Power system control device, power system control system, and power system control method

Abstract

To provide a power system control device which appropriately evaluates a power system state deviation, caused by a prediction error, from a proper range and develops such a highly reliable and efficient control plane as to reduce influences of the deviation.SOLUTION: In a power system control system 1, a power system control device 2 comprises a control unit 10 and a recording unit 20. The recording unit holds power demand data 203 indicating a past power demand, factor data 204 including information that may be a factor of the past power demand, and prediction error data 206 indicating a tendency of a prediction error with respect to an actual power demand, of a power demand 205 predicted based on the factor data in the past. The control unit predicts a future power demand based on the power demand data and the factor data and calculates a numerical value to be set to an electric apparatus for controlling a power system in such a manner that measurements in a power system 3 satisfy a second restriction condition based on the predicted future power demand.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a technique for controlling a power system. [Background technology]

[0002] 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 higher satisfaction through optimization calculations such as optimal power flow calculations.

[0003] 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 a comparison result between a predicted value and an actual value to make corrections when making a renewable energy transmission plan based on a predicted value. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2020-54085 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Patent Document 1 has a problem that, although it is possible to correct the control deviation caused by the deviation between the predicted value and the actual value, it is not possible to suppress the deviation of the operating conditions caused by the deviation between the predicted value and the actual value. 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 difference occurs from the assumed future power system state due to the occurrence of a prediction error, 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 voltage of the substation will fluctuate from the expected value, and if the fluctuation is large, it will deviate from the voltage range condition, causing a problem that the reliability of the power system cannot be maintained.

[0006] The present invention has been made in consideration of the above points, and proposes a power system control device that, even when efficient control is planned and applied based on prediction of the future power system state, properly evaluates deviations of the power system state from an appropriate range caused by prediction errors, and plans highly reliable and efficient control that can reduce the effects of these deviations. [Means for solving the problem]

[0007] In order to solve such a problem, 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 error of a power demand predicted based on the factor data in the past with respect to an 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 electric devices 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 to be set in an electric device constituting the power system based on the prediction error data. the method includes estimating a prediction error of future power demand, setting the calculated numerical value to the electrical equipment, calculating an amount of deviation of a measured value in the power system from the first constraint condition when the estimated prediction error occurs, generating a second constraint condition by modifying the first constraint condition used for calculating the numerical value to be set to the electrical equipment so that an amount of deviation of the measured value in the power system from the first constraint condition when the estimated prediction error occurs is reduced, and calculating a numerical value 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. Effect of the Invention

[0008] According to one aspect of the present invention, in an operation system that performs efficient control through optimization based on a predicted future power grid state, it is possible to create and apply an efficient and reliable control plan based on an appropriate evaluation of deviations from an appropriate range of the power grid state caused by prediction errors. Problems, configurations, and effects other than those described above will become apparent from the description of the following embodiments. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a configuration of a power system control system according to a first embodiment of the present invention. [Diagram 2]FIG. 2 is an explanatory diagram showing an example of environmental state data at a specific measurement point in the power system according to the first embodiment of the present invention. [Diagram 3] FIG. 2 is an explanatory diagram illustrating an example of device setting data of an electric device connected to a power system according to the first embodiment of the present invention. [Figure 4] 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. [Diagram 5] FIG. 2 is an explanatory diagram illustrating an example of power system configuration data of a power transmission line in the power system according to the first embodiment of the present invention. [Figure 6] 1 is a flowchart showing an example of a process in which a prediction processing unit creates power system prediction data in the first embodiment of the present invention. [Figure 7] FIG. 2 is an explanatory diagram showing an example of power system prediction data of a specific measurement point in the first embodiment of the present invention. [Figure 8] FIG. 4 is an explanatory diagram showing an example of prediction error data at a specific measurement point in the first embodiment of the present invention. [Figure 9] FIG. 2 is an explanatory diagram showing an example of power system control data for putting the power system into a predetermined state in the first embodiment of the present invention. [Figure 10] 5 is a flowchart showing an example of a process in which a control generation execution unit according to the first embodiment of the present invention generates a control for a power system state. [Figure 11] FIG. 4 is an explanatory diagram showing an example of deviation countermeasure data in the first embodiment of the present invention. [Figure 12] FIG. 11 is an explanatory diagram showing an example of a prediction error display screen displayed in the first embodiment of the present invention. [Figure 13] FIG. 2 is an explanatory diagram showing an example of a deviation countermeasure display screen displayed in the first embodiment of the present invention. [Figure 14] 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. [Figure 15] 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. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. EXAMPLES

[0011] FIG. 1 is a block diagram showing a configuration of a power system control system 1 according to a first embodiment of the present invention.

[0012] The power system control system 1 includes a power system controller 2 and a power system 3, and the power system controller 2 and the power system 3 are connected via a communication network 4 so as to be able to communicate with each other.

[0013] 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 predetermined 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.

[0014] 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. The functions of the control unit 10 and the optimum calculation unit 30 are not limited to those realized by a single device, but may be realized by multiple devices connected to each other so as to be able to 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 for realizing the respective functions.

[0015] The recording unit 20 is realized by a storage device. The recording unit 20 of this embodiment holds power system state data 201, power system configuration data 202, demand data 203, factor data 204, power system prediction data 205, prediction error data 206, system control data 207, and deviation countermeasure data 208. These will be described in detail later.

[0016] 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.

[0017] The power system control device 2 can acquire measurement data of the power system 3 from measuring devices 5 of the power system 3 via a communication network 4 connected to a communication unit 40. The power system control device 2 can also control the operating state of the electric devices 6 of the power system 3 by transmitting control commands to the electric devices 6. When the operating state is controlled by a control command, the control may be entirely automatic, or may include some manual operation.

[0018] 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 devices 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 bus bars, but may also include various other facilities related to power transmission and distribution.

[0019] The measuring equipment 5 indicates a plurality of measuring devices that measure the state at a specific measuring point of the power system 3. The 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, 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 the measurement items. The measurement points include the busbar of the power generation equipment, the busbar of the substation equipment, the power distribution equipment, the consumer equipment, etc., and one measuring point may have one or more measuring equipment. 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 date and time of measurement together with the measured value.

[0020] The electric devices 6 are devices that change the power flow state of the electric power system 3, such as power generation equipment, transformers, circuit breakers, and phase modifying equipment, among the devices that make up the electric power system 3, but may also include various other devices. Furthermore, when the electric devices 6 receive a control command transmitted by the electric power system control device 2 via the communication network 4 directly or indirectly via manual intervention, they can change their operating state in response to the control command.

[0021] 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 and execution unit 104, a deviation calculation unit 105, and a deviation countermeasure creation unit 106.

[0022] The power system status acquisition unit 102 acquires various data transmitted by the measuring equipment 5 via the communication unit 40 periodically (e.g., at five-minute intervals) or when instructed by the control unit 10 (e.g., at a time desired by the user), and records the data in the recording unit 20 as power system status data 201 together with the measurement date and time.

[0023] 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.

[0024] The environmental state 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 of the device installation location.

[0025] FIG. 2 is an explanatory diagram showing an example of the environmental state data 211 at a specific measurement point in the power system 3 according to the first embodiment of the present invention.

[0026] 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. The measurement date and time D102, temperature D103, active power D104, and reactive power D105 are items related to the measurement, and the values ​​of each item are changed appropriately depending on the measurement at the measurement point. The temperature D103 is an example of factor data, and may be replaced with or added to other factor data. Examples of other factor data will be described later.

[0027] The device setting data 212 includes information on the setting state of the electric device 6 .

[0028] FIG. 3 is an explanatory diagram illustrating an example of the device setting data 212 of the electric device 6 connected to the power system 3 according to the first embodiment of the present invention.

[0029] 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 electric devices 6 in chronological order. Note that 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 electric devices 6, and a combination of items that can be acquired from the measuring device 5 as information included in the device setting data 212 can be appropriately adopted.

[0030] The device status data 213 includes information (operation status information) indicating the operation status of the electric devices 6, such as power distribution facilities and power generation equipment, that change the power flow status.

[0031] FIG. 4 is an explanatory diagram illustrating an example of the device status data 213 of the electric device 6 connected to the power system 3 according to the first embodiment of the present invention.

[0032] As shown in Fig. 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 an open / close state of a switchgear such as a circuit breaker or a switch, a power transmission status of a power transmission line, and the like. The items of the equipment status data 213 may also be a combination of items of operation states of equipment that change the power flow state such as a power distribution facility or a power generation device, and items of operation states of other equipment acquired from the measuring device 5.

[0033] The power system configuration data 202 is recorded in advance in the recording unit 20. The power system configuration data 202 includes information related to the configuration of the power system 3.

[0034] FIG. 5 is an explanatory diagram showing an example of the power system configuration data 202 of the power transmission lines in the power system 3 according to the first embodiment of the present invention.

[0035] 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 electrical characteristics of the devices constituting the power system 3, operational conditions such as transmission capacity and voltage range, and information on items that do not usually change in a short time, such as the electrical connection form between the devices. Fig. 5 shows the case of a transmission line, but different items may be provided for other devices.

[0036] FIG. 6 is a flowchart showing an example of a process in which the prediction processing unit 103 creates power system prediction data in the first embodiment of the present invention.

[0037] First, when the prediction processing unit 103 receives a request to create power system prediction data for a specified future date and time (hereinafter also referred to as a specified date and time), the prediction processing unit 103 acquires demand data 203 consisting of past demand information and the like 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 prediction data 205 that predicts future demand from the statistical correlation between the acquired demand data 203 and factor data 204 (step S103).

[0038] 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.

[0039] In step S102, the factor data 204 includes, for example, meteorological data such as temperature, humidity, solar radiation, wind speed, or atmospheric 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 schedule, calendar data such as a flag value indicating the date, day of the week, or an arbitrarily set day type, data indicating the occurrence or non-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.

[0040] The demand data 203 may include, for example, active or reactive power for each substation or for each line of a substation, or a total value of active or reactive power for multiple substations or multiple lines.

[0041] In step S103, the prediction processing unit 103 generates prediction data regarding future demand by utilizing the statistical relationship between the demand data 203 and the factor data 204. For example, active power is taken as the demand data 203, and temperature is taken as the factor data 204, and a time series pattern of the demand data 203 against the factor data 204 for a certain period of time is created, and future demand data may be predicted using the factor data 204 corresponding to a future time as an input. Alternatively, a 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.

[0042] 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 forecasted and grasping their interrelationships, the power system configuration data 202 may be used in addition to the power system state data 201.

[0043] FIG. 7 is an explanatory diagram illustrating an example of the power system prediction data 205 of a specific measurement point in the first embodiment of the present invention.

[0044] The power system prediction data 205 is data that stores multiple predicted values ​​at specific measurement points, and includes items such as a unique ID D501 for identifying the power system prediction data, a prediction execution date and time D502 indicating the date and time when the prediction was calculated, a prediction date and time D503 indicating the target date and time for the prediction, 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 with or added to other factor data, or the power system prediction data 205 may not include factor data.

[0045] FIG. 8 is an explanatory diagram showing an example of the prediction error data 206 at a specific measurement point in the first embodiment of the present invention.

[0046] The prediction error data 206 is data that stores multiple estimates of prediction errors occurring at specified measurement points, and includes items such as a serial number D601, a measurement point ID D602, a repeating unit of a prediction error period D603, a start date and time D604, an end date and time D605, a prediction error occurrence probability 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.

[0047] In this data example, if the repeat unit is every day and the occurrence probability is recorded as high from 6:00 to 8:00, this may be understood to indicate that there is a certain high possibility that a forecast error of the specified error amount 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 time period when forecast errors are likely to occur, the location on the power system where forecast errors are likely to occur, the occurrence probability 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.

[0048] In addition, in this data, prediction errors occurring in the same repeat unit may be considered 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 portion of the prediction error from 6:00 to 10:00 every day at measurement point A1 from 6:00 to 8:00 are considered to occur simultaneously.

[0049] However, as long as an assumption or estimate of the probability of simultaneous occurrence can be expressed, the prediction error data may include items different from 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 excursions in the past, and prediction errors that are likely to cause serious voltage excursions as operational knowledge.

[0050] In this embodiment, a control for reducing the effect of voltage deviation caused by prediction error will be described. However, voltage deviation is one example of a measurement value indicating the state of the power grid, and this embodiment can be applied to reduce the effect of deviation of any measurement value other than voltage, such as power amount, from a predetermined range.

[0051] The control generation and execution unit 104 generates power system control data 207 for calculating an optimal power system state for the specified date and time, generates a control command for realizing a 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 set in advance, or may be a date and time specified as appropriate by an instruction via the input unit 50.

[0052] FIG. 9 is an explanatory diagram showing an example of the power system control data 207 for putting the power system 3 into a predetermined state in the first embodiment of the present invention.

[0053] 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 device setting data 212. The equipment to be controlled is an object of control included in the electric device 6, and may be, for example, a circuit breaker and a switch, or may be a combination of other devices. The application date and time D702 records the date and time when the power system control data is applied.

[0054] FIG. 10 is a flowchart showing an example of a process in which the control generation and execution unit 104 according to the first embodiment of the present invention generates a control for a power system state.

[0055] The control generation execution unit 104 provides the power system state data 201, the power system configuration data 202, and the power system forecast data 205 relating to a specified time to the optimization calculation unit 30, acquires a power system plan for achieving the calculated optimal power system state, and stores it as power system control data 207 (step S201).

[0056] Next, the control generation execution unit 104 instructs the deviation calculation unit 105 to calculate the voltage deviation amount, acquires the 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).

[0057] 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, the power system configuration data 202, the power system prediction data 205, the 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).

[0058] 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 judges whether the calculated risk is equal to or greater than a threshold (step S205) for the deviation countermeasure creation unit 106. If the calculated risk is equal to or greater than the threshold (step S205: Y), the control generation execution unit 104 stores a change setting of the voltage range such that the risk becomes equal to or less than the threshold in the deviation countermeasure data 208 (step S206).

[0059] Next, the control generation execution unit 104 acquires the change setting of the voltage range from the deviation countermeasure data 208, and using the data to which the change setting has been applied, recalculates optimal device settings again by the same process as in S201, and overwrites 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).

[0060] FIG. 11 is an explanatory diagram showing an example of the deviation countermeasure data 208 in the first embodiment of the present invention.

[0061] Deviation countermeasure data 208 is data related to an estimated deviation amount at a location where a condition is set and the countermeasure. The location where a condition is set is, for example, a bus bar, but may be other items. Fig. 11 shows an example related to bus bar voltage. Deviation countermeasure data 208 shown in Fig. 11 includes an ID D801 indicating a serial number, a countermeasure application date and time D802, a bus bar ID D803, an estimated deviation item D804, an estimated deviation amount D805, an estimated risk D806, a risk threshold D807, and a countermeasure content D808 for the deviation.

[0062] In step S203, the control generation and execution unit 104 may simply uniformly incorporate fluctuations that are specified as having a high probability of occurring at the same time. For example, when occurrences of contradictory fluctuations are stored at one point, the control generation and 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 both the active power and reactive power of multiple loads located in the vicinity of the busbar connection network are reduced, the voltage rise fluctuation becomes larger and voltage deviation is 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, a more practical deviation countermeasure can be realized.

[0063] The control generation and execution unit 104 also 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.

[0064] When calculating the deviation amount of the voltage in step S204, the control generation and execution unit 104 calculates the deviation amount for each set of prediction errors extracted in step S203, and calculates all the deviation amounts.

[0065] When calculating the risk of deviation in step S205, the control generation and execution unit 104 can determine that there is a risk when the voltage deviates from the specified range due to a prediction error by, for example, setting the estimated risk to 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 to 0 points otherwise, and setting the threshold to 5 points.

[0066] In addition, when the operating conditions are set with a certain margin, the risk level may be calculated in a manner appropriate to these conditions. For example, when the operating conditions are such that a deviation is deemed dangerous if it continues for a certain period of time, it may be determined that a deviation with a risk level equal to or greater than the threshold level exists when the cumulative value of the deviation amount or risk level calculated in the past exceeds a predetermined threshold level.

[0067] In step S206, as a method of changing the voltage range so that the risk is equal to or less than the threshold, for example, when 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 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 upper voltage limit of -0.001 and the risk calculation performed in S205, and adopting the range change when the risk becomes equal to or less than the threshold, and any method may be used as long as it changes the voltage range so that the risk is equal to or less than the threshold.

[0068] FIG. 12 is an explanatory diagram showing an example of a prediction error display screen displayed in the first embodiment of the present invention.

[0069] 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, and therefore detailed description will be omitted. The prediction error display screen 1200 may display a part of the prediction error data 206, or may also display data 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 [Return] button that closes the screen and returns to the menu screen that called it.

[0070] FIG. 13 is an explanatory diagram showing an example of a deviation countermeasure display screen displayed in the first embodiment of the present invention.

[0071] 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 explained in FIG. 11, and therefore detailed explanation will be omitted. Furthermore, the deviation countermeasure display screen 1300 may display a part 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 part 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.

[0072] 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 constraint conditions of the power flow equation of the power system 3 and other equality constraint conditions and inequality constraint conditions such as voltage range and whether or not equipment control is possible. As the variables, the voltage of each bus, the input amount of phase modifying equipment, the tap position of the transformer, etc. are used.

[0073] The calculations in the optimum calculation unit 30 can be performed using known techniques such as optimal power flow calculation, but any other method may be used as long as it is a method for calculating the optimum state of the power system 3 for the desired purpose.

[0074] According to the above-described first embodiment, even when efficient control is planned and applied based on a future predicted power system state, it is possible to appropriately evaluate deviation of the power system state from an appropriate range caused by a prediction error and to plan appropriate avoidance measures, thereby making it possible to plan an efficient and reliable control plan. EXAMPLES

[0075] In the second embodiment, the power system control device 2 has a configuration in which a data correction unit 107 corrects demand data used in the prediction processing unit 103 in addition to the configuration and functions of the first embodiment. 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 Figs. 1 to 13 with the same reference numerals, and therefore the description thereof will be omitted.

[0076] FIG. 14 is a block diagram showing an example of the configuration of the data correction unit 107 in the second embodiment of the present invention.

[0077] The control unit 10 of the second embodiment includes a correction unit 107 in addition to the main control unit 101 and the deviation countermeasure creation unit 106 similar to those of the first embodiment. The correction unit 107 includes a first correction unit 171, a second correction unit 172, and a third correction unit 173.

[0078] First, the first correction unit 171 receives the demand data 203 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 a 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 a date and time when a 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.

[0079] Next, 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 the data into a predetermined time granularity. The predetermined time granularity is, for example, a 24-hour granularity. The second correction unit 172 then extracts frequency components from each sample. A known method may be applied to extract the frequency components, and the known method is, for example, a Fourier transform or a wavelet transform.

[0080] Next, the second correction unit 172 obtains the observation time granularity of a predetermined 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. Then, the second correction unit 172 outputs second corrected data by restoring the frequency components to their original waveforms.

[0081] Then, the third correction unit 173 receives the demand data 203 and the second correction data, corrects the second correction data so that the measurement 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 the data as third correction data 209 (not shown). With this, the processing of the data correction unit 107 is completed.

[0082] Furthermore, the first correction unit 171 may automatically generate a portion of the prediction error data 206 by recording the date and time when a detected sudden demand fluctuation occurred and the fluctuation range, and calculating the fluctuation period and occurrence probability for recurring fluctuations.

[0083] 14, all correction processes are completed before input to the prediction processing unit 103, but the present invention is not limited to this, and the output of the prediction processing unit 103 may be corrected by the third correction unit 173. This configuration will be described with reference to FIG.

[0084] FIG. 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.

[0085] 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. Here, 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 predetermined period in the past in which observation data exists, based on the input correction data. Then, the prediction data output by the prediction processing unit 103 is 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 the measured value of the demand data 203 in the predetermined period in the past in which the above observation data exists is matched or the deviation is minimized, and outputs it as new prediction data.

[0086] According to the above-described second embodiment, even when efficient control is planned and applied based on the predicted future power system state, it is possible to appropriately evaluate deviation of the power system state from the appropriate range caused by prediction error and to plan appropriate avoidance measures, thereby making it possible to plan an efficient and reliable control plan. Furthermore, the second embodiment also has the following effects.

[0087] By correcting the sudden demand fluctuation data in the first correction unit 171, it is possible to reduce deterioration of modeling error caused by the influence of the sudden demand fluctuation data in identification of the prediction model in the prediction processing unit 103. This makes it possible to further improve the accuracy of the predicted value output by the prediction processing unit 103. In addition, it is possible to automatically generate the sudden demand fluctuation as prediction error data 206.

[0088] Moreover, the second correction unit 172 removes frequency components less than the observation time granularity of the factor data 204 from the first corrected data, thereby making it 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 deterioration of modeling errors in the prediction processing unit 103.

[0089] 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 follow the case where a sudden demand fluctuation occurs at the latest observation date and time. This makes it possible to further improve the prediction accuracy at the time of a sudden demand fluctuation.

[0090] Furthermore, the system according to the embodiment of the present invention may be configured as follows.

[0091] (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 a power demand predicted in the past based on the factor data with respect to an 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 electric devices 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. The method calculates a numerical value to be set in the electrical equipment (e.g., step S201), estimates a prediction error of future power demand based on the prediction error data (e.g., steps S202-S203), sets the calculated numerical value in the electrical equipment, calculates the amount of deviation of the measurement value in the power system from the first constraint condition when the estimated prediction error occurs (e.g., step S204), generates a second constraint condition by modifying the first constraint condition used to calculate the numerical value to be set in the electrical equipment so that the amount of deviation of the measurement value in the power system from the first constraint condition when the estimated prediction error occurs is reduced (e.g., step S206), and calculates a numerical value to be set in the electrical equipment to control the power system based on the predicted future power demand so that the measurement value in the power system satisfies the second constraint condition (e.g., step S207).

[0092] As a result, in an operational system that performs efficient control through optimization based on predicted future power grid states, it is possible to develop 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.

[0093] (2) In (1) above, 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 to D605), and the amount of the prediction error (e.g., D607 to D608), and the control unit calculates the amount of deviation when two or more prediction errors occur simultaneously, which are determined to be likely to occur simultaneously and to lead to a large fluctuation in the measurement value, based on the timing of the prediction error and the amount of the prediction error (e.g., steps S203 to S204).

[0094] This makes it possible to carry out appropriate control for prediction errors that are of great need to be addressed.

[0095] (3) In the above (1), the control unit calculates the degree of risk based on the deviation amount, and generates a second constraint condition when the degree of risk exceeds a predetermined threshold (eg, steps S205: Y, S206).

[0096] This allows appropriate controls to be implemented to prevent risky deviations from occurring.

[0097] (4) In the above (3), the control unit generates a second constraint condition when a cumulative value of the degree of risk calculated in the past exceeds a predetermined threshold value.

[0098] This allows appropriate controls to be implemented to prevent risky deviations from occurring.

[0099] (5) In the above (3), the control unit generates a second constraint condition so that the degree of risk does not exceed a predetermined threshold (for example, step S206).

[0100] This allows appropriate controls to be implemented to prevent risky deviations from occurring.

[0101] (6) In (3) above, the control unit sets a numerical value calculated based on the second constraint condition in the electrical equipment, calculates the amount of 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 amount of deviation exceeds a threshold, generates a second constraint condition by adding an amount of change to the first constraint condition, and repeats adding the amount of change to the first constraint condition and calculating the amount of deviation based on the second constraint condition until the risk becomes a value that does not exceed the threshold (for example, repeating steps S206 to S207).

[0102] This allows appropriate controls to be implemented to prevent risky deviations from occurring.

[0103] (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 Figure 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.

[0104] This makes it possible to generate a highly accurate prediction model.

[0105] (8) In the above (1), when the control unit detects a sudden demand fluctuation based on the difference value of the power demand data for each unit time, 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 (for example, the processing of the third correction unit 173 in Figure 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 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.

[0106] This makes it possible to generate a highly accurate prediction model.

[0107] (9) In the above (1), the measurement value is a voltage, and the first constraint and the second constraint are a voltage range.

[0108] This allows for appropriate control to be exercised to prevent the voltage from deviating from a predetermined range.

[0109] (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 of an unexpected event, and information related to the economic situation.

[0110] This makes it possible to carry out appropriate control targeting errors in power demand predictions due to various factors.

[0111] The present invention is not limited to the above-mentioned embodiment, but includes various modifications. For example, the above-mentioned embodiment has been described in detail for a better understanding of the present invention, and is not necessarily limited to those including all of the configurations described. Also, it is possible to replace a 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. Also, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0112] In addition, the above-mentioned configurations, functions, processing units, processing means, etc. may be realized in part or in whole by hardware, for example, by designing them as integrated circuits. In addition, the above-mentioned configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as a program, table, file, etc. that realizes each function can be stored in a storage device such as a non-volatile semiconductor memory, a hard disk drive, or an SSD (Solid State Drive), or in a computer-readable non-transitory data storage medium such as an IC card, an SD card, or a DVD.

[0113] In addition, the control lines and information lines are those considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0114] 1 Power system control system 2. Power System Control Device 3 Power system 4. Communication Network 5. Measuring Instruments 6. Electrical Equipment 10 Control section 20 Recording Section 30 Optimal Calculation Unit 40 Communications Department 50 Input section 60 Display section

Claims

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

9. 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;

10. 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.

11. 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.

12. 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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