JP6689732B2 - Power system control device and power system control method - Google Patents

Description

  The present invention relates to a technique for controlling a power system.

  Renewable energy is being introduced to reduce greenhouse gas emissions. Renewable energy power generation includes, for example, wind power generation and solar power generation. The power generation amount (power generation amount) of wind power generation or solar power generation changes due to climate change. Therefore, the power transmission amount (power transmission amount) of the power system fluctuates, making the power system unstable and reliable. May decrease.

  In order to improve the reliability of the electric power system, it is conceivable to give a margin to the entire equipment of the electric power system so that it can cope with a sudden change in the power transmission amount. It is possible to improve the reliability of the power system by ensuring redundancy by duplicating power transmission paths or equipment, or by improving resistance to state changes by increasing the capacity that can be transmitted by each path. .

  On the other hand, however, there is an increasing demand for cost reduction in the course of market liberalization. Although it is intended to improve reliability, large capital investment is not preferable. It is also preferable to keep the operating cost as low as possible.

  Non-Patent Document 1 discloses an optimum power flow calculation method for calculating an optimum power system state in response to various requirements such as maintaining or improving reliability, suppressing capital investment, and reducing operating costs. Further, Patent Document 1 describes a method for dealing with the problem that the optimum control amount calculated in the optimum power flow calculation is not optimum at the time of actual control due to the passage of time.

Yasuji Sekine and 6 others, "Optimal Power Flow Calculation for Electric Power System << OPF; Optimal Power Flow >>", Japan Electric Association, March 2002

JP, 2010-136600, A

  The technology disclosed in Patent Document 1 uses a metaheuristic method to predict the demand for each future substation based on the data of the demand power measured at the substation, and based on the data of the predicted demand. The optimal control amount is calculated, and the electric power system is controlled using the calculated control amount. Active power and reactive power are measured as demand power, and used to predict future demand for each substation. At that time, considering the time required from the calculation of the control amount to the application, the calculation is performed so that the optimum control amount is obtained when the control is applied. That is, in Patent Document 1, the future state of the power system is predicted in advance.

  However, there is a limit to the accuracy of the preliminary prediction, and the actual state of the power system may differ greatly from that predicted in advance. Even in such a case, the technique of Patent Document 1 applies the calculation result predicted in advance, and thus it may not always be possible to appropriately control the power system.

  An object of the present invention is to provide a technique that enables appropriate control in control of a power system that requires time for calculation of prediction.

  An electric power system control device according to an aspect of the present invention is a power system control device that generates control data of an electric power system at a predetermined time and controls the electric power system based on the control data at the predetermined time. A system state acquisition unit that acquires state data indicating a state of the power system, and a plurality of states of the power system at a specified time that is a future time to execute control related to the power system based on the acquired state data. A plurality of state prediction data that predicts, a prediction calculation unit that generates a plurality of control candidate data that is control data corresponding to each of the plurality of state prediction data, and the state data acquired at the specified time. Utilizing a comparison unit that selects one of the plurality of state prediction data, and a control unit based on control candidate data corresponding to the selected state prediction data. Having an execution unit for executing control related power system, the.

  According to the present invention, a plurality of state prediction data and control candidate data corresponding to them are created in advance, and control is executed with the control candidate data selected based on the actual state. Even if the state changes by the time of executing, more appropriate control can be executed.

2 is a block diagram showing an example of a system configuration of Example 1. FIG. 5 is a diagram showing an example of environmental condition data 211 regarding a specific measurement point in Example 1. FIG. 5 is a diagram showing an example of device setting data 212 in Example 1. FIG. 6 is a diagram showing an example of device state data 213 in Example 1. FIG. 3 is a diagram showing an example of system configuration data 202 in Embodiment 1. FIG. 5 is a flowchart showing a processing example in the control unit 10 according to the first embodiment for performing control to an optimum power system state. 5 is a flowchart showing an example of a process in which the prediction calculation unit 102 creates systematic prediction data in the first embodiment. FIG. 5 is a diagram showing an example of systematic prediction data 203 of a specific measurement point in the first embodiment. 5 is a diagram showing an example of system control data 204 in Example 1. FIG. 7 is a flowchart illustrating a processing example in which the comparison unit 103 calculates an application priority order in the first embodiment. 10 is a flowchart showing a processing example in which the determination unit 104 selects system control data to be applied in the second embodiment. 9 is a block diagram showing an example of a system configuration of Example 3. FIG. FIG. 13 is a diagram showing an example of systematic prediction data 203 at a specific measurement point in the fourth embodiment. FIG. 14 is a diagram showing an example of system control data 204 in the fourth embodiment. 16 is a flowchart illustrating a processing example in which a comparison unit 103 calculates an application priority order in a fourth example. 16 is a flowchart illustrating an example of processing in which the determination unit 104 selects system control data to be applied in the fifth embodiment. FIG. 16 is a diagram showing an example of a control data selection screen in the fifth embodiment.

  Various embodiments will be described with reference to the drawings. In the drawings, the same reference numerals are given to the same or similar parts.

  FIG. 1 is a block diagram illustrating an example of the system configuration of the first embodiment. In FIG. 1, 1 is a system controller, 2 is a communication network, 3 is a power system, 4 is measuring equipment, and 5 is electrical equipment.

  In this embodiment, based on various data of the grid acquired by the measuring device 4 of the power grid 3, a plurality of states of the power grid 3 at a future specified date and time are predicted to generate a plurality of grid prediction data, and each prediction is performed. The optimum grid control data corresponding to the above is calculated, and at the specified date and time, the plurality of predicted grid prediction data and the actually measured status data of the power grid 3 are compared, and the grid corresponding to the plurality of grid prediction data is calculated. Priorities are assigned to the respective control data, and the electric device 5 is controlled based on the system control data having a high priority.

  According to this embodiment, the control suitable for the actual state of the power system 3 at the time when the control is executed can be applied to the devices of the power system 3.

  The system control device 1 includes a control unit 10, a recording unit 20, an optimum calculation unit 30, a communication unit 40, an input unit 50, and a display unit 60.

  The system control device 1 is, for example, a server having a CPU, a memory, a storage device such as a hard disk or SSD, and a communication function. The control unit 10 and the optimum calculation unit 30 are realized by a CPU that performs processing according to a control program stored in the memory. The recording unit 20 is realized by a memory and a storage device. The communication unit 40 performs communication via the communication network 2. The system control device 1 may be implemented by a plurality of servers that are communicably connected to each other, instead of being physically a single server.

The input unit 50 includes at least one device that inputs information, such as a keyboard, a pointing device such as a mouse, a touch panel, and a voice instruction device. The display unit 60 includes at least one device that outputs information, such as a display device, a printer, and a voice output device.

  The system control device 1 can acquire state data indicating the state of the power system 3 measured by a plurality of measuring devices 4 provided in the power system 3 via the communication network 2 connected to the communication unit 40. it can. In addition, the operating state of the electric device 5 can be controlled by transmitting a control command including data to be applied to control to the plurality of electric devices 5 included in the power system 3.

  The power system 3 includes equipment (not shown) of a power plant that generates electric power using a power generator, equipment of a consumer who consumes the power generated by the power plant, and power distribution that sends power from the power station to the consumer. Including equipment. Various electric devices 5 are included in these facilities. Further, the power system 3 includes a measuring device 4 that measures the states of these facilities.

  As the power generation device, power generation devices by various power generation methods including thermal power generation, hydraulic power generation, nuclear power generation, geothermal power generation, solar power generation, and wind power generation can be considered.

  The power distribution facility is a facility related to power transmission and distribution, including an overhead power transmission line, an underground power transmission line, a transformer, a circuit breaker, and a phasing facility.

  The measuring device 4 is a device that measures the state of a specific measurement item at a specific measurement point of the power system 3. The electric power system 3 is provided with measuring devices 4 at a plurality of measuring points.

  The measurement items include, for example, electric power information of the electric power system 3, weather information, setting state information of the electric device 5, operating state information of the electric device 5, operating state information of a device that changes the electric power flow state, and various other items. Contains the value. The electric power information of the electric power system 3 includes, for example, a voltage value, a phase, an electric power value, etc. of a specific point of the electric power system 3. The meteorological information includes temperature, wind speed, wind direction, precipitation amount, solar radiation amount, and the like. The device that changes the power flow state is, for example, a power distribution facility or a power generation device.

  The measurement points include busbars of power plants and substations, power distribution facilities, and consumers. One measurement device 4 may be provided at one measurement point, or a plurality of measurement devices 4 may be provided.

  The value measured by the measuring device 4 is transmitted as state data to the system control device 1 via the communication network 2. At this time, the measurement date and time information indicating the time when the value is measured may be transmitted together with the measured value.

  The electric device 5 is a device that uses or handles electricity among the devices that form the power system 3, and is, for example, a device that can change the power flow state of the power system 3. The electric device 5 includes, for example, a device for changing the power flow state of the power system 3, such as a power generator, a transformer, a circuit breaker, and a phase adjusting facility. However, the electric device 5 may include various other devices. When the electric equipment 5 receives the control command transmitted from the system control device 1 via the communication network 2, the electric device 5 can change the setting state and the operating state according to the control command.

  The control unit 10 of the system control device 1 includes a system state acquisition unit 101, a prediction calculation unit 102, a comparison unit 103, and a determination unit 104.

The system status acquisition unit 101 acquires, via the communication unit 40, the system status data transmitted by the measuring device 4 on a regular basis or when receiving an instruction from the control unit 10, and measures the system status data 201 as the measurement date and time. It is recorded together with the recording unit 20.
When recording the system state data 201, when the system state acquisition unit 101 can estimate the state of the power system 3, the system state acquisition unit 101 acquires the system configuration data 202 from the recording unit 20 and calculates the system state by state estimation calculation. The data may be corrected and the corrected data may be stored in the recording unit 20.

  The system status data 201 includes environmental status data 211, device setting data 212, and device status data 213.

  The environmental condition data 211 includes weather information, information on active power and reactive power flowing through the load, and information on active power generated by the power generation facility.

  The device setting data 212 includes setting values set for the electric device 5.

  The device state data 213 includes operating states of devices such as the electric device 5, the power distribution facility, and the power generator that change the power flow state.

  FIG. 2 is a diagram illustrating an example of the environmental state data 211 regarding a specific measurement point according to the first embodiment.

  The environmental condition data 211 is table data in which measured values at specific measurement points are stored in time series, and as an example of items, items such as serial number D101, measurement date / time D102, temperature D103, active power D104, and reactive power D105 are included. included. D103 to D105 are items in which measured values are stored, and measured values that change over time are stored together with D101 and D102.

  The temperature D103 of this embodiment is meteorological information, and the temperature measured at the measurement point is stored. However, instead of recording the values measured at actual measurement points, the measurement values at topographically adjacent measurement points may be recorded.

  Further, as another example, instead of the temperature D103, or in addition to the temperature D103, other weather information may be recorded in the environmental condition data 211. Specifically, one or more of temperature, wind speed, wind direction, precipitation amount, and solar radiation amount may be recorded in the environmental condition data 211 as the weather information. Further, other weather information may be included in the environmental condition data 211.

  Active power D104 and reactive power D105 are examples of power information. The environmental state data 211 may include, as the power information, one or more of active power, reactive power, apparent power, phase, voltage, and current, or may include other power information.

  FIG. 3 is a diagram illustrating an example of the device setting data 212 according to the first embodiment.

  The device setting data 212 is table data in which information on setting values set in the device is stored in time series. The device setting data 212 includes, as items, a serial number D201, a measurement date / time D202, an input amount D203 of the phase adjusting equipment 1A, an input amount D204 of the phase adjusting equipment 1B, a tap position D205 of the transformer 1A, and a tap position D206 of the transformer 1B. Is included.

  D203 to D206 shown here are examples of the setting information of the electric device 5, and may be a combination of other setting information. The setting information may be setting information acquired from the electric device 5 by the measuring device 4 attached to the electric device 5, or may be setting information set in the electric device 5.

  FIG. 4 is a diagram illustrating an example of the device status data 213 according to the first embodiment.

  The equipment state data 213 is table data in which the operation states of the equipment are stored in time series, and the items are a serial number D301, a measurement date / time D302, an operation state D303 of the phase adjusting equipment 1A, an operation state D304 of the phase adjusting equipment 1B, It includes an operating state D305 of the transformer 1A and an operating state D306 of the transformer 1B.

  D303 to 306 shown here are examples, and the open / close state of a switchgear such as a circuit breaker or a switch, and the power transmission status of a power transmission line may be included. In addition, it is a combination of various operating states of the electrical equipment 5 that changes the power flow state of the power distribution facility or the power generator, and arbitrary information selected from various operating information of other equipment acquired by the measuring equipment 4. Is also good.

  FIG. 5 is a diagram illustrating an example of the system configuration data 202 according to the first embodiment. Here, an example of a transmission line is shown. D401 is a serial number, D402 is a name, D403 is a rated voltage, D403 is a rated capacity, D405 is a reference length, D405 is a line length, D406 is a resistance value, and D407 is a parameter such as reactance, which are included in the system configuration data 202 as values for each device. Is stored. The system configuration data 202 stores information that does not change in a short time, such as parameter information of the devices that configure the power system 3 or mutual electrical connection information of the devices among the information necessary for analyzing the power system. It

  Although FIG. 5 shows the case of a power transmission line, different parameter items may be stored for other devices.

  FIG. 6 is a flowchart illustrating an example of processing performed by the control unit 10 according to the first embodiment to perform control to an optimum power system state.

  First, the control unit 10 predicts that the user is instructed via the input unit 50 or is set in advance to instruct calculation of the state of the optimum power system 3 at the designated date and time and application of control to the power system 3. The computing unit 102 uses the system status data 201 and the system configuration data 202 to create a plurality of system prediction data at the specified date and time (step S101).

  At that time, the prediction calculation unit 102 predicts a plurality of possible changes in the state of the electric power system 3 from the present to the designated date and time based on the state of the electric power system 3 from the past to the present and the configuration of the electric power system 3. , Systematic prediction data for each case may be created. Specifically, the prediction calculation unit 102 predicts a plurality of environmental conditions at a designated date and time based on the data accumulated in the system status data 201, creates system prediction data based on each prediction, and the system prediction data 202 To record as.

  Next, the control unit 10 causes the optimum calculation unit 30 to calculate system control data for achieving an optimum system state based on each of the plurality of system prediction data (step S102).

  Next, when the specified date and time, that is, the date and time when the application of the system control data is started, is approached, the control unit 10 causes the comparison unit 103 to calculate the latest system state data and each system on which the system control data is calculated. The prediction data is compared to determine the priority order of application to each system control data (step S103).

  The timing of determining the priority order by the comparison unit 103 may be a timing that is traced back from the designated date and time by a predetermined time in consideration of the time required for control and calculation so that the system control data can be applied to the control at the designated date and time. Good.

  Further, the priority order of the grid control data may be calculated such that the grid prediction data corresponding to the grid control data has a higher priority as it is closer to the latest grid status data.

  Next, the control unit 10 causes the determination unit 104 to select the highest priority system control data from the plurality of system control data (step S104).

  Finally, the control unit 10 causes the execution unit 105 to execute control based on the system control data selected by the determination unit 104 (step S105). In that case, the execution part 105 produces | generates the control command which implement | achieves the control value described in the selected system control data, and transmits to the electric equipment 5 of the control object in the electric power system 3.

  FIG. 7 is a flowchart illustrating an example of a process in which the prediction calculation unit 102 creates systematic prediction data according to the first embodiment.

  First, when the prediction calculation unit 102 receives an instruction to create systematic prediction data of a specified future date and time (specified date and time), the prediction calculation unit 102 acquires the environmental condition data 211 (step S201).

  Next, the prediction calculation unit 201 predicts a plurality of weather conditions at the designated date and time based on the acquired environmental condition data and creates a plurality of predicted weather information (step S202).

  Next, the prediction calculation part 201 produces | generates the load amount corresponding to each of the produced forecast weather information by the statistical calculation with respect to environmental condition data (step S203). The load amount (predicted load amount) generated here is an example of system prediction data.

  As a simple example, the method of creating the forecast weather information in step S202 is to extract the data at the same time as the specified date and time in the past year and several days before and after the specified date and time from the accumulated environmental condition data 211. It is possible to use a method in which a plurality of statistical quantities having different statistical attributes are calculated based on the data and each of the calculated plurality of statistical quantities is used as predicted weather information.

  For example, regarding the temperature, it is conceivable to use a plurality of extracted temperature data and set the average value, the highest value, and the lowest value thereof as the plurality of forecast weather information.

  However, the method of creating the forecast weather information is not particularly limited as long as it is possible to forecast the weather at a future date and time. As another example, the forecasted weather information may be created by another statistical calculation, or the forecasted weather information may be created by a known metaheuristic method. Further, not only the system state data 201 but also the system configuration data 202 may be used to create the forecast weather information.

  As a method of creating the predicted load amount in step S203, for example, there is a simple method of setting the predicted load amount to the load amount corresponding to the data closest to the predicted weather from the accumulated environmental condition data. The predicted load amount may be generated by the statistical calculation of ## EQU1 ## or a metaheuristic method such as the known example 1, and the method is not limited as long as the load amount at a future date and time can be predicted. When generating the predicted load amount without using the weather information, the execution of step S202 and the weather information of the environmental condition data 211 may be unnecessary. If necessary for data generation, the system configuration data 202 may be used in addition to the system status data 201.

  FIG. 8 is a diagram illustrating an example of the systematic prediction data 203 at a specific measurement point according to the first embodiment.

  The systematic prediction data 203 is table data that stores a plurality of predicted values at specific measurement points, and includes a unique ID D501 for specifying the systematic prediction data and a prediction execution indicating the date and time when the prediction calculation is performed. Date / time D502, designated date / time D503 indicating the date / time (designated date / time) designated as the target of prediction, temperature D504 indicating the predicted temperature, active power D505 indicating the predicted active power, and invalid indicating the predicted reactive power. Electric power D506 is included.

  The air temperature D504 is an example of weather information. As the temperature D504, a predicted temperature value at an actual measurement point may be used, or a predicted temperature value at a position topographically close to the measurement point may be used.

  Further, the systematic prediction data 203 may include any one or more of temperature, wind speed, wind direction, precipitation amount, and solar radiation amount as weather information, and may include information indicating other weather conditions. .

  Active power D505 and reactive power D506 are examples of power information. The system prediction data 203 may include, as the power information, one or more of active power, reactive power, apparent power, phase, voltage, and current, and may also include other power-related information.

  The optimum calculation unit 30 generates grid control data that realizes an optimum power grid state based on the grid prediction data at the designated date and time in the grid prediction data 203 illustrated in FIG. 8 and the latest device setting data 212. , To the control unit 10. The control unit 10 adds the ID of the system prediction data corresponding to the received system control data and records it in the system control data 204. The system configuration data 202 and / or the latest device status data 213 may be used to generate the system control data.

  The calculation for obtaining the optimum power system state by the optimum calculation unit 30 is intended to reduce the power loss or improve the stability of the voltage, and calculate the optimum power system state for that purpose. The actual calculation is, for example, a combination of variables that give an objective function expressing power loss or reduction and satisfy the constraints of the power flow equation of the power system and other equality constraints and inequality constraints such as the voltage range. From among the above, a method of computing a combination of variables that minimizes or maximizes the above objective function may be used. As the variables, each bus voltage, the input amount of the phase adjusting equipment, the transformer tap position, etc. can be used. As a calculation method of the optimum calculation unit 30, for example, a known method described in Non-Patent Document 1 can be used, but the calculation method is not limited to that method. Any method may be used as long as it is a calculation method capable of calculating the optimum power system state.

  FIG. 9 is a diagram illustrating an example of the system control data 204 according to the first embodiment.

The system control data 204 is table data that stores setting parameters applied to control of the power system 3. The system control data 204 stores a plurality of system control data corresponding to a plurality of system prediction data. The system control data 204 includes, as items, a serial number D601 of the system control data, an ID D602 of the original system prediction data, a priority D603 to which the system control data is applied, and a date and time when the system control data is applied. The start date and time D604, the input amount D605 of the phase adjusting equipment 1A, the input amount D606 of the phase adjusting equipment 1B, the tap position D607 of the transformer 1A, and the tap position D608 of the transformer 1B are included.

  D605 to D608 are setting values set in the electric device 5. Other than those shown in FIG. 9, the target value of the terminal voltage of the generator, the open / closed state of the switch, etc. may be included.

  The value of the application priority order D603 is added by the comparison unit 103. The comparison unit 103 calculates which system prediction data appropriately indicates the actual state based on the latest environmental condition data, and assigns an application priority to each of the plurality of system control data according to the calculation result. Give.

  FIG. 10 is a flowchart illustrating a processing example in which the comparison unit 103 calculates the application priority order according to the first embodiment.

  First, when the control unit 10 is instructed by the control unit 10 to calculate the priority of specific system control data, the comparison unit 103 acquires data indicating the latest environmental state (latest environmental state data) from the environmental state data 211 ( Step S301).

  Further, the comparison unit 103 acquires the comparison calculation data 205 representing the comparison calculation method (step S302).

  Further, the comparison unit 103 acquires the system prediction data corresponding to each system control data specified by the prediction data ID of the system control data illustrated in FIG. 9 (step S303).

  Next, the comparison unit 103 uses the comparison calculation data 205, compares each system prediction data with the latest environmental condition data by a predetermined comparison calculation method, and indicates a state close to the environmental condition indicated by the latest environmental condition data. Priorities are given to the respective system prediction data so that the system prediction data has a high priority (step S304).

  Lastly, the comparing unit 103 adds the priority order calculated in step S304 to the system control data corresponding to each system prediction data, with reference to the prediction data ID D602 of the system control data 204 (step S305). .

  The comparison calculation data 205 is data indicating a method of comparison calculation for comparing the system prediction data and the latest environmental condition data in step S304. As the method of the comparison calculation in step S304, for example, the sum of weighted mean squared errors of each predicted value of the systematic prediction data and the corresponding latest environmental state data is calculated, and the systematic prediction data having a smaller value is prioritized. The calculation may be performed so that the rank becomes higher. The weighted mean square error of the present embodiment can be calculated using the equation (1).

ここで、Ｅ_ｉはｉ番目（ｉ＝１，２，…，Ｎ）の系統予測データについての重み付き平均二乗誤差である。ｘ_ｉ，ｊはｉ番目の系統予測データのｊ番目（ｊ＝１，２，…，Ｍ）の項目の予測値である。ｙ_ｊはｘ_ｉ，ｊと同じ項目の最新環境状態データの値である。ｗ_ｊはｊ番目の項目に対する重み付けである。

Here, E _i is a weighted mean square error for the i-th (i = 1, 2, ..., N) systematic prediction data. x _{i, j} is the predicted value of the j-th (j = 1, 2, ..., M) item of the i-th system prediction data. y _j is the value of the latest environmental state data of the same item as x _{i, j} . w _j is a weight for the j-th item.

w _j is a positive value that can be arbitrarily designated in consideration of the importance of each item and the range of values. As w _j , for example, a value obtained by calculating the reciprocal of the average value of each item and increasing the value by the degree of importance of the item of importance may be adopted. However, it is not limited to this, and other values may be adopted. The w _j may be recorded as the comparison calculation data 205.

  The determination unit 104 selects the system control data 204 with the highest priority calculated as described above as the system control data to be actually applied.

  As described above, when the optimum power system state is calculated based on future predictions and the calculated result is applied to the power system, it is possible to control the power system state according to the actual state at the time of application. Becomes

  The contents included in this embodiment can be organized as follows.

  The system control device 1 generates control data of the power system 3 at a predetermined time and controls the power system 3 based on the control data at a predetermined time. In the grid control device 1, the grid status acquisition unit 101 acquires status data (grid status data) indicating the status of the power grid 3. The prediction calculation unit 102 predicts, based on the acquired state data, a plurality of state prediction data that predicts a plurality of states of the power system 3 at a designated time (designated date and time) that is a future time at which control regarding the power system 3 is executed. (System prediction data) is generated, and a plurality of control candidate data (system control data) that is control data corresponding to each of the plurality of state prediction data is generated. The comparison unit 103 uses the state data acquired at the designated time to select any one of the plurality of state prediction data. The executing unit 105 executes the control regarding the electric power system 3 based on the control candidate data corresponding to the selected state prediction data. As described above, since a plurality of state prediction data and control candidate data corresponding to them are created in advance and control is executed with the control candidate data selected based on the actual state, it takes time to calculate the prediction and control is performed. Appropriate control can be executed even if the state changes before the execution.

  Further, the comparison unit 103 may calculate the degree of similarity (priority order) between the state indicated by the plurality of state prediction data and the state of the power system at the designated time, and select the state prediction data having the highest degree of similarity. . Since the control candidate data corresponding to the state prediction data closest to the actual state is selected from the plurality of control candidate data and the control is executed, the control according to the state close to the actual state can be executed.

  Further, the comparison unit 103 calculates a weighted mean square error between the state indicated by the plurality of state prediction data and the state of the power system 3 at the specified time, and calculates the state prediction data having the smallest weighted average two-row error as the degree of similarity. May be judged to be the highest. With the weighted average situation error, the state prediction data indicating the state closest to the actual state can be easily obtained with high accuracy.

  Further, the control related to the power system 3 may include determination of a set value of the output voltage of the power generation device, determination of the input amount of the phase adjusting equipment, or determination of the tap position of the transformer.

  Further, the state prediction data may include data that predicts the power consumption of the load of the power system 3 and data that predicts the amount of power generation of the power generation device.

  In addition, the state data may include data on the measured temperature, wind speed, wind direction, precipitation amount, or solar radiation amount.

  In the second embodiment, in addition to the function of the device of the first embodiment, the system control device 1 depends on the selected control candidate data when there is a large state change in the power system 3 after the system prediction data is generated. It has a function to prevent the control from being executed. In the present embodiment, it is possible to perform more preferable control even if the device of the power system 3 is broken or changed such that the calculated system control data becomes unsuitable.

  Here, the difference between the second embodiment and the first embodiment will be mainly described.

  In the second embodiment, the processing when the determination unit 104 selects the system control data to be actually applied is different from that in the first embodiment.

  The determination unit 104 selects the highest priority of the system control data 204 as the system control data to be actually applied when the failure or change in the device state data that is higher than the specified value has not occurred, and the system control data 204 is generated. If there is, select no system control data.

  FIG. 11 is a flowchart illustrating a processing example in which the determination unit 104 selects system control data to be applied in the second embodiment.

  First, when the system control data of a specific date and time is requested, the determination unit 104 acquires a plurality of system control data 204 of the relevant date and time (step S401) and selects the system control data with the highest priority from among them. (Step S402).

  Next, the determination unit 104 acquires the device status data 213 (step S403). Furthermore, the determination unit 104 determines, based on the device state data 213, whether or not a failure above the regulation or a change in the state above the regulation has occurred in the power system 3 (step S404).

  If a failure above the stipulation and a change in the state above the stipulation have not occurred (N in step S404), the determination unit 104 sets the grid control data selected in step S402 as the grid control data to be actually applied, and the grid control The data is notified to the control unit 10.

  On the other hand, if a failure above the regulation or a change in the state above the regulation has occurred (Y in step S404), the determination unit 104 notifies the control unit 10 that there is no system control data to be applied (step S406). .

  As an example of the determination method in step S404, the latest device control data may be acquired, and when two or more faults occur, it may be determined that a fault more than the specified fault has occurred. .

  In addition, as an example of another determination method, the system control data at the prediction execution date and time of the system prediction data corresponding to the system control data selected in step S402 and the latest device state data 213 are compared, and two or more locations are compared. When the state of the circuit breaker changes, it may be determined that the state change more than the specified has occurred.

  As described above, when the optimum power system state is calculated based on the future prediction and the calculated result is applied to the power system, when the state of the power system 3 changes significantly, unintended control is executed. It is possible to perform control to the power system state in accordance with the actual state at the time of application while avoiding the accident.

  The contents of this embodiment can be organized as follows.

  In the present embodiment, the determination unit 104 determines not to execute the control based on the selected control candidate data when the power system 3 has a predetermined state change after the state prediction data is generated. If there is a state change that cannot be handled within the range of multiple state prediction data, the control by the selected control candidate data is not executed. It is possible to prevent appropriate control from being executed.

  In addition, the determination unit 104 may determine not to execute the control based on the selected control candidate data when two or more failures occur in the power system 3 after the state prediction data is generated. For example, a clear design of the control method should be adopted such that a method of predicting the state is set on the assumption that no double failure is assumed, and if an unexpected double failure occurs, control will not be executed by the selected control candidate data. It is possible.

  The third embodiment is different from the first embodiment in that the grid control device 1 connects to an information source outside the power grid 3 to acquire external data and uses the data to generate grid prediction data. The third embodiment will be described below mainly with respect to the differences from the first embodiment.

  FIG. 12 is a block diagram showing an example of the system configuration of the third embodiment. The system of the second embodiment shown in FIG. 12 includes a second communication network 6, a weather information server 7, and a power information server 8 in addition to the system of the first embodiment shown in FIG. The system control device 1 of the second embodiment has a second communication unit 70 in addition to the system control device 1 of the first embodiment shown in FIG.

  The system control device 1 can acquire data from the weather information server 7 and the power information server 8 via the second communication network 6 connected to the second communication unit 70. The second communication unit 70 and the second communication network 6 may be physically configured by the same device.

  The weather information server 7 is a server that provides current and future weather information. The current weather information provided from the weather information server 7 can be used as the weather information of the environmental condition data. Further, future weather information provided from the weather information server 7 can be used as weather information of system prediction data. It can be assumed that the data provided by the weather information server 7 is provided by a weather information service corporation that implements a service that provides weather information.

  The power information server 8 is a server that provides current and future power information. The current power information provided from the power information server 8 can be used as the power information of the environmental condition data of each measurement point. The future power information provided by the power information server 8 can be used as the power information of the system prediction data. This power information is provided with power information such as a power generation operation plan, a power generation amount condition, and a demand response control plan.

  The data provided from the power information server 8 is, for example, data provided from a higher-ranking organization that supervises the power system 3, such as a wide area operating organization of the power system, or data provided from the public power market. Is also good.

  The system status acquisition unit 101 stores both or one of the environmental status data provided by the measuring device 4 and the environmental status data provided by the weather information server 7 and the power information server 8.

  The prediction calculation unit 102 can store, in the system prediction data 203, either or both of the weather information and power information data predicted based on the environmental state data 211 and the data provided by the weather information server 7 and the power information server 8. .

  As described above, according to this embodiment, when the optimum state of the power system 3 is calculated based on the future prediction and the calculated result is applied to the power system 3, the prediction accuracy is improved and the prediction range is expanded. It is possible to control the power system state according to the actual state at the time of application.

  This embodiment can be organized as follows.

  The system status acquisition unit 101 is further connected to an external information server (weather information server 7, power information server 8) outside the power system 3 and providing predetermined external data via the second communication network 6, Get external data from the information server. The prediction calculation unit 102 generates state prediction data based on the state data and external data. Therefore, the plurality of states of the power system 3 are predicted in consideration of not only the state of the power system 3 but also the external environment, so that the state of the power system can be predicted with high accuracy.

  Further, the above-mentioned state data may include data indicating the load on the power system 3, and the external data may include weather data provided from the weather information server. The meteorological data not only directly influences the load on the power system 3 such that the usage of the air conditioner increases when the temperature is high, but is also a parameter that causes the temperature to change the characteristics of the power system 3. If the load of the power system 3 is predicted using, it is possible to predict with high accuracy.

  In addition, the external data may include power generation prediction data that predicts the power generation amount of renewable energy. The power generation prediction of renewable energy can be taken into consideration in the state prediction of the power system 3.

  In addition, the external data may include power generation plan data indicating a power generation plan. The power generation plan can be considered in the state prediction of the power system 3.

  In the fourth embodiment, the grid control device 1 predicts a plurality of time-series states of the power system 3, and creates a plurality of time-series control data corresponding to each of the plurality of time-series states. This is different from the first embodiment in that the power system 3 is controlled based on the time-series control data corresponding to the time-series state that is closest to the current state and the state expected from the current state among the sequence states. Hereinafter, the parts of the fourth embodiment that are different from the first embodiment will be mainly described.

  The basic system configuration of the fourth embodiment is similar to that of the first embodiment shown in FIG. In the present embodiment, the optimum operation unit 30 does not use the system prediction data for a single date and time but the system control data for realizing the optimum state of the power system 3 based on the system prediction data for a plurality of dates and times in a certain period. To generate. A known method may be used for the calculation for calculating the optimum state of the power grid 3 for the grid power data at a plurality of dates and times. As an example, there is a method described in Non-Patent Document 1.

  In the present embodiment, in step S202, the prediction calculation unit 102 predicts a plurality of time-series weather conditions from the specified date and time to a predetermined time after the specified date and time, and predicts the time-series weather as data to be included in the system prediction data. Create multiple sets of information.

  FIG. 13 is a diagram illustrating an example of the systematic prediction data 203 at a specific measurement point according to the fourth embodiment. The systematic prediction data 203 includes a unique ID D701 for identifying the systematic predicted data, a predicted execution date / time D702 that is the date and time when the prediction was performed, a designated date / time D703 that is the date / time designated as the target of the prediction, and a plurality of designated dates and times. It includes items such as detailed date / time D704 in which the date / time is designated as a time series, temperature D705 at each detailed date / time, active power D706, and reactive power D707. The temperature D705 belongs to the forecast weather information that predicts the weather condition. The active power D706 and the reactive power D707 are, for example, predicted power information that predicts transmitted power. In this way, the systematic prediction data 203 may store prediction information of a plurality of items at specific measurement points. The unique ID D701 is assigned to one type of time-series systematic prediction data. FIG. 13 shows time-series systematic prediction data in which the unique ID D701 is A1 and time-series systematic prediction data in which the unique ID D701 is A2. They are two types of system prediction data corresponding to the same designated time.

  The optimal calculation part 30 calculates the control content which implement | achieves the optimal state of the electric power system 3 with respect to each detailed date and time contained in system prediction data, and collects these control content, and produces | generates system control data.

  FIG. 14 is a diagram illustrating an example of the system control data 204 according to the fourth embodiment. The system control data 204 includes a serial number D801, a unique ID D802 of the original predicted data, a priority D803 to which the system control data is applied, an application start date / time D804 of the system control data, and a plurality of date / time after the application start date / time. Includes items such as the detailed date and time D805 designated as a time series, the input amount D806 of the phase adjusting equipment 1A, the input amount D807 of the phase adjusting equipment 1B, the tap position D808 of the transformer 1A, and the tap position D809 of the transformer 1B for each detailed date and time. . The input amount D806 of the phase adjusting equipment 1A, the input amount D807 of the phase adjusting equipment 1B, the tap position D808 of the transformer 1A, and the tap position D809 of the transformer 1B for each detailed date and time are control parameters applied to the detailed date and time. Thus, the system control data may include control parameters set for a plurality of facilities.

  The input amount D806 of the phase adjusting equipment 1A, the input amount D807 of the phase adjusting equipment 1B, the tap position D808 of the transformer 1A, and the tap position D809 of the transformer 1B shown here are examples of the setting state of the electric device 5. As another control parameter, for example, a combination including a target value of the terminal voltage of the generator, a switching state of the switch, and the like may be used.

  The priority order D803 is an item added by the comparison unit 103.

  The detailed date and time D805 is determined according to the calculation method of the optimum calculation unit 30, and may be the same as or different from the detailed date and time of the original system prediction data.

  FIG. 15 is a flowchart illustrating a processing example in which the comparing unit 103 calculates the application priority order in the fourth embodiment.

  When the control unit 10 is instructed by the control unit 10 to calculate the priority of the system control data having a specific application start date and time, the comparison unit 103 acquires the comparison prediction data from the prediction calculation unit 102 (step S501). The priority calculation is instructed when the application start date and time approaches. The comparison prediction data is data that predicts the environmental condition from the application start date and time to a predetermined time later. Since the application start date and time are approaching, the comparison prediction data has high prediction accuracy.

  Further, the comparison unit 103 acquires the comparison calculation data 205 representing the comparison calculation method (step S502). Furthermore, the comparison unit 103 acquires the system prediction data corresponding to each system control data from the system prediction data 203 illustrated in FIG. 13 based on the unique ID of the system control data (step S503).

  Next, the comparison unit 103 compares the system prediction data with the comparison prediction data by the comparison calculation method based on the comparison calculation data 205. Then, the comparison unit 103 calculates the priority order of the systematic prediction data such that the higher the similarity to the comparison prediction data, the higher the priority order (step S504).

  Finally, the comparison unit 103 adds the priority order of each system prediction data calculated in step S504 to the system control data corresponding to each system prediction data (step S505).

  With respect to the comparison unit 103, in step S501, the prediction calculation unit 102 determines the highest occurrence probability in the case of assuming the latest environmental condition data among the time-series systematic prediction data from the application start date and time. The systematic prediction data is created and provided to the comparison unit 103 as comparative prediction data.

  As the method of the comparison operation in step S504, for example, the comparison operation is performed such that the smaller the sum of the weighted mean square errors of each prediction value of the system prediction data and the corresponding comparison prediction data is, the higher the priority is. It is possible to do it. The weighted mean square error can be calculated by the equation (2).

ここで、Ｅ_ｉはｉ番目（ｉ＝１，２，…，Ｎ）の系統予測データについての重み付き平均二乗誤差、ｘ_{ｉ，ｊ，ｋ}はｉ番目の系統予測データのｊ番目（ｊ＝１，２，…，Ｍ）の項目におけるｋ番目（ｋ＝１，２，…，Ｌ）の詳細日時の予測値、ｙ_ｊ，ｋはｘ_{ｉ，ｊ，ｋ}と同じ項目と同じ詳細日時の比較用予測データの値、ｗ_ｊはｊ番目の項目に対する重み、ｖ_ｋはｋ番目の詳細日時データに対する重みである。

Here, E _i is the weighted mean square error for the i-th (i = 1, 2, ..., N) system-predicted data, and x _{i, j, k} is the j-th (j = j = j-th) of the i-th system-predicted data. 1, 2, ..., M) The predicted value of the k-th (k = 1, 2, ..., L) detailed date / time, y _{j, k} is the same item as x _{i, j, k} The value of the prediction data for comparison, w _j is the weight for the j-th item, and v _k is the weight for the k-th detailed date and time data.

The weight w _j is a positive value that can be arbitrarily designated in consideration of the importance of each item, the variable range, and the like. For example, the reciprocal of the average value of each item may be used as the weight w _j in consideration of the variable range. Alternatively, considering the importance of each item, a value obtained by performing a process of increasing the value of the item to be emphasized with respect to the reciprocal of the average value may be used as the weight w _j . Alternatively, another value may be used.

The weight v _k is a positive value that can be arbitrarily set in consideration of the importance of each detailed date and time. For example, the weight v _k may be set to a large value in consideration of the importance of the item close to the application start date and time.

Since the weight w _j and the weight v _k determine the comparison calculation method, they may be recorded as the comparison calculation data 205.

  As described above, according to the present embodiment, the optimum state of the power system 3 is calculated based on the system prediction data of a plurality of dates and times within a certain period, instead of the system prediction data of a single designated date and time, and the calculation result is the power. It can be applied to the grid 3 to control the power grid 3 suitable for the actual state at the time of application.

  It can also be organized as in this embodiment.

  The prediction calculation unit 102 generates a plurality of state prediction time-series data (time-series system prediction data) that predicts the state of the power system 3 from a designated time to a predetermined time after the designated time. Control candidate time series data, which is time series control data corresponding to each series data, is generated in advance. Then, when the designated time comes, the prediction calculation unit 102 predicts the state data indicating the actual state of the power system 3 at the designated time (application start time) and the state of the power system 3 until a predetermined time after the designated time. The latest prediction data (comparison prediction data) is generated. The comparing unit 103 uses the state data acquired at the designated time and the latest prediction data, and has the highest degree of similarity with the state from the designated time to the prescribed time after the designated time from the plurality of state prediction time series data. Select the state prediction time series data that indicates. The execution unit 105 executes the control regarding the power system 3 based on the control candidate time series data corresponding to the selected state prediction time series data. Since a plurality of time-series states are predicted and the control is executed using the control candidate time-series data selected based on the actual state at the specified time and the predicted state immediately after that, it is appropriate for the power system 3. Time-series control can be performed.

  The fifth embodiment differs from the first embodiment in that the system control device 1 reflects the user's selection in the control of the power system 3. The basic system configuration of the fifth embodiment is similar to that of the first embodiment shown in FIG. In this embodiment, the determination unit 104 is different from the first embodiment in the processing when selecting the system control data to be actually applied.

  When the comparison unit 103 gives priority to a plurality of system control data, the determination unit 104 causes the display unit 60 to display the system control data and prompts the user to select any system control data. For example, a plurality of system control data may be displayed in descending order of priority.

  When the user selects any system control data, the determination unit 104 determines the system control data selected by the user as the system control data to be actually applied.

  FIG. 16 is a flowchart illustrating an example of processing in which the determination unit 104 selects system control data to be applied in the fifth embodiment.

  When the system control data of a specific date and time is requested, the determination unit 104 acquires a plurality of system control data of the specific date and time (step S401) and displays the acquired system control data on the display unit 60 (step S407). ).

  Next, when the user selects any system control data (step S408), the determination unit 104 notifies the control unit 10 of the selected system control data as system control data to be applied to actual control. (Step S409).

  In addition, in step S408, when the user receives a signal from the input unit 50 indicating that no system control data has been selected, the determination unit 104 applies the predetermined system control data to actual control. You may Alternatively, in that case, the determination unit 104 may apply the system control data with the highest priority to the actual control. Further, when the determination unit 104 sets a timer for a predetermined time and times out without receiving a signal indicating that the system control data has been selected by the user, the determination unit 104 determines a predetermined system control data or a system with the highest priority. The control data may be applied to actual control.

  FIG. 17 is a diagram illustrating an example of the control data selection screen according to the fifth embodiment. On the control data selection screen G101, items such as an application priority order G102, a phase adjusting equipment input amount G103, and a transformer tap position G104 are displayed.

  The display content is created based on the system control data. For example, the control data selection screen G101 displays all or part of the system control data. Specifically, in the input amount G103 of the phase adjusting equipment and the tap position G104 of the transformer, the input amount D605 of the phase adjusting equipment 1A and the input of the phase adjusting equipment 1B included in the system control data illustrated in FIG. The values of the quantity D606, the tap position D607 of the transformer 1A, and the tap position D608 of the transformer 1B are displayed.

  The priority order G102 displays the priority order D603 of the system control data as an example. However, data such as error data used when the comparison unit 103 calculates the priority order, which can determine the result of comparing each system control data with the system prediction data, may be displayed instead of the priority order.

  Further, it is not necessary to display all the generated plurality of system control data on the control data selection screen G101. For example, it is possible to display a predetermined number of system control data with higher priority and not display system control data with lower priority.

  Further, the priority order G102, the input amount G103 of the phase adjusting equipment, and the tap position G104 of the transformer may be collectively displayed on the same screen as shown in FIG. 17, or may be linked to each system control data. It may be displayed on another screen.

  Further, a button displaying "no control" may be displayed so that the user can select not to apply the control of any system control data.

  As described above, according to the present embodiment, the future state related to the power system 3 is predicted, and the prediction result is presented to the user to reflect the priority order based on the state of the power system 3 and the user's selection. Control using the system control data can be executed. It can assist the user in applying the appropriate grid control data.

  The contents included in this embodiment can be organized as follows.

  The determination unit 104 presents the processing result of the comparison unit 103 to the user and causes the user to select any of the control candidate data. The execution unit 105 executes control based on the control candidate data selected by the user. As described above, in this embodiment, the control to be executed can be confirmed in advance from the user's point of view, so that it is possible to prevent the inappropriate control from being automatically executed.

  Although various embodiments of the present invention have been described above, the present invention is not limited to the above-mentioned embodiments and includes various modifications. For example, the above-described embodiments have been described in detail for the purpose of explaining the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of a certain embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of a certain embodiment. Further, it is possible to add / delete / replace other configurations with respect to a part of the configurations of the respective embodiments.

  Further, the above-described respective configurations, functions, processing units, processing means, etc. may be realized by hardware by designing a part or all of them with, for example, an integrated circuit. Further, each of the above-described configurations, functions and the like may be realized by software by a processor interpreting and executing a program for realizing each function. Information such as a program, a table, and a file that realizes each function can be placed in a memory, a hard disk, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC card, an SD card, and a DVD.

  Further, the control lines and information lines are shown as being considered necessary for explanation, and not all control lines and information lines are shown in the product. In reality, it may be considered that almost all the configurations are connected to each other.

DESCRIPTION OF SYMBOLS 1 ... System control device, 10 ... Control part, 101 ... System state acquisition part, 101 ... Control data selection screen G, 102 ... Prediction calculation part, 103 ... Comparison part, 104 ... Judgment part, 105 ... Execution part, 2 ... Communication Network, 20 ... Recording unit, 201 ... Prediction calculation unit, 3 ... Power system, 30 ... Optimal calculation unit, 4 ... Measuring device, 40 ... Communication unit, 5 ... Electrical device, 50 ... Input unit, 6 ... Communication network, 60 ... Display unit, 7 ... Weather information server, 70 ... Communication unit, 8 ... Power information server

Claims (15)

A power system control device that generates control data of a power system at a predetermined time and controls the power system based on the control data at the predetermined time,
A system status acquisition unit that acquires status data indicating the status of the power system,
On the basis of the acquired state data, a plurality of state prediction data that predicts a plurality of states of the power system at a specified time, which is a future time when the control related to the power system is executed, are generated, and the plurality of state predictions are performed. A prediction calculation unit that generates a plurality of control candidate data that are control data corresponding to each of the data,
Using the state data acquired at the specified time, a comparison unit that selects one from the plurality of state prediction data,
An electric power system control device comprising: an execution unit that executes control related to the electric power system based on control candidate data corresponding to the selected state prediction data. The comparison unit calculates the degree of similarity between the state indicated by the plurality of state prediction data and the state of the power system at the designated time, and selects the state prediction data having the highest degree of similarity.
The power system control device according to claim 1. The comparison unit calculates a weighted mean square error between the state indicated by the plurality of state prediction data and the state of the power system at the designated time, and the state prediction data in which the weighted average two-row error is the smallest is Judge that the similarity is the highest,
The power system control device according to claim 2.   The power according to claim 1, further comprising: a determination unit that determines not to perform the control based on the selected control candidate data when the power system has a predetermined state change after the state prediction data is generated. System control device.   The said determination part determines that the control by the selected control candidate data is not performed, when two or more faults generate | occur | produce in the said electric power system after the said state prediction data are produced | generated. Power system controller. The system status acquisition unit is further connected to an external information server that is outside the power system and provides predetermined external data via a communication network, and acquires the external data from the external information server,
The prediction calculation unit generates the state prediction data based on the state data and the external data,
The power system control device according to claim 1. The state data includes data indicating the load of the power system,
The external data includes weather data provided by a weather information server,
The power system control device according to claim 6. The external data includes power generation prediction data that predicts the power generation amount of renewable energy,
The power system control device according to claim 6. The external data includes power generation plan data indicating a power generation plan,
The power system control device according to claim 6. The prediction calculation unit generates a plurality of state prediction time series data that predicts a state of the power system from the designated time to a predetermined time after the designated time, and corresponds to each of the plurality of state prediction time series data. The control candidate time-series data that is time-series control data is generated in advance, and when the designated time comes, the state data indicating the actual state of the power system at the designated time and the predetermined time after the designated time. Generate the latest prediction data that predicts the state of the power system,
The comparison unit uses the state data and the latest prediction data acquired at the designated time, and from the plurality of state prediction time series data, the state from the designated time to the predetermined time after the designated time and Select the state prediction time series data showing the state with the highest similarity of
The execution unit executes control relating to the power system based on control candidate time series data corresponding to the selected state prediction time series data,
The power system control device according to claim 1. Presents the processing result of the previous SL comparison unit on the user, further comprising a determination unit which Ru is selected by the user one of the control candidate data,
The execution unit executes control based on the control candidate data selected by the user,
The power system control device according to claim 1.   The electric power system control device according to claim 1, wherein the control relating to the electric power system includes determination of a set value of an output voltage of a power generation device, determination of an input amount of a phase adjusting facility, or determination of a tap position of a transformer. .   The power system control device according to claim 1, wherein the state prediction data includes data that predicts power consumption of a load of the power system and data that predicts power generation amount of a power generation device.   The power system control device according to claim 1, wherein the state data includes data of measured temperature, wind speed, wind direction, precipitation amount, or insolation amount. A power system control method for generating control data of a power system at a predetermined time, and controlling the power system based on the control data at the predetermined time,
The system status acquisition means acquires status data indicating the status of the power system,
Prediction calculation means, based on the acquired state data, to generate a plurality of state prediction data that predicts a plurality of states of the power system at a specified time, which is a future time to execute control related to the power system, Generating a plurality of control candidate data which is control data corresponding to each of the plurality of state prediction data,
Comparing means, using the state data acquired at the specified time, selects any one of the plurality of state prediction data,
A power system control method, wherein the executing means executes control relating to the power system based on control candidate data corresponding to the selected state prediction data.

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