JP2011142790A - Prediction system of solar power generation amount and prediction method of solar power generation amount - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To attain stabilization of a power system by suppressing increase in the number of capacitors and the standby amount of generators, even when a photovoltaic power generator is increased. <P>SOLUTION: In the prediction system of solar power generation amount which predicts the amount of photovoltaic power generation of photovoltaic power generators dispersed and installed in the power system, a data input means collects each pieces of data, including the predicted values and result values of atmospheric temperature and the amount of solar radiation in each region and the electrical energy of the power system. A parameter-estimating means utilizes the atmospheric temperature, the result value regarding the amount of solar radiation, and the electrical energy at substantially the same time, regarding a plurality of different days in each collected data, and computes regression coefficients, by using the atmospheric temperature and the results value regarding the amount of solar radiation as predicted variables and the electrical energy as an object variable in each or the plurality of regions. A solar power generation amount predicting means computes the predictor of the amount of solar power generation at each region from the regression coefficient at each region computed by the parameter estimating means and the predicted values of the amount of solar radiation. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a technology for predicting a power generation amount of a solar power generation apparatus, and more particularly to a solar power generation amount prediction system and a solar power generation amount prediction method used for supply and demand planning and system stabilization control in an electric power system.

  In the electric power system, various controls are performed in order to suppress fluctuations in frequency and voltage within a certain range. For example, in the entire system under the jurisdiction of the power company, when the demand increases or decreases with respect to the amount of power generation, it appears as a change in frequency, so the output of the generator for system stabilization is controlled. In some areas, if the balance between power generation and demand is lost, it will appear as a change in voltage. Therefore, the voltage is adjusted by changing the tap of the transformer or increasing or decreasing the reactive power.

  Generally, it takes time to change the power control device from a stopped state to a controllable state. For example, depending on the type of the generator, it may take one day from the stop state to the standby state. To that end, the power company predicts the power demand from weather and calendar information, makes the generator stand by so that there is no excess or deficiency in the power demand, and shifts from the standby state to the operating state when control is required. Let me control.

  On the other hand, in recent years, the amount of natural energy introduced as represented by solar power generation has increased. Since the power generation amount of sunlight changes depending on the amount of solar radiation, the power generation amount cannot be controlled like other generators. When power from solar power generation is supplied to the grid, the balance between demand and power generation, taking into account uncontrollable power generation, such as solar power, that was controlled according to changes in demand. Had to control to take. For this reason, with the increase in the power generated by solar power generation, the control accuracy (secondary battery (NAS), distributed power supply) that can improve the prediction accuracy of the power generation amount by solar power generation and can sufficiently control the fluctuation of the solar power generation amount. It is important to wait (fuel cell, gas turbine), SVC, SVR, etc.).

As a basic formula for predicting the amount of photovoltaic power generation, Non-Patent Document 1 shows the following formula (1).
Photovoltaic power generation amount = Facility capacity x Solar radiation x Coefficient (1)
Here, the coefficient is a coefficient that is composed of factors that decrease the amount of power generation due to various factors such as contamination of the solar panel, changes in the panel temperature, and loss of the inverter.

  However, when using formula (1) to predict the amount of power generation in a certain region / district, the “irradiation amount” can be obtained from the weather forecast, but “equipment capacity” and “coefficient” may not be known. There are so many that it cannot be predicted easily. This is because a solar power generator is generally provided in each home. It is possible to estimate the installed capacity to some extent by investigating the contract contents of each household, but it is difficult to grasp the coefficient. This is because the coefficient is influenced by various factors such as the installation direction of the panel and the presence or absence of obstacles such as buildings.

  For this reason, in Patent Documents 1 and 2, as a technique for predicting the amount of photovoltaic power generation without knowing the equipment capacity and coefficient without using the equation (1), typical weather conditions such as sunny, cloudy, and rain, A technique for predicting the power generation amount on the corresponding day from the past actual power generation value is disclosed. Specifically, the amount of power generation and weather conditions per unit time in the past are accumulated in units of control devices, and prediction is based on the data.

  However, currently, the information that can be grasped by the power company is the amount of power in minutes on the sending side (transmission end) of substations (ie, the value obtained by subtracting the amount of solar power generated from each household from the actual amount of power demand) Therefore, in order for the electric power company to realize the invention of Patent Document 1, it is necessary to collect information such as the amount of power generation of each household in units of minutes, which requires a large facility cost.

  In addition, since an error occurs in the predicted value and the actual value in practice, in the technique of Patent Document 2, the number of power storage devices and power generation are reduced in order to absorb the error when the amount of power generation increases. It is necessary to increase the standby amount of the machine.

JP 2009-60704 A JP 2006-33908 A

Japanese Industrial Standard JIS C 8907: 2005 (Method for estimating the amount of power generated by a photovoltaic power generation system)

  The present invention has been made in view of the above-described circumstances, and it is possible to efficiently predict the amount of power generated by a solar power generation device and enable a highly accurate supply and demand plan, and increase the number of solar power generation devices in each household. The purpose of the present invention is to provide a photovoltaic power generation amount prediction system and a photovoltaic power generation amount prediction method capable of realizing stabilization of the power system by suppressing an increase in the number of power storage devices and generator standby amounts. And

  In order to achieve the above object, a photovoltaic power generation amount prediction system according to the present invention is a photovoltaic power generation amount prediction system that predicts the amount of photovoltaic power generation of photovoltaic power generation devices distributed and installed in an electric power system. Data input means for collecting each data of predicted temperature and actual value of solar radiation amount, solar radiation amount and electric energy of power system, and actual temperature and solar radiation amount results at substantially the same time for a plurality of different days among the collected data Parameter estimation means for calculating a regression coefficient (coefficient a to be described later) for one or a plurality of regions, using the value and the electric energy as an explanatory variable with the actual temperature and the amount of solar radiation as an explanatory variable and the electric energy as an objective variable And a photovoltaic power generation amount prediction means for calculating a predicted value of the photovoltaic power generation amount for each region from the regression coefficient for each region calculated by the parameter estimation means and the solar radiation amount forecast value.

  In the present invention, temperature, actual solar radiation value and electric energy at substantially the same time are accumulated, and the regression coefficient (coefficient a) is calculated using these past data. Then, the photovoltaic power generation amount is predicted from the regression coefficient and the solar radiation amount forecast value.

  Preferably, it is preferable to use data around noon where the power demand is less susceptible to changes in solar radiation.

  The solar power generation amount prediction system according to the present invention is a solar power generation amount prediction system for predicting the solar power generation amount of the solar power generation devices distributed and installed in the power system, and forecasts the solar radiation amount for each region. Data input means for collecting each value data, actual value, and electric energy of the power system, and using the collected data, the amount of change in unit time of solar radiation actual value is an explanatory variable, and unit time of electric energy The parameter estimation means for calculating the regression coefficient for each region or regions with the amount of change in the objective variable as the objective variable, the regression coefficient for each region calculated by the parameter estimation means, and the solar radiation forecast value And a photovoltaic power generation amount prediction means for calculating a predicted value for each region.

  In the present invention, a regression coefficient (coefficient a) is calculated from the amount of solar radiation and the amount of power, and the photovoltaic power generation amount is predicted using this regression coefficient. The unit time may be an arbitrary time such as a minute unit, but it is preferable to match the response time of the control device for stabilizing the system.

  Further, the photovoltaic power generation amount prediction means of the photovoltaic power generation amount prediction system according to the present invention calculates the confidence interval of the regression coefficient, and calculates the solar radiation amount forecast value error from the predicted value and the actual value of the solar radiation amount collected by the data input means. And calculating a prediction value including an upper limit value and a lower limit value of the amount of photovoltaic power generation based on the confidence interval of the regression coefficient and the confidence interval of the solar radiation amount forecast value error. .

  In the present invention, by calculating the predicted value as the upper and lower limit values of the confidence interval, it is possible to improve the accuracy of power supply and demand planning and output control of the control device.

  Moreover, the photovoltaic power generation amount prediction method according to the present invention is a photovoltaic power generation amount prediction method for predicting the amount of photovoltaic power generation without measuring the actual value of the amount of power generation of the photovoltaic power generation devices distributed and installed in the power system. A data input stage for collecting the forecasted and actual values of solar radiation amount for each region and the power amount data at the transmission end of the power system, and using the collected data, the unit of solar radiation amount and power amount A parameter estimation stage that calculates the regression coefficient for each region using the amount of solar radiation as the explanatory variable and the amount of power as the objective variable from the actual value of the amount of change in time, the confidence interval of the regression coefficient of arbitrary probability, and the amount of solar radiation A prediction interval that includes the confidence interval of the forecast value, and includes the calculated confidence interval value, the regression coefficient for each region calculated in the parameter estimation stage, and the upper and lower limits of photovoltaic power generation from the solar radiation forecast value Show values by region A photovoltaic power generation amount prediction step of, characterized by comprising prediction value transmission step of transmitting the predicted value of the region for each region to the controller for system stabilization having jurisdiction over the area, the.

  Further, the photovoltaic power generation amount prediction method according to the present invention is based on the predicted value of the photovoltaic power generation amount calculated in the photovoltaic power generation amount prediction stage, and determines whether to start or stop the control device, and outputs the control device. It is characterized by determining.

  In the present invention, since the determination of the start / stop of the control device and the output are determined based on the predicted upper and lower limit values, the control device can be efficiently operated.

  In the present invention, among the basic formulas for predicting the amount of photovoltaic power generation, it is possible to predict the amount of photovoltaic power generation for each region by treating the installed capacity and coefficient as a unit. It is not necessary to acquire the power generation amount, coefficient, and equipment capacity of the power generation device, and work efficiency can be improved and equipment costs can be reduced. In addition, the control device can be operated efficiently by predicting the amount of photovoltaic power generation with a range of upper and lower limits for each region.

1 is a configuration diagram of a photovoltaic power generation amount prediction system including an electric power system according to an embodiment of the present invention. It is a flowchart which shows the process outline | summary of the solar energy generation amount prediction system of FIG. It is explanatory drawing of the view of the estimation of the amount of photovoltaic power generation by embodiment of this invention. It is a conceptual diagram of the selection method of the data used for calculation of the coefficient a of the parameter estimation means of FIG. It is an example of the input data file 31 used for the parameter estimation process of the method 1 by embodiment of this invention. It is explanatory drawing which shows the relationship between temperature, the weather, and electric energy. It is an example of the input data file 31 used for the parameter estimation process of the method 2 by embodiment of this invention. It is explanatory drawing of the error distribution (FIG.8 (a)) of the solar radiation amount, and the error distribution (FIG.8 (b)) of the coefficient a. FIG. 9A is an explanatory diagram of the confidence interval of the solar radiation error, FIG. 9A is an explanatory diagram of the relationship between the weather and the solar radiation error, and FIG. 9B is the upper and lower limits of the confidence interval from the daily solar radiation error. It is explanatory drawing of the method to obtain | require. It is explanatory drawing of the confidence interval of the coefficient a of photovoltaic power generation amount prediction processing, Fig.10 (a) is a data example containing the coefficient a calculated | required as a result of parameter estimation processing, FIG.10 (b) is photovoltaic power generation. It is explanatory drawing of how to obtain | require the upper and lower limit value of the confidence interval of the coefficient a of quantity prediction processing. It is an example of the data of the confidence interval of the coefficient a preserve | saved at the parameter file 32 of FIG. It is an example of the data of the confidence interval of the solar radiation amount estimated value preserve | saved at the parameter file 32 of FIG. It is explanatory drawing of an example of how to obtain | require the control amount with respect to a supply-and-demand plan and a control apparatus from the predicted value of the upper and lower limit value of photovoltaic power generation. It is an example of the output data file 33 of FIG. It is a flowchart which shows the process sequence of the daily photovoltaic power generation amount prediction value (upper / lower limit value) calculation routine of the solar power generation amount prediction means of FIG. It is a flowchart which shows the process sequence of the hourly photovoltaic power generation amount predicted value calculation routine of the photovoltaic power generation amount prediction means of FIG.

  Embodiments of the present invention will be described below. FIG. 1 is a diagram showing a configuration of a photovoltaic power generation amount prediction system according to an embodiment of the present invention and an outline of a power system to which the system is applied.

  In this figure, the electric power generated at the power plant 5 is supplied to each household 3 that is an electric power consumer through an electric power system (including substation equipment). Each home 3 is provided with a solar power generation device 4 that usually covers part or all of the power consumption of the home while supplying surplus power to the power system. Thus, each household is a power consumer as well as a power supplier. In the present embodiment, a case where the solar power generation device 4 is provided in a general household will be described as an example, but an operator such as a factory may be used.

  The electric power system is divided into regions, and a control device 2, a thermometer 7, and a solar radiation meter 8 for power stabilization are provided in each region. The control device 2 is a generator for frequency / voltage stabilization, a secondary battery (NAS), or a fuel cell, and is connected to the power system.

  Furthermore, the thermometer 7 and the solar radiation meter 8 are also installed for each region, and measure the temperature and the solar radiation amount, respectively. In addition, the area division in which the control apparatus 2 is installed and the area division in which the thermometer 7 and the solar radiation meter 8 are installed may be the same or different.

  The photovoltaic power generation amount prediction system 1 collects power amount data from a watt hour meter 6 provided at the transmission end of a substation in the power system, and also records temperature data from a thermometer 7 and a solar radiation amount meter 8 in each region. , Collecting solar radiation data. The watt-hour meter 6 is not limited to a device that directly collects the amount of power, but also includes a device that collects electrical data that can calculate the amount of power. For example, since the electric energy can be calculated from the electrical data collected via PT and CT provided in the electric power system, PT and CT are also included in the watt-hour meter.

  In addition, weather forecasts, temperature values, and solar radiation forecast values are sequentially input from the weather trader's server 9. These data can be collected by providing a communication function to the watt-hour meter 6, the thermometer 7, and the solar radiation meter 8 and collecting the data by data communication or via a dedicated data collection device (not shown). You may make it do.

    The photovoltaic power generation amount prediction system 1 includes a watt-hour meter 6, a thermometer 7, a solar radiation meter 8, a communication device 11 that performs data communication with a server 9 of a weather trader, an arithmetic processing unit 12 that executes data arithmetic processing, and data It is comprised from the memory | storage part 13 which memorize | stores. The storage unit 13 and the arithmetic processing unit 12 can each be configured as a server.

  Further, the arithmetic processing unit 12 is a data input means 21 for storing the received data passed from the communication device 11 in the storage unit 13, and a parameter for calculating a parameter for photovoltaic power generation amount prediction based on the received various data. An estimation unit 22, a photovoltaic power generation amount prediction unit 23 that calculates a predicted value of the photovoltaic power generation amount using the calculated parameters, and a predicted value transmission unit 24 that transmits the prediction value to the control device 2 are provided. Each means 21 to 24 is a function that can be executed by the CPU as a program.

  The temperature data and solar radiation data may be input directly from the local thermometer 7 and solar radiation meter 8 via a communication line, or may be input from a weather company.

Next, the operation of the photovoltaic power generation amount prediction system 1 having the above configuration will be described.
The photovoltaic power generation amount prediction system 1 measures the electric power sent to the electric power system in units of minutes and seconds via the watt-hour meter 6. The power amount data sent from the watt hour meter 6 is received by the communication device 11 and stored in the input data file 31 of the storage unit 13 by the data input means 21 of the arithmetic processing unit 12.

Here, the amount of power collected via the data input means 21 is the amount of power generated by the solar power generation device of each home (hereinafter referred to as the amount of power consumed by each home) as shown in the following equation (2). , Referred to as “photovoltaic power generation amount”).
Electricity amount = Electricity demand amount-Solar power generation amount (2)

  On the other hand, the temperature and solar radiation amount are similarly measured in units of minutes and seconds from the thermometer 7 and solar radiation meter 8 in each region, and the measured data is received by the communication device 11 and is stored in the storage unit 13 by the data input means 21. It is stored in the input data file 31.

  In addition, about solar radiation amount data, it replaces with inputting from the solar radiation meter 8, and the coefficient and installation capacity of the solar power generation device 4 in one certain household are preserve | saved in the memory | storage part 13 of the solar power generation amount prediction system 1. Alternatively, the solar power generation amount of the household may be measured in minutes or seconds, and the solar radiation amount may be calculated from equation (1).

  The photovoltaic power generation amount prediction system 1 uses the data stored in the input data file 31 of the storage unit 13 to predict the photovoltaic power generation amount. As shown in FIG. 2, the processing procedure includes a process for estimating a solar power generation amount prediction executed by the parameter estimation unit 22 (hereinafter referred to as “parameter estimation process”), and solar power generation. It consists of the process (henceforth "solar power generation prediction process") which estimates the amount of photovoltaic power generation performed by the amount prediction means 23.

  In addition, although a weather forecast value is delivered from the server 9 of a weather company, the forecast value of solar radiation amount shall be delivered as a weather forecast value here.

<Step S1: Parameter estimation processing>
Next, the processing content of the parameter estimation means 22 of the photovoltaic power generation amount prediction system 1 will be described.

  The parameter estimation unit 22 is activated by an operator activation request or an arbitrary timing set in advance. First, the solar radiation amount data accumulated for a certain period is used to cut out a time zone in which the solar radiation amount data is instantaneously changing. (Step S1a).

  The cutting conditions include a case where the amount of solar radiation per unit time changes by a certain value or more, a certain ratio changes, or a certain frequency component or more. Data is extracted under this cutting condition.

  Next, the parameter estimation means 22 calculates the relationship between the change in the amount of solar radiation and the change in the amount of photovoltaic power generation in the time period cut out in step S1a (step S1b). At this time, if the characteristic formula (1) of photovoltaic power generation is to be used, the equipment capacity and coefficient must be obtained. However, it is not realistic for an electric power company to collect the amount of photovoltaic power generation data for each household because of the great cost. For this reason, in this embodiment, the amount of photovoltaic power generation follows the change in the amount of solar radiation very quickly, while the demand for power does not change immediately even if the amount of solar radiation changes suddenly. . That is, the total power demand shown in the graph of FIG. 3 (c) is the amount of photovoltaic power generation that changes momentarily during the day shown in FIG. 3 (b) and generated by the electric power company shown in FIG. 3 (a). It is assumed that it is the sum of the amount of electric power (that is, the amount of electric power measured by the electric energy meter 6), and the amount of photovoltaic power generation in FIG. 3 (b) is estimated based on the measured value of FIG. 3 (a) It is. In addition, since it becomes dark when the amount of solar radiation becomes small, there is a case where the light is turned on and the power demand amount changes, but in many cases the blinking of the light does not change instantaneously because it is operated by a human, and the power generated by that operation Changes are also averaged over a time range.

  As a parameter estimation method, as will be described later, a method (method 1) for estimating by measuring changes in the amount of solar radiation and the amount of power, and the amount of power, temperature, and amount of solar radiation at a predetermined time (for example, noon) are measured. There is a method (method 2) of estimating by doing. Method 2 is more effective than method 1 for a relatively large power system.

(Method 1. Estimation by measuring changes in solar radiation and electric energy)
Specifically, the coefficient a of the following equation (3) is obtained from the time data cut out in step S1a by the least square method.
△ Solar power generation amount = Coefficient a × △ Solar radiation amount (3)
Here, coefficient a = “equipment capacity” × “coefficient”. Further, Δ solar power generation amount means a change amount (hereinafter referred to as “change amount”) of the solar power generation amount with respect to previously collected data, and Δ solar radiation amount also means a change amount with respect to the previous sampling value.

  The coefficient a in the equation (3) corresponds to the product of “equipment capacity” and “coefficient” in the equation (1). There is no need to separately grasp “equipment capacity” and “coefficient” as in the past.

  FIG. 4 is a conceptual diagram of a data selection method used for calculating the coefficient a. In this figure, FIG. 4 (a1) is a graph of a daily time change in the amount of power generated by an electric power company (that is, the amount of power measured by the watt hour meter 6), and FIG. 4 (a2) is a graph of FIG. 4 (a1). It is a graph of the time change of the solar radiation amount on the same day. Usually, sampling is performed every minute, but the sampling interval is not limited to this. Collect data for multiple days.

  Next, from these data, as shown in FIG. 4 (b1) and FIG. 4 (b2), changes are calculated for the electric energy and the solar radiation amount, respectively. Here, there is a relationship of Δ power amount = −Δ solar power generation amount. Then, the amount of change in the amount of solar power generated when the amount of change in the amount of solar radiation exceeds a predetermined value is extracted. In the example of FIG. 4 (b2), the amount of change in solar power generation when the amount of change in solar radiation becomes greater than or equal to the threshold C1 and the amount of change in solar power generation when the change in solar radiation becomes less than or equal to the threshold C2 are shown. Extracted from the data of 4 (b1).

  With this extraction process, as shown in FIG. 4C, when the amount of solar radiation change is taken on the horizontal axis and the amount of power generation change is taken on the vertical axis, the extracted data is in the first and third quadrants. Since they appear densely to some extent, the coefficient a of the linear approximation formula is calculated by a method such as the least square method. Thereby, the variation | change_quantity of the photovoltaic power generation correlated with the solar radiation amount change can be calculated | required simply, suppressing processing load.

In addition, when the system range is relatively large, it is necessary to install multiple solar radiation meters.For each of the predetermined areas where the solar radiation meters are installed, multiple variables with the right side as multiple terms The coefficients a1, a2,... For each region are calculated by a method such as analysis.
△ Solar power generation = coefficient a1 x △ solar radiation 1 + coefficient a2 x △ solar radiation 2 +
... (3a)
Here, the coefficient a i and the Δ solar radiation amount i mean the coefficient a and the amount of solar radiation change in the region division i (i is a natural number), respectively. Moreover, Δ solar power generation amount means a change in solar power generation amount.

  The parameter estimation unit 22 stores the calculated coefficient a i in the parameter file 32 of the storage unit 13. In the following description, unless otherwise specified, the coefficient a means a coefficient ai of an arbitrary regional section i.

Next, the parameter estimation process will be described with a simple numerical example. FIG. 5 shows an example of an input data file 31 in which data input every minute is accumulated. In this figure, the electric energy is the electric energy on the sending side to the electric power system measured by the electric power company. The amount of solar radiation is the amount of solar radiation installed in the area. Here, the Δ electric energy means a change from the electric energy one minute ago. Note that the Δ solar power generation amount (that is, the change from the solar power generation amount one minute ago) can be generally regarded as the following equation as described above.
ΔPhotovoltaic power generation amount = −ΔPower amount Therefore, the amount of change in the solar power generation amount can be grasped by collecting data corresponding to the power change.

In the present embodiment, if only those having a solar radiation amount of 10 W / m ² or more are extracted, in the example of FIG. 5, the data of the rows of 12:02 and 12:03 are extracted. Of course, if there is data of other time when the Δ solar radiation amount has changed by 10 or more, it is also extracted at the same time.
Then, the coefficient a is obtained from the extracted data by the least square method. In the data of FIG. 5, the coefficient a is about 1.

  The “coefficient” in equation (1) and “coefficient a” in equation (3) also depend on the surface temperature of the solar panel, and the temperature can be estimated from the air temperature. Therefore, it is necessary to investigate the characteristics of the photovoltaic power generation devices installed in individual households. However, according to the method described in the following method 2, the coefficient a considering the temperature element can be calculated without conducting this investigation.

(Method 2. Estimation based on the amount of photovoltaic power generation at a given time)
The amount of power generated by the solar power generation device for each time period is estimated using the property that the power demand depends on the temperature and the weather. FIG. 6 is a diagram showing the relationship between the temperature and the amount of power (the amount of power measured by the power meter 6) when the weather is used as a parameter. When compared for each temperature, when it is clear, the amount of photovoltaic power generation is large, so the amount of power viewed from the power company is small. On the other hand, when there is rain, there is almost no photovoltaic power generation, so a large amount of power is measured. Since the weather affects the amount of solar radiation, the amount of power, temperature, and amount of solar radiation are in the relationship of the following equation (4). And the coefficient a can be calculated | required from following Formula using the data of the same time slot | zone (for example, 12:00).
Electric energy = f (temperature)-coefficient a x solar radiation amount (4)
Here, f (temperature) is usually a quadratic expression, but it may be a linear expression by dividing a range such as a region where the temperature is high or a region where the temperature is low. In addition to temperature, other factors such as humidity and calendar information may be included. The data to be used is not data in minutes / seconds, but one-hour values are preferable because they are less affected by variations.

Incidentally, when the expression (4) is described more accurately, the following expression is obtained.
Electricity amount = f (air temperature) −coefficient a × temperature correction coefficient b (temperature) × irradiation amount temperature correction coefficient b = 1− (air temperature−reference temperature) × coefficient c / 100
Here, the reference temperature and the coefficient c are coefficients that vary depending on the type of solar panel, and are not corrected when the temperature is lower than the reference temperature. However, the change in the temperature correction coefficient b is compared with the measurement error. Since it is small, there is no practical problem if the equation (4) is used.

Next, a specific example to which the expression (4) is applied will be described with a simple numerical example. FIG. 7 shows the amount of power and other input data for 1 minute from noon. A multiple regression equation is constructed with the amount of solar radiation and temperature as explanatory variables and the amount of electricity as an objective variable. Using the data of FIG. 7, the above equation (4) becomes as follows.
Electricity amount = (2 × temperature + 100) −1 × irradiation amount (5)
That is, the coefficient a is 1.

  Although this numerical example is constructed by a linear expression for simplicity, the term of temperature may be a quadratic expression, or other weather data such as humidity may be used.

  Note that the change in the amount of solar radiation affects not only the amount of photovoltaic power generation but also the above-described amount of power demand from the change in the power demand for lighting (lighting demand). In other words, the coefficient a is strictly a coefficient obtained by adding not only the amount of photovoltaic power generation but also the change in lighting demand. However, since the amount of photovoltaic power generation is generally large around 12:00 and the demand for lighting is small around 12:00, the coefficient of solar radiation 1 in the above equation (5) is regarded as almost the amount of photovoltaic power generation. Can do. Therefore, the above equation (5) can be regarded as approximating the coefficient a.

In practice, it is preferable to obtain the coefficient a for each region by the following equation (4a) corresponding to the above equation (3a).
Electric energy = f (temperature 1, temperature 2, ...)-coefficient a1 x solar radiation 1-coefficient a2 x solar radiation 2-
... (4a)
Here, the temperature i, the coefficient ai, and the amount of solar radiation i indicate the temperature, the coefficient a, and the amount of solar radiation of the region division i (i is a natural number), respectively.

<Solar power generation forecasting process>
Next, the processing content of the photovoltaic power generation amount prediction means 23 will be described.

The photovoltaic power generation amount prediction means 23 calculates the photovoltaic power generation amount for each region based on the following equation (6).
Photovoltaic power generation amount i = coefficient ai × irradiation amount i (6)
Here, i corresponds to the identification number of the region segment.

  In addition, the value delivered from a weather trader is used for the amount of solar radiation. The coefficient ai may be corrected from the difference between the sequential prediction result and the actual value. Thereby, even when a household solar power generation device is introduced in the region of the regional section i, it is possible to correct the sequential coefficient ai and predict the solar power generation amount with high accuracy.

Next, how to obtain the upper and lower limit values of the predicted value will be described.
The error factors in the prediction of the amount of photovoltaic power generation can be classified into two categories: “meteorological forecast error” and “prediction model error” (that is, error of coefficient a). Therefore, the upper and lower limit values of the photovoltaic power generation amount prediction are obtained from these two error factors.

  First, the weather forecast error will be described. The distribution of the error (forecast value−actual value) is obtained from the weather forecast (irradiation amount) and the actual weather value accumulated in the past. The distribution of errors in the amount of solar radiation is a graph having a shape as shown in FIG. The reason is that when it is predicted to be sunny, the amount of solar radiation does not exceed that, and an error always occurs only in the rain direction (see FIG. 9A for an example of data).

  In FIG. 9 (a), when the actual value of the solar radiation amount at a fixed time on a plurality of days is u and the predicted value of the solar radiation amount is v, the solar radiation amount error (v−u) is calculated, and this solar radiation amount error data Will accumulate.

  Next, an error range that falls within a confidence interval of, for example, ± 95% is determined for each weather (sunny, cloudy, rainy) from the accumulated solar radiation amount error data. The 95% confidence interval is a range excluding 2.5% in which the error deviates downward and 2.5% in which the error deviates upward. Of course, other confidence intervals such as 99% may be obtained.

  If there is sunny data for 200 days, as shown in Fig. 9 (b), the solar radiation errors are arranged in order of magnitude, and the solar radiation for 190 days excluding the upper 5 days and the lower 5 days is solar radiation. The data is within the 95% confidence interval of the quantity error. In the example of FIG. 9B, the data on the upper side of the 95% confidence interval of the solar radiation amount error is 13, and the data on the lower side of the 95% confidence interval of the solar radiation amount error is 1.

  Next, the error of the prediction model is obtained. The error of the prediction model means a confidence interval of the coefficient a. The coefficient a obtained by the above-described parameter estimation processing method 1 or method 2 is an average value obtained from a large amount of sample data. When the distribution of the coefficient a obtained for each sample data is obtained in the same manner, the shape shown in FIG. The confidence interval can be obtained in the same manner as described above (see FIG. 10A for a numerical example).

  In FIG. 10A, Δ solar power generation amount is a value obtained by subtracting the current value from the previous value of the electric energy, and Δ solar radiation amount is a value obtained by subtracting the previous value from the current value of the solar radiation amount. Then, Δ solar power generation amount / Δ solar radiation amount, that is, coefficient a is calculated for each sampling time.

As a simple calculation method of the coefficient a, if the coefficient a is obtained from 200 pieces of data, as shown in FIG. 10 (b), the coefficients a are arranged in the order of magnitude, The data for 190 pieces excluding the lower five pieces is the data within the 95% confidence interval of the coefficient a. In the example of FIG. 10B, the upper data of the 95% confidence interval of the coefficient a is 1.15, and the lower data is 0.84. FIGS. 10A and 10B show the coefficient a obtained by the method 1, but the confidence interval can be obtained by the same process for the coefficient a obtained by the method 2.
When the coefficient a is a normal distribution, it can be calculated by the following arithmetic expression.

Confidence interval above 95% confidence interval of coefficient a = average value of coefficient a + standard deviation of coefficient a x 2
Lower confidence interval of 95% confidence interval of coefficient a = average value of coefficient a-standard deviation of coefficient a x 2

The calculated confidence interval of the coefficient a and the confidence interval of the solar radiation amount forecast value are stored in the parameter file 32 of the storage unit 13. FIG. 11 shows a data example of the confidence interval of the coefficient a, and FIG. 12 shows a data example of the confidence interval of the solar radiation amount forecast value.
From the above, the upper and lower limit values of the predicted value of the amount of photovoltaic power generation can be obtained from the following equation.

Upper limit of photovoltaic power generation amount = confidence interval on the upper side of (equipment capacity x coefficient) x confidence interval on the upper side of forecasted solar radiation value = confidence interval on the upper side of coefficient ai x (predicted solar radiation amount for region division i-solar radiation error) Upper confidence interval)

Lower limit of photovoltaic power generation amount = lower confidence interval of (equipment capacity x coefficient) x lower confidence interval of forecasted solar radiation value = lower confidence interval of coefficient ai x (irradiance forecast value of region division i- Lower confidence interval for solar radiation error)

  The upper and lower limit values of the calculated photovoltaic power generation amount are stored in the output data file 33 of the storage unit 13.

  Based on the upper limit value and the lower limit value of the predicted value of the photovoltaic power generation amount, the control device 2 that is operated so as to absorb the fluctuation of the power generation amount including a predetermined margin at least in the range of the upper and lower limit values. To the standby state. For example, on the day before the operation day, the predicted value of the upper and lower limit of the photovoltaic power generation amount of the next day (operation day) is calculated, and the control device 2 in the stopped state so that the amount of power in the range of the upper and lower limit value can be supplied. To the standby state.

  Further, based on the lower limit value of the photovoltaic power generation amount, the power generation amount of a thermal power generator (generator for base load operation) having high efficiency but slow output fluctuation is determined. Specifically, in FIG. 13 (a), the power supply amount in the region (B) lower than the lower limit value of the predicted amount of photovoltaic power generation is the planned supply and demand value of the generator for base load operation that does not consider solar power generation. Deduct from. Further, the range (A) from the lower limit value of the photovoltaic power generation amount to the upper limit value is the control device 2, that is, a small generator (distributed power source), fuel cell, or secondary battery (with low output fluctuation, although the efficiency is not high. NAS) and the standby amount of the control device 2 such as a hydroelectric generator. Thus, by obtaining the upper limit value and the lower limit value of the amount of photovoltaic power generation, a power generation plan with high energy efficiency as a whole becomes possible.

  Moreover, in the operational state of the day, as shown in FIG. 13 (b), the upper limit value y (t) of the predicted amount of photovoltaic power generation at time t was calculated from equation (6) based on the amount of solar radiation at that time. A value obtained by subtracting the actual value z (t) is stored in the output data file 33 of the storage unit 13 and set as a target value of the control device 2. At this time, when the upper limit and the lower limit exist in the coefficient ai used in the equation (6), the median value or the average value is used. The calculation of the target output is performed in units of regions that the control device has jurisdiction over. The calculation result is stored in the output data file 33 shown in FIG. This output data file 33 is created for each region, and the weather input data for the day (sunny, cloudy, rain, etc.) and the solar radiation amount forecast value for each time are input in advance by the data input means 21.

Hereinafter, the processing procedure of the above-mentioned photovoltaic power generation amount prediction means 23 will be described in detail with reference to FIGS. 15 and 16.
When the solar power generation amount prediction value (upper / lower limit value) calculation routine of the solar power generation amount prediction means 23 is started at a certain time before sunrise or the previous day of the day, first, the parameter file 32 is accessed. Then, confidence interval data (lower limit value, upper limit value) of the solar radiation amount error corresponding to the weather of the day is extracted (S103). Then, the upper limit value of the solar radiation amount error confidence interval is subtracted from the solar radiation amount predicted value at each time stored in the output data file 33, and the subtraction result is stored as the lower limit value of the solar radiation amount predicted value in the output data file 33 ( S104). Similarly, a value obtained by subtracting the lower limit value of the solar radiation amount error confidence interval from the predicted solar radiation amount at each time is stored as the upper limit value of the predicted solar radiation amount in the output data file 33 (S105). Note that the weather forecast data may change during the day. If the weather forecast data changes, the parameter file 32 indicates the solar radiation amount error confidence interval data corresponding to the new weather forecast data from the switching time. And the upper and lower limit values of the predicted solar radiation amount are calculated using this data.

  Next, the photovoltaic power generation amount predicting means 23 multiplies the lower limit value of the predicted solar radiation amount by the lower limit value of the confidence interval of the coefficient a stored in the parameter file 32, thereby calculating the predicted photovoltaic power generation amount. Is stored in the output data file 33 (S106). Similarly, the photovoltaic power generation amount predicting unit 23 multiplies the upper limit value of the predicted solar radiation amount by the upper limit value of the confidence interval of the coefficient a stored in the parameter file 32 to obtain the predicted photovoltaic power generation amount value. Is stored in the output data file 33 (S107). The above processing is executed for all times and regions.

  When the above-described processing is completed, the photovoltaic power generation amount predicting unit 23 transmits the lower limit value for each region of the photovoltaic power generation amount predicted value for each region to the central power supply command station (not shown) via the predicted value transmitting unit 24 (S108). ). At the Central Power Supply Command Center, the lower limit value for each hour of the predicted amount of photovoltaic power generation received from the planned supply and demand values for generators for base load operation in each region (the demand and supply plan values not considering the amount of photovoltaic power generation) Subtract and calculate the supply and demand plan value considering the amount of photovoltaic power generation. The generator for base load operation is operated according to this supply and demand plan value.

  Thereafter, during the day of the day, the actual solar radiation amount value is input from time to time via the data input means 21, and the input data is stored as the actual solar radiation amount value in the output data file 33.

  The hourly solar power generation amount prediction value calculation routine of the solar power generation amount prediction means 23 is started every time the input of the solar radiation amount actual value at each sampling time is completed, and the coefficient a of the parameter file 32 is added to the input solar radiation amount actual value. The solar power generation amount (z) based on the actual solar radiation amount on that day is calculated by multiplying the median values of the confidence intervals and stored in the output data file 33 (S202). And the photovoltaic power generation amount prediction means 23 calculates the difference (yz) between the upper limit value (y) of the predicted photovoltaic power generation amount and the photovoltaic power generation amount (z), and outputs this difference value as output data. The file 33 is stored (S203).

  This difference (yz) is transmitted to the control device 2 side having jurisdiction over the area via the predicted value transmitting means 24 (S205). The control device 2 secures standby capacity based on the received difference value.

  Moreover, the photovoltaic power generation amount prediction means 23 calculates the difference between the predicted value of the photovoltaic power generation amount at the next time and the actual value of the current photovoltaic power generation amount (S204), and uses this calculation result as an output command value. It transmits to the control apparatus 2 which has jurisdiction over the area via the predicted value transmission means 24 (S205). The control device 2 changes the output based on this output command value.

  According to the present embodiment, the amount of photovoltaic power generation can be predicted for each region with the upper and lower limits, so that the control device can be operated efficiently. Moreover, since it is not necessary to acquire the power supply amount, coefficient, and equipment capacity of the solar power generation device provided in each home, it is possible to improve work efficiency and reduce equipment costs.

  The present invention is not limited to the above-described embodiment, and can be implemented with various modifications without departing from the scope of the invention. For example, the photovoltaic power generation amount prediction system may calculate the supply and demand plan and the target output to the control device in combination with not only photovoltaic power generation but also other power adjustment values such as wind power generation. In addition, the photovoltaic power generation amount prediction system can be realized as a dedicated device for executing the photovoltaic power generation amount prediction processing, but for example, it is realized as a part of the supply and demand plan calculation function of the central power supply command station. It is also possible to realize it as a unit with other functions.

1 Photovoltaic power generation prediction system 2 Control device 3 Home (electric power consumer)
DESCRIPTION OF SYMBOLS 4 Solar power generation device 5 Power plant 6 Electricity meter 7 Thermometer 8 Solar radiation meter 9 Meteorological server 11 Communication device 12 Arithmetic processing unit 13 Storage unit 21 Data input means 22 Parameter estimation means 23 Solar power generation amount prediction means 24 Predicted value transmission means 31 Input data file 32 Parameter file 33 Output data file

Claims (6)

A photovoltaic power generation amount prediction system for predicting the amount of photovoltaic power generation of photovoltaic power generation devices distributed in a power system,
A data input means for collecting each data of temperature, forecast value and actual value of solar radiation amount and electric power amount of electric power system for each region,
Of the collected data, when the temperature, the solar radiation actual value, and the electric energy at substantially the same time for a plurality of different days are used as the explanatory variables and the electric energy is the target variable Parameter estimation means for calculating the regression coefficient of each region or regions,
A solar power generation amount prediction means for calculating a prediction value of the solar power generation amount for each region from the regression coefficient for each region calculated by the parameter estimation means and the solar radiation amount forecast value;
A photovoltaic power generation amount prediction system characterized by comprising: A photovoltaic power generation amount prediction system for predicting the amount of photovoltaic power generation of photovoltaic power generation devices distributed in a power system,
A data input means for collecting the forecast value and actual value of solar radiation amount for each region and the power amount data of the power system;
Using each collected data, the regression coefficient is calculated for each region or regions where the amount of change in unit time of solar radiation actual value is used as an explanatory variable and the amount of change in unit time of power is used as a target variable. Parameter estimation means for
A solar power generation amount prediction means for calculating a prediction value of the solar power generation amount for each region from the regression coefficient for each region calculated by the parameter estimation means and the solar radiation amount forecast value;
A photovoltaic power generation amount prediction system characterized by comprising:   The photovoltaic power generation amount prediction means calculates a confidence interval of the regression coefficient and calculates a confidence interval of a solar radiation forecast value error from the forecast value and actual value of the solar radiation amount collected by the data input means, and the regression coefficient The solar power generation according to claim 1 or 2, wherein a predicted value including an upper limit value and a lower limit value of the solar power generation amount is calculated based on the confidence interval of the solar power generation amount and the confidence interval of the solar radiation amount forecast value error. Quantity prediction system.   It is provided with a predicted value transmission means for transmitting the predicted value of the photovoltaic power generation amount calculated by the photovoltaic power generation amount prediction means for each region to a control device for system stabilization having jurisdiction over the region. The photovoltaic power generation amount prediction system according to any one of claims 1 to 3.   5. The photovoltaic power generation amount prediction according to claim 4, wherein a target value of the output of the control device is calculated based on a predicted value of the photovoltaic power generation amount calculated by the photovoltaic power generation amount prediction unit. system. A photovoltaic power generation amount prediction method for predicting the amount of photovoltaic power generation without measuring the actual value of the amount of power generation of the photovoltaic power generation devices distributed in the power system,
A data input stage that collects forecast and actual values of solar radiation for each region and data on the power consumption at the transmission end of the power system,
Using each collected data, parameter estimation to calculate the regression coefficient for each region using the amount of solar radiation as the explanatory variable and the amount of power as the objective variable from the actual values of the amount of solar radiation and the amount of change in unit time Stages,
Calculate the confidence interval of the regression coefficient and the prediction value of the solar radiation amount with an arbitrary probability, and also calculate the confidence interval value, the regression coefficient for each region calculated in the parameter estimation step, and the solar radiation amount prediction value. A photovoltaic power generation prediction stage for calculating a predicted value including an upper limit value and a lower limit value of the solar power generation amount for each region;
A predicted value transmission stage for transmitting the predicted value of the region for each region to a control device for system stabilization that has jurisdiction over the region,
A method for predicting a photovoltaic power generation amount, comprising determining whether the control device is activated or stopped and determining an output of the control device based on the predicted value calculated in the photovoltaic power generation amount prediction step.