment control, and suppression of the output of the renewable energy power source as necessary in preparation for the occurrence of an assumed accident.

JP2013-208042A

  Renewable energy power sources that use renewable energy such as sunlight and wind power vary greatly depending on the environment. However, there has been no prior art that evaluates the supply reliability at the time of an assumed accident in consideration of the characteristics of such a renewable energy power source.

  Therefore, the problem of the present embodiment is to appropriately evaluate the supply reliability when an assumed accident occurs in an electric power system including a renewable energy power source.

  The power system reliability evaluation system of the embodiment evaluates the power supply reliability when an assumed accident occurs in a power system including a renewable energy power source. The power system reliability evaluation system includes: measurement information including at least one of a voltage, a phase, a load, a generator output, and a power flow at a plurality of locations in the power system; a predicted value of an output related to the renewable energy power source; Renewable energy output prediction information including a value larger by a predetermined amount than a value and a value smaller by a predetermined amount than the predicted value, demand prediction information of power in the power system, and operation related to power supply in the power system A system state estimation unit for estimating a future system state based on the plan information, a future system state estimated by the system state estimation unit, assumed accident information including a plurality of assumed accident aspects, and the renewable energy output A reliability evaluation unit that evaluates the supply reliability based on prediction information.

FIG. 1 is a diagram illustrating an example of a configuration of a reliability evaluation system according to an embodiment. FIG. 2 is a diagram illustrating an example of assumed accident information according to the embodiment. FIG. 3 is a diagram illustrating an example of the probability distribution of the renewable energy output prediction according to the embodiment. FIG. 4 is a diagram illustrating an example of the renewable energy output prediction information according to the embodiment. FIG. 5 is a diagram illustrating an example of system state information estimated in the embodiment. FIG. 5A is a diagram illustrating an example of node data in the system state information. FIG. 5B is a diagram illustrating an example of branch data in the system state information. FIG. 6 is an image diagram of the reliability evaluation model of the embodiment. FIG. 7 is an image diagram showing the relationship between the overload capacity and the time in the embodiment. FIG. 8 is an image diagram of a PV curve showing a voltage stability limit point. FIG. 9 is a flowchart illustrating an example of a process for creating a reliability evaluation model according to the embodiment. FIG. 10 is a data set example of a tidal current section and an accident aspect for creating a reliability evaluation model in the embodiment. FIG. 11 is an image diagram showing a state in which input variables are clustered. FIG. 12 is a flowchart illustrating an example of processing during operation of the embodiment. FIG. 13 is a flowchart illustrating an example of the process of S22 of FIG. FIG. 14 is an image diagram for explaining an example of selecting a reliability evaluation model in the embodiment.

  Embodiments of the present invention will be specifically described below with reference to the drawings. First, the precondition will be described. Supply reliability (hereinafter also simply referred to as “reliability”) in the power system (hereinafter also simply referred to as “system”) has two concepts, adequacy and security, from the standpoint of the system operator. . Adequacy is the reliability of static supply, and it is related to whether or not the equipment configuration meets the power demand (hereinafter also simply referred to as “demand”) in consideration of operational constraints such as planned outages of grid equipment. It is a concept.

  Security, on the other hand, is dynamic supply reliability, and is a concept related to whether the stability of the system can be maintained and the frequency and voltage can be kept normal against disturbances such as sudden failures. is there.

  As a reference for maintaining these supply reliability levels when the system operator operates the power system in real time, one facility (for example, two installed in parallel) is included in the facilities constituting the system. The “N-1 reliability standard” is widely adopted so that even if one of the power transmission lines of the power line breaks down, the power supply can be continued only with the remaining equipment. In addition, for important facilities, etc., “N-2 reliability standard” that allows the power supply to continue even if two facilities (for example, both of the two transmission lines provided in parallel) fail. May be adopted.

  Among the controls for maintaining the supply reliability, there is a concept of preventive control in which measures are taken in advance before the occurrence of the assumed accident in order to avoid a problem in power supply when the assumed accident occurs. In preventive control, current system information is acquired to estimate the system state, and the reliability when an assumed accident occurs in the estimated system state is examined. If supply disruption occurs, consider advance measures such as generator output adjustment and phase control. However, in normal preventive control, control is performed while taking into account both the disadvantages of reliability due to supply failures and the economic disadvantages of proactive measures.

  In recent years, there is a concern about the uncertainty of power supply in the future grid due to the reform of the power system and the increase of renewable energy power sources using renewable energy such as sunlight and wind power. In order to maintain the supply reliability dynamically in the presence of uncertainty, it is necessary to search for operating points (control values such as voltage, phase, and generator output) with a certain margin. It is necessary to evaluate supply reliability in advance and preventive control. Conventional systems do not realize reliability evaluation and preventive control in consideration of such uncertainties such as renewable energy output.

  As renewable energy sources increase, the number of factors that grid operators cannot manage and control will increase. In other words, uncertainty related to power supply in the power system (hereinafter also simply referred to as “uncertainty”) increases, and it becomes difficult to estimate a possible current cross section (tidal current distribution). The tidal current means the flow of electricity, and can be expressed by the magnitude and direction of active power, reactive power, and the like. Given the uncertainties of renewable energy sources in each region of the grid, the predicted tidal current pattern can cause a combined explosion. In other words, using conventional reliability evaluation and preventive control methods, it is possible to evaluate whether stable operation of the system is possible by performing tidal current calculations and transient stability calculations for each of the enormous number of tidal sections that can be taken in the future. Therefore, enormous calculation time is required, and it becomes difficult to evaluate and control the N-1 reliability or N-2 reliability of the system in real time.

  Therefore, in the present embodiment, a reliability evaluation system (power system reliability evaluation system) that can appropriately evaluate the supply reliability at the time of occurrence of an assumed accident in a power system including a renewable energy power source will be described.

  FIG. 1 is a diagram illustrating an example of a configuration of a reliability evaluation system 1 according to the embodiment. The reliability evaluation system 1 evaluates the power supply reliability when an assumed accident occurs in the power system 21 including the renewable energy power source 23. The power system 21 includes a generator 22 (22a, 22b), a renewable energy power source 23 (23a, 23b, 23c, 23d), and a customer 24 (24a).

  The generator 22 is a large-scale generator that performs thermal power generation, hydroelectric power generation, nuclear power generation, and the like. The renewable energy power source 23 is a power source that generates power using sunlight, wind power, or the like, which is a renewable energy, and has various scales. Individual renewable energy power sources 23 and customers 24 can be grouped for each region, and may be handled as a group of regions 31 (31a, 31b, 31c).

  The power system 21 includes a measuring device 25 (25a, 25b,..., 25n) and a control terminal device 26 (26a, 26b,..., 26n) as devices for cooperating with the reliability evaluation system 1. Are provided. The measuring device 25 measures system information such as voltage, phase, power flow at various locations of the power system 21 and states of various power system facilities such as a transformer, a generator 22, a renewable energy power source 23, and the measurement information is trusted. To the degree evaluation system 1.

  The control terminal device 26 receives a control command from the reliability evaluation system 1 and controls the system control device and the generator 22 (output amount, etc.) and suppresses the output of the renewable energy power source 23. Here, the system control device includes, for example, a phase advance capacitor, a shunt reactor, an SVC (Static Var Compensator), and the like.

  The reliability evaluation system 1 estimates the future system operation state (for example, 30 minutes or 60 minutes later from the current time) from the current system operation state, and supplies when an assumed accident occurs in the future system operation state. This is a computer device that evaluates the reliability and calculates and outputs the content of preventive control if a problem occurs in power supply. Specific evaluation items for supply reliability include, for example, overload, transient stability, voltage stability, frequency stability, and the like. If these evaluation items exceed the specified management value (threshold value), equipment failure or dropout may occur, causing a power failure.

  The reliability evaluation system 1 includes a processing unit 11, a storage unit 12, an input unit 13, and a display unit 14. In addition, although the reliability evaluation system 1 is also provided with a communication part, in order to simplify description, illustration and description are abbreviate | omitted.

  The processing unit 11 includes a system information collection unit 111, a system state estimation unit 112, a reliability evaluation unit 113, a control content calculation unit 114, and a control command transmission unit 115. In addition, hereinafter, when the processing unit 11 other than the units 111 to 115 performs processing, or when any one of the units 111 to 115 and any one or more of the units 111 to 115 performs processing, the operation subject is referred to as “processing unit”. 11 ".

  The system information collection unit 111 acquires various types of information related to the current power system from the measurement device 25 installed in the power system 21. In addition to the operation program of the processing unit 11 and various data, the storage unit 12 includes a reliability evaluation model (details will be described later), demand prediction information input by the grid operator, operation plan information, assumed accident information, Energy output prediction information (renewable energy output prediction information; the same applies hereinafter) and the like are stored.

  The demand prediction information is information that predicts the magnitude of demand in each time slot (for example, every 30 minutes) of the day, and is information that can be predicted with high accuracy from the system operator's rule of thumb. The operation plan information is information on an operation plan of a predetermined system such as turning on / off the phase adjusting equipment, starting / stopping the generator 22, and increasing / decreasing the output of the generator 22.

  The assumed accident information is information on an accident assumed in the power system 21. Typical examples of such an accident include power transmission line failure, bus line failure, generator failure (generator 22 failure), transformer failure, and the like. FIG. 2 is a diagram illustrating an example of assumed accident information according to the embodiment. As shown in FIG. 2, in the assumed accident information, an accident point (accident target location) and an accident aspect are associated with each identification information (No.).

  Returning to FIG. 1, the renewable energy output prediction information is information related to prediction in consideration of uncertainty regarding the output of the renewable energy power supply 23 in the future (for example, 30 minutes or 60 minutes after the present time). This renewable energy output prediction information can be represented by, for example, a probability distribution.

  FIG. 3 is a diagram illustrating an example of the probability distribution of the renewable energy output prediction according to the embodiment. In the graph of FIG. 3, the vertical axis represents the occurrence probability, and the horizontal axis represents the renewable energy output. As shown in FIG. 3, it is assumed that the renewable energy output has some probability distribution, and in order to express uncertainty, for example, the range of the renewable energy output is within ± σ of the predicted value (68.27%). It is defined as This range can be arbitrarily determined, for example, may be defined as ± 2σ or ± 3σ of the predicted value, or an index other than the standard deviation, for example, the capacity ratio of the renewable energy power source 23 May be defined as the inside of ± 10% of the predicted value. Renewable energy output prediction information including such uncertainties is stored in the storage unit 12 for each renewable energy power supply 23 or region set 31.

  Here, FIG. 4 is a diagram illustrating an example of the renewable energy output prediction information of the embodiment. As shown in FIG. 4, in this renewable energy output prediction information, a renewable energy output predicted value and a value corresponding to ± σ in the probability distribution are given for each area (regional set 31). By using such renewable energy output prediction information, the reliability evaluation system 1 will improve reliability in the power system 21 in the future for all combination patterns of renewable energy output caused by such uncertainty. It can be evaluated whether it can be maintained.

  Returning to FIG. 1, the system state estimation unit 112 outputs measurement information including at least one of voltage, phase, load, generator output, and power flow at a plurality of locations in the power system 21 and output related to the renewable energy power source 23. Renewed energy output prediction information including a predicted value, a value larger than the predicted value by a predetermined amount (for example, predicted value + σ), and a value smaller than the predicted value by a predetermined amount (for example, predicted value−σ), and power in the power system 21 The future grid state is estimated based on the demand forecast information and the operation plan information regarding the power supply in the power grid 21.

  FIG. 5 is a diagram illustrating an example of system state information estimated in the embodiment. FIG. 5A is a diagram illustrating an example of node data in the system state information. FIG. 5B is a diagram illustrating an example of branch data in the system state information.

  In the node data of FIG. 5A, for each node name, voltage, phase, generator output (active power output, reactive power output), load (active power load, reactive power load), and information on the phase adjusting equipment Are associated.

  In the branch data of FIG. 5B, information on active power flow, reactive power flow, active power loss, and reactive power loss is associated with each branch name.

  Returning to FIG. 1, the reliability evaluation unit 113 supplies based on the future system state estimated by the system state estimation unit 112, the assumed accident information including a plurality of assumed accident aspects, and the renewable energy output prediction information. Evaluate confidence. Specifically, for example, it is as follows.

  The reliability evaluation unit 113 uses the reliability evaluation model created by the offline preliminary analysis, and calculates the future system state estimated by the system state estimation unit 112, the assumed accident information, and the renewable energy output prediction information. By inputting, at least one of overload margin, transient stability margin, voltage stability margin, and frequency stability margin is acquired as an evaluation index of supply reliability.

  In addition, the reliability evaluation unit 113, during online operation in real time, a plurality of tidal current sections with different outputs from the plurality of renewable energy power sources 23 for the power system 21, a plurality of assumed accident aspects in the assumed accident information, Is used to calculate the evaluation index based on the reliability evaluation model. In addition to the reliability evaluation unit 113, an evaluation model creation unit (not shown) that performs offline analysis includes information on at least one of voltage, phase, load, generator output, and power flow, and an evaluation index. Using the correlation analysis results, select information with a high correlation with the evaluation index as high as a predetermined value as an input variable of the reliability evaluation model, and learn the selected input variable and evaluation index as teacher data to evaluate the reliability A model is created in advance and stored in the storage unit.

  When the control content calculation unit 114 determines that there is a problem with the supply reliability evaluated by the reliability evaluation unit 113, among the devices in the power system 21, the input variable whose correlation with the evaluation index is higher than a predetermined value. Select a device that can control the device to be controlled.

  In addition, when it is determined that there is a problem with the supply reliability evaluated by the reliability evaluation unit 113, the control content calculation unit 114 calculates the control content of the control target device, and the power system after control with the control content Estimate the state. Thereafter, the reliability evaluation unit 113 uses the power system state estimated by the control content calculation unit 114 to evaluate the supply reliability in the power system state.

  The control command transmission unit 115 transmits a control command based on the control content finally calculated by the control content calculation unit 114 to the control terminal device 26. The control terminal device 26 controls the system control device and the generator 22 (output amount, etc.) and suppresses the output of the renewable energy power source 23 based on the received control command.

  The input unit 13 is a means by which a user (system operator) of the reliability evaluation system 1 inputs information, and is, for example, a keyboard or a mouse. For example, when the user inputs assumed accident information, operation plan information, and the like using the input unit 13, the information is stored in the storage unit 12 via the processing unit 11.

  The display unit 14 is information display means, and is, for example, an LCD (Liquid Crystal Display). For example, the display unit 14 displays the evaluation result of the reliability by the reliability evaluation unit 113. Moreover, the display part 14 can implement | achieve the decision support regarding the control by a system | operator by displaying the control content etc. which were calculated by the control content calculation part 114, for example.

<About reliability evaluation model>
Next, the reliability evaluation model will be described. The reliability evaluation unit 113 evaluates the reliability of the future system using a reliability evaluation model. FIG. 6 is an image diagram of the reliability evaluation model of the embodiment.

  As shown in FIG. 6, information input to the reliability evaluation model includes, for example, estimated future system state information (P is active power, Q is reactive power, V is voltage, and θ is phase). These are assumed accident aspects and renewable energy output prediction (probability distribution). In addition, the information output from the reliability evaluation model includes, for example, an overload margin, a transient stability margin as an evaluation index corresponding to an overload, transient stability, voltage stability, and frequency stability as evaluation items, Voltage stability margin and frequency stability margin. A reliability evaluation model is prepared for each evaluation item. As each evaluation index, a margin up to the stability limit point is used.

  An example of an evaluation index (a margin up to the stability limit point) in each evaluation item will be described. The overload stability limit point is determined according to the overload capability and elapsed time of the transmission line and the transformer. FIG. 7 is an image diagram showing the relationship between the overload capacity and the time in the embodiment. As shown in FIG. 7, as an example, when the overload withstand capacity is continuous withstand capacity, it becomes a continuous capacity, but when the overload withstand capacity is 30 minutes withstand capacity, it becomes 30 minutes capacity, and when the overload withstand capacity is 10 minutes withstand capacity, 10 minutes capacity. That is, the power system 21 must be operated so that the tidal current flowing through the transmission line does not exceed the overload capacity for each time zone. The overload margin can be represented by, for example, an integrated value of “overload tolerance−transmission line power flow”.

  FIG. 8 is an image diagram of a PV curve showing the voltage stability limit point. In the graph of FIG. 8, the vertical axis represents system voltage and the horizontal axis represents demand. As shown in FIG. 8, the point P1 is a point corresponding to the voltage and demand after the convergence of the system fluctuation caused by the accident. Point P2 corresponds to the stable limit voltage and the stable limit demand (stable limit point). In other words, it becomes unstable when demand exceeds the stability limit. Therefore, the voltage stability margin (load margin) can be expressed, for example, as “stability limit point−current operating point” regarding the load.

Transient stability is a measure of whether or not a plurality of generators 22 can be operated in synchronization with a disturbance such as a system fault. If there is a generator 22 that is out of synchronization and abnormally accelerates (steps out), It becomes unstable. As an index for quantitatively indicating the transient stability, there is generally an index based on the energy function method. Here, CCT (Critical Clearing Time) is used as an example. Using CCT, it can be determined whether the transient stability is stable or unstable as follows.
CCT ≧ accident elimination time: stable CCT <accident elimination time: unstable Therefore, for example, “CCT−accident elimination time” can be set as a transient stability margin.

  In addition, about the evaluation index, what was mentioned above is only an example and it is also possible to define evaluation indexes other than the above. For example, the transient stability margin may be defined as “deceleration energy−acceleration energy” or “180 ° −generator phase” based on the energy function method.

Next, a method for creating a reliability evaluation model will be described. When the renewable energy output prediction (renewable energy output predicted value (hereinafter also simply referred to as “predicted value”) and probability distribution) is as shown in FIG. 4, there are N areas where the renewable energy power supply 23 exists, renewable energy output area can take "predicted value -σ", "predictive value", considering the three patterns of the "predicted value + sigma", power flow state can take line is the combination of 3 ^N. However, if the transient stability calculation of “3 ^N × number of assumed accidents” is performed, depending on the number of areas in which the renewable energy power source 23 exists, it takes a huge amount of calculation time, and the reliability can be evaluated in real time. become unable. Therefore, the reliability evaluation model in this embodiment is created in advance by offline analysis.

  With reference to FIG. 9, the process of creating a reliability evaluation model will be described. FIG. 9 is a flowchart illustrating an example of a process for creating a reliability evaluation model according to the embodiment. The flowchart of FIG. 9 is roughly divided into two phases. S1 to S4 are teacher data creation phases used for learning in S9, and S6 to S9 are reliability evaluation model creation phases used in the reliability evaluation system 1. .

  In S <b> 1, the processing unit 11 sets a tidal current section and an accident aspect used for calculation based on data input using the input unit 13 by the system operator. With reference to FIG. 10, the example of a data set of a tidal current section and an accident aspect will be described. FIG. 10 is a data set example of a tidal current section and an accident aspect for creating a reliability evaluation model in the embodiment.

As shown in FIG. 10, the renewable energy output of each area is allocated at regular intervals with respect to the base power flow section (for example, the output of the total renewable energy power source is 70% of the rated capacity). If the step size of the renewable energy output is 10% in 10% increments of 10% to 100%, and the number of areas is N and the number of assumed accident aspects is M, (10 ^N × M) data sets are prepared. In addition, since the supply and demand imbalance occurs when the renewable energy output changes from the base power flow cross section, for example, the change in the renewable energy output is distributed according to the capacity ratio of the supply and demand adjustment generator. Adjust the output of the supply and demand adjustment generator in each data set.

  Returning to FIG. 9, in S <b> 2, the processing unit 11 performs tidal current calculation. This tidal current calculation result is used as an initial value for the transient stability calculation in S3. In addition, parameters such as voltage, phase, generator output, load, power flow of each branch, and loss obtained by this power flow calculation are used as input variable candidates in S6 and S7.

  Next, the processing unit 11 performs transient stability calculation in S3, and calculates each evaluation index of supply reliability in S4 using the calculation result. For example, when evaluating the overload margin, the processing unit 11 uses the result that the transient stability is stabilized and the system fluctuation is converged, so that “overload tolerance−transmission line power flow” of each branch after the accident. Calculate Also, when evaluating the voltage stability margin, the processing unit 11 creates a PV curve by repeatedly increasing the load and repeatedly calculating the power flow using the result of convergence of the system fluctuation. Further, when evaluating the transient stability margin, the processing unit 11 repeatedly calculates the transient stability while gradually changing the accident removal time to obtain the CCT.

  In S5, the processing unit 11 determines whether or not the processes in S1 to S4 have been completed in all cases (all data sets in FIG. 10). If Yes, the process proceeds to S6. If No, the process returns to S1.

In S6, the processing unit 11 uses the power flow calculation result in S2 and the evaluation index calculated in S4 to calculate the voltage, phase, generator output, load, power flow in each branch, loss, etc. Analyzes the correlation between system information and each evaluation index. As an example of correlation analysis, it is conceivable to use a correlation coefficient represented by the following equation (1).

Here, x _i is an input variable of the i-th data set, x˜ is an expected value of the input variable, y _i is an evaluation index of the i-th data set, and y˜ is an expected value of the evaluation index. By calculating the correlation coefficient between each input variable candidate and the evaluation index, an input variable having a high correlation with the evaluation index can be determined.

  In S7, the processing unit 11 selects an input variable by excluding unnecessary input variables having low correlation from a large number of input variable candidates using the correlation analysis result obtained in S6. Further, an input variable may be selected in consideration of multicollinearity and the like. In order to utilize the correlation analysis result also in the control content calculation unit 114, a sensitivity matrix of each evaluation index and each input variable may be created. Here, the sensitivity matrix is obtained by calculating how the evaluation index changes when each value changes (variability) and expressing it as a matrix.

  In S8, in order to improve the accuracy of the reliability evaluation model, the processing unit 11 clusters input variables and creates a reliability evaluation model for each cluster. When clustering, first, the cluster is divided for each accident aspect. This is because even if the tidal conditions before the accident are the same, the behavior of the power system varies greatly depending on the aspect of the accident. In addition, a clustering method such as k-means or hierarchical type may be used.

  In S9, the processing unit 11 performs learning for each cluster divided in S8, and creates a reliability evaluation model corresponding to each cluster. As an example of the learning method, it is conceivable to use a technique such as a neural network or a random forest. In addition, a technique such as regression analysis (for example, linear regression analysis or multiple regression analysis) may be used.

  Through the above procedure, a reliability evaluation model is created in advance by offline analysis and stored in the storage unit 12 so that the reliability evaluation unit 113 can use it. The N-1 reliability or the N-2 reliability can be evaluated without executing the degree calculation (details will be described later).

  Next, a state in which input variables are clustered will be described with reference to FIG. FIG. 11 is an image diagram showing a state in which input variables are clustered. Note that the actual input variable is multivariable, but in FIG. 11, the input variable is represented by one variable so that the image can be easily captured. In FIG. 11, as an example, a cluster 1 having a regression equation 1 and a cluster 2 having a regression equation 2 are divided. In addition, a reliability evaluation model is created for each of the clusters 1 and 2. Therefore, for example, when there is an input variable α corresponding to the cluster 1 during operation, the input variable α is applied to the reliability evaluation model corresponding to the cluster 1 so that the stability (overload margin, transient stability) Degree margin, voltage stability margin, frequency stability margin) β.

  Next, with reference to FIG. 12, processing during operation by the reliability evaluation system 1 will be described. FIG. 12 is a flowchart illustrating an example of processing during operation of the embodiment. First, in S21, the system state estimation unit 112 estimates a future system state 30 minutes or 60 minutes after the present time.

  Next, in S22, the reliability evaluation unit 113 uses the system state, the renewable energy output prediction information (probability distribution), the assumed accident information, and the reliability evaluation model estimated in S21, or N-1 reliability, or Evaluate N-2 reliability.

  The process of S22 will be described with reference to FIG. FIG. 13 is a flowchart illustrating an example of the process of S22 of FIG. First, in S221, the reliability evaluation unit 113 acquires an input data set based on the renewable energy output prediction information (predicted value and probability distribution) and the assumed accident aspect as shown in FIG. This input data set covers, for example, all combined patterns of the renewable energy output prediction and the assumed accident aspect, but may be created by the system operator.

  In S222, the reliability evaluation unit 113 selects a reliability evaluation model corresponding to each input data set. As an example, with reference to FIG. 14, selection of a reliability evaluation model assuming that there are four (four areas) renewable energy power sources 23 in the power system 21 will be described. FIG. 14 is an image diagram for explaining an example of selecting a reliability evaluation model in the embodiment.

  In the input data set shown in FIG. 14A, the renewable energy output prediction and the accident aspect of each area (areas 1 to 4) are set for each input data set number. In the renewable energy output prediction, a predicted value is represented as “0”, a value larger by σ than the predicted value is represented as “+ σ”, and a value smaller than the predicted value by σ is represented as “−σ”.

  Further, as shown in FIG. 14 (b), the reliability evaluation model shows that each accident aspect ((b1) accident is one of N-1 accidents. (B2) accident is one of N-2 accidents. ) According to the renewable energy output level (each cluster). That is, (b1) includes (reliability) evaluation model numbers a1, a2,..., And (b2) includes (reliability) evaluation model numbers b1, b2,.・ A reliability evaluation model is prepared.

  And as shown in FIG.14 (c), the reliability evaluation model corresponding to an accident aspect and a renewable energy output prediction is selected for every input data set number.

  Returning to FIG. 13, in S223, the reliability evaluation unit 113 uses the future system state and the reliability evaluation model estimated in S21, and uses a plurality of evaluation indexes (overload margin, transient stability margin, voltage stability). Margin, frequency stability margin). However, because the reliability evaluation model applied to each input data set is different, each input data set has its own evaluation items (overload, transient stability, voltage stability, frequency stability, frequency stability, etc.) An evaluation index is calculated.

  In S224, the reliability evaluation unit 113 performs a statistical process using the plurality of evaluation indexes calculated in S223, and calculates a final reliability evaluation result. The statistical process may be, for example, adopting an evaluation index having a minimum margin to the stability limit point as a final evaluation result.

  Returning to FIG. 12, after S22, in S23, the reliability evaluation unit 113 determines whether or not there is a problem in the supply reliability. If Yes, the process proceeds to S24, and if No, the process ends. .

  In S24, the control content calculation unit 114 selects a control target device (for example, a generator 22, a renewable energy power source 23, a transformer, a phase advance capacitor, a shunt reactor, an SVC, etc.). As a method for selecting a control target device, for example, a control target device capable of controlling an input variable with high correlation may be selected from a correlation analysis result between the input variable of the used reliability evaluation model and the evaluation index. A table (information) for referring to devices that can control each input variable is created in advance.

  In S25, the control content calculation unit 114 calculates the control amount of the control target device selected in S24. For example, the control content calculation unit 114 prepares a sensitivity matrix of each control target device and each input variable, and a sensitivity matrix of each input variable and the evaluation index, and performs control so that each evaluation index does not exceed the stability limit point. Determine the amount.

  In S26, the control content calculation unit 114 performs post-control state estimation in order to evaluate the reliability after control. After S26, the process proceeds to S22. Here, the reason why the process of S22 is performed again after S26 is that, for example, if the control content of the control target device is changed in order to improve one evaluation index, another evaluation index may be deteriorated. . Therefore, by repeating the processing of S22 to S26 until No in S23, all the evaluation indexes for the power system 21 can be made to be in a comprehensively good state.

  When the control content calculation unit 114 calculates the final control content, the control command transmission unit 115 transmits a control command based on the control content to the control target device (such as the control terminal device 26).

  As described above, according to the reliability evaluation system 1, it is possible to appropriately evaluate the supply reliability at the time of occurrence of an assumed accident in the power system 21 having uncertainty by including the renewable energy power source 23. That is, the power system 21 can be maintained in a state where there is no problem in supply reliability even if an accident occurs.

  Next, as a modified example, it is considered that optimization calculation is applied to processing during operation in the above-described embodiment using high-speed reliability evaluation based on a reliability evaluation model not including transient stability calculation.

  For example, the reliability evaluation system 1 calculates the control content of the control target device by the control content calculation unit 114 and evaluates the supply reliability in the power system state after the control by the control content by the reliability evaluation unit 113. By repeating, the control content which optimizes the predetermined objective function regarding supply reliability is calculated.

  Specifically, for example, in the flowchart of FIG. 12, a method of perturbing using random numbers is applied to the selection of the control target device in S24 and the control amount calculation in S25. By repeatedly performing the loop, it is possible to optimize the control amount so as to satisfy an arbitrary constraint condition and minimize an arbitrary objective function. As a specific optimization method, for example, if the reliability evaluation model is represented by a regression equation, a mathematical programming method such as a linear programming method or a quasi-Newton method is applied. For example, if the reliability evaluation model is represented by a learning model such as a neural network model, a genetic algorithm or metaheuristics such as PSO (Particle Swarm Optimization) is applied.

  Although the embodiments and modifications of the present invention have been described, these are presented as examples and are not intended to limit the scope of the invention. These novel embodiments and modifications can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and modifications are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

  For example, the predicted value of the renewable energy output and its probability distribution are input by the system operator, but the prediction function itself may be incorporated in the reliability evaluation system 1. The probability distribution may not be a normal distribution as shown in FIG.

  In addition, the reliability evaluation system 1 is a function that compares both the economic disadvantages of preventive control and the disadvantages caused by supply failure, and determines whether to perform preventive control, as in the conventional reliability evaluation system. May be included.

  In the above-described embodiment, the uncertainty is expressed by the standard deviation of the probability distribution. However, other indices (for example, a fixed value such as a predicted value ± 25% using the capacity ratio of the renewable energy power source 23) are used. ) May be used. Moreover, you may use other than the thing quoted as embodiment as an evaluation parameter | index of a reliability evaluation model.

  In the above-described embodiment, the reliability evaluation system 1 includes the control command transmission unit 115 that transmits a control command to the control device in the system. However, this is not always necessary, and the reliability evaluation result or the control content is displayed. May be used to support decision-making by the operator.

  The program executed in the reliability evaluation system 1 of the present embodiment is a file in an installable format or an executable format, and is a CD (Compact Disc) -ROM (Read Only Memory), a flexible disc (FD), a CD. It can be provided by being recorded on a recording medium readable by a computer device such as -R (Recordable) or DVD (Digital Versatile Disk). Further, the program executed in the reliability evaluation system 1 of the present embodiment may be provided or distributed via a network such as the Internet.

DESCRIPTION OF SYMBOLS 1 Reliability evaluation system 11 Processing part 12 Storage part 13 Input part 14 Display part 21 Electric power system 22 Generator 23 Renewable energy power supply 24 Consumer 25 Measuring apparatus 26 Control terminal apparatus 31 Area set 111 System information collection part 112 System state estimation 112 Unit 113 reliability evaluation unit 114 control content calculation unit 115 control command transmission unit

Claims (6)

A power system reliability evaluation system that evaluates the power supply reliability in the event of an assumed accident in a power system including a renewable energy power source,
Measurement information including at least one of voltage, phase, load, generator output, power flow at a plurality of locations in the power system, a predicted value of output related to the renewable energy power supply, a value larger than the predicted value by a predetermined amount, And based on renewable energy output prediction information including a value smaller than the predicted value by a predetermined amount, demand prediction information of power in the power system, and operation plan information related to power supply in the power system. A system state estimation unit for estimating the system state;
A reliability evaluation unit that evaluates the supply reliability based on the future system state estimated by the system state estimation unit, the assumed accident information including a plurality of assumed accident aspects, and the renewable energy output prediction information; Power system reliability evaluation system. The reliability evaluation unit uses a reliability evaluation model created by offline analysis,
By inputting the future system state estimated by the system state estimation unit, the assumed accident information, and the renewable energy output prediction information with respect to the reliability evaluation model, overload margin, transient stability The power system reliability evaluation system according to claim 1, wherein at least one of a power margin, a voltage stability margin, and a frequency stability margin is acquired as a supply reliability evaluation index. Input selected as information whose correlation with the evaluation index is higher than a predetermined value by using a correlation analysis result between the evaluation index and information on at least one of voltage, phase, load, generator output, and power flow The reliability evaluation model is created in advance by learning a variable and the evaluation index as teacher data,
The reliability evaluation unit
The evaluation index is calculated based on the reliability evaluation model using a plurality of tidal current sections with different outputs from the plurality of renewable energy power sources for the power system and a plurality of assumed accident aspects in the assumed accident information. The power system reliability evaluation system according to claim 2. The power system reliability evaluation system further includes:
When it is determined that there is a problem with the supply reliability evaluated by the reliability evaluation unit, among the devices in the power system, a device that can control an input variable having a predetermined correlation or higher with the evaluation index is controlled. The electric power system reliability evaluation system of Claim 3 provided with the control content calculation part selected as object apparatus. When the control content calculation unit determines that there is a problem in the supply reliability evaluated by the reliability evaluation unit, the control content calculation unit calculates the control content of the control target device, and the power system after control with the control content Estimate the state,
The reliability evaluation unit uses the power system state estimated by the control content calculation unit,
The power system reliability evaluation system according to claim 4, wherein supply reliability in the power system state is evaluated. The power system reliability evaluation system is:
By repeating the calculation of the control content of the control target device by the control content calculation unit and the evaluation of the supply reliability in the power system state after the control with the control content by the reliability evaluation unit, the supply reliability The power system reliability evaluation system according to claim 5, wherein the control content for optimizing a predetermined objective function related to degree is calculated.

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