JP7608281B2 - Power system control device, power system control system, and power system control method - Google Patents

Description

The present invention relates to technology for controlling power grids.

Conventionally, the entire equipment that produces and consumes electricity, such as power generation equipment, power transmission equipment, substation equipment, distribution equipment, and consumer equipment (hereinafter referred to as "power system"), is known. Power system monitoring and control systems exist to maintain a high-quality power supply in power systems. Power system operators use the power system monitoring and control systems to measure the voltage, frequency, power, etc. at specific measurement points in the power system, and monitor and control the power system equipment so that the voltage, frequency, power flow, etc. are set to predetermined appropriate values in response to the fluctuating power consumption of consumers. Furthermore, in recent years, there have been attempts to determine control values that provide greater satisfaction through optimization calculations such as optimal power flow calculations.

In addition, in devices that monitor and control electricity, control plans are made based on predicted values, such as making control plans based on predicted future demand. JP 2020-54085 A (Patent Document 1) discloses a control method that uses the results of comparing predicted values with actual values to make corrections when making a renewable energy transmission plan based on predicted values.

JP 2020-54085 A

However, while the technology described in Patent Document 1 can correct control deviations caused by the discrepancy between predicted and actual values, it has the problem that it cannot suppress deviations in operating conditions caused by the discrepancy between predicted and actual values. In a system that calculates a control amount based on a prediction of the future state of the power system and applies it to each device, if a prediction error occurs and a difference occurs from the assumed future power system state, the state of the power system to which the calculated control amount is applied may deviate from the appropriate range set at the time of calculation. For example, when controlling the voltage of a substation, if the demand prediction for each substation or consumer is incorrect, the substation voltage will fluctuate from the expected value, and if the fluctuation is large, it will deviate from the voltage range conditions, causing problems such as the reliability of the power system being compromised.

The present invention has been made in consideration of the above points, and proposes a power system control device that appropriately evaluates deviations from the appropriate range of the power system state caused by prediction errors, even when efficient control is planned and applied based on predictions of future power system states, and plans highly reliable and efficient control plans that can reduce the impact of these deviations.

In order to solve such problems, for example, the configuration described in the claims is adopted. For example, a power system control device having a control unit and a recording unit, the recording unit holds power demand data indicating past power demand, factor data including information that may be a factor of the past power demand, and prediction error data indicating a tendency of prediction errors of power demand predicted in the past based on the factor data with respect to actual power demand, the control unit predicts future power demand based on the power demand data and the factor data, calculates values to be set in electrical equipment constituting the power system based on the predicted future power demand, in order to control the power system so that a measurement value in the power system satisfies a first constraint condition, and calculates a value based on the prediction error data. The method is characterized in that a prediction error of future power demand is estimated, the calculated numerical value is set to the electrical equipment, the deviation of the measured value in the power system from the first constraint condition when the estimated prediction error occurs is calculated, a second constraint condition is generated by modifying the first constraint condition used to calculate the numerical value set to the electrical equipment so that the deviation of the measured value in the power system from the first constraint condition when the estimated prediction error occurs is reduced, and a numerical value is calculated to be set to the electrical equipment based on the predicted future power demand in order to control the power system so that the measured value in the power system satisfies the second constraint condition.

According to one aspect of the present invention, in an operation system that performs efficient control through optimization based on predicted future power grid states, it is possible to create and apply efficient and reliable control plans based on appropriate evaluation of deviations from the appropriate range of the power grid state caused by prediction errors. Problems, configurations, and effects other than those described above will be made clear through the explanation of the following examples.

1 is a block diagram showing a configuration of a power system control system according to a first embodiment of the present invention; FIG. 2 is an explanatory diagram illustrating an example of environmental state data at a specific measurement point in the power system according to the first embodiment of the present invention. FIG. 2 is an explanatory diagram illustrating an example of device setting data of an electrical device connected to a power system according to the first embodiment of the present invention. FIG. 4 is an explanatory diagram illustrating an example of device status data of an electric device connected to a power system according to the first embodiment of the present invention. FIG. 2 is an explanatory diagram illustrating an example of power system configuration data of a transmission line in the power system according to the first embodiment of the present invention. 5 is a flowchart illustrating an example of a process in which a prediction processing unit creates power system prediction data in the first embodiment of the present invention. FIG. 2 is an explanatory diagram illustrating an example of power system forecast data of a specific measurement point in the first embodiment of the present invention. FIG. 11 is an explanatory diagram illustrating an example of prediction error data at a specific measurement point in the first embodiment of the present invention. FIG. 2 is an explanatory diagram illustrating an example of power system control data for putting the power system into a predetermined state in the first embodiment of the present invention. 5 is a flowchart illustrating an example of a process in which a control generation and execution unit according to the first embodiment of the present invention generates a control for a power system state. FIG. 4 is an explanatory diagram illustrating an example of deviation countermeasure data in the first embodiment of the present invention. FIG. 11 is an explanatory diagram showing an example of a prediction error display screen displayed in the first embodiment of the present invention. FIG. 11 is an explanatory diagram showing an example of a deviation countermeasure display screen displayed in the first embodiment of the present invention. FIG. 11 is a block diagram showing an example of a configuration of a data correction unit according to a second embodiment of the present invention. FIG. 11 is a block diagram showing another example of the configuration of the data correction unit in the second embodiment of the present invention.

The following drawings will explain in detail the embodiments of the present invention.

Figure 1 is a block diagram showing the configuration of a power system control system 1 according to a first embodiment of the present invention.

The power system control system 1 includes a power system control device 2 and a power system 3, and the power system control device 2 and the power system 3 are communicatively connected via a communication network 4.

The power system control device 2 is a circuit, a printed circuit board, a server, or an information processing device that includes a controller such as a CPU (Central Processing Unit) or a microprocessor, a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), and a communication device. The power system control device 2 includes a control unit 10 that performs various controls, a recording unit 20 that records (stores) various information, an optimum calculation unit 30 that calculates an optimum solution under specified conditions, a communication unit 40 that communicates with various devices via a communication network 4, an input unit 50 that inputs various information, and an output unit 60 that outputs various information.

The control unit 10 and the optimum calculation unit 30 may be realized by software in which a controller performs processing according to various programs stored in a storage device, or may be realized by hardware such as a circuit. Furthermore, the functions of the control unit 10 and the optimum calculation unit 30 are not limited to being realized by a single device, but may be realized by multiple devices connected to each other so that they can communicate with each other. The functions of the control unit 10 and the optimum calculation unit 30 may be realized by different hardware, or may be realized by the same hardware, for example by the same processor executing programs to realize the respective functions.

The recording unit 20 is realized by a storage device. In this embodiment, the recording unit 20 holds power system state data 201, power system configuration data 202, demand data 203, factor data 204, power system forecast data 205, forecast error data 206, system control data 207, and deviation countermeasure data 208. These will be described in detail later.

The communication unit 40 is realized by a communication device. The input unit 50 is realized by including at least one of a keyboard, a pointing device such as a mouse, a touch panel, a voice instruction device, etc. The output unit 60 is realized by including at least one of a display device, a printer, a voice output device, etc.

The power system control device 2 can acquire measurement data of the power system 3 from the measuring devices 5 of the power system 3 via the communication network 4 connected to the communication unit 40. The power system control device 2 can also control the operating state of the electrical devices 6 of the power system 3 by sending control commands to the electrical devices 6. When the operating state is controlled by the control command, the control may be entirely automatic, or may include some manual operation.

The power system 3 includes power generation facilities (e.g., power plants) that generate electricity using power generation equipment, consumer equipment that consumes the electricity generated by the power generation equipment, power distribution facilities (power transmission facilities, substation facilities, power distribution facilities, etc.) that transmit electricity from the power generation facilities to the consumer equipment, and measuring instruments 5 that measure the status of these facilities. The power generation facilities are devices that perform thermal power generation, hydroelectric power generation, nuclear power generation, geothermal power generation, solar power generation, wind power generation, etc., but may also be devices that generate electricity using various other power generation methods. The power distribution facilities include overhead transmission lines, underground transmission lines, transformers, circuit breakers, switches, phase modifying equipment, and busbars, but may also include various other facilities related to power transmission and distribution.

Measuring equipment 5 refers to a number of measuring devices that measure the state at specific measurement points of the power system 3. Measurement items include power items such as the voltage, phase, and power of the power system 3, weather items such as temperature, wind speed, wind direction, precipitation, and solar radiation, items of the setting state and operation state of the electrical equipment 6, and items of the operation state of equipment that changes the power flow state such as power distribution equipment and power generation equipment, and various other items can be measurement items. Measurement points include the busbar of the power generation equipment, the busbar of the substation equipment, power distribution equipment, consumer equipment, etc., and there may be one or more measuring devices for one measurement point. The value measured by the measuring equipment 5 is transmitted to the power system control device 2 via the communication network 4. At this time, the measuring equipment 5 may transmit information on the measurement date and time together with the measured value.

The electric equipment 6 is a device for changing the power flow state of the electric power system 3, such as a power generation device, a transformer, a circuit breaker, and a phase modifying device, among the devices that make up the electric power system 3, but may also include various other devices. Furthermore, when the electric equipment 6 receives a control command sent by the electric power system control device 2 via the communication network 4, either directly or indirectly via manual intervention, it can change its operating state in response to the control command.

The control unit 10 of the power system control device 2 includes a main control unit 101, a power system state acquisition unit 102, a prediction processing unit 103, a control generation execution unit 104, a deviation calculation unit 105, and a deviation countermeasure creation unit 106.

The power system status acquisition unit 102 periodically (e.g., at 5-minute intervals) or when instructed by the control unit 10 (e.g., at a time desired by the user), acquires various data transmitted by the measuring device 5 via the communication unit 40, and records the data in the recording unit 20 as power system status data 201 together with the measurement date and time.

As shown in FIG. 1, the power system status data 201 includes environmental status data 211, equipment setting data 212, and equipment status data 213.

The environmental condition data 211 includes information such as the active power and reactive power flowing to the load (electrical equipment that consumes power in the power system 3), the active power and reactive power generated by the power generation device, and the temperature at the location where the device is installed.

Figure 2 is an explanatory diagram showing an example of environmental state data 211 at a specific measurement point of the power system 3 in Example 1 of the present invention.

As shown in FIG. 2, the environmental condition data 211 includes information such as a serial number D101, date and time D102, temperature D103, active power D104, and reactive power D105. The environmental condition data 211 stores values (measured values) at specific measurement points in chronological order. Note that the measurement date and time D102, temperature D103, active power D104, and reactive power D105 are items related to measurement, and the values of each item are changed as appropriate depending on the measurement at the measurement point. Temperature D103 is an example of factor data, and may be replaced or added with other factor data. Examples of other factor data will be described later.

The device setting data 212 includes information on the setting state of the electrical device 6.

Figure 3 is an explanatory diagram showing an example of device setting data 212 for an electrical device 6 connected to a power system 3 in Example 1 of the present invention.

3, the device setting data 212 includes information such as a serial number D201, a measurement date and time D202, an input amount D203 of the phase modifying equipment R1, an input amount D204 of the phase modifying equipment R2, a tap position D205 of the transformer T1, and a tap position D206 of the transformer T2. The device setting data 212 stores information (setting values) indicating the settings of the electrical equipment 6 in chronological order. The input amount D203 of the phase modifying equipment R1, the input amount D204 of the phase modifying equipment R2, the tap position D205 of the transformer T1, and the tap position D206 of the transformer T2 are examples of items related to the settings of the electrical equipment 6, and a combination of items that can be acquired from the measuring equipment 5 can be appropriately adopted as information included in the device setting data 212.

The equipment status data 213 includes information (operation status information) indicating the operating status of the electrical equipment 6, which is the power distribution equipment and the equipment that changes the power flow status, such as the power generation equipment.

Figure 4 is an explanatory diagram showing an example of device status data 213 for an electrical device 6 connected to a power system 3 in Example 1 of the present invention.

4, the equipment status data 213 includes information such as a serial number D301, a measurement date and time D302, an operation state D303 of the phase modifying equipment R1, an operation state D304 of the phase modifying equipment R2, an operation state D305 of the transformer T1, and an operation state D306 of the transformer T2. The equipment status data 213 stores operation state information of the electric equipment 6 in chronological order. The operation state D303 of the phase modifying equipment R1, the operation state D304 of the phase modifying equipment R2, the operation state D305 of the transformer T1, and the operation state D306 of the transformer T2 may include items such as the open/close state of the switchgear such as a circuit breaker and a switchgear, and the power transmission status of the power transmission line. The items of the equipment status data 213 may also be a combination of items of the operation state of the equipment that changes the power flow state such as the power distribution equipment and the power generation equipment, and items of the operation state of other equipment acquired from the measuring equipment 5.

The power system configuration data 202 is pre-recorded in the recording unit 20. The power system configuration data 202 includes information related to the configuration of the power system 3.

Figure 5 is an explanatory diagram showing an example of power system configuration data 202 for a transmission line in power system 3 in Example 1 of the present invention.

The power system configuration data 202 includes information such as a serial number D401, a name D402, a rated voltage D403, a reference capacity D404, a line length D405, a resistance D406, and a reactance D407, and is stored for each transmission line. The power system configuration data 202 includes information necessary for analyzing the power system 3, such as the electrical characteristics of the equipment constituting the power system 3, operational conditions such as transmission capacity and voltage range, and the electrical connection form between the equipment, which do not usually change in a short time. Although FIG. 5 shows the case of a transmission line, different items may be provided for other equipment.

Figure 6 is a flowchart showing an example of the process in which the prediction processing unit 103 creates power system prediction data in Example 1 of the present invention.

First, when the prediction processing unit 103 receives a request to create power system forecast data for a specified future date and time (hereinafter also referred to as the specified date and time), it acquires demand data 203 consisting of past demand information from the recording unit 20 (step S101). Next, the prediction processing unit 103 acquires factor data 204 consisting of information related to fluctuations in demand from the recording unit 20 (step S102). Next, the prediction processing unit 103 generates power system forecast data 205 that predicts future demand from the statistical correlation between the acquired demand data 203 and factor data 204 (step S103).

In step S101, the demand data 203 may be a virtual data table linked to data in other tables in the recording unit 20, and may be the same as the environmental condition data 211 in which the demand is stored.

In step S102, the factor data 204 includes, for example, meteorological data such as temperature, humidity, solar radiation, wind speed, or air pressure; fuel data such as trading volume or trading price of crude oil or natural gas; power line data such as the transmission capacity of a power line; generator operation status data such as generator operation or maintenance schedules; calendar data such as a flag value indicating the date, day of the week, or an arbitrarily set type of day; data indicating the occurrence of a sudden event such as a typhoon or other event; and data indicating economic conditions such as the number of electricity consumers, industrial trends, or business climate index. The factor data 204 may also include past observation data of the above-mentioned prediction target itself, or a prediction value of the prediction target at the prediction target date and time.

Demand data 203 may include, for example, active or reactive power for each substation or for each line of a substation, or the combined active or reactive power of multiple substations or multiple lines.

In step S103, the prediction processing unit 103 generates prediction data regarding future demand by utilizing the statistical relationship between these demand data 203 and factor data 204. For example, it is possible to take active power as the demand data 203 and temperature as the factor data 204, create a time series pattern of the demand data 203 against the factor data 204 for a certain period of time, and predict future demand data using the factor data 204 corresponding to a future time as input. Alternatively, the demand forecast may be generated by other known statistical calculations or metaheuristic methods. There are no limitations on the prediction method as long as it is possible to predict demand at a future date and time.

The known method may be, for example, a method called a parametric model, such as a multiple regression model, a generalized linear model, or a Lasso regression, or a method called a non-parametric model that uses a regression tree, such as a support vector regression, a Gaussian process regression, or a random forest. Furthermore, if necessary for demand forecasting, such as identifying points to be predicted and grasping interrelationships, the power system configuration data 202 may be used in addition to the power system state data 201.

Figure 7 is an explanatory diagram showing an example of power system forecast data 205 for a specific measurement point in Example 1 of the present invention.

The power system forecast data 205 is data that stores multiple forecast values at specific measurement points, and includes items such as a unique ID D501 for identifying the power system forecast data, a forecast execution date and time D502 indicating the date and time when the forecast was calculated, a forecast date and time D503 indicating the target date and time for the forecast, a temperature D504 indicating the predicted temperature, an active power D505 indicating the predicted active power, and a reactive power D506 indicating the predicted reactive power. The temperature D504 is an example of factor data, and may be replaced or added with other factor data, or the power system forecast data 205 may not include factor data.

Figure 8 is an explanatory diagram showing an example of prediction error data 206 for a specific measurement point in Example 1 of the present invention.

The prediction error data 206 is data that stores multiple estimates of prediction errors that occur at specified measurement points, and includes items such as a serial number D601, a measurement point ID D602, a repeating unit of the prediction error period D603, a start date and time D604, an end date and time D605, a probability of occurrence of a prediction error D606, a typical active power error amount D607, and a typical reactive power error amount D608. The prediction error data may be stored in advance in the recording unit 20, or may be obtained from an external server via the communication network 4.

In this data example, if the repeat unit is recorded as every day from 6:00 to 8:00 with a high occurrence probability, this can be interpreted as indicating that there is a relatively high possibility that a specified amount of forecast error will occur at the same time every day. However, as long as it is possible to express assumptions or estimates such as the time or period when forecast errors are likely to occur, the location on the power system where forecast errors are likely to occur, the probability of occurrence of forecast errors, or the magnitude of forecast errors, the forecast error data may include items different from those shown in FIG. 8 or may be data in a different format.

In addition, in this data, prediction errors that occur in the same repeating unit can be understood to occur simultaneously. For example, the prediction error from 6:00 to 8:00 every day at measurement point A1 shown in FIG. 8 and the prediction error from 6:00 to 10:00 every day at measurement point A1 from 6:00 to 8:00 can be understood to occur simultaneously.

However, as long as an assumption or estimate of the probability of simultaneous occurrence can be expressed, the prediction error data may include items other than those shown in FIG. 8, and may be in a different format. This makes it possible to register in advance prediction errors that have caused voltage deviations in the past, and prediction errors that are likely to cause serious voltage deviations as operational knowledge.

In this embodiment, control is described that reduces the effects of voltage deviations caused by prediction errors. However, voltage deviations are only one example of a measurement value that indicates the state of the power system, and this embodiment can be applied to reduce the effects of deviations from a specified range of any measurement value other than voltage, such as the amount of power.

The control generation execution unit 104 generates power system control data 207 for calculating the optimal power system state for the specified date and time, generates a control command for realizing the set value described in the power system control data 207, and transmits it to each target electrical device 6 in the power system 3. The specified date and time may be a date and time specified at a fixed or irregular interval by a pre-set setting, or may be a date and time specified as appropriate by an instruction via the input unit 50.

Figure 9 is an explanatory diagram showing an example of power system control data 207 for putting power system 3 into a specified state in Example 1 of the present invention.

The power system control data 207 includes, for example, an ID D701 for identifying each data, an application date and time D702, and setting values D703 to D708 of the equipment to be controlled. The setting values D703 to D708 of the equipment to be controlled may be the same as the setting values D203 to D206 of the equipment setting data 212. The equipment to be controlled is an object of control included in the electric equipment 6, and may be, for example, a circuit breaker and a switch, or a combination of other equipment. The application date and time D702 records the date and time when the power system control data is applied.

Figure 10 is a flowchart showing an example of the process in which the control generation execution unit 104 of Example 1 of the present invention generates control for the power system state.

The control generation execution unit 104 provides the power system state data 201, power system configuration data 202, and power system forecast data 205 for the specified time to the optimization calculation unit 30, obtains a power system plan for achieving the calculated optimal power system state, and stores it as power system control data 207 (step S201).

Next, the control generation execution unit 104 instructs the deviation calculation unit 105 to calculate the voltage deviation amount, acquires prediction error data 206 (step S202), and extracts one or more simultaneously occurring prediction errors that will lead to a large voltage fluctuation at the specified date and time (step S203).

Next, the control generation execution unit 104 calculates the amount of voltage deviation when a prediction error occurs in the optimal power system state at the specified date and time from the power system state data 201, power system configuration data 202, power system forecast data 205, power system control data 207, and the multiple prediction error amounts extracted in S203, and stores the amount of voltage deviation in deviation countermeasures data 208 (step S204).

Next, the control generation execution unit 104 calculates the risk of the deviation based on the stored data such as the deviation amount and past history, and determines whether the calculated risk is equal to or greater than a threshold (step S205). If the calculated risk is equal to or greater than a threshold (step S205: Y), the control generation execution unit 104 stores a change setting for the voltage range so that the risk is equal to or less than the threshold in the deviation countermeasure data 208 (step S206).

Next, the control generation execution unit 104 obtains the change settings for the voltage range from the deviation countermeasure data 208, and using the data to which the change settings have been applied, recalculates the optimal device settings again in the same process as in S201, and overwrites the settings to the power system control data 207 (step S207). Finally, when the application date and time of the power system control data 207 approaches, the control generation execution unit 104 applies these settings to the corresponding equipment (S208).

Figure 11 is an explanatory diagram showing an example of deviation countermeasure data 208 in Example 1 of the present invention.

The deviation countermeasure data 208 is data related to the estimated deviation amount at the location where the condition is set and the countermeasure. The location where the condition is set is, for example, a busbar, but may be other items. FIG. 11 is an example related to busbar voltage. The deviation countermeasure data 208 shown in FIG. 11 includes an ID D801 indicating a serial number, a countermeasure application date and time D802, a busbar ID D803, an estimated deviation item D804, an estimated deviation amount D805, an estimated risk D806, a risk threshold D807, and a countermeasure content for the deviation D808.

In step S203, the control generation execution unit 104 may simply incorporate all the fluctuations that are specified as having a high probability of occurring at the same time. For example, when the occurrence of conflicting fluctuations is stored at one point, the control generation execution unit 104 may use a method of extracting prediction errors that lead to large voltage fluctuations from among prediction errors with a high probability of simultaneous occurrence, based on the connection network of the power system. For example, when a prediction error occurs in a direction in which the active power and reactive power of multiple loads located in the vicinity of the busbar connection network are both reduced, the voltage rise fluctuation becomes larger and voltage deviation becomes more likely to occur. This extraction can be realized by extracting such prediction errors from the occurrence probability of prediction errors of each load and the signs of the active power error amount and the reactive power error amount. By using this procedure, for example, by eliminating voltage fluctuations for prediction errors that are extremely unlikely to occur simultaneously, more practical deviation countermeasures can be realized.

The control generation and execution unit 104 also separately extracts prediction errors in the direction of decreasing the voltage and sets of different simultaneous prediction errors, and evaluates the voltage fluctuation in step S204. In this way, it is possible to appropriately evaluate the voltage deviation caused by one or more prediction errors.

When calculating the voltage deviation amount in step S204, the control generation execution unit 104 calculates the deviation amount for each set of prediction errors extracted in step S203, and then calculates all the deviation amounts.

When calculating the risk of deviation in step S205, the control generation execution unit 104 can determine that there is a risk when the voltage falls outside the specified range due to a prediction error by, for example, setting the estimated risk at 10 points if the estimated voltage deviation amount at the application date and time in the deviation countermeasure data 208 is other than 0, and setting the estimated risk at 0 points otherwise, and setting the threshold at 5 points.

In addition, if the operating conditions are set with a generous margin, the risk level may be calculated in a manner appropriate to these conditions. For example, if the operating conditions are such that a deviation that continues for a certain period of time is deemed dangerous, it may be determined that a deviation with a risk level equal to or greater than the threshold level exists when the amount of deviation or cumulative value of the risk level calculated in the past exceeds a predetermined threshold level.

In step S206, as a method of changing the voltage range so that the risk is below the threshold, for example, if the estimated deviation item is the upper voltage limit and the estimated deviation amount is 0.02, the countermeasure may be to change the upper voltage limit to -0.02. With this range change method, the power system cross section in which the optimal device settings recalculated in step S207 are realized can keep the voltage within the range even if a deviation of 0.02 occurs due to a prediction error. The method of changing the voltage range may further be a method according to the calculation method of the risk in S205, such as repeatedly performing the voltage upper limit of -0.001 and the risk calculation performed in S205, and adopting the range change when the risk becomes below the threshold, and any method may be used as long as it changes the voltage range so that the risk is below the threshold.

Figure 12 is an explanatory diagram showing an example of a prediction error display screen displayed in Example 1 of the present invention.

The prediction error display screen 1200 is called from the menu screen and displays the contents of the prediction error data 206. The contents of the prediction error data 206 displayed on the prediction error display screen 1200 are the same as those described in FIG. 8, so detailed description will be omitted. The prediction error display screen 1200 may display a portion of the prediction error data 206, or may also display data that is not included in the prediction error data 206. The prediction error display screen 1200 may include a [Change] button that allows the user to change part of the prediction error information, and a [Back] button that closes the screen and returns to the menu screen that called it.

Figure 13 is an explanatory diagram showing an example of a deviation countermeasure display screen displayed in Example 1 of the present invention.

The deviation countermeasure display screen 1300 is called from the menu screen and displays the contents of the deviation countermeasure data 208. The contents of the deviation countermeasure data 208 displayed on the deviation countermeasure display screen 1300 are the same as those described in FIG. 11, so detailed description will be omitted. The deviation countermeasure display screen 1300 may display a portion of the deviation countermeasure data 208, or may also display data that is not included in the deviation countermeasure data 208. The deviation countermeasure display screen 1300 may include a [Change] button that allows the user to change some of the deviation countermeasure information, such as thresholds, and a [Back] button that closes the screen and returns to the menu screen that called it.

Next, the calculation by the optimum calculation unit 30 will be described. The optimum calculation unit 30 calculates the state of the power system 3 that is optimal for a given objective, such as reducing power loss or improving voltage stability. The optimum calculation unit 30 determines the variables of the power system 3 so that an index representing the objective is improved. For example, the optimum calculation unit 30 gives an objective function that expresses a reduction in power loss, and calculates a combination of variables that minimizes or maximizes the objective function from among combinations of variables that satisfy the constraints of the power flow equation of the power system 3 and other equality constraints and inequality constraints such as the voltage range and whether or not equipment control is possible. The variables used include the voltage of each bus, the input amount of phase modifying equipment, and the tap position of the transformer.

The calculations of the optimum calculation unit 30 can be performed using known techniques such as optimal power flow calculations, but any other method that calculates the optimum state of the power system 3 for the desired purpose may also be used.

According to the above-described first embodiment, even when efficient control is planned and applied based on the predicted future state of the power system, it is possible to appropriately evaluate deviations from the appropriate range of the power system state caused by prediction errors and to plan appropriate avoidance measures, thereby making it possible to plan efficient and reliable control plans.

In the second embodiment, the power system control device 2 has the configuration and functions of the first embodiment, and is further configured to include a data correction unit 107 that corrects the demand data used in the prediction processing unit 103. Except for the differences described below, each unit of the system in the second embodiment has the same function as each unit in the first embodiment shown in Figures 1 to 13 and denoted with the same reference numerals, and therefore the description of those units is omitted.

Figure 14 is a block diagram showing an example of the configuration of the data correction unit 107 in Example 2 of the present invention.

The control unit 10 of the second embodiment includes a main control unit 101 and a deviation countermeasure creation unit 106 similar to those of the first embodiment, and further includes a correction unit 107. The correction unit 107 includes a first correction unit 171, a second correction unit 172, and a third correction unit 173.

First, the first correction unit 171 receives the demand data 203 as input, and corrects the sudden demand fluctuation in the demand data 203. Specifically, the first correction unit 171 calculates a one-time difference value of the demand data 203, which is time-series data, and calculates the standard deviation of the calculated one-time difference value. The first correction unit 171 then detects a date and time having a one-time difference value that exceeds a predetermined multiple of the calculated standard deviation as the date and time at which the sudden demand fluctuation occurred. The first correction unit 171 then accumulates only the one-time difference value at the detected date and time in a time series, and outputs the first correction data by dividing the accumulated value from the demand data 203.

Then, the second correction unit 172 receives the first correction data and the factor data 204, and corrects the short-period fluctuation components in the first correction data. Specifically, the second correction unit 172 divides the first correction data into a plurality of samples by dividing it into a predetermined time granularity. The predetermined time granularity is, for example, 24-hour granularity. The second correction unit 172 then extracts frequency components for each sample. A publicly known method may be applied to extract the frequency components, and the publicly known method is, for example, a Fourier transform or a wavelet transform.

Then, the second correction unit 172 obtains the observation time granularity of a specific factor in the factor data 204, and corrects all frequency components in the frequency components that are less than the observation time granularity to zero. The second correction unit 172 then outputs second correction data by restoring the frequency components to their original waveform.

Then, the third correction unit 173 receives the demand data 203 and the second correction data, corrects the second correction data so that the measured value at the latest date and time of the demand data 203 matches the value of the second correction data at the same date and time, and outputs it as third correction data 209 (not shown). With this, the processing of the data correction unit 107 is completed.

Furthermore, the first correction unit 171 may record the date and time when a detected sudden demand fluctuation occurred and the range of fluctuation, and calculate the fluctuation period and occurrence probability for recurring fluctuations, thereby automatically generating a portion of the prediction error data 206.

In addition, in the configuration described using FIG. 14, all correction processes are completed before input to the prediction processing unit 103, but this is not limited thereto, and the output of the prediction processing unit 103 may be corrected by the third correction unit 173. This configuration will be described using FIG. 15.

Figure 15 is a block diagram showing another example of the configuration of the data correction unit 107 in the second embodiment of the present invention.

The first correction unit 171 and the second correction unit 172 in FIG. 15 are the same as the correction units described above with reference to FIG. 14. In the configuration of FIG. 15, the correction data of the second correction unit 172 is input to the prediction processing unit 103. The prediction processing unit 103 outputs prediction data for the prediction target date and time and a specified period of the past in which observation data exists, based on the input correction data. The prediction data output by the prediction processing unit 103 is then input to the third correction unit 173. The third correction unit 173 corrects the prediction data output by the prediction processing unit 103 so that it matches or minimizes the deviation from the measured value of the demand data 203 in the specified period of the past in which the above observation data exists, and outputs it as new prediction data.

According to the above-described second embodiment, even when efficient control is planned and applied based on the future predicted power system state, it is possible to appropriately evaluate deviations from the appropriate range of the power system state caused by prediction errors and to plan appropriate avoidance measures, thereby making it possible to plan efficient and reliable control plans. Furthermore, the second embodiment also has the effects described below.

By correcting the sudden demand fluctuation data in the first correction unit 171, it is possible to reduce the deterioration of modeling errors caused by the influence of the sudden demand fluctuation data in the identification of the prediction model in the prediction processing unit 103. This makes it possible to further improve the accuracy of the predicted values output by the prediction processing unit 103. In addition, it is possible to automatically generate the sudden demand fluctuation as prediction error data 206.

In addition, in the second correction unit 172, by removing frequency components less than the observation time granularity of the factor data 204 from the first correction data, it is possible to remove short-period fluctuation components of the demand data 203 that are difficult to model with the observation time granularity of the factor data 204. This makes it possible to reduce the deterioration of modeling errors in the prediction processing unit 103.

In addition, the third correction unit 173 corrects the second correction data so that the observed value at the latest observation date and time in the demand data 203 matches the value of the second correction data at the same date and time, thereby enabling the prediction model to track when a sudden demand fluctuation occurs at the latest observation date and time. This makes it possible to further improve the prediction accuracy when a sudden demand fluctuation occurs.

The system of the present invention may also be configured as follows:

(1) A power system control device (e.g., power system control device 2) having a control unit (e.g., control unit 10) and a recording unit (e.g., recording unit 20), in which the recording unit holds power demand data (e.g., demand data 203) indicating past power demand, factor data (e.g., factor data 204) including information that may be a factor of the past power demand, and prediction error data (e.g., prediction error data 206) indicating a tendency of prediction error of power demand predicted in the past based on the factor data with respect to actual power demand, and the control unit predicts future power demand based on the power demand data and the factor data (e.g., steps S101 to S103), and sets electrical equipment constituting the power system to control the power system so that a measurement value in the power system satisfies a first constraint condition based on the predicted future power demand. A numerical value to be set for the electrical equipment is calculated (e.g., step S201), a prediction error of future power demand is estimated based on the prediction error data (e.g., steps S202-S203), the calculated numerical value is set for the electrical equipment, and the deviation of the measured value in the power system from the first constraint condition when the estimated prediction error occurs is calculated (e.g., step S204), a second constraint condition is generated by modifying the first constraint condition used to calculate the numerical value to be set for the electrical equipment so that the deviation of the measured value in the power system from the first constraint condition when the estimated prediction error occurs is reduced (e.g., step S206), and a numerical value to be set for the electrical equipment is calculated based on the predicted future power demand in order to control the power system so that the measured value in the power system satisfies the second constraint condition (e.g., step S207).

As a result, in an operational system that performs efficient control through optimization based on predicted future power grid states, it is possible to create and apply efficient and reliable control plans based on appropriate evaluation of deviations from the appropriate range of power grid states caused by prediction errors.

(2) In the above (1), the prediction error data includes information indicating the measurement point of the power demand (e.g., D602), the timing of the prediction error (e.g., D603-D605), and the amount of the prediction error (e.g., D607-D608), and the control unit calculates the amount of deviation when two or more prediction errors that are likely to occur simultaneously and that are determined to lead to a large fluctuation in the measurement value occur simultaneously based on the timing of the prediction error and the amount of the prediction error (e.g., steps S203-S204).

This allows appropriate control to be implemented for prediction errors that are most in need of addressing.

(3) In the above (1), the control unit calculates the degree of risk based on the amount of deviation, and generates a second constraint condition if the degree of risk exceeds a predetermined threshold (e.g., step S205: Y, S206).

This allows appropriate controls to be implemented to prevent potentially dangerous deviations from occurring.

(4) In the above (3), the control unit generates a second constraint condition when the cumulative value of the previously calculated risk exceeds a predetermined threshold.

This allows appropriate controls to be implemented to prevent potentially dangerous deviations from occurring.

(5) In (3) above, the control unit generates a second constraint condition so that the risk does not exceed a predetermined threshold (e.g., step S206).

This allows appropriate controls to be implemented to prevent potentially dangerous deviations from occurring.

(6) In the above (3), the control unit sets a value calculated based on the second constraint condition in the electrical device, calculates the deviation of the measured value in the power system from the first constraint condition when an estimated prediction error occurs, and if the risk calculated based on the deviation amount exceeds a threshold, generates a second constraint condition by adding a change amount to the first constraint condition, and repeats adding the change amount to the first constraint condition and calculating the deviation amount based on the second constraint condition until the risk becomes a value that does not exceed the threshold (e.g., repeating steps S206 to S207).

This allows appropriate controls to be implemented to prevent potentially dangerous deviations from occurring.

(7) In the above (1), when the control unit detects a sudden demand fluctuation based on the difference value per unit time of the power demand data, the control unit executes a first correction process (e.g., the process of the first correction unit 171) to generate first correction data by removing the sudden demand fluctuation from the power demand data, a second correction process (e.g., the process of the second correction unit 172) to generate second correction data by removing from the first correction data components that fluctuate in a period shorter than the time granularity of the information contained in the factor data, and a third correction process (e.g., the process of the third correction unit 173 in FIG. 14) to generate third correction data by adding to the second correction data a value corresponding to the demand fluctuation detected from data of the power demand data within a predetermined time range close to the present, and predicts future power demand based on the third correction data and the factor data.

This allows for the generation of highly accurate predictive models.

(8) In the above (1), when the control unit detects a sudden demand fluctuation based on the difference value per unit time of the power demand data, the control unit executes a first correction process to generate first correction data by removing the sudden demand fluctuation from the power demand data, and a second correction process to generate second correction data by removing from the first correction data components that fluctuate in a period shorter than the time granularity of the information contained in the factor data, and executes a third correction process (e.g., the process of the third correction unit 173 in FIG. 15 ) to generate a prediction model that predicts future power demand from the factor data based on the second correction data and the factor data, and output a value obtained by adding a value corresponding to the demand fluctuation detected from the data of the power demand data within a predetermined time range close to the present to the output of the prediction model as a result of predicting future power demand.

This allows for the generation of highly accurate predictive models.

(9) In (1) above, the measurement value is a voltage, and the first constraint and the second constraint are a voltage range.

This allows for appropriate control to be implemented to prevent the voltage from deviating from the specified range.

(10) In the above (1), the factor data includes at least one of information related to weather, information related to fuel transactions, information related to the operation status of the generator, information related to the calendar date, information related to the occurrence or non-occurrence of an unexpected event, and information related to the economic situation.

This allows for appropriate control to be implemented in response to errors in power demand forecasts based on various factors.

The present invention is not limited to the above-described embodiments, but includes various modified examples. For example, the above-described embodiments have been described in detail to provide a better understanding of the present invention, and are not necessarily limited to those having all of the configurations described. It is also possible to replace part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. It is also possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

The above configurations, functions, processing units, processing means, etc. may be realized in part or in whole in hardware, for example by designing them as integrated circuits. The above configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information on the programs, tables, files, etc. that realize each function can be stored in storage devices such as non-volatile semiconductor memory, hard disk drives, and SSDs (Solid State Drives), or in computer-readable non-transitory data storage media such as IC cards, SD cards, and DVDs.

In addition, the control lines and information lines shown are those considered necessary for the explanation, and not all control lines and information lines on the product are necessarily shown. In reality, it can be assumed that almost all components are interconnected.

REFERENCE SIGNS LIST 1 Power system control system 2 Power system control device 3 Power system 4 Communication network 5 Measuring device 6 Electric device 10 Control unit 20 Recording unit 30 Optimal calculation unit 40 Communication unit 50 Input unit 60 Display unit

Claims (12)

A power system control device having a control unit and a recording unit,
the recording unit holds power demand data indicating past power demand, factor data including information that may be a factor of the past power demand, and prediction error data indicating a tendency of prediction error of the power demand predicted in the past based on the factor data with respect to an actual power demand;
The control unit is
predicting future power demand based on the power demand data and the factor data;
calculating, based on the predicted future power demand, values to be set in electrical devices constituting the power system in order to control the power system so that a measurement value in the power system satisfies a first constraint condition;
estimating a forecast error of future electricity demand based on the forecast error data;
The calculated value is set in the electrical device, and a deviation amount of a measured value in the power system from the first constraint condition is calculated when the estimated prediction error occurs;
generating second constraint conditions by modifying the first constraint conditions used to calculate values to be set in the electrical equipment so that a deviation of a measured value in the power system from the first constraint conditions when the estimated prediction error occurs is reduced;
a power system control device that calculates, based on the predicted future power demand, a numerical value to be set in the electrical device in order to control the power system so that a measurement value in the power system satisfies the second constraint condition. The power system control device according to claim 1,
the prediction error data includes information indicating a point at which the power demand is measured, a timing at which the prediction error occurs, and an amount of the prediction error;
the control unit calculates the amount of deviation when two or more prediction errors that are determined to be likely to occur simultaneously and to lead to a large fluctuation in the measurement value occur simultaneously, based on the timing of occurrence of the prediction errors and the amount of the prediction errors. The power system control device according to claim 1,
The control unit calculates a degree of risk based on the deviation amount,
The power system control device generates the second constraint condition when the degree of risk exceeds a predetermined threshold. The power system control device according to claim 3,
The power system control device according to claim 1, wherein the control unit generates the second constraint condition when a cumulative value of the degree of risk calculated in the past exceeds a predetermined threshold. The power system control device according to claim 3,
The power system control device according to claim 1, wherein the control unit generates the second constraint condition so that the degree of risk does not exceed the predetermined threshold value. The power system control device according to claim 3,
The control unit is
a value calculated based on the second constraint condition is set in the electrical device, and a deviation amount of a measured value in the power system from the first constraint condition is calculated in the case where the estimated prediction error occurs;
generating the second constraint condition by adding a change amount to the first constraint condition when the risk calculated based on the deviation amount exceeds the threshold value;
a power system control device which repeats adding a change amount to the first constraint condition and calculating the deviation amount based on the second constraint condition until the degree of risk becomes a value that does not exceed the threshold value. The power system control device according to claim 1,
The control unit is
a first correction process for generating first correction data by removing an unexpected demand fluctuation from the power demand data when the unexpected demand fluctuation is detected based on a difference value of the power demand data per unit time;
a second correction process for generating second correction data by removing, from the first correction data, components that vary at a period shorter than the time granularity of information included in the factor data;
a third correction process for generating third correction data by adding a value corresponding to a demand fluctuation detected from data within a predetermined time range close to the present among the power demand data to the second correction data;
A power system control device which predicts future power demand based on the third correction data and the factor data. The power system control device according to claim 1,
The control unit is
a first correction process for generating first correction data by removing an unexpected demand fluctuation from the power demand data when the unexpected demand fluctuation is detected based on a difference value of the power demand data per unit time;
a second correction process for generating second correction data by removing, from the first correction data, components that vary at a period shorter than the time granularity of information included in the factor data;
generating a forecasting model for forecasting a future power demand from the factor data based on the second correction data and the factor data;
a third correction process for adding a value corresponding to a demand fluctuation detected from the power demand data within a predetermined time range close to the present to the output of the prediction model, and outputting the result as a prediction of future power demand. The power system control device according to claim 1,
the measurement is a voltage,
2. A power system control device, comprising: a power system controller, the power system controller comprising: a power system controller configured to control a power system; The power system control device according to claim 1,
The power system control device, characterized in that the factor data includes at least one of information regarding weather, information regarding fuel transactions, information regarding the operating status of generators, information regarding calendar dates, information regarding the occurrence of unexpected events, and information regarding economic conditions. A power system control system including a power system and a power system control device connected to the power system via a communication network,
the power system includes a plurality of measuring devices and a plurality of electrical devices;
The power system control device has a control unit, a recording unit, and a communication unit,
the communication unit is connected to the communication network,
the recording unit holds power demand data indicating past power demand, factor data including information that may be a factor of the past power demand, and prediction error data indicating a tendency of prediction error of the power demand predicted in the past based on the factor data with respect to an actual power demand;
The control unit is
predicting future power demand based on the power demand data and the factor data;
calculating a value to be set in the electrical device for controlling the power grid based on the predicted future power demand so that a measured value in the power grid satisfies a first constraint;
estimating a forecast error of future electricity demand based on the forecast error data;
The calculated value is set in the electrical device, and a deviation amount of a measured value in the power system from the first constraint condition is calculated when the estimated prediction error occurs;
generating second constraint conditions by modifying the first constraint conditions used to calculate values to be set in the electrical equipment so that a deviation of a measured value in the power system from the first constraint conditions when the estimated prediction error occurs is reduced;
calculating a value to be set in the electrical device for controlling the power grid based on the predicted future power demand so that a measured value in the power grid satisfies the second constraint;
The power system control system is characterized in that the calculated numerical value is set in the electrical equipment via the communication unit. A power system control method executed by a power system control device having a control unit and a recording unit,
the recording unit holds power demand data indicating past power demand, factor data including information that may be a factor of the past power demand, and prediction error data indicating a tendency of prediction error of the power demand predicted in the past based on the factor data with respect to an actual power demand;
The power system control method includes:
a step of the control unit predicting a future power demand based on the power demand data and the factor data;
a step of calculating, by the control unit, a numerical value to be set in an electrical device constituting the power system, based on the predicted future power demand, in order to control the power system so that a measurement value in the power system satisfies a first constraint condition;
a step of the control unit estimating a prediction error of a future power demand based on the prediction error data;
a step of calculating an amount of deviation of a measured value in the power system from the first constraint condition when the calculated value is set in the electric device and the estimated prediction error occurs, by the control unit;
a step of generating a second constraint condition by modifying the first constraint condition used for calculating a numerical value to be set in the electrical device, so that a deviation amount of a measured value in the power system from the first constraint condition when the estimated prediction error occurs is reduced by the control unit;
and a step of calculating, by the control unit, a numerical value to be set in the electrical equipment, based on the predicted future power demand, in order to control the power system so that a measurement value in the power system satisfies the second constraint condition.

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