JP2012023816A - Information processing equipment, and program thereof - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing equipment capable of easily estimating a power generation quantity from a power generation facility utilizing natural energy.SOLUTION: This information processing equipment includes a similar day selecting means 11 to select at least two past days having weather transition similar to that of an object day for estimating a power generation quantity from weather information indicating transition of past weather memorized in an information storage part 10, a time series data producing means 12 to obtain the power generation quantity data of the day selected by the similar day selecting means 11 from the information storage part 10, and produce the power generation quantity data obtained as time series data at intervals of a fixed time, a mathematical model producing means 13 to produce a mathematical model at least indicating correlation of the power generation quantity between times of the day selected by the similar day selecting means 11 by using the time series data, and a scenario producing means 14 to determine the probability distribution of the power generation quantity at the intervals of the fixed time by applying the mathematical model to a prescribed probability density function and produce a plurality of power generation quantity scenarios indicating the estimated value of the power generation quantity of a power generation facility at the intervals of the fixed time by producing a plurality of sets of random numbers based on the probability distribution.

Description

  The present invention relates to the power generation amount of a power generation facility using natural energy in an electric power system including at least one power generation facility (for example, a solar power generation facility, a wind power generation facility, etc.) using natural energy (or also referred to as renewable energy). The present invention relates to an information processing apparatus capable of prediction.

  Power generation facilities that use natural energy have the advantages of low fuel costs and low greenhouse gas emissions, but on the other hand, the power generation amount is uncertain and predictable because it depends heavily on weather conditions. Is difficult.

  As related representative techniques, Patent Documents 1 to 3 shown below can be cited.

JP 2004-289918 A JP 2007-228676 A JP 2004-72900 A

  In Patent Document 1, the amount of power generated by a power generation facility that uses natural energy is predicted based on weather information, and the shortage is compensated by the amount of power generated by another power generation facility. Not.

  Moreover, if the balance between the power generation amount and the demand amount is not maintained, the power system becomes unstable, such as the frequency fluctuating. For this reason, when natural energy is introduced, special care is required in managing the power system.

  A power generation plan is defined as the timing of starting, stopping, and increasing or decreasing the output of one or more power generation facilities over a certain period. For example, when there is one power generation facility, the power generation facility is always operated as long as power demand is present, and the output thereof may be adjusted according to the power demand. Further, when there are a plurality of power generation facilities, it is necessary to determine which power generation facility is used when. Normally, power generation facilities with low power generation costs are used preferentially. Moreover, when using power generation equipment such as thermal power generation, not only fuel costs are incurred during operation of the power generation equipment, but also costs for starting up the power generation equipment. Therefore, when there are a plurality of power generation facilities, it is not easy to determine when to use which power generation facility, and a complicated mathematical programming problem arises.

  When operating a power system having a plurality of power generation facilities, whether large or small, it is necessary to generate a power generation plan for the next day at least on the previous day. In this case, for example, an optimal power generation plan (or start / stop plan) of the power generation facility is generated based on the prediction result of the power demand on the next day. For this purpose, for example, mathematical programming methods such as dynamic programming, Lagrange relaxation, and quadratic programming are used. Patent document 2 is mentioned as a prior art in such a field | area.

  However, Patent Document 2 considers fluctuations in demand, but in generating individual power generation plans, power generation plans are generated with respect to fixed demand fluctuations. For this reason, when a power generation facility using natural energy is included in the power system, it is difficult to generate a power generation plan for the next day because the amount of power generation necessary to maintain a balance between supply and demand is not determined.

  The simplest solution to this is to consider power generation facilities that use natural energy as negative demand. This solution may be used when there is only one power generation facility using natural energy and it can be assumed that the demand does not fluctuate. However, when there are a plurality of power generation facilities using natural energy or when there is a demand fluctuation, it is difficult to use this solution because it is necessary to consider a combination of all fluctuations. Moreover, even when the power generation amount of the power generation facility using natural energy exceeds the demand amount, the power generation amount of the power generation facility capable of controlling the power generation amount becomes negative, so this method cannot be used.

  As described above, it is difficult to predict the power generation amount of the power generation facility using natural energy including the demand fluctuation and generate the most appropriate power generation plan by the conventional method.

  In addition, as another solution when using power generation equipment that uses natural energy, a storage battery with a sufficiently large capacity is used, and the output of the storage battery is controlled to generate power using the charge / discharge amount of the storage battery and natural energy. One method is to keep the total amount of power generated by the equipment constant. Patent document 3 is mentioned as a prior art in such a field | area.

  In patent document 3, the inflow / outflow (tidal current) of the electric power from another electric power system is used, and its own power generation equipment is not used. In other words, power supply is procured and transmitted as required to maintain a balance between supply and demand, and generation of the power generation plan is left to others.

  Since it is difficult to predict the amount of power generated by a power generation facility that uses natural energy, there is a way of giving up the prediction of the previous day and minimizing fluctuations in demand by using the storage battery and the control technology on the day of power generation. However, even if it is basically difficult to predict, it is necessary to generate the best power generation plan by predicting the power generation amount as much as possible the day before.

  In addition, in the conventional technology, for example, when predicting the amount of photovoltaic power generation, basically, the amount of solar radiation is predicted, and complex astronomical calculations and geometric calculations are performed. The amount of power generation had to be predicted by evaluating the efficiency of the. Similarly, in the case of predicting the power generation amount of wind power generation, the power generation amount has to be predicted from the wind conditions of the weather forecast.

  As described above, in a power system including a power generation facility that uses natural energy, it is difficult to make a prediction in consideration of an error when a power generation amount is predicted on the previous day or the like.

  This invention is made | formed in view of the said situation, and it aims at providing the information processing apparatus which can estimate easily the electric power generation amount from the power generation equipment using natural energy.

    An information processing apparatus according to an aspect of the present invention is an information processing apparatus that manages a power system including at least one power generation facility that uses natural energy. Weather information that indicates past weather changes and past power generation facilities. An information storage unit as a storage medium that stores at least power generation amount data indicating the transition of power generation amount, and a target of power generation amount prediction from weather information indicating past weather transitions stored in the information storage unit A similar day selection unit that selects at least two past days with similar weather transitions, and the amount of power generated on the day selected by the similar day selection unit is acquired from the information storage unit and acquired. Time-series data generating means for generating the power generation amount data as time-series data for every fixed time, and the time of the day selected by the similar date selecting means using the time-series data A mathematical model generating means for generating a mathematical model showing at least the correlation of the power generation amount, and applying the mathematical model to a predetermined probability density function to determine a probability distribution of the power generation amount at a constant time and generating a random number according to the probability distribution Scenario generating means for generating a plurality of power generation scenarios indicating the predicted value of the power generation per fixed time of the power generation facility by generating a plurality of sets.

  ADVANTAGE OF THE INVENTION According to this invention, the information processing apparatus which can estimate easily the electric power generation amount from the power generation equipment using natural energy can be provided.

The figure which shows an example of the hardware constitutions of the electric power system common to each embodiment of this invention. 2 is a functional block diagram illustrating an example of functions of the information processing apparatus according to the first embodiment of the present invention. FIG. The figure which shows an example of the method of classifying the weather information which shows transition of the past weather according to a weather using a classification algorithm. The graph which shows an example of distribution of the global solar radiation amount of Tokyo at 12:00 of a certain year. The graph which shows an example of the relationship between the time of the global solar radiation amount of Tokyo of a certain year. The graph which shows an example of the total solar radiation amount for every fixed time of Tokyo in December of a certain year. The graph which shows an example of the total solar radiation amount for every fixed time only on the day of "sunny" or "sunny" in Tokyo in December of a certain year. 2 is a diagram showing two examples of specific operations of the information processing apparatus according to the embodiment. FIG. The graph which shows an example of the electric power generation scenario which the information processing apparatus which concerns on the same embodiment produces | generates. The graph which shows an example of the demand amount scenario which the information processing apparatus which concerns on the same embodiment produces | generates. It is a functional block diagram which shows an example of the function of the information processing apparatus which concerns on the embodiment, and is a figure which shows an example which produces | generates the electric power generation amount scenario of a wind power generation facility. The functional block diagram which shows an example of a structure of the information processing apparatus which concerns on the 2nd Embodiment of this invention. The figure which shows an example of the specific operation | movement of the information processing apparatus which concerns on the embodiment. The graph which shows an example of the required electric power generation scenario which the information processing apparatus which concerns on the embodiment produces | generates. The functional block diagram which shows an example of a structure of the information processing apparatus which concerns on the 3rd Embodiment of this invention.

  Embodiments of the present invention will be described below with reference to the drawings.

(First embodiment)
First, a first embodiment of the present invention will be described with reference to FIGS.

  FIG. 1 is a diagram illustrating an example of a hardware configuration of a power system common to the embodiments of the present invention.

  An information processing apparatus 1 applied to a power system common to the embodiments of the present invention includes a CPU 2, a memory 3, an input device 4, and a display device 5. Various power generation facilities connected to the power system 100, demand Can communicate various data and commands with facilities, storage batteries, etc. (Acquire power generation data indicating changes in past power generation from various power generation facilities, and acquire demand data indicating changes in past demand from demand facilities. Or a command for instructing charging / discharging to the storage battery based on the power generation plan). The various power generation facilities are classified into power generation facilities such as solar power generation 111 to 11M using natural energy and wind power generation 121, and other power generation facilities 131 to 13N (for example, thermal power generation and nuclear power generation). In addition, the power system 100 is provided with a storage battery 141 that charges and discharges power generated from a power generation facility that uses natural energy, and a power converter 151 that converts power from direct current to alternating current. The power supplied to the power system 100 is supplied to the demand facility 161.

  Although FIG. 1 shows a case where there are a plurality of power generation facilities that use natural energy, in this first embodiment, natural energy is used in order to make the basic part of the technology easy to understand at first. Description will be made assuming that the power generation facility is, for example, only one “solar power generation facility”.

  FIG. 2 is a functional block diagram illustrating an example of functions of the information processing apparatus according to the first embodiment of the present invention.

  The information processing apparatus 1 according to the first embodiment of the present invention includes an information storage unit 10, a similar date selection unit 11, a time series data generation unit 12, a mathematical model generation unit 13, and a scenario generation unit 14.

  The information storage unit 10 is a storage medium that stores at least weather information indicating the transition of past weather and power generation amount data indicating the transition of the past power generation amount of the power generation facility using natural energy. The data stored in the information storage unit 10 is not limited to the above-described data. For example, demand data indicating the demand amount of demand equipment connected to the power system 100, and power generation using natural energy included in the power system 100 The necessary power generation amount data indicating the power generation amount of facilities other than the facilities (here, including a storage battery or the like) is included. The weather information indicating the transition of past weather stored in the information storage unit 10 is classified according to the weather using, for example, a classification algorithm as shown in FIG. The classification algorithm shown in FIG. 3 uses the announcement of the Japan Meteorological Agency, etc. to classify frequently occurring weather patterns as class 1 to class 3 along the time of the day, About 5 to 20 kinds of high weather patterns are listed, and the weather information is classified by attaching a pattern number to each of the listed weather patterns. In classification 1 and classification 3, weather classification such as “clear”, “clear”, “cloudy”, “rain”, “snow” is performed, and in “category 2”, “sometimes”, “after”, “temporary” , “One” and other conditions are classified. In category 4, the results of categories 1 to 3 are used to classify and list weather patterns with a high frequency of appearance such as “sunny”, “sunny”, “sunny and cloudy”. The classification algorithm shown in FIG. 3 is an example and is not limited to this. Specific examples of other classification algorithms include cluster analysis and support vector machines.

  The similar date selection unit 11 uses a past date (for example, a past date) in which the transition of the meteorological amount is similar to the day that is the target of the prediction of the power generation amount, based on the weather information indicating the past meteorological change stored in the information storage unit 10. It has a function to select at least two days in a certain period of days that have the most similar weather pattern of the day or the most similar change in the amount of solar radiation per day (hereinafter referred to as “similar days”). . For example, the similar day selection unit 11 selects a similar day by selecting a weather pattern number similar to the day on which the power generation amount is predicted from the weather pattern with the pattern number shown in FIG. . In addition, in the classification of similar days, it is possible to perform more detailed classification based on the cloud data indicating the cloud amount announced by the Japan Meteorological Agency and the clear index (= surface solar radiation amount / external solar radiation amount). In addition, as the number of days for selecting similar days increases, the power generation amount prediction accuracy described later increases.

  The time-series data generation unit 12 acquires the power generation amount data for the day selected by the similar day selection unit 11 from the information storage unit 10, and acquires the generated power generation data for every predetermined time (for example, “every hour”, “ For example, “every minute” or the like). The specific processing of the time series data generation unit 12 will be described later.

  The mathematical model generation unit 13 has a function of generating a mathematical model that indicates at least the correlation of the power generation amount between times on similar days, using the time series data generated by the time series data generation unit 12. A specific example of the mathematical model to be generated includes a variance-covariance matrix described later.

  The scenario generation unit 14 applies the mathematical model generated by the mathematical model generation unit 13 to a predetermined probability density function to determine the probability distribution of the power generation amount at a certain time, and at the same time random numbers according to the determined probability distribution By generating a plurality of power generation scenarios indicating the predicted value of the power generation amount per fixed time of the power generation facility using natural energy. A specific calculation formula using the probability density function will be described later.

  In addition to the above-described functions, the information processing apparatus 1 shown in FIG. 2 obtains the theoretical upper limit of the amount of photovoltaic power generation from the information storage unit 15 that stores astronomical data indicating the position of the sun and the solar panel characteristics. A theoretical upper limit generation unit 16 and a parameter generation unit 17 that generates a probability density function parameter for determining the probability distribution are included. The information storage unit 15, the theoretical upper limit generation unit 16, and the parameter generation unit 17 are not necessarily included in the information processing apparatus 1 according to the embodiment. Further, the data stored in the information storage unit 15 is not limited to that described above.

  Next, with reference to FIGS. 4-7, the relationship between the fluctuation | variation of the total solar radiation amount and the fluctuation | variation of the electric power generation amount of a solar power generation facility is demonstrated.

  FIG. 4 is a graph showing an example of the distribution of global solar radiation in Tokyo at 12:00 in a certain year, with “date” on the horizontal axis and “global solar radiation” on the vertical axis.

  As shown in the figure, there is an upper limit to the amount of solar radiation, and the distribution is randomly distributed from the upper limit to the lower limit (0). Examples of factors that cause such a random distribution include a change in cloud amount.

  FIG. 5 shows an example of the relationship between global solar radiation between Tokyo times in a certain year, with “9 o'clock global solar radiation” on the horizontal axis and “12 o'clock global solar radiation” on the vertical axis. It is a graph which shows.

  As shown in the figure, as the amount of solar radiation at 9 o'clock increases, the amount of solar radiation at 12 o'clock also increases. For this reason, it can be said that there is a correlation between the amount of solar radiation between times.

  FIG. 6 is a graph showing an example of the amount of global solar radiation at regular intervals in Tokyo in December of a certain year, with “time” on the horizontal axis and “total solar radiation” on the vertical axis.

  In FIG. 7, the horizontal axis is “time”, and the vertical axis is “total solar radiation”. It is a graph which shows an example of the amount of solar radiation.

  As shown in FIG. 6 and FIG. 7, there is an upper limit to the total solar radiation amount, and it fluctuates greatly during the day (from 6 o'clock to 18 o'clock). As a factor that the amount of global solar radiation greatly fluctuates during the day in this way, for example, a change in cloud amount at certain time intervals and the like can be mentioned.

  4 to 7 show that there is an upper limit to the amount of global solar radiation, and there is a correlation between the amount of solar radiation between times. On the other hand, it is possible to roughly predict the change in cloud cover at the stage the day before the prediction, but it is difficult to accurately predict the cloud cover at a certain point in time, and it is necessary to make a prediction that takes into account errors. It becomes. For this reason, it is appropriate to perform prediction using a probability density function that can take error into account. Further, FIG. 7 shows only “sunny” or “sunny” days, but it is very difficult to accurately observe the global solar radiation on other weather days (for example, “rainy”, “cloudy”, etc.). is there. Therefore, in the information processing apparatus 1 according to the embodiment, the global solar radiation amount is not deterministically predicted but is predicted including the variation of the global solar radiation amount.

  Next, with reference to FIG. 8, two examples of specific operations of the information processing apparatus 1 according to the embodiment will be described.

  FIG. 8A is a diagram illustrating an example of an operation until the information processing apparatus 1 generates a power generation amount scenario indicating a predicted value of the power generation amount for each fixed time of the power generation facility using natural energy.

  First, the similar date selection unit 11 selects at least two similar dates from the information storage unit 10 (step S101).

  Next, the time-series data generation unit 12 acquires the power generation amount data on the selected similar day from the information storage unit 10, and generates time-series data for every fixed time from the acquired power generation amount data on the similar day (step S102). ).

  Next, the mathematical model generation unit 13 generates a mathematical model indicating the correlation of the amount of power generation between times from the generated time series data (step S103).

  Finally, the scenario generation unit 14 applies the generated mathematical model to a predetermined probability density function to determine the probability distribution of the amount of power generation per fixed time, and generates a plurality of sets of random numbers according to the probability distribution. A plurality of power generation scenarios indicating the predicted value of the power generation per certain time of the power generation facility using the power generation are generated (step S104).

  FIG. 8B shows an operation for generating a power generation amount scenario indicating the predicted value of the power generation amount for each fixed time of the power generation facility using natural energy from the global solar radiation amount data, as in FIG. 8A. It is a figure which shows an example.

  First, the similar date selection unit 11 selects at least two similar dates from the information storage unit 10 (step S201).

  Next, the time-series data generation unit 12 acquires the global solar radiation amount data on the selected similar day from the information storage unit 10, and generates time-series data at regular intervals from the acquired global solar radiation amount data on the similar day (Step S202).

  Next, the mathematical model generation unit 13 generates a mathematical model indicating the correlation of the amount of solar radiation between times from the generated time series data (step S203).

  Next, the scenario generation unit 14 applies the generated mathematical model to a predetermined probability density function to determine the probability distribution of the global solar radiation amount for every fixed time, and generates a plurality of random number sets according to the probability distribution. A plurality of global solar radiation amount scenarios indicating the predicted values of the global solar radiation amount for each hour are generated (step S204).

  Finally, the scenario generation unit 14 performs conversion from the total solar radiation amount to the power generation amount for the total solar radiation amount scenario, so that the power generation amount indicating the predicted value of the power generation amount for each fixed time of the power generation facility using natural energy is shown. A plurality of scenarios are generated (step S205).

  Next, a specific example of processing performed by the time series data generation unit 12, the mathematical model generation unit 13, and the scenario generation unit 14 will be described.

The time-series data generation unit 12 acquires the power generation amount data on the similar date selected by the similar date selection unit 11 from the information storage unit 10, and summarizes the acquired power generation amount data, for example, every hour. The power generation amount data {x _t } obtained by collecting the power generation amount data for every hour of the day is generated. Further, the time series data generation unit 12 generates hourly time series data for N days (24 hours × N days) by arranging the generated power generation amount data {x _t } from x ₁ to x _24. To do. Note that “t” described above is a variable indicating time, and “N” described above is an integer of 2 or more indicating the number of days of the selected similar day. In addition, the time series data in the embodiment is generated as data for every hour of the acquired power generation amount data, but is not limited to this, and is generated as data for every minute or even shorter time. You may make it do.

The mathematical model generated by the mathematical model generation unit 13 is generated, for example, as in the following equation (1).

The var (x _t ) shown in the above equation (1) represents the variance of the power generation amount data {x _t } obtained from the time series data generation unit 12, and cov (x _t , x _u ) is the power generation amount. The covariance between the data {x _t } and the power generation amount data {x _u } is represented. The fluctuation range of the power generation amount at each time is indicated by the variance of each time, and the correlation of the power generation amount between the times is indicated by the covariance. In addition, “u” described above is a variable indicating a time different from time t, and “{x _u }” indicates power generation amount data at time u. In order to obtain such variance and covariance in the mathematical model generation unit 13, it is necessary to select at least two similar days, and the larger the number of similar days to be selected, the higher the prediction accuracy of the power generation amount. .

The probability distribution determined by the scenario generation unit 14 is determined using, for example, a probability density function as shown in the following equation (2).

In the equation (2), μ represents an average vector of x μ = (μ ₁ , μ ₂ , μ ₃ ,..., Μ ₂₄ ), and T represents a transposed matrix. Further, since the power generation amount data {x _t } for one day can be regarded as a 24-dimensional vector, an arbitrary number of scenarios can be generated by generating a large number of sets of 24 random numbers according to the equation (2). Is possible. The scenario generated in this way reproduces the statistical properties of similar days. For example, a method described in Japanese Patent Application No. 2009-57682 may be used to generate a set of random numbers.

  The probability distribution used in the scenario generation unit 14 is not limited to the multidimensional normal distribution shown in the above equation (2). For example, a log normal distribution, an exponential distribution, a Weibull distribution, a beta distribution, or the like can be used. The probability distribution can also be determined by function approximation from the power generation amount data acquired from the information storage unit 10.

  Hereinafter, a case where the scenario generation unit 14 uses a beta distribution as a specific example using a distribution other than the multidimensional normal distribution will be described.

The probability distribution function of the beta distribution is shown in the following equation (3).

  As with solar power generation, the beta distribution is based on astronomical data where the lower limit of power generation is 0 and the upper limit of power generation indicates the position of the sun, etc., the positional relationship between the sun and the solar power generation panel, and the power generation efficiency. It is used when the upper limit and the lower limit are determined, and it is possible to improve the accuracy of fitting.

  In the equation (3), B (p, q) is a beta function, a is a lower limit of the distribution, b is an upper limit of the distribution, and p and q are parameters for determining the distribution.

  In addition, when using the beta distribution according to the equation (3), by first generating a plurality of random numbers according to the beta distribution and then applying the above-described variance-covariance matrix, the power generation amount at least between the times is applied to the plurality of random numbers. You may make it have correlation of.

  Next, a scenario generated by the information processing apparatus 1 will be described with reference to FIGS. 9 and 10.

  FIG. 9A is a graph showing an example of transition of past power generation amount stored in the information storage unit 10 with “time” on the horizontal axis and “power generation” on the vertical axis. The amount of power generation data for the day is shown. FIG. 9B shows a scenario of the power generation amount generated by the information processing apparatus 1 in the embodiment, with “time” on the horizontal axis and “power generation” on the vertical axis, as in FIG. 9A. It is a graph which shows an example. FIG. 9B shows the results of generating 30 scenarios using a set of random numbers generated by the scenario generation unit 14, but the number of generated scenarios is not limited to this. Even if there are only two similar days to be selected, it is possible to generate one million scenarios, for example.

  Comparing FIG. 9 (a) and FIG. 9 (b), it can be said that the power generation amount scenario of FIG. 9 (b) reproduces the correlation of the power generation amount between the times shown in FIG. 9 (a).

  Moreover, although FIG. 9 demonstrated the electric power generation amount scenario which shows the predicted value of the electric power generation amount for every fixed time of a solar power generation facility, the information processing apparatus 1 which concerns on the embodiment is shown in FIG. It is also possible to generate a demand amount scenario indicating a predicted value of the demand amount for each fixed time of the demand equipment connected to.

  FIG. 10A is a graph showing an example of the transition of the past demand amount stored in the information storage unit 10 with “time” on the horizontal axis and “demand amount” on the vertical axis. FIG. 10B shows a demand scenario generated by the information processing apparatus 1 according to the embodiment, with “time” on the horizontal axis and “demand” on the vertical axis, as in FIG. It is a graph which shows an example.

  Comparing FIG. 10 (a) and FIG. 10 (b), it can be said that the demand amount scenario of FIG. 10 (b) reproduces the correlation of the demand amount between the times shown in FIG. 10 (a).

  Note that the information processing apparatus 1 according to the embodiment is not limited to the solar power generation equipment described above, but can be used for power generation equipment using other natural energy. For example, as shown in FIG. It can also be used for power generation facilities.

  FIG. 11 is a functional block diagram illustrating an example of the function of the information processing apparatus 1 according to the embodiment, and illustrates an example of generating a power generation amount scenario indicating a predicted value of the power generation amount for each fixed time of the wind power generation facility.

  In the case of the wind power generation facility shown in FIG. 11, after obtaining the theoretical upper limit of the wind speed from the wind condition data indicating the wind condition of each region stored in the information storage unit 15, the theoretical upper limit of the wind speed and the wind turbine characteristics are obtained. The theoretical upper limit generation unit 16 obtains the theoretical upper limit of the power generation amount of the wind power generation facility, and the parameter generation unit 17 generates a probability density function parameter that determines the probability distribution. Note that the similar date selection unit 11, time series data generation unit 12, mathematical model generation unit 13, and scenario generation unit 14 illustrated in FIG. 11 are stored in the information storage unit 10 in the past power generation amount by the wind power generation facility. It functions in the same manner as described above using data and the like.

  According to the first embodiment, in an electric power system including a power generation facility using natural energy, it is possible to easily generate a power generation amount scenario that takes into account at least the correlation of the power generation amount between times.

(Second Embodiment)
Next, a second embodiment of the present invention will be described with reference to FIGS.

  In the second embodiment, parts that are the same as those in the first embodiment shown in FIG. 2 are given the same reference numerals, and redundant descriptions are omitted. Below, it demonstrates centering on a different part from 1st Embodiment.

  In the second embodiment, generation of a required power generation scenario indicating a predicted value of required power generation per fixed time required for facilities other than the power generation facilities using natural energy included in the power system 100, and activation of the power generation facilities The generation of a power generation plan in which the timing of stopping and output increase / decrease is determined over a certain period will be described.

  FIG. 12 is a functional block diagram showing an example of the configuration of the information processing apparatus 1 according to the second embodiment of the present invention.

  The information processing apparatus 1 according to the embodiment has the power generation plan generation unit 18, the power generation plan selection unit 19, the charge / discharge amount determination unit 20, the capacity determination unit 21, and the configuration shown in the first embodiment. The expected value calculation unit 22 is further provided.

  However, the information storage unit 10 according to the embodiment includes the past demand of the demand facility in addition to the meteorological information indicating the transition of the past weather, the power generation amount data indicating the transition of the past power generation amount of the power generation facility using the natural energy. Demand amount data indicating the transition of the amount is stored.

  Further, the similar date selection unit 11 uses the weather information indicating the past meteorological change stored in the information storage unit 10 to determine whether the power generation amount and the demand amount are predicted and the past day in which the meteorological change is similar. Select at least two days.

  Further, the time series data generation unit 12 acquires the power generation amount data and the demand amount data for the day selected by the similar day selection unit 11 from the information storage unit 10, and the acquired power generation amount data and the demand amount data at regular intervals. Generate as time series data.

  In addition, the mathematical model generation unit 13 uses the time series data generated by the time series data generation unit 12 to indicate the correlation of the power generation amount between the times selected by the similar day selection unit 11 and between the times. A mathematical model is generated that shows the correlation between demand quantities and also shows the correlation between the power generation quantity and the demand quantity.

  In addition, the scenario generation unit 14 applies the mathematical model generated by the mathematical model generation unit 13 to a predetermined probability density function, and generates a probability distribution of power generation per fixed time and a probability distribution of demand per fixed time. And generating multiple sets of random numbers that follow each of these probability distributions to generate a power generation scenario that shows the predicted value of power generation for each power generation facility that uses natural energy and for each fixed time for a demand facility A plurality of demand amount scenarios showing predicted values of the demand amount are generated, and by subtracting the power generation amount scenario from the demand amount scenario, it is required for facilities other than the power generation facility using natural energy (here, including storage batteries and the like). A plurality of required power generation scenarios indicating predicted values of the required power generation for every fixed time are generated.

  The power generation plan generation unit 18 has a function of generating a power generation plan in which the power generation facility activation, stop, and output increase / decrease timings are determined over a certain period using the necessary power generation amount scenario generated by the scenario generation unit 14. Have. For example, the power generation plan generation unit 18 generates a power generation plan including control for charging and discharging the storage battery 141 in accordance with a change in the average value of the required power generation amount scenario generated by the scenario generation unit 14.

  The power generation plan selection unit 19 has a function of selecting an optimal power generation plan from a plurality of generated power generation plans. The function of the power generation plan selection unit 19 may be included in the power generation plan generation unit 18.

  The charge / discharge amount determining unit 20, the capacity determining unit 21, and the expected value calculating unit 22 will be described later.

  Next, an example of a specific operation of the information processing apparatus 1 according to the embodiment will be described with reference to FIG.

  FIG. 13 is a diagram illustrating an example of operations from when the information processing apparatus 1 generates a necessary power generation amount scenario indicating a correlation between power generation amounts between times and a correlation between demand amounts between times to generate a power generation plan. is there.

  First, the similar date selection unit 11 selects at least two similar dates from the information storage unit 10 (step S301).

Next, the time series data generation unit 12 acquires the power generation amount data and the demand amount data on the selected similar day from the information storage unit 10, and generates time series data for every predetermined time from the acquired similar day data. (Step S302). For example, the time-series data generation unit 12 acquires the power generation amount data and the demand amount data on the similar date selected by the similar date selection unit from the information storage unit 10, and summarizes the acquired data every hour. Generate hourly power generation data {x _t } and hourly demand data {y _t }. Further, the time series data generation unit 12 arranges the generated power generation amount data {x _t } and the demand amount data {y _t } from x ₁ to x ₂₄ and y ₁ to y ₂₄ , respectively, for 1 hour. Forty-eight time-series data are generated every time.

  Next, the mathematical model generation unit 13 generates a mathematical model indicating the correlation between the power generation amounts between the times and the correlation between the demand amounts between the times from the generated time series data (step S303). For example, the mathematical model generation unit 13 generates a 48 × 48 variance-covariance matrix indicating the correlation between the power generation amounts between the times and the demand amount correlation between the times from the generated 48 time-series data.

  Next, the scenario generation unit 14 applies the generated mathematical model to a predetermined probability density function to determine the probability distribution of the power generation amount for every fixed time and the probability distribution of the demand amount for every fixed time. By generating multiple sets of random numbers that follow each probability distribution, a power generation scenario that indicates the predicted power generation amount for each fixed time of a power generation facility that uses natural energy and a predicted value of the demand amount for a fixed time for a demand facility A plurality of demand volume scenarios shown are generated (step S304). For example, the scenario generation unit 14 applies the generated mathematical model to a 48-dimensional normal distribution to determine the probability distribution of the power generation amount and the probability distribution of the demand amount per fixed time, and follows each determined probability distribution By generating a set of 48 random numbers, a power generation scenario and a demand scenario are generated.

  Next, the scenario generation unit 14 subtracts the power generation amount scenario from the generated demand amount scenario, and the necessary power generation scenario indicating the predicted value of the required power generation per fixed time required for facilities other than the power generation facility using natural energy is obtained. A plurality are generated (step S305). For example, among the 48 random number sets generated by the scenario generation unit 14, the first 24 are used for generating a power generation scenario, and the remaining 24 are used for generating a demand scenario. A difference between the scenario and the power generation scenario is obtained, and a necessary power generation scenario is generated.

  Finally, the power generation plan generation unit 18 (and the power generation plan selection unit 19) generates a power generation plan using the necessary power generation amount scenario generated by the scenario generation unit 14 (step S306). For example, a power generation plan that is generated by the scenario generation unit 14 and that determines the timing of starting, stopping, and increasing / decreasing the power generation facility over a certain period from the power generation scenario, demand scenario, and required power generation scenario. Generate.

  Although FIG. 12 shows the case where there is one power generation facility using natural energy, it is not limited to this. When there are a plurality of power generation facilities that use natural energy, for example, the mathematical model generated by the mathematical model generation unit 13 indicates the correlation between the power generation amounts of each of the plurality of power generation facilities that use natural energy. Thus, it is possible to easily generate a power generation amount scenario, a demand amount scenario, a necessary power generation amount scenario, and a power generation plan for a plurality of power generation facilities using natural energy.

  FIG. 14 is a graph showing an example of a required power generation amount scenario generated by the information processing apparatus 1 according to the embodiment, where “time” is taken on the horizontal axis and “required power generation” is taken on the vertical axis.

  The necessary power generation scenario in FIG. 14 shows the result of subtracting the power generation scenario in FIG. 9B from the demand scenario in FIG. 10B.

  Since the information processing apparatus 1 according to the embodiment does not need to consider all combinations of fluctuations as in the past even when there is no large correlation between the power generation amount and the demand amount, the required power generation scenario can be easily set. It is possible to generate.

  Next, the charge / discharge amount determination unit 20, the capacity determination unit 21, and the expected value calculation unit 22 of the information processing apparatus 1 according to the embodiment will be described.

  The scenario and the power generation plan generated by the information processing apparatus 1 according to the embodiment can be used for the charge / discharge amount determining unit 20, the capacity determining unit 21, and the expected value calculating unit 22 shown in FIG.

  The charge / discharge amount determination unit 20 has a function of averaging the values of the scenarios generated by the scenario generation unit 14 and obtaining the charge / discharge amount to be charged / discharged in the storage battery included in the power system 100. In this way, the charge / discharge amount determining unit 20 can control the charge and discharge of the storage battery by obtaining the charge / discharge amount of the storage battery, and the amount of power generation of the power generation facility that uses natural energy that varies in a complicated manner. Variations can be smoothed.

  The capacity determination unit 21 has a function of determining the capacity of the storage battery included in the power system 100 according to each value of the scenario generated by the scenario generation unit 14. As described above, the capacity determining unit 21 can smooth the fluctuation of the power generation amount efficiently by determining an appropriate capacity of the storage battery.

  Based on the power generation plan generated by the scenario generation unit 14, the expected value calculation unit 22 obtains at least one of the expected value of the power generation amount of the power generation facility using natural energy, the expected value of the power generation cost, and the expected value of revenue. It has the required function.

  Further, the power generation plan selection unit 19, the charge / discharge amount determination unit 20, the capacity determination unit 21, and the expected value calculation unit 22, for example, take proactive measures such as using a storage battery when the power generation plan is difficult to execute. It is also possible to perform a simulation in preparation for an emergency situation, and to perform a process in accordance with the simulation when an emergency situation occurs.

  According to the second embodiment, it is possible to easily generate not only the power generation amount scenario but also the demand amount scenario and the necessary power generation amount scenario and the power generation plan in the power system including the power generation facility using natural energy. Become.

(Third embodiment)
Next, a third embodiment of the present invention will be described with reference to FIG.

  In the third embodiment, a scenario and generation of a power generation plan when there are a plurality of power generation facilities using natural energy will be described.

  FIG. 15 is a functional block diagram showing an example of the configuration of the information processing apparatus 1 according to the third embodiment of the present invention.

  In the figure, the past power generation amount data of M solar power generation facilities and one wind power generation facility, which are power generation facilities using natural energy, and the past demand amount data of the demand facility are stored in the information processing apparatus 1. The case where it is memorize | stored in the information storage part 10 is illustrated.

  Here, an example of an operation in which the information processing apparatus 1 generates a scenario when there are a plurality of power generation facilities using natural energy will be described.

  First, the similar date selection unit 11 selects at least two similar dates from the information storage unit 10 in order to generate time-series data for one day. Next, the time series data generation unit 12 acquires the power generation amount data and the demand amount data on the similar day selected from the information storage unit 10, for example, K pieces of time series data of solar power generation facilities for one day, 1 L pieces of time-series data of wind power generation facilities for one day and 24 pieces of time-series data of demand facilities for one day are generated. Next, the mathematical model generation unit 13 shows a component of (K × M + L + 24) × (K × M + L + 24) indicating the correlation of the power generation amount between times and the correlation of the demand amount between times from the generated time series data. Generate a variance-covariance matrix with Finally, the scenario generation unit 14 applies the generated mathematical model to a predetermined probability density function to determine the distribution of the power generation amount and the demand amount for each fixed time, and follows the determined distribution (K × M + L + 24) A set of random numbers for each fixed time of a dimension is generated, and a scenario of a photovoltaic power generation facility, a wind power generation facility, and a demand facility is generated.

  Note that the number of time series data generated by the time series data generation unit 12 is not limited to the number described above.

  Moreover, since the power generation amount scenario and the demand amount scenario generated as described above are scenarios generated under the same conditions, they have a high possibility of occurring at the same time. For this reason, it is possible to generate a necessary power generation amount scenario in the same manner as in FIG. 12, and it is also possible to generate a power generation plan from the generated necessary power generation amount scenario. In addition, the storage battery contained in an electric power system can be handled similarly to a pumped-storage power generation facility, and is used reversibly when the power demand increases.

  According to the third embodiment, in the case where there are a plurality of power generation facilities that use natural energy, not only the correlation between the power generation amount, the demand amount, and the required power generation amount in each power generation facility but also the power generation facility It is possible to generate a power generation scenario, a demand scenario, a necessary power generation scenario, and a power generation plan that also take into account the correlation between them.

  The various functions and processing procedures described in each embodiment described above are stored as a computer program in a computer-readable storage medium (for example, a magnetic disk, an optical disk, and a semiconductor memory), and the processor and processor are used as necessary. May be read out and executed. Such a computer program can also be distributed by transmitting from one computer to another computer via a communication medium.

  The present invention is not limited to the above-described embodiments as they are, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. In addition, various inventions can be formed by appropriately combining a plurality of components disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

  DESCRIPTION OF SYMBOLS 1 ... Information processing apparatus, 2 ... CPU, 3 ... Memory, 4 ... Input device, 5 ... Display apparatus, 10 ... Information storage part, 11 ... Similar date selection part, 12 ... Time series data generation part, 13 ... Mathematical model generation 14 ... Scenario generation unit 15 ... Information storage unit 16 ... Theoretical upper limit generation unit 17 ... Parameter generation unit 18 ... Power generation plan generation unit 19 ... Power generation plan selection unit 20 ... Charge / discharge amount determination unit 21 DESCRIPTION OF SYMBOLS ... Capacity determination part, 22 ... Expected value calculation part, 100 ... Electric power system, 111,11M ... Solar power generation equipment, 121 ... Wind power generation equipment, 131, 13N ... Power generation equipment, 141 ... Storage battery, 151 ... Power converter, 161 ... demand equipment.

Claims (10)

In an information processing apparatus that manages a power system including at least one power generation facility that uses natural energy,
An information storage unit as a storage medium that stores at least weather information indicating the transition of past weather, and power generation amount data indicating the transition of past power generation amount of the power generation facility;
Similar day selection means for selecting at least two days in the past that are similar to the day of the forecast of the amount of power generation and the change in weather from the weather information indicating the transition of the past weather stored in the information storage unit; ,
Time-series data generation means for acquiring the power generation amount data for the day selected by the similar day selection means from the information storage unit, and generating the acquired power generation amount data as time-series data for every fixed time;
Using the time series data, a mathematical model generating means for generating a mathematical model that indicates at least the correlation of the power generation amount between the times of days selected by the similar date selecting means;
Applying the mathematical model to a predetermined probability density function to determine the probability distribution of the power generation amount at a constant time and generating a plurality of sets of random numbers according to the probability distribution to predict the power generation amount at a constant time of the power generation facility Scenario generating means for generating a plurality of power generation scenarios indicating values;
An information processing apparatus comprising: In an information processing apparatus that manages a power system including at least one power generation facility that uses natural energy,
An information storage unit that stores at least weather information that indicates past weather changes, power generation data that indicates changes in past power generation amount of the power generation facility, and demand data that indicates changes in past demand amount of the demand facility When,
Similar days for selecting at least two days in the past that have similar weather transitions and days that are subject to prediction of power generation and demand from weather information indicating past weather transitions stored in the information storage unit A selection means;
When generating the power generation amount data and demand amount data on the day selected by the similar day selection means from the information storage unit, and generating the acquired power generation amount data and the demand amount data as time-series data for every fixed time Series data generation means;
Using the time series data, showing the correlation of the power generation amount between the times selected on the day selected by the similar day selection means, showing the correlation of the demand amount between the times, and showing the correlation between the power generation amount and the demand amount A mathematical model generating means for generating a mathematical model also indicating:
The mathematical model is applied to a predetermined probability density function to determine a probability distribution of the power generation amount at a certain time and a probability distribution of the demand amount at a certain time, and a set of random numbers according to each of these probability distributions. Generating a plurality of power generation scenarios indicating the predicted value of the power generation amount per fixed time of the power generation facility and a plurality of demand amount scenarios indicating the predicted value of the demand amount per fixed time of the demand facility, the demand Scenario generation means for generating a plurality of required power generation scenarios indicating predicted values of required power generation for each fixed time required for equipment other than the power generation equipment by subtracting the power generation scenario from the amount scenario;
An information processing apparatus comprising:   The mathematical model generated by the mathematical model generation unit also indicates a correlation between the power generation amounts of each of a plurality of power generation facilities using natural energy. Information processing device.   The information processing apparatus according to any one of claims 1 to 3, wherein the probability density function forms a multidimensional normal distribution.   The information processing apparatus according to claim 1, wherein the probability density function forms a beta distribution.   Power generation plan generation means for generating a power generation plan in which the timing of starting, stopping, and output increase / decrease of each power generation facility is determined over a certain period using the required power generation scenario generated by the scenario generation means is further provided. The information processing apparatus according to claim 2, wherein the information processing apparatus is an information processing apparatus.   The power generation plan generation unit generates a power generation plan including control for charging / discharging a storage battery included in an electric power system according to a change in an average value of a necessary power generation amount scenario generated by the scenario generation unit. The information processing apparatus according to claim 5. The information processing apparatus according to claim 6, further comprising capacity determining means for determining a capacity of a storage battery included in an electric power system according to a required power generation amount scenario generated by the scenario generating means.   The apparatus further comprises expectation value calculation means for obtaining at least one of an expected value of power generation amount, an expected value of power generation cost, and an expected value of profit based on the power generation plan generated by the power generation plan generation means. The information processing apparatus according to any one of claims 5 to 7. A computer that manages the power system including at least one power generation facility that uses natural energy.
A similar day selection function for selecting at least two days in the past that have similar weather transitions from the weather information indicating the past weather transitions stored in the storage medium; and
A time series data generation function for acquiring power generation amount data indicating a transition of power generation amount on a day selected by the similar day selection function from the storage medium, and generating the acquired power generation amount data as time series data for every predetermined time When,
Using the time series data, a mathematical model generation function that generates a mathematical model that indicates at least a correlation of power generation amount between times of days selected by the similar day selection function;
Applying the mathematical model to a predetermined probability density function to determine the probability distribution of the power generation amount at a constant time and generating a plurality of sets of random numbers according to the probability distribution to predict the power generation amount at a constant time of the power generation facility Scenario generation function to generate multiple power generation scenarios that show values,
A program to realize

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