Power system monitoring device, power system operation monitoring method, and program

Abstract

To provide a power system monitoring device that can avoid overestimating predicted values for dynamic line ratings.SOLUTION: A power system monitoring device according to an embodiment includes a power flow value calculation unit that calculates a future predicted power flow value of a power transmission and distribution facility installed in a power system on the basis of past weather forecast data, a conductor temperature calculation unit that calculates the change in conductor temperature when the predicted power flow value is applied to the power transmission and distribution facility under weather conditions on the basis of actual weather data for the same period as the past weather forecast data, a conductor temperature constraint determination unit that determines whether the conductor temperature satisfies preset constraint conditions, and a condition change unit that changes the calculation conditions for the predicted power flow value when the conductor temperature does not satisfy the constraint conditions.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a power system monitoring device, a power system operation monitoring method, and a program. [Background technology]

[0002] Due to the recent liberalization of the electric power industry, it has become common for electricity generated by renewable energy power generation equipment such as wind power generation equipment and solar power generation equipment to be transmitted and distributed via existing power transmission and distribution facilities. In addition to promoting the effective use of renewable energy power generation equipment, there has been active discussion about how to make effective use of existing power transmission and distribution facilities.

[0003] Among these, the application of dynamic line rating, which determines the maximum current amount taking into account weather conditions (temperature, wind, sunshine), is expected.Dynamic line rating is a technology that increases the transmission capacity of the power system by changing the predetermined power flow value of the power transmission and distribution equipment to a value that keeps the equipment below its upper limit temperature. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-94441 Summary of the Invention [Problem to be solved by the invention]

[0005] The factors that determine the thermal capacity of a transmission line in dynamic line rating include joule loss, convection loss, radiation loss, and solar heat absorption. In particular, thermal capacity is largely influenced by convection loss, which is highly correlated with wind conditions. When using dynamic line rating in power generation planning, it is necessary to calculate predicted values ​​of dynamic line rating for future periods. Because predicted values ​​of dynamic line rating depend on weather forecast values, they contain uncertainty. In particular, if the prediction is overestimated compared to the true value of the real-time dynamic line rating, there is a possibility that the temperature of the transmission line will exceed the allowable range.

[0006] The problem to be solved by the present invention is to provide a power system monitoring device, a power system operation monitoring method, and a program that are capable of avoiding overestimation of predicted values ​​related to dynamic line ratings. [Means for solving the problem]

[0007] A power system monitoring device according to one embodiment includes a power flow value calculation unit that calculates a future predicted power flow value of a power transmission and distribution facility installed in a power system based on past weather forecast data; a conductor temperature calculation unit that calculates the change in conductor temperature when the predicted power flow value is applied to the power transmission and distribution facility under weather conditions based on actual weather data for the same period as the past weather forecast data; a conductor temperature constraint determination unit that determines whether the conductor temperature satisfies preset constraint conditions; and a condition change unit that changes the calculation conditions for the predicted power flow value if the conductor temperature does not satisfy the constraint conditions. [Effects of the Invention]

[0008] According to this embodiment, it is possible to avoid overestimation of the predicted value for the dynamic line rating. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a block diagram showing the overall configuration of a power operation system using a power system monitoring device according to a first embodiment. [Figure 2]1 is a block diagram showing the configuration of a power system monitoring device according to a first embodiment. [Figure 3] 3 is a flowchart showing the procedure of an operation performed by the power system monitoring device according to the first embodiment to monitor a power system. [Figure 4] (a) is a diagram showing an example of the time series change in conductor temperature when a continuous allowable capacity is passed through the conductor, and (b) is a diagram showing an example of the time series change in conductor temperature when a short-term allowable capacity is passed through the conductor. [Figure 5] 10 is a flowchart showing the procedure of an operation performed by a power system monitoring device according to a fourth embodiment to monitor a power system. [Figure 6] FIG. 10 is a schematic diagram for explaining a power flow value optimization process. [Figure 7] FIG. 10 is a block diagram showing the configuration of a power system monitoring device according to a fifth embodiment. [Figure 8] FIG. 10 is a schematic diagram illustrating an example of a learning model construction process. [Figure 9] FIG. 13 is a schematic diagram for explaining a condition change process in the fifth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. The present invention is not limited to the following embodiments.

[0011] (First embodiment) Fig. 1 is a block diagram showing the overall configuration of a power operation system using a power system monitoring device according to Embodiment 1. The power operation system shown in Fig. 1 includes a power system monitoring device 1, a power control device 8, and a power system 3.

[0012] First, the power system 3 will be described. The power system 3 is a power supply network that supplies electric power. The power system 3 includes a generator 4, a renewable energy power generation device 5, a transformer 6, and a transmission line 7. As an example, the power system 3 is configured by a plurality of power systems 3a, 3b, 3c, 3d, and 3e. The power system 3 may also include circuit breakers, disconnecting switches, phase modifiers, and the like.

[0013] The power system 3a is composed of a generator 4a, a transformer 6a, and a transmission line 7a. The generator 4a is a facility that generates electricity using hydroelectric power, thermal power, nuclear power, etc. The output power of the generator 4a is controlled by a power control device 8. The transformer 6a transforms the power output from the generator 4a. The transformer 6a has a tap for switching voltage. The transmission line 7a transmits the power transformed by the transformer 6a.

[0014] The power system 3b is made up of a generator 4b, a transformer 6b, and a transmission line 7b. The electric power generated by the generator 4b is transformed by the transformer 6b and transmitted via the transmission line 7b.

[0015] The power system 3c is made up of a renewable energy power generation device 5, a transformer 6c, and a power transmission line 7c. The renewable energy power generation device 5 is a device that generates power using renewable energy such as wind power or solar power. The output power of the renewable energy power generation device 5 is controlled by a power control device 8.

[0016] Power system 3d is composed of transformer 6d and transmission line 7d. Transformer 6d transforms the electric power supplied from power system 3a, power system 3b, and power system 3c. Transmission line 7d transmits the electric power transformed by transformer 6a.

[0017] The power system 3e is composed of a transformer 6e and a transmission line 7e. The electric power supplied from the power system 3a, the power system 3b, and the power system 3c is transformed by the transformer 6e and transmitted via the transmission line 7e.

[0018] Each of the transformers 6a to 6e in the power systems 3a to 3e is provided with a measuring device 61a to 61e, respectively. The measuring device 61 measures the current temperature, power, voltage, current, and frequency of the transformer 6. The measuring device 61 is composed of measuring circuits and transmitting / receiving circuits for temperature, power, voltage, current, and frequency. Note that it is not necessary to provide a measuring device for every transformer. For example, in a transformer without a temperature sensor, the conductor temperature may be derived from weather conditions and a mathematical formula.

[0019] Measuring devices 71a to 71e are provided on the transmission lines 7a to 7e of the power systems 3a to 3e, respectively. Measuring device 71 measures the current temperature, power, voltage, current, and frequency of the transmission line 7. Measuring device 71 is composed of measuring circuits and transmitting / receiving circuits for temperature, power, voltage, current, and frequency. Note that it is not necessary to provide measuring devices on all transmission lines. For example, in transmission lines without temperature sensors, the conductor temperature may be derived from weather conditions and a mathematical formula.

[0020] The power system monitoring device 1 is a device that calculates the amount of power generated by a generator 4 or a renewable energy power generation device 5 when the temperature of existing power transmission and distribution equipment is below a predetermined upper limit value, without being limited to a defined rated value.

[0021] The power system monitoring device 1 is connected to the power system 3 via a communication line 91, and is also connected to the power control device 8 via a communication line 93. The power system monitoring device 1 is configured with a computer or the like. The power system monitoring device 1 is installed in the office of a business operator that monitors or controls the power system 3. The configuration of the power system monitoring device 1 will now be described with reference to FIG. 2.

[0022] 2 is a block diagram showing the configuration of the power system monitoring device according to the first embodiment. The power system monitoring device 1 includes a data acquisition unit 11, a transmission unit 12, a calculation unit 13, a storage unit 14, a display unit 15, and an operation unit 16. Each unit will be described below.

[0023] The data acquisition unit 11 is connected to the measurement devices 61 and 71 of the power system 3 via a communication line 91. The communication line 91 may be a dedicated line for wired or wireless communication, an internet line, a telephone line, or the like.

[0024] The data acquisition unit 11 acquires measurement data D11, weather forecast data D12, actual weather data D13, constraint data D14, supply and demand plan data D15, and system data D16. At this time, the data acquisition operation of the data acquisition unit 11 is controlled by the calculation unit 13. In addition, the data acquisition unit 11 transfers the acquired data to the calculation unit 13.

[0025] The measurement data D11 is data indicating the temperature, power, voltage, current, and frequency of the transformer 6 measured by the measuring device 61, and data indicating the temperature, power, voltage, current, and frequency of the transmission line 7 measured by the measuring device 71.

[0026] The weather forecast data D12 includes, for example, at least one of Japan Meteorological Agency forecast data, weather forecast GPV (Grid Point Value) data, weather forecast SCW site (Super C Weather) data, meteorological reanalysis data such as ERA-5, and data on the results of WRF (Weather Research and Forecasting) analysis such as numerical weather models. Japan Meteorological Agency forecast data is data predicted by the Japan Meteorological Agency. Weather forecast GPV data is past and future weather forecast data calculated by a supercomputer at grid points preset on a map. ERA-5 data is data used for weather forecasting by the European Centre for Medium-Range Weather Forecasts. WRF is a forecast model for predicting wind conditions such as wind speed and direction.

[0027] The weather record data D13 is, for example, weather data actually measured at an observation point. For example, data measured by the Japan Meteorological Agency's AMeDAS (Automated Meteorological Data Acquisition System) is applied to the weather record data D13.

[0028] The constraint data D14 indicates, for example, a constraint condition on the conductor temperature of the power transmission line 7. The constraint data D14 may be stored in the storage unit 14 in advance. The supply and demand plan data D15 is data indicating a power generation plan, for example, every 30 minutes or every 5 minutes, regarding the generators 4a, 4b and the renewable energy power generation device 5. The data acquisition unit 11 may receive the supply and demand plan data D15 from an external device (not shown) or from the power control device 8.

[0029] The system data D16 includes data related to the power generation facilities and power transmission and distribution facilities installed in the power system 3. For example, the system data D16 includes data such as the rated power and upper limit temperature of the generators 4a and 4b, the renewable energy power generation device 5, the transformer 6, and the transmission line 7.

[0030] The transmitter 12 is configured by a transmission circuit. The transmitter 12 is connected to the power control device 8 via a communication line 93. The communication line 93 may be a dedicated line for wired or wireless communication, an internet line, a telephone line, or the like. The transmitter 12 is also connected to the calculation unit 13. The transmitter 12 transmits various data created by the calculation unit 13 to the power control device 8.

[0031] The display unit 15 is configured by a liquid crystal display, a plasma display, etc. The display unit 15 displays various data created by the calculation unit 13.

[0032] The operation unit 16 receives input operations from the user and includes input devices such as a mouse and a keyboard.

[0033] The calculation unit 13 is configured by a computer. The calculation unit 13 is connected to the data acquisition unit 11, the transmission unit 12, the storage unit 14, the display unit 15, and the operation unit 16. The calculation unit 13 has a calculation target determination unit 131, a prediction time determination unit 132, a power flow value calculation unit 133, a conductor temperature calculation unit 134, a conductor temperature constraint determination unit 135, a condition change unit 136, and a condition setting unit 137. Each unit is configured by a program or a software module within the computer.

[0034] The calculation target determination unit 131 determines the power transmission section and calculation period in the power system 3 that are the targets of power flow value calculation, based on an input operation on the operation unit 16 by the user.

[0035] The prediction time determination unit 132 determines the time period for which the power flow value of the power transmission and distribution facility is predicted, based on an input operation on the operation unit 20 by the user.

[0036] The power flow value calculation unit 133 calculates a power flow value using various data acquired by the data acquisition unit 11. The conductor temperature calculation unit 134 calculates the change in conductor temperature when the predicted power flow value calculated by the power flow value calculation unit 133 is caused to flow in the target section. The conductor temperature constraint determination unit 135 determines whether the conductor temperature calculated by the conductor temperature calculation unit 134 deviates from the constraint condition. The condition change unit 136 changes the calculation condition for the predicted power flow value when the conductor temperature constraint determination unit 135 determines that the conductor temperature deviates from the constraint condition. When the conductor temperature constraint determination unit 135 determines that the conductor temperature does not deviate from the constraint condition, the condition setting unit 137 sets the conductor allowable temperature at that time as the calculation condition for the predicted power flow value.

[0037] The storage unit 14 is configured with a storage medium such as a semiconductor memory or a hard disk. The storage unit 14 is connected to the calculation unit 13. The writing and reading of data into and from the storage unit 14 is controlled by the calculation unit 13. The storage unit 14 stores various data such as computer programs for causing each part of the calculation unit 13 to execute processing.

[0038] Next, returning to FIG. 1 , the power control device 8 will be described. The power control device 8 is a device that actually controls the power system 3. The power control device 8 is connected to the power system 3 via a communication line 92, and is also connected to the power system monitoring device 1 via a communication line 93. The power control device 8 is configured by a computer or the like. The power control device 8 is installed in a substation of a general electricity transmission and distribution company that monitors or controls the power system 3.

[0039] Furthermore, the power control device 8 instructs control of the power generation amount of, for example, the generator 4 and the renewable energy power generation device 5 based on the supply and demand plan data D15. Furthermore, the power control device 8 receives control commands from the power system monitoring device 1. The power control device 8 controls the power system 3 based on the received control commands.

[0040] The operation of the above-described power system monitoring device 1 for monitoring the power system 3 will be described below.

[0041] FIG. 3 is a flowchart showing the procedure of the operation of the power system monitoring device 1 according to the first embodiment to monitor the power system 3.

[0042] 3, first, the calculation target determination unit 131 of the calculation unit 13 performs a calculation target determination process (step S101). In this calculation target determination process, the calculation target determination unit 131 determines a transmission section in the power system 3 selected by a user's input operation on the operation unit 20, such as the power transmission line 7a, as a target section for power flow value calculation.

[0043] Furthermore, the calculation target determination unit 131 determines a past period selected by a user's input operation on the operation unit 20, such as the period from January 1, 2021 to December 31, 2021, as the target period for the power flow value calculation. The determined target section and target period are notified to the data acquisition unit 11.

[0044] Next, the prediction time determination unit 132 of the calculation unit 13 performs a prediction time determination process (step S102). In this prediction time determination process, the prediction time determination unit 132 determines, for the target section determined in the calculation target determination process, a future prediction period selected by the user's input operation on the operation unit 20, such as a month, a season, or a future prediction time (e.g., 12 hours ahead), as the prediction target time for the current value.

[0045] Next, the data acquisition unit 11 performs a weather forecast data acquisition process (step S103). In this weather forecast data acquisition process, the data acquisition unit 11 acquires past weather forecast data D12 for the target section and target period notified by the calculation target determination unit 131. The data acquisition unit 11 transfers the acquired past weather forecast data D12 to the calculation unit 13.

[0046] Next, the data acquisition unit 11 performs a weather record data acquisition process (step S104). In this weather record data acquisition process, the data acquisition unit 11 acquires past weather record data D13 for the target section and target period notified by the calculation target determination unit 131. The time period of the acquired past weather record data D13 is the same as the time period of the past weather forecast data D12 acquired earlier in the weather forecast data acquisition process. The data acquisition unit 11 transfers the acquired past weather record data D13 to the calculation unit 13.

[0047] Next, power current calculation unit 133 of operation unit 13 performs a predicted power current calculation process (step S105). In this predicted power current calculation process, power current calculation unit 133 uses past weather forecast data D12 transferred from data acquisition unit 11 to calculate power current values ​​for the target section determined by calculation target determination unit 131. Power current calculation unit 133 sets the calculated power current values ​​as predicted power current values ​​for the target prediction time determined by prediction time determination unit 132. Note that if the weather forecast data D12 is based on predictions made at multiple forecast points arranged in a mesh pattern at intervals smaller than the target section, i.e., if there are multiple forecast points within the target section, power current calculation unit 133 may calculate a predicted power current value for each forecast point.

[0048] In the predicted power flow value calculation process, power flow value calculation unit 133 calculates at least one of a continuous allowable capacity and a short-term allowable capacity. The continuous allowable capacity and the short-term allowable capacity will now be described.

[0049] The continuous allowable capacity is a power flow value calculated on the assumption that the weather conditions will continue for a specified time (for example, about one hour), and can be calculated using, for example, the following equation (1). Equation (1) is an equation at the equilibrium point where the heat inflow term and the heat outflow term are balanced (steady-state equation).

[0050]

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[0051] On the other hand, the short-term allowable capacity is the maximum allowable current when the current equivalent to two lines flows through the healthy line when a fault is removed from one of two transmission lines, and indicates the current at which the short-term allowable temperature is reached after, for example, 10 minutes.

[0052] Next, the conductor temperature calculation unit 134 of the operation unit 13 performs a conductor temperature change calculation process (step S106). In this conductor temperature change calculation process, the conductor temperature calculation unit 134 calculates the temperature change of the conductor of the power transmission line 7a when the predicted power flow value calculated by the power flow value calculation unit 133 is caused to flow in the target section under the weather conditions indicated in the actual weather data D13 transferred from the data acquisition unit 11. The conductor temperature change can be calculated using, for example, the following equations (2) and (3). Equation (3) is an approximation of equation (2).

[0053]

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[0054]

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[0055] Next, the conductor temperature constraint determination unit 135 determines whether the conductor temperature calculated in step S106 deviates from the constraint conditions indicated in the constraint data D14 (step S107). Here, the constraint conditions for the conductor temperature will be described with reference to Figures 4(a) and 4(b).

[0056] Fig. 4(a) is a diagram showing an example of the time series change in conductor temperature when a continuous allowable capacity is applied to the conductor. In the continuous allowable capacity shown in Fig. 4(a), the equilibrium temperature is preset to, for example, 90°C as a constraint.

[0057] Fig. 4(b) is a diagram showing an example of the time series change in conductor temperature when a short-term allowable capacity is applied to the conductor. In the short-term allowable capacity shown in Fig. 4(b), the constraint is set such that the rise in conductor temperature occurring within a predetermined time (e.g., 10 minutes) is less than or equal to a predetermined temperature (e.g., 120°C).

[0058] Here, an example of a method for analytically calculating the equilibrium temperature and the time required to reach it of a conductor will be described.

[0059] For example, Bonface Ngoko, Hideharu Sugihara, Tsuyoshi Funaki (Osaka Univ.) "Validation of a Simplified Model for Estimating Overhead Conductor Temperatures under Dynamic Line Ratings - Comparison with the CIGRE Model -" IEEJ Transactions on Power and Energy, 2018 Volume 138 Issue 4 Pages 284-296 states that first, the radiation loss q in the above equation (1) is r The radiation loss P corresponds to r is expressed by the following equation (5).

[0060]

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[0061]

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[0062] Here, the temperature T A is T X It's much larger than 4T. X 2 T A、 T X 3 can be ignored. Therefore, h r can be transformed into the following equation (6).

[0063]

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[0064]

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[0065]

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[0066]

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[0067]

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[0068] On the other hand, in the case of natural convection, the convection coefficient hc can be expressed by the following equation (13).

[0069]

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[0070] Here, the more important range, G r ≧10 4 , P r ≦10 4 Considering A=0.480 and m=0.250, h c is transformed into the following equation (14).

[0071]

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[0072]

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[0073]

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[0074] Next, the solar heat absorption q in the above equation (1) s The radiation loss P corresponds to s is expressed by the following equation (17).

[0075]

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[0076] Next, the Joule loss P j is expressed by the following equation (18).

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[0077]

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[0078] In the approximate calculation of the equilibrium temperature, heat generation and heat loss are balanced, so the relationship in equation (20) below holds.

[0079]

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[0080]

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[0081]

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[0082]

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[0083]

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[0084]

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[0085] Another example of a method for analytically calculating the equilibrium temperature and the time required to reach it of a conductor will be described below.

[0086] In this example, a differential equation representing the temperature of the power transmission line 7 is used, which is expressed by the following equation (26).

[0087]

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[0088]

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[0089]

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[0090]

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[0091]

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[0092] The method for calculating the approximate short-term capacity will be described below.

[0093] JPEG2025138254000037.jpg22165

[0094]

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[0095]

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[0096] Returning to the flowchart shown in FIG. 3, if the conductor temperature deviates from the constraint condition (step S107: Yes), the condition change unit 136 performs a condition change process to change the calculation conditions for the predicted power flow value (step S108). In this condition change process, the condition change unit 136 changes the wind speed (V W) gradually decreases the upper limit value. At this time, the condition changing unit 136 sets the upper wind speed limit value according to the month, season, or future prediction time based on the prediction time determined in the prediction time determination process. Note that the condition changing unit 136 may decrease the upper wind speed limit value before the change (for example, 5 m / s) by a fixed value (for example, 0.1 m / s). Furthermore, if the upper wind speed limit value before the change is set to 100%, the condition changing unit 136 may gradually decrease the upper wind speed limit value by multiplying it by a predetermined correction coefficient (0.9, 0.8, ...). Furthermore, the upper limit of the gradual decrease, i.e., the upper limit of the change, may be set according to the amount of change in wind speed within a predetermined time interval.

[0097] In this embodiment, the condition change unit 136 also changes the wind speed (V W ) instead of the effective wind speed (V W ·K angle The upper limit of the constant K may be gradually decreased. angle can be calculated using the following equation (33). Note that φ in equation (4) indicates the wind direction. K angle Instead of Kδ, the above-mentioned Kδ may be used.

[0098]

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[0099] Once the power flow recalculation process is complete, the above-described steps S107 to S109 are repeated until the conductor temperature satisfies the constraints. Based on the initial conductor temperature calculation results, the condition change unit 136 may extract a time slice for the time period for which the conductor temperature is to be calculated, where the maximum conductor temperature for that time period exceeds a predetermined value, or where a temperature rise occurring within a predetermined time interval for that time period exceeds a reference value, and change the upper limit of the wind speed for the extracted time slice. This eliminates the need to update the power flow calculation conditions for all time slices, thereby reducing the load on the calculation process.

[0100] If the conductor temperature satisfies the constraint condition (step S107: No), the condition setting unit 137 performs a condition determination process to set the upper limit value of the wind speed at that time as a calculation condition for the predicted power flow value (step S110). This ends the process performed according to the flowchart in FIG.

[0101] According to the present embodiment described above, when it is predicted that the conductor temperature in the transmission section will not satisfy the constraints, the calculation conditions for the power flow value are changed. By setting the calculation conditions using past data and utilizing the set calculation conditions when predicting the dynamic line rating during actual operation, it is possible to avoid overestimation of the predicted value of the dynamic line rating.

[0102] (Second embodiment) A second embodiment will be described. Here, differences from the first embodiment will be mainly described, and overlapping descriptions will be omitted. In this embodiment, the content of the condition change process (step S108) differs from that of the first embodiment.

[0103] If the conductor temperature deviates from the constraint condition (step S107: Yes), in the condition change process of this embodiment, the condition change unit 136 gradually increases the lower limit value of the ambient temperature of the conductor (power transmission line 7a) indicated in the weather forecast data D12 transferred from the data acquisition unit 11. At this time, the condition change unit 136 may increase the lower limit value of the temperature before the change by a fixed value. Alternatively, if the lower limit value of the temperature before the change is set to 100%, the condition change unit 136 may gradually increase the temperature by multiplying it by a predetermined correction coefficient (1.1, 1.2, ...). Furthermore, an upper limit of the gradual increase may be set depending on the change in the ambient temperature from the time section before the change.

[0104] Next, as in the first embodiment, the power flow value calculation unit 133 performs a process of recalculating the predicted power flow value (step S109). At this time, when the power flow value calculation unit 133 calculates the continuous allowable capacity as the predicted power flow value, the convection loss q c The value of decreases as the ambient temperature of the conductor increases. In the condition change process of this embodiment, the lower limit of the ambient temperature is gradually increased, so the recalculated predicted power flow value is smaller than the previously calculated predicted power flow value. As a result, the power flow calculation conditions are fed back in a direction that causes the conductor temperature to satisfy the constraint condition.

[0105] Thereafter, when the conductor temperature satisfies the constraint condition (step S107: No), the condition setting unit 137 sets the lower limit value of the ambient temperature at that time as the calculation condition for the predicted power flow value. This completes the processing performed according to the flowchart of this embodiment.

[0106] According to the present embodiment described above, when it is predicted that the conductor temperature in the transmission section does not satisfy the constraints, the calculation conditions for the power flow value are changed in the same way as in the first embodiment. This makes it possible to avoid overestimation of the predicted value of the dynamic line rating.

[0107] (Third embodiment) A third embodiment will be described. Here, differences from the first embodiment will be mainly described, and overlapping descriptions will be omitted. In this embodiment, the content of the condition change process (step S108) differs from that of the first embodiment.

[0108] If the conductor temperature deviates from the constraint condition (step S107: Yes), in the step condition change process of this embodiment, the condition change unit 136 gradually decreases the allowable temperature of the conductor (power transmission line 7a) included in the constraint data D14 transferred from the data acquisition unit 11. At this time, the condition change unit 136 may decrease the allowable temperature of the conductor by a fixed value from the allowable conductor temperature before the change. Alternatively, if the allowable conductor temperature before the change is set to 100%, the condition change unit 136 may decrease the allowable conductor temperature by multiplying it by a predetermined correction coefficient (0.9, 0.8, . . .). Furthermore, an upper limit of the gradual decrease may be set depending on the change in the allowable conductor temperature from the time section before the change.

[0109] Next, as in the first embodiment, the power flow value calculation unit 133 performs a process to recalculate the predicted power flow value (step S109). At this time, when the power flow value calculation unit 133 calculates the continuous allowable capacity as the predicted power flow value, it gradually decreases the conductor allowable temperature (value given as Tavg) in equation 1 or the upper limit of the conductor temperature change calculated by equation 4. Because the conductor allowable temperature is gradually decreased in step S108 of this embodiment, the recalculated predicted power flow value will be smaller than the predicted power flow value calculated previously. As a result, the power flow calculation conditions are fed back in a direction that causes the conductor temperature to satisfy the constraint conditions.

[0110] Thereafter, when the conductor temperature satisfies the constraint condition (step S107: No), the condition setting unit 137 sets the conductor allowable temperature at that time as the calculation condition for the predicted power flow value, thereby completing the processing performed according to the flowchart of this embodiment. In the condition changing process of this embodiment, the condition changing unit 136 may gradually increase the allowable temperature of the conductor (power transmission line 7a). In this case, the condition setting unit 137 sets the allowable conductor temperature when the conductor temperature deviates from the constraint condition as the calculation condition for the predicted power flow value.

[0111] According to the present embodiment described above, when it is predicted that the conductor temperature in the transmission section does not satisfy the constraints, the calculation conditions for the power flow value are changed in the same way as in the first embodiment. This makes it possible to avoid overestimation of the predicted value of the dynamic line rating.

[0112] (Fourth embodiment) Fig. 5 is a flowchart showing the procedure of the operation of the power system monitoring device according to the fourth embodiment to monitor the power system 3. In the flowchart shown in Fig. 5, the contents from the calculation target determination process (step S201) to the predicted power flow value calculation process (step S205) are the same as those in the first embodiment, so their explanations will be omitted. In addition, the contents from the conductor temperature change calculation process (S207) to the condition determination process (S211) are also the same as those in the first embodiment, so their explanations will be omitted.

[0113] On the other hand, this embodiment differs from the first embodiment in that the power flow value calculation unit 133 performs a power flow value optimization process (step S206) following the predicted power flow value calculation process (step S205). Here, the power flow value optimization process will be described with reference to FIG.

[0114] 6 is a schematic diagram for explaining the power flow optimization process. In this power flow optimization process, for example, the objective is set to minimize power generation costs. Furthermore, the balance of power supply and demand based on the supply and demand plan data D15, the upper and lower limits of voltage in each power system, and the upper and lower limits of power output from the power source of each bus are set as first constraints of the OPF (Optimal Power Flow). Furthermore, the predicted power flow calculated in the predicted power flow calculation process (step S205) is added as a second constraint of the OPF.

[0115] The power flow calculation unit 133 calculates a solution to minimize the power generation cost based on the supply and demand plan data D15, without violating the first and second constraints. The predicted power flow calculated in the predicted power flow calculation process (step S205) is the maximum current that can flow through each transmission line depending on weather conditions. However, the predicted power flow is not necessarily the optimal power flow to achieve the goal of minimizing power generation costs.

[0116] Therefore, in this embodiment, the power flow value calculation unit 133 calculates the optimal power flow value by adding a new second constraint condition to the conventional first constraint condition. Therefore, according to this embodiment, it is possible to minimize the power generation cost while avoiding overestimation of the predicted value of the dynamic line rating.

[0117] In this embodiment, the predicted power flow value may be added as a constraint condition not limited to the constraint condition of the OPF. The predicted power flow value may be added to a specific rule that is preset for the power transmission and distribution equipment of the power system 3.

[0118] (Fifth embodiment) Fig. 7 is a block diagram showing the configuration of a power system monitoring device according to the fifth embodiment. In Fig. 7, components similar to those of the power system monitoring device 1 according to the first embodiment described above are given the same reference numerals, and redundant explanations will be omitted.

[0119] In the power system monitoring device 1 according to this embodiment, the calculation unit 13 further includes a learning model construction unit 138. The storage unit 14 stores position data D21, topographical data D22, and a learning model D23.

[0120] The location data D21 is data relating to the location such as the latitude and longitude indicating the location of each power transmission and distribution facility in the power system 3, and the height of the power transmission line 7.

[0121] The topographical data D22 is data relating to the elevation of the installation location and the topography around the installation location of each piece of power transmission and distribution equipment in the power system 3. Note that the topographical data D22 may be, for example, data provided by the Geospatial Information Authority of Japan.

[0122] The learning model D23 is constructed by machine learning in the learning model construction unit 138. Here, a process in which the learning model construction unit 138 constructs the learning model D23 for machine learning will be described.

[0123] FIG. 8 is a schematic diagram illustrating an example of a learning model construction process. In this embodiment, weather forecast data D12, actual weather data D13, system data D16, location data D21, and topographical data 22 are used as input data for machine learning. A plurality of these input data exist for each time slice and measurement location. The machine learning period is set by a user's input operation on the operation unit 20. For the weather forecast data D12, wind speed data for a confidence interval with the smallest error may be used instead of data for the entire interval. Alternatively, if multiple wind speed data exist for a certain time slice or measurement location, the median value of these wind speed data may be used.

[0124] By applying machine learning to the input data in the initial hidden layer, updated values ​​of the calculation conditions for the power flow value that satisfy the constraint condition for the conductor temperature are output as output data. These updated values ​​correspond to the upper wind speed limit described in the first embodiment, the lower ambient temperature limit described in the second embodiment, or the allowable conductor temperature described in the third embodiment.

[0125] The learning model construction unit 138 calculates the predicted power flow value for the target period of machine learning based on the weather forecast data and the system data D16, using the calculation formula explained in the first embodiment. Next, the learning model construction unit 138 calculates the conductor temperature when power is transmitted at the predicted power flow value to the target section of machine learning under the weather conditions of the actual weather data D13 for the target period of machine learning.

[0126] Next, if the calculated conductor temperature deviates from the constraints, the learning model construction unit 138 recalculates the predicted power flow value and conductor temperature using the updated values ​​of the output data. If the conductor temperature still deviates from the constraints with this updated value, the learning model construction unit 138 updates the hidden layer. In this way, the learning model construction unit 138 updates the hidden layer until a conductor temperature that satisfies the constraints is calculated using the updated values ​​of the output data. The learning model construction unit 138 then stores the final version of the learning model D23, including the hidden layer, in the memory unit 14.

[0127] Thereafter, in the condition change process (step S108), the condition change unit 136 changes the calculation conditions for the power flow value using the learning model D23.

[0128] 9 is a schematic diagram illustrating the condition change process in the fifth embodiment. In this embodiment, the condition change unit 136 inputs weather forecast data D12, actual weather data D13, system data D16, location data D21, and topographical data D22 to a learning model D23 read from the storage unit 14. The updated values ​​(upper wind speed limit, lower ambient temperature limit, and allowable conductor temperature) output by machine learning using the learning model D23 satisfy the constraint conditions for the conductor temperature. Therefore, the condition setting unit 137 sets these updated values ​​as the calculation conditions for the predicted power flow value.

[0129] According to the present embodiment described above, when it is predicted that the conductor temperature in the transmission section does not satisfy the constraints, the calculation conditions for the power flow value are updated, as in the other embodiments. In this case, in this embodiment, the learning model D23 calculates an updated value that satisfies the constraints for the conductor temperature. This eliminates the need for the predicted power flow value recalculation process (step S109).

[0130] Therefore, it is possible to quickly perform processing that prevents overestimation of the predicted value of the dynamic line rating.

[0131] Although several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel system described in this specification can be embodied in various other forms. Furthermore, various omissions, substitutions, and modifications can be made to the forms of the system described in this specification without departing from the spirit of the invention. The appended claims and their equivalents are intended to cover such forms and modifications that fall within the scope and spirit of the invention. [Explanation of symbols]

[0132] 1,5:Power system monitoring equipment 133: Power flow calculation unit 134: Conductor temperature calculation unit 136: Condition change section 138: Learning model construction unit

Claims

1. a power flow value calculation unit that calculates a predicted future power flow value of a power transmission and distribution facility installed in the power system based on past weather forecast data; a conductor temperature calculation unit that calculates a change in conductor temperature when the predicted power flow value is applied to the power transmission and distribution facility under weather conditions based on the past weather forecast data and actual weather data for the same period; and a conductor temperature constraint determination unit that determines whether the conductor temperature satisfies a preset constraint condition; a condition changing unit that changes the calculation conditions for the predicted power flow value when the conductor temperature does not satisfy the constraint condition; A power system monitoring device comprising:

2. 2. The power system monitoring device according to claim 1, wherein the condition changing unit changes, as the calculation condition, an upper limit of a wind speed indicated in the weather forecast data, a lower limit of an ambient temperature of the power transmission and distribution equipment, or an allowable conductor temperature used in calculating the predicted power flow value.

3. The power system monitoring device according to claim 1 , wherein the condition changing unit changes an upper limit value of an effective wind speed as the calculation condition.

4. The power system monitoring device according to claim 1 , wherein the condition changing unit, when changing the calculation conditions for the predicted power flow value, multiplies the calculated value before the change by a predetermined correction coefficient.

5. The power system monitoring device according to claim 1 , wherein the power flow value calculation unit calculates an optimal power flow value by adding the predicted power flow value to a condition for optimization processing related to the power transmission and distribution facility.

6. The power system monitoring device according to claim 1 , wherein the power flow value calculation unit applies the predicted power flow value to a specific rule that is preset for the power transmission and distribution facility.

7. The power system monitoring device according to claim 2 , wherein the condition changing unit changes the upper limit of the wind speed depending on a prediction time period for which the predicted power flow value is to be calculated.

8. The power system monitoring device according to claim 2 , wherein the condition changing unit changes the upper limit of the wind speed based on an upper limit that is set in advance in accordance with an amount of change in wind speed within a predetermined time interval.

9. The power system monitoring device according to claim 1 , wherein the constraint condition is set such that the rise in the conductor temperature occurring within a predetermined time period is less than or equal to a predetermined value.

10. 2. The power system monitoring device according to claim 1, wherein the condition changing unit extracts, for a time period for which the conductor temperature is to be calculated, a time section in which a maximum value of the conductor temperature for the time period exceeds a predetermined value or a temperature rise occurring within a predetermined time interval of the conductor temperature for the time period exceeds a reference value, based on a result of an initial conductor temperature calculation, and changes the upper limit value of the wind speed for the extracted time section.

11. The power system monitoring device according to claim 1 , wherein the conductor temperature calculation unit calculates the conductor temperature using the following formula: [Equation 1]

12. 2. The power system monitoring device according to claim 1, wherein when a plurality of forecast points of the weather forecast data are arranged in a mesh pattern within the section for which the predicted power flow value is to be calculated, the power flow value calculation unit calculates the predicted power flow value for each forecast point.

13. a learning model construction unit that performs machine learning on input data including the weather forecast data and the actual weather data to construct a learning model for outputting updated values ​​of the calculation conditions for the predicted power flow value; The power system monitoring device according to claim 1 , wherein the condition changing unit changes the calculation conditions for the predicted power flow value using the learning model when the conductor temperature does not satisfy the constraint condition.

14. Based on past weather forecast data, the system calculates future predicted power flow values ​​for power transmission and distribution facilities installed in the power grid. calculating a change in conductor temperature when the predicted power flow value is applied to the power transmission and distribution facility under weather conditions based on the past weather forecast data and actual weather data for the same period; determining whether the conductor temperature satisfies a preset constraint; If the conductor temperature does not satisfy the constraint condition, the calculation condition for the predicted power flow value is changed. Power system operation monitoring method.

15. A process of calculating future predicted power flow values ​​of power transmission and distribution facilities installed in the power grid based on past weather forecast data; a process of calculating a change in conductor temperature when the predicted power flow value is applied to the power transmission and distribution facility under weather conditions based on the past weather forecast data and actual weather data for the same period; A process of determining whether the conductor temperature satisfies a preset constraint condition; a process of changing a calculation condition for the predicted power flow value when the conductor temperature does not satisfy the constraint condition; A program that causes a computer to execute the following.

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