JP5014213B2 - Water storage facility operation support system, operation support method and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To support the operation of a water storage facility so as to increase a power generation quantity, or a power sales amount by hydroelectric power generation. <P>SOLUTION: An optimal storage water level calculation system 200 is provided with: a model storing part 252 for storing an amount of water intake calculation model 6 for calculating an amount Q of water intake to be used for hydroelectric power generation on the basis of a water storage amount difference &Delta;V from a start point of time to an end point of time of a unit period and an inflow amount R, a water level calculation model 1 for calculating an operation water level H of the water storage facility on the basis of a water storage amount Q, an electric power calculation model 10 for calculating generated power En on the basis of the amount Q of water intake and the operation water level H, and a money amount of power selling calculation model 11 for calculating the money amount of power selling; a predicted inflow amount acquiring part 213 for acquiring a predicted value of the inflow amount; and an operation water level planning part 214 for determining a combination of water storage amounts V at which the generated power En or the money amount of power selling is maximum, applying each of the determined water storage amounts to the water level calculation model 1 to calculate the operation water level H and outputting the calculated operation water level H. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to a system, method, and program for supporting the operation of a water storage facility.

In order to efficiently perform hydropower generation, a computer-based operation support system is used. For example, in Patent Document 1, a generator operation plan and a water level plan that are automatically calculated based on the amount of discharged water per day, a past generator operation plan and actual data of the water level plan, etc. are displayed, and corrections are made as necessary. Accepting and preparing a power generation operation plan.
JP 2006-39838 A

  The higher the position of water, the greater the potential energy, the faster the flow rate, and the greater the amount of power generated in hydropower generation. However, in the apparatus described in Patent Document 1, although the water level is planned according to the amount of water discharged and the actual results, the water level is not increased due to the relationship between the water level plan and the generated power, and the generated power is increased. You can't plan.

  This invention is made | formed in view of such a background, and it aims at providing the system, method, and program which support operation | use of a water storage facility so that the electric power generated by hydroelectric power generation can be increased.

The invention according to claim 1 of the present invention for solving the above-mentioned problems is a system for supporting the operation of a water storage facility for hydroelectric power generation, and is provided to the water storage facility in each unit period within a predetermined period. A predicted inflow amount acquisition unit for acquiring a predicted value of the inflow amount of water, and the hydraulic power based on the stored water amount difference that is the difference in the stored water amount in the water storage facility from the start point to the end point of the unit period and the inflow amount A water intake amount calculation model for calculating a water intake amount that is an amount of water used for power generation, a water level calculation model for calculating a water level of the water storage facility based on the water storage amount, and the water intake amount and the water level A unit storing a power amount calculation model for calculating the amount of power generated by the hydropower generation in the unit period, and for each unit period in the predetermined period, the unit period With changing the water volume of the reservoir property at the start, from the predicted value of the inflow in the water volume and the unit period is the change, and calculates the intake amount by using the intake quantity calculation model, the change in The water level is calculated from the stored water amount using the water level calculation model , and the power amount is calculated from the calculated water intake and the calculated water level using the power amount calculation model. An optimal water storage amount determining unit that determines an optimal combination of the water storage amounts according to the amount of electric power, and applying each of the determined water storage amounts to the water level calculation model to calculate the water level, and calculating the calculated water level An operation water level output unit for outputting is provided.

  According to the water storage facility operation support system of the present invention, simulation can be performed by changing the amount of stored water, and an optimal water level can be presented according to the amount of power generated within a predetermined period. As a result, the operator of the water storage facility can manage the water level of the water storage facility according to the output from the water storage facility operation support system of the present invention, and can manage the optimal water level. The water level that has been managed based on this can be used by inexperienced ones.

The invention according to claim 2 of the present invention is the water storage facility operation support system according to claim 1, wherein the optimum water storage amount determination unit includes the combination of the changed water storage amounts. It is assumed that the optimum combination of the water storage amounts is determined so that the total amount of power calculated for each unit period within a predetermined period is the maximum.
In this case, it is possible to present a water level that maximizes the amount of power generated within a predetermined period. Therefore, efficient hydroelectric power generation can be performed.

The invention according to claim 3 of the present invention is the water storage facility operation support system according to claim 1, further comprising a power price acquisition unit that acquires a price per unit power in each period, The optimum water storage amount determination unit has a maximum total value of values obtained by multiplying each of the electric energy calculated for each unit period within the predetermined period by the price among the combinations of the changed water storage amounts. Is determined as the optimum combination of water storage amounts.
In this case, since the operation water level can be determined so as to maximize the final sales price, the operation of the water storage facility can be supported so as to be more effective for the commercial enterprise. Further, for example, even when the power price fluctuates greatly, it is possible to determine an effective water level so that a large amount of power can be generated at a high price.

  The invention according to claim 4 of the present invention is the water storage facility operation support system according to any one of claims 1 to 3, wherein the water storage amount setting stores the upper limit value and the lower limit value of the water storage amount. A value storage unit, wherein the optimum water storage amount determination unit changes the water storage amount at the start of the unit period for each predetermined step from the lower limit value to the upper limit value, and The amount of power is calculated.

Moreover, invention of Claim 5 among this invention is a water storage facility operation | use assistance system of Claim 4, Comprising: The said optimal water storage amount determination part of the said optimal water storage amount is carried out by dynamic programming. The combination will be determined.
In this case, by using the dynamic programming method, it is possible to more efficiently determine an enormous combination of water storage amounts.

Further, the invention according to claim 6 of the present invention is the water storage facility operation support system according to claim 1, and stores the actual value of the inflow and the actual value of the rainfall every predetermined period. And the difference between the rainfall amount in a certain first period and the inflow amount in the second period before the first period from the first period and the equilibrium inflow amount that is a predetermined constant as the explanatory variable, A model storage unit for storing a regression model whose objective variable is an increase amount of the inflow from the period to the first period; an actual value of the inflow and an actual value of the rainfall stored in the database; And an inflow amount model estimation unit that performs regression analysis based on the regression model stored in the model storage unit and estimates a regression coefficient for each of the explanatory variables, and a prediction period that is a period to be predicted A predicted rainfall amount acquisition unit for acquiring a predicted value of the rainfall amount in the system; and a result value of the inflow amount in the period before the predetermined period from the prediction period is acquired from the database, the predicted value of the rainfall amount, the inflow An actual volume value, and an increase amount prediction unit that calculates the predicted value of the increase amount in the prediction period by applying each regression coefficient to the regression model, and the predicted inflow amount acquisition unit includes: The predicted value of the inflow amount in the prediction period is calculated by adding the predicted value of the increase amount to the actual value of the inflow amount.
In this case, since the inflow rate can be predicted based on the actual value of the inflow rate, the inflow rate can be predicted more easily and accurately than in the case where the inflow rate is predicted based only on the rainfall amount. it can. Since the theoretical value of the generated power amount depends on the predicted value of the inflow amount, the theoretical value of the generated power amount can be calculated more accurately by accurately predicting the inflow amount. Thereby, the optimal operational water level based on a more accurate theoretical value can be presented.

Moreover, invention of Claim 7 among this invention is a water storage facility operation | use assistance system of Claim 6, Comprising: The said database further memorize | stores the actual value of the amount of snowmelt for every said predetermined period, The regression model stored in the model storage unit further includes the snow melting amount in the first period as an explanatory variable, and a predicted snow melting amount acquisition unit that acquires a predicted value of the snow melting amount in the prediction period. The inflow amount model estimation unit, the actual value of the inflow amount stored in the database, the actual value of the rainfall amount, the actual value of the snowmelt amount, and the model storage unit Based on the regression model, regression analysis is performed and the regression coefficient is estimated.
In this case, the amount of water flowing into the water storage facility can be predicted in consideration of the predicted value of the amount of snowmelt, so there was no precipitation compared to the case where the amount of inflow was predicted based solely on the prediction of precipitation. Even in such a case, it is possible to predict that the inflow amount may increase due to melting snow. Therefore, it is possible to predict the inflow amount with higher accuracy. Since the theoretical value of the generated power amount depends on the predicted value of the inflow amount, the theoretical value of the generated power amount can be calculated more accurately by accurately predicting the inflow amount. Thereby, the optimal operational water level based on a more accurate theoretical value can be presented.

  Moreover, invention of Claim 8 among this invention is a water storage facility operation | use assistance system of Claim 1, Comprising: For every predetermined period, the actual value of the said inflow, the actual value of rainfall, and the temperature A database storing actual values and actual values of snow cover, the amount of rainfall in a certain first period, the amount of snow melt in the first period, and the inflow in a second period before the first period from the first period An inflow model that is a regression model in which a difference from the equilibrium inflow that is a predetermined constant is an explanatory variable, and an increase in the inflow from the second period to the first period is an objective variable; A value obtained by subtracting a predetermined constant indicating the temperature at which snow melting starts from the temperature in one period or a larger value of zero and the amount of snow in the second period, and the amount of rainfall in the first period As an explanatory variable, A model storage unit for storing a snowmelt amount model that is a regression model having the snowmelt amount as a target variable in a period; an actual value of the temperature, an actual value of the snowfall amount, and an actual rainfall amount stored in the database; A snowmelt amount model estimation unit that performs regression analysis based on the value and the snowmelt amount model stored in the model storage unit, and estimates a regression coefficient for each of the explanatory variables related to the snowmelt amount model; Applying the actual temperature value, the actual snow accumulation value, the actual rainfall value, and the estimated regression coefficient stored in the database to the snow melting model, the snow melting amount for each predetermined period The actual value of the inflow amount stored in the database, the actual value of the rainfall amount, the theoretical value of the calculated snowmelt amount, and the module are calculated. An inflow model estimation unit that performs regression analysis based on the inflow rate model stored in the storage unit and estimates a regression coefficient for each of the explanatory variables related to the inflow rate model, and a prediction target period. A predicted rainfall amount acquisition unit that acquires a predicted value of the rainfall amount in a certain prediction period; a predicted temperature acquisition unit that acquires a predicted value of the temperature in the prediction period; and the prediction period in the period before the predetermined period. The actual value of the snow amount is acquired from the database, and the predicted value of the temperature, the acquired actual value of the snow amount, the predicted value of the rainfall amount, and the respective explanatory variables related to the snow melt amount model, the snow melt amount A predicted snowmelt amount acquisition unit that calculates a predicted value of the snowmelt amount in the prediction period by applying to the model; and the actual value of the inflow amount in a period before the predetermined period from the prediction period Obtained from the database, applying the predicted value of the rainfall, the predicted value of the snowmelt, the actual value of the inflow, and the regression coefficients to the inflow model, An increase amount prediction unit that calculates a predicted value, wherein the predicted inflow amount acquisition unit adds the predicted value of the increase amount to the actual value of the inflow amount, and the predicted value of the inflow amount in the prediction period Is calculated.

  In this case, the theoretical value of the amount of snowmelt is calculated using a snowmelt amount model with temperature, rainfall, and snow cover as explanatory variables, and the amount of water flowing into the water storage facility is predicted in consideration of the amount of snowmelt. be able to. Therefore, even if measured values of snowmelt cannot be obtained from the Japan Meteorological Agency or private weather companies, etc., the amount of snowmelt is generally based on the predicted values of temperature and rainfall that are easy to obtain and the actual value of snow cover. The predicted value can be calculated. This makes it possible to predict the inflow of water into the water storage facility, taking into account that the inflow can increase due to snow melting, even if the predicted value of the amount of snow melt cannot be easily obtained or when there is no precipitation. Therefore, it is possible to present the optimum operational water level based on the predicted value of the inflow amount with higher accuracy.

  Other problems and solutions to be disclosed by the present application will be made clear by the embodiments of the invention and the drawings.

  ADVANTAGE OF THE INVENTION According to this invention, operation | use of a water storage facility can be supported so that the electric power generated by hydroelectric power generation can be increased.

  Hereinafter, a water level operation support system in a water storage facility according to an embodiment of the present invention will be described. The water level operation support system according to the present embodiment supports planning of the water level of a water storage facility such as a dam used for hydroelectric power generation. The water level operation support system according to the present embodiment predicts the amount of water flowing into the water storage facility, and based on the predicted amount of inflow and weather information, the amount of power generated and its price (hereinafter referred to as the amount of power sold). ) And the water level for each predetermined unit period (in this embodiment, 1 day) is presented so that the amount of power sold is maximized.

== System configuration ==
FIG. 1 is a diagram illustrating an overall configuration of a water level operation support system according to the present embodiment.
The water level operation support system of the present embodiment calculates an optimal water level in a water storage facility, and an inflow prediction system 100 that predicts the amount of water flowing into a water facility from a river (hereinafter referred to as inflow, referred to as R). The sub-system is configured to include two sub-systems with the optimum water level calculation system 200. Each of the inflow prediction system 100 and the optimum water storage level calculation system 200 is a computer such as a personal computer, a workstation, or a PDA (Personal Digital Assistance). The inflow amount prediction system 100 and the optimum water storage level calculation system 200 may be configured by a plurality of computers.

  The inflow amount prediction system 100 and the optimum water storage level calculation system 200 are connected to each other via a communication network 300 so as to communicate with each other. The communication network 300 is, for example, the Internet or a LAN (Local Area Network). The communication network 300 is constructed by, for example, Ethernet (registered trademark), a public telephone line network, a wireless communication network, or the like.

== Inflow prediction system 100 ==
The inflow amount prediction system 100 improves the prediction accuracy by predicting the inflow amount in consideration of the snowmelt amount.
FIG. 2 is a graph showing changes in the inflow amount. Even when there is no precipitation or snowmelt, there is a predetermined inflow due to, for example, oozing from a forest. Therefore, if there is no precipitation or snowmelt, the inflow will gradually decrease to a predetermined equilibrium value (hereinafter referred to as equilibrium inflow) (a). On the other hand, if there is precipitation, the amount of water flowing temporarily increases accordingly, but the precipitation stops and starts decreasing again toward the equilibrium inflow (b). On the other hand, when the temperature rises, snow melting occurs and the amount of running water increases accordingly, but the effect of snow melting on the inflow is more gradual than that of precipitation (c). This is a case where, for example, snow melting continuously occurs when the average temperature rises during the period from winter to spring.

  Therefore, in the inflow prediction system 100 of the present embodiment, paying attention to precipitation, snowmelt, and equilibrium inflow, parameters are set based on actual values of weather data such as past temperature and precipitation and a regression model described later. Estimated and estimated parameters, for example, predicted values of temperature (hereinafter referred to as predicted temperatures) obtained from, for example, weather forecasts, and predicted values of precipitation (hereinafter referred to as predicted rainfall) are applied to the regression model. Calculate the predicted value of the quantity.

  FIG. 3 is a diagram illustrating a hardware configuration of the inflow prediction system 100 according to the present embodiment. The inflow amount prediction system 100 includes a CPU 101, a memory 102, a storage device 103, a communication interface 104, an input device 105, and an output device 106. The storage device 103 stores various programs and data, for example, a hard disk drive, a flash memory, a CD-ROM drive, or the like. The CPU 101 implements various functions by reading a program stored in the storage device 103 into the memory 102 and executing it. The communication interface 104 is an interface for connecting to the communication network 300. For example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone line network, a communication device for performing wireless communication, etc. It is. The input device 105 is, for example, a keyboard, a mouse, a touch panel, or a microphone that receives data input from a user. The output device 106 is, for example, a display, a printer, or a speaker that outputs data.

  FIG. 4 is a diagram illustrating a software configuration of the inflow prediction system 100 according to the present embodiment. The inflow prediction system 100 according to the present embodiment includes a snowfall temperature estimation unit 111, a snowmelt model estimation unit 112, an inflow model estimation unit 113, a predicted temperature acquisition unit 114, a predicted precipitation acquisition unit 115, and a predicted snowmelt acquisition unit 116. , An inflow amount prediction unit 117, a model storage unit 151, a parameter storage unit 152, and a meteorological and inflow amount result database 153. In addition, the snowfall temperature estimation unit 111, the snowmelt amount model estimation unit 112, the inflow amount model estimation unit 113, the predicted temperature acquisition unit 114, the predicted precipitation amount acquisition unit 115, the predicted snowmelt amount acquisition unit 116, and the inflow amount prediction unit 117 This is realized by the CPU 101 of the inflow amount prediction system 100 reading out the program stored in the storage device 103 to the memory 102 and executing it.

  The meteorological and inflow history database 153 stores a history of information (hereinafter referred to as meteorological inflow history information) including various actual values of meteorology and actual values of inflow. FIG. 5 is a diagram illustrating a configuration example of the weather inflow history information stored in the weather and inflow history database 153. As shown in the drawing, the meteorological inflow amount information includes temperature, precipitation, snowfall, snow cover, snowmelt, and inflow in association with the date. The temperature is the average daily temperature. Precipitation, snowfall and snowmelt are cumulative values of daily precipitation, snowfall and snowmelt. The snow cover is the snow cover observed on that day. The inflow is the cumulative amount of water that has flowed into a water storage facility such as a dam per day. The temperature, precipitation, snowfall, and snow cover are data provided by, for example, the Japan Meteorological Agency or a private weather company. The inflow is a measured value measured at the water storage facility. The inflow amount may be, for example, a measurement value of a river flow rate provided by a local government that manages the river. The amount of snowmelt may be measured by the Japan Meteorological Agency, a private weather company, a surveying company, or the like, or a value calculated by a model described later may be recorded as an actual value.

The model storage unit 151 stores various statistical models based on the meteorological inflow history information. The parameter storage unit 152 stores parameters such as regression coefficients and constants applied to the models stored in the model storage unit 151. Is memorized. The model storage unit 151 includes a precipitation statistical model (hereinafter referred to as precipitation model 1), a snowfall statistical model (hereinafter referred to as snowfall model 2), and a snowfall statistical model (hereinafter referred to as snowfall model). 3), a statistical model of snowmelt (hereinafter referred to as snowmelt model 4), and a statistical model (hereinafter referred to as inflow model 5) relating to the increase in inflow from the previous day. In the parameter storage unit 152, the temperature at which the rain changes to snow (hereinafter referred to as snow temperature) δ, the equilibrium inflow amount μ ₁ , the temperature μ _{2 at which} snow melting begins, and the regression coefficients α _{1 to} α ₃ , β ₁ , β ₂ are stored. It is stored in the parameter storage unit 152. It is assumed that the equilibrium inflow amount μ ₁ and the temperature μ _{2 at} which snow melting starts are stored in the parameter storage unit 152 in advance as predetermined constants.

In the following description, the temperature at the date t, rainfall, snowfall, snowfall, snowmelt and inflow _{respectively,} and _{T t, P t, S t} , D t, M t , and _{F t.} Let ΔFt be the increase in the inflow from the previous day. The precipitation when the temperature Tt is higher than δ is P1 _t , and the precipitation when the temperature T _t is δ or less is P2 _t .

降雪量モデル２は、気温Ｔ_ｔがδ以下である場合の降水量Ｐ２ｔを説明変数とし、降雪量Ｓｔを目的変数とした回帰モデルであり、次式で表される。

積雪量モデル３は、積雪量Ｄ_ｔが、前日までの積雪量Ｄ_ｔ−１に当日の降雪量Ｓ_ｔを加え、そこから融雪量Ｍ_ｔを引いたものに一致するという関係を示すモデルであり、次式で表される。

融雪量モデル４は、気温Ｔ_ｔから融雪が始まる気温μ_２を減じた値に積雪量Ｄ_ｔを乗じた値と、Ｐ１_ｔとを説明変数とし、融雪量Ｍ_ｔを目的変数とした回帰モデルであり、次式で表される。

融雪量モデル４において、気温Ｔ_ｔがμ_２よりも低ければ第１項は０になり、前日までの積雪量Ｄ_ｔ−１が０であれば融雪量Ｍ_ｔは０になる。
流入量モデル５は、均衡流入量μ_１から前日の流入量Ｆ_ｔ−１を減じた値と、当日の気温Ｔ_ｔがδより高い場合の降水量Ｐ１_ｔと、当日の融雪量Ｍ_ｔとを説明変数とし、流入量の増加量ΔＦ_ｔを目的変数とした回帰モデルであり、次式で表される。

Precipitation Model 1, precipitation P _t is, the boundary of snow temperature [delta], is a model that indicates that the P1 _t or P2 _t, is expressed by the following equation.

The snowfall model 2 is a regression model in which the precipitation P2t when the temperature T _t is equal to or less than δ is an explanatory variable and the snowfall St is an objective variable, and is represented by the following equation.

Snowfall model 3, snow accumulation D _t is a model showing the relationship of addition of the day of snowfall S _t in snowfall D _t-1 of the previous day, matches therefrom minus snowmelt M _t Yes, it is expressed by the following formula.

The snow melting amount model 4 is a regression model in which a value obtained by subtracting the temperature μ _{2 at} which the snow melting starts from the temperature T _t is multiplied by the snow accumulation amount D _t , P1 _t is an explanatory variable, and the snow melting amount M _t is an objective variable. And is represented by the following equation.

In the snowmelt amount model 4, the first term is 0 if the temperature T _t is lower than μ ₂ , and the snowmelt amount M _t is 0 if the snow accumulation amount D _t−1 up to the previous day is 0.
Inflow model 5, a value obtained by subtracting the inflow F _t-1 of the previous day from equilibrium inflow mu _1, and precipitation P1 _t where temperature T _t of the day is higher than [delta], and the day of snowmelt M _t Is an regression variable, and an inflow increase ΔF _t is a target variable, which is expressed by the following equation.

The snowfall temperature estimation unit 111 estimates the snowfall temperature δ based on the precipitation model 1 and the weather inflow result information, and registers the estimated snowfall temperature δ in the parameter storage unit 152. Specifically, the snowfall temperature estimation unit 111 acquires the weather inflow actual result information with a snowfall larger than 0 from the weather and inflow actual result database 153, and based on the acquired weather inflow actual result information, the model snowfall = a X Regression coefficients a and b are estimated by regression analysis of x temperature + b x precipitation. Next, the snowfall temperature estimation unit 111 “a × temperature of meteorological inflow result information + b × precipitation amount of meteorological inflow result information” when the temperature is δ or less, and “0” when the temperature is higher than δ. ”Is calculated as the estimated snowfall amount, and δ is calculated such that the value obtained by squaring the difference between the estimated snowfall amount and the snowfall amount of the meteorological inflow result information is squared. The snowfall temperature estimation unit 111, for example, for δ in which the temperature in a predetermined range is increased for each predetermined step, if each meteorological inflow result information is higher than δ, the value obtained by multiplying the temperature by the regression coefficient a and the regression The sum of the coefficient b multiplied by the precipitation amount is calculated as the estimated snowfall amount, and the value obtained by squaring the difference between the estimated snowfall amount and the snowfall amount of the actual inflow information is calculated as the square of the error. Then, the one with the smallest error square can be determined as δ. A general statistical method can be used for the process of determining δ so that the square of the error is minimized. The snowfall temperature estimation unit 111 registers δ determined as described above in the parameter storage unit 152. The snow melting temperature δ is determined as described above.

Moreover, the snowfall temperature estimation part 111 reads the meteorological inflow result information corresponding to the date t from the meteorological and inflow result database 153 for each date t, and the precipitation and temperature of the read weather inflow result information, P2 _t is calculated by applying the estimated snowfall temperature δ to the precipitation model 1. The snowfall temperature estimation unit 111 performs regression analysis based on the meteorological inflow information, P2 _t , and the snowfall model 2 to estimate the regression variable γ. The snowfall temperature estimation unit 111 registers the estimated regression variable γ in the parameter storage unit 152.

を回帰分析して、回帰係数α_２及びα_３を推計する。融雪量モデル推計部１１２は、推計した回帰係数α_２及びα_３をパラメタ記憶部１５２に登録する。 The snow melting amount model estimation unit 112 is an equation in which the snow accumulation amount model 3 is substituted for the snow melting amount model 4.

And regression coefficients α ₂ and α ₃ are estimated. The snowmelt amount model estimation unit 112 registers the estimated regression coefficients α ₂ and α ₃ in the parameter storage unit 152.

The inflow amount model estimation unit 113 estimates the regression coefficients α ₁ , β ₁ , and β ₂ based on the inflow amount model 5 and the weather inflow actual amount information. Specifically, for each date t, the inflow model estimation unit 113 calculates P1 _t based on the precipitation in the meteorological inflow performance information corresponding to the precipitation models 1 and δ and the date t, and each date t The inflow model 5 is subjected to regression analysis by using the actual inflow information of the weather and the corresponding P1 _t , and the regression coefficients α ₁ , β ₁ , and β ₂ are estimated. When there is no snowmelt amount in the actual inflow information, the inflow amount model is calculated using M _t calculated using the snowmelt model 4 using the snow cover amount on the date t-1 and other weather information on the date t. It is also possible to estimate regression coefficients α ₁ , β ₁ , and β ₂ by regression analysis of 5. The inflow model estimation unit 113 registers the estimated regression coefficients α ₁ , β ₁ , and β ₂ in the parameter storage unit 152.

  The predicted temperature acquisition unit 114 acquires a predicted temperature value (hereinafter referred to as a predicted temperature). For example, the predicted temperature acquisition unit 114 may receive an input of the predicted temperature from the user, or may access the computer of the Japan Meteorological Agency or a private weather company to acquire the predicted temperature. Moreover, you may make it the estimated temperature acquisition part 114 estimate temperature based on weather inflow amount performance information. In this case, for example, a statistical model used for general temperature prediction is stored in the model storage unit 151, and the predicted temperature acquisition unit 114 performs a regression analysis based on the statistical model and the weather inflow result information. The estimated temperature can be calculated by applying the estimated parameter and the meteorological inflow history information to the statistical model.

  The predicted precipitation acquisition unit 115 acquires a predicted value of precipitation (hereinafter referred to as predicted precipitation). For example, the predicted precipitation acquisition unit 115 may receive an input of predicted precipitation from a user, or may access a computer of the Japan Meteorological Agency or a private weather company to acquire the predicted precipitation. Further, the predicted precipitation amount acquiring unit 115 may predict the precipitation amount based on the weather inflow result information, similarly to the predicted temperature acquiring unit 114.

  The predicted snowmelt amount acquisition unit 116 acquires a predicted value of snowmelt amount (hereinafter referred to as a predicted snowmelt amount). In the present embodiment, as will be described later, the predicted snowmelt amount acquisition unit 116 calculates the snowmelt amount based on the snowmelt amount model 4. For example, the predicted snowmelt amount may be received from the user. The predicted snowmelt amount may be acquired by accessing a computer of the Japan Meteorological Agency or a private weather company. Further, the predicted snow melting amount acquisition unit 116 may store a history of actual values of snow melting amount and perform prediction based on the actual values.

  The inflow prediction unit 117 includes the predicted temperature acquired by the predicted temperature acquisition unit 114, the predicted precipitation acquired by the predicted precipitation acquisition unit 115, the parameters estimated by the snowfall temperature estimation unit 111, the weather inflow result information, and the inflow A predicted value of the inflow amount (hereinafter referred to as a predicted inflow amount) is calculated using the model 5.

The inflow prediction unit 117 reads δ from the parameter storage unit 152, δ in the precipitation model 1, the predicted temperature T _t acquired by the predicted temperature acquisition unit 114, and the predicted precipitation P acquired by the predicted precipitation acquisition unit 115. P1 _t is calculated by applying _t . That is, P1 _t = P _t if the predicted temperature T _t is greater than δ, and P1 _t = 0 if T _t is less than or equal to δ.
The inflow amount prediction unit 117 reads α _{1 to} α ₃ , β ₁ , β ₂ , μ ₁ , and μ ₂ from the parameter storage unit 152, and the weather corresponding to the previous day t−1 from the meteorological and inflow amount result database 153. Read inflow volume result information. The inflow amount predicting unit 117 sets the snow amount of the read weather inflow amount result information as D _t−1 and the read out amount of the weather inflow amount result information as F _t−1 .
The predicted snow melting amount acquisition unit 116 substitutes α ₂ , T _t , μ ₂ , D _t−1 , α ₃ , and P1 _t into the snow melting amount model 4 to calculate a predicted value M _t of the snow melting amount.
The inflow amount prediction unit 117 substitutes α ₁ , μ ₁ , F _t−1 , β ₁ , P1 _t , β ₂ , and M _t into the inflow amount model 5 to calculate a predicted value ΔF _t of the inflow increase amount. , F _t−1 is added with ΔF _t to calculate the predicted inflow amount F _t .

  As described above, according to the inflow amount prediction system 100 of the present embodiment, the inflow amount is predicted in consideration of the precipitation amount and the snowmelt amount based on the predicted temperature, the predicted precipitation amount, and the weather inflow actual result information. It can be carried out. Even when there is no precipitation, the amount of inflow increases due to melting of snow, so that the accuracy of prediction can be improved by predicting the amount of inflow taking into account the amount of snow melting.

  Moreover, according to the inflow amount prediction system 100 of the present embodiment, the snowmelt amount can be calculated from the precipitation amount and the temperature. Prediction of precipitation and temperature has various methods as weather forecasting methods and is easily available. Therefore, even if it is difficult to predict the amount of snowmelt, it is possible to easily predict the inflow amount in consideration of the amount of snowmelt by predicting the amount of snowmelt based on the easily obtained precipitation and temperature predicted values. can do.

  Also, in the inflow model 5 described above, the first term is the difference between the balanced inflow and the inflow, and the balanced inflow is taken into account. Compared to the case where the inflow is simply used as an explanatory variable, More accurate inflow prediction can be performed.

== Optimal reservoir level calculation system 200 ==
The optimum water storage level calculation system 200 is based on constraints such as the maximum / minimum value of the water level in the water storage facility and the maximum / minimum value of the amount of water used for hydroelectric power generation (hereinafter referred to as water intake, Q). The water level is simulated using the predicted value of the inflow amount predicted by the prediction system 100, and the plan for the water level that maximizes the amount of power sales is calculated.

  FIG. 6 is a diagram illustrating a hardware configuration of the optimum water storage level calculation system 200. As shown in the figure, the optimum water storage level calculation system 200 includes a CPU 201, a memory 202, a storage device 203, a communication interface 204, an input device 205, and an output device 206. The storage device 203 stores various data and programs, for example, a hard disk drive, a flash memory, a CD-ROM drive, and the like. The CPU 201 implements various functions by reading a program stored in the storage device 203 into the memory 202 and executing it. The communication interface 204 is an interface for connecting to the communication network 300. For example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone line network, and a communication for performing wireless communication Such as a vessel. The input device 205 is a keyboard, mouse, touch panel, microphone, or the like that accepts data input. The output device 206 is, for example, a display, a printer, or a speaker that outputs data.

  FIG. 7 is a diagram showing a software configuration of the optimum water storage level calculation system 200. As shown in the figure, the optimum water level calculation system 200 includes a specification input unit 211, a stored water amount set value input unit 212, a predicted inflow amount acquisition unit 213, an operational water level planning unit 214, a specification storage unit 251, and a model storage. A unit 252 and a power price database 253. In addition, as for the specification input part 211, the stored water amount setting value input part 212, the predicted inflow amount acquisition part 213, and the operation water level plan part 214, CPU201 with which the optimal water level calculation system 200 is provided is memorize | stored in the memory | storage device 203. This is realized by reading the program into the memory 202 and executing it. The specification storage unit 251 and the power price database 253 are realized as storage areas provided by the memory 202 and the storage device 203 provided in the optimum water storage level calculation system 200. The specification storage unit 251, the model storage unit 252, and the power price database 253 are managed by a database server different from the optimal storage level calculation system 200, and the optimal storage level calculation system 200 accesses the database server. Also good.

  The specification storage unit 251 stores information (hereinafter referred to as specification information) including setting values of various specifications such as a water storage facility, a river, and a power generation facility. FIG. 8 is a diagram illustrating a configuration example of the specification information stored in the specification storage unit 251. As shown in the figure, the specification information includes a specification name, a unit, and a setting value.

  The specification input unit 211 receives input of specification information and registers the received specification information in the specification storage unit 251. The specification input unit 211 may receive input of each item of specification information from the input device 205 such as a keyboard and a mouse, for example, or access the specification information by accessing the host computer of the power company, for example. You may make it acquire.

In addition, the specification input unit 211 preliminarily includes the maximum value of the water level (maximum operating water level; hereinafter referred to as H _max . The unit is m) and the minimum value of the water level (minimum) as the specifications relating to the water storage facility. Operational water level; hereinafter referred to as H _min . The unit is m.) The specification information including the highest operational water level received and the specification information including the lowest operational water level are created and specifications are created. Registered in the storage unit 251, accepts an input of a maintenance flow rate (hereinafter referred to as S0. The unit is m ³ / s) as specifications relating to the river, and creates specification information including the received maintenance flow rate Then, the maximum amount of water that can be used for power generation (maximum water intake; hereinafter referred to as Q _max . The unit is m ³ / s. ), The minimum amount of water available for power generation (minimum water intake; hereinafter, Q Indicated as _min . The unit is m ³ / s.), the height of water discharged after power generation (water discharge level; hereinafter referred to as H _out . The unit is m), and the loss head (hereinafter referred to as H _(The unit is m.), and the specification information including the maximum water intake, the specification information including the minimum water intake, the specification information including the discharge level, and the loss head are received. It is assumed that the specification information including it is created and registered in the specification storage unit 251.

  The power price database 253 stores the power price for each date. FIG. 9 is a diagram illustrating a configuration example of the power price database 253. As shown in the figure, the power price database 253 stores a power price (unit: yen / kWh) in association with the date. In this embodiment, it is assumed that the power price can be changed for each date, and the power price for each day is input in advance from the user and registered in the power price database 253.

  The water storage amount set value input unit 212 includes a period (hereinafter referred to as an operation period. In this embodiment, the length of the operation period is 6 days) and a start point of the first day of the operation period ( 0:00) and the target value (hereinafter referred to as the final value) of the stored water amount at the end of the last day (which is also the starting point on the seventh day). The target water storage amount) is received. In addition, the water storage amount setting value input unit 212 may acquire, for example, the current water storage amount of the water storage facility and set it as the initial water storage amount. In addition, the water storage amount setting value input unit 212 may predict either the initial water storage amount or the final target water storage amount from the past water level.

  The predicted inflow amount acquisition unit 213 accesses the inflow amount prediction system 100 and acquires the predicted value of the inflow amount R for each day in the operation period predicted by the inflow amount prediction system 100. Note that the predicted inflow amount acquisition unit 213 may accept input of a predicted value of the inflow amount from the input device 205 such as a keyboard or a mouse.

モデル２は、最高運用水位Ｈ_ｍａｘに基づいて貯水量の上限（以下、上限貯水量といい、Ｖ_ｍａｘと表記する。）を算出するためのものであり、次式により表される。

モデル３は、最低運用水位Ｈ_ｍｉｎに基づいて貯水量の下限（以下、下限貯水量といい、Ｖ_ｍｉｎと表記する。）を算出するためのものであり、次式により表される。

モデル４は、１日の０時から２４時（すなわち次の日の０時）の貯水量に基づいて、単位期間の開始時点から終了時点までの貯水量の差（以下、貯水量差といい、ΔＶと表記する。）を算出するものであり、ある日付ｔの０時における貯水量をＶ_ｔとして、次式により表される。

モデル５は、流入量から維持流量及び貯水量差を引いた水量（Ｒ０）を算出するためのものであり、次式により表される。

モデル６は、最小取水量Ｑ_ｍｉｎ、最大取水量Ｑ_ｍａｘ、及びＲ０に基づいて、取水量Ｑを決定するためのもの（取水量算出モデル）であり、次式により表される。

すなわち、Ｒ０が、最小取水量Ｑ_ｍｉｎ以上であり、かつ、最大取水量Ｑ_ｍａｘ以下である場合には、Ｒ０が取水量Ｑとなり、Ｒ０が最小取水量Ｑ_ｍｉｎよりも小さい場合には最小取水量Ｑ_ｍｉｎが取水量Ｑとなり、Ｒ０が最大取水量よりも大きい場合には最大取水量Ｑ_ｍａｘが取水量Ｑとなる。 The operational water level planning unit 214 (corresponding to the optimum water storage amount determining unit of the present invention) calculates the optimum water level (hereinafter referred to as the operational water level, referred to as H) on each day during the operation period by simulation. Then, the calculated operational water level for each day is output to the output device. The statistical model used by the operational water level planning unit 214 for simulation is stored in the model storage unit 252. The model storage unit 252 stores the following models 1 to 11 Model 1 is for calculating the operating water level H based on the water storage amount V (water level calculation model), and is expressed by the following equation. The Note that a is a constant specific to the water storage facility.

Model 2 is for calculating the upper limit of the water storage amount (hereinafter referred to as the upper limit water storage amount and expressed as V _max ) based on the maximum operational water level H _max and is represented by the following equation.

The model 3 is for calculating the lower limit of the water storage amount (hereinafter referred to as the lower limit water storage amount and expressed as V _min ) based on the minimum operating water level _Hmin , and is represented by the following equation.

Model 4 is based on the amount of stored water from 0:00 to 24:00 on the first day (that is, 0:00 on the next day). , And expressed as ΔV), and is expressed by the following equation, where V _t is the amount of water stored at 0:00 on a certain date t.

The model 5 is for calculating a water amount (R0) obtained by subtracting the maintenance flow rate and the stored water amount difference from the inflow amount, and is represented by the following equation.

The model 6 is for determining the water intake amount Q based on the minimum water intake amount Q _min , the maximum water intake amount Q _max , and R0 (water intake amount calculation model), and is expressed by the following equation.

That, R0 is, and the minimum intake quantity _{Q min} or more, and equal to or less than the maximum intake amount _{Q max} is, R0 is intake amount Q, and the minimum intake if R0 is smaller than the minimum intake quantity _{Q min} The amount Q _min becomes the water intake amount Q, and when R0 is larger than the maximum water intake amount, the maximum water intake amount Q _max becomes the water intake amount Q.

モデル８は、１日の終了時点における運用推移、放水位Ｈ_ｏｕｔ及び損失落差Ｈ_ｌｏｓに基づいて、有効落差ｈｎを算出するためのものであり、日付ｔの０時における運用水位をＨ_ｔとし、水位を海抜高さに変換するための所定の定数をｂとして、次式により表される。

モデル９は、取水量Ｑ及び有効落差ｈｎに基づいて１日に発電される発電電力Ｐｎを算出するためのものであり、発電の変換効率に係る係数をｃ、重力加速度をｇとして、次式により表される。

モデル１０は、発電電力Ｐｎに基づいて１日に発電される発電電力量Ｅｎを算出するためのもの（電力量算出モデル）であり、次式により表される。

モデル１１は、ある日付についての発電電力量Ｅｎと、その日付に対応する電力価格とに基づいて売電額を算出するためのものであり、次式により表される。

The model 7 is for calculating the amount of water discharged in the water storage facility on the basis of R0 and the amount of water intake Q (hereinafter referred to as normal discharge flow rate, expressed as S). expressed.

The model 8 is for calculating the effective head hn based on the operation transition at the end of the day, the discharge level H _out and the loss head H _los , and the operating water level at 0:00 on the date t is H _t. , Where b is a predetermined constant for converting the water level to the sea level, and is expressed by the following equation.

The model 9 is for calculating the generated power Pn generated on the basis of the water intake amount Q and the effective head hn, where c is a coefficient relating to conversion efficiency of power generation and g is a gravitational acceleration. It is represented by

The model 10 is for calculating the amount of generated power En generated per day based on the generated power Pn (power amount calculation model), and is represented by the following equation.

The model 11 is for calculating the amount of power sold based on the generated power amount En for a certain date and the power price corresponding to the date, and is represented by the following equation.

The operational water level planning unit 214 sets the initial water storage amount received by the water storage amount setting value input unit 212 to V _{1 on} the first day of the operation period, and sets the final target water storage amount received by the water storage amount setting value input unit 212 for the seventh day. as water storage _{V 7,} for each date t (t = 2~6), the water volume _{V t} at time zero predetermined step between _{V max} from _{V min} (e.g., 0.1 and 0.5, 1 ), The simulation is performed, and V _t is determined so that En becomes maximum. The operational water level planning unit 214 uses dynamic programming in this simulation. By using dynamic programming, it can be quickly calculated optimal combination of V _t.

== Screen example ==
FIG. 10 is a diagram illustrating an example of a screen 60 used for the simulation of the operational water level. The screen 60 includes an operation period input field 611, an initial water storage amount input field 612, a final target water storage amount input field 613, and various specifications display fields 621 to 627.

The operation water level planning unit 214 reads the setting values (H _min and H _max ) corresponding to the minimum operation water level and the maximum operation water level from the specification storage unit 251, and displays the read H _min and H _max in the display column 621 and the display column. Displayed at 622. The operational water level planning unit 214 reads the set value (S0) corresponding to the maintenance flow rate from the specification storage unit 251 and displays the read S0 in the display field 623. The operational water level planning unit 214 reads the set values (Q _min and Q _max ) corresponding to the minimum water intake and the maximum water intake from the specification storage unit 251, and displays the read Q _min and Q _max in the display columns 624 and 625. Display each. The operational water level planning unit 214 reads the set value (H _out ) corresponding to the water discharge level from the specification storage unit 251, and displays the read H _out in the display column 626. Operation level planning unit 214, a specification storage unit 251, setting values corresponding to the loss drop reads _{(H los),} displays the read _{H los} the display column 627.
Further, the operational water level planning unit 214 calculates V _max and V _min using the above-described model 2 and model 3, and displays the calculated V _max and V _min in the display column 631 and the display column 632, respectively.

  The predicted inflow amount acquisition unit 213 accesses the inflow amount prediction system 100, acquires the predicted value of the inflow amount R for each day in the operation period predicted by the inflow amount prediction system 100, and displays the acquired R This is displayed in the column 633.

  When the operation period, the initial water storage amount and the final target water storage amount are input to the input fields 611, 612 and 613, and the button 641 is pressed, the water storage amount setting value input unit 212 is input to the input fields 611, 612 and 613. The operation water level planning unit 214 receives the input of the operation period, the initial water storage amount, and the final target water storage amount, and simulates the water storage amount V for each day during the operation period. The operational water level planning unit 214 performs a simulation of the water storage amount V as follows, for example. In the example of FIG. 10, it is assumed that the coefficient a = 30000 unique to the water storage facility and the constant b = 500 for converting the water level to the height above sea level.

The operational water level planning unit 214 sets the initial water storage amount input in the input field 612 as the water storage amount V ₁ on the first day, and sets the final target water storage amount input in the input field 613 as the water storage amount V ₇ on the seventh day. The water storage amount V _t from the first day to the sixth day is simulated, and the water storage amount V is displayed in the display column 651.

The operational water level planning unit 214 calculates the operational water level H by applying the stored water amount V for each date to the model 1 and displays the calculated operational water level H in the display field 652. The operational water level planning unit 214 applies V _t and V _{t + 1} to the model 4 for the date t on the first to sixth days, calculates the water storage amount difference ΔV _t for each date t, and calculates the calculated water storage amount difference ΔV. _t is displayed in the display field 653.

  The operational water level planning unit 214 calculates the R0 by applying the predicted value R of the inflow rate, the maintenance flow rate S0, and the stored water amount difference ΔV to the model 5, and determines the intake amount Q according to the R0 by the model 6. The amount of water taken Q is displayed in the display field 654. The operational water level planning unit 214 calculates the normal discharge flow rate S by applying R0 and the water intake amount Q to the model 7, and displays the calculated S in the display column 655.

The operational water level planning unit 214 applies the calculated operational water level H _{t + 1} , the water discharge level H _out and the loss head H _los to the model 8 for each day t of the first to sixth days, calculates the effective head hn, The calculated hn is displayed in the display field 656.

  The operational water level planning unit 214 applies the water intake amount Q and the effective head hn to the model 9, calculates the generated power Pn and displays it in the display column 657, and applies the calculated Pn to the model 10 to generate the generated power amount. En is calculated, and the calculated En is displayed in the display field 660.

  The operational water level planning unit 214 reads out the power price corresponding to the date of each day during the operation period from the power price database 253 and displays it on the display field 659, and applies the En and the read power price to the model 11. The power sale amount is calculated, and the calculated power sale amount is displayed in the display field 658.

The operation water level planning unit 214 performs simulation by changing the water storage amount V _t (t = 2 to 6) from the maximum V _min to V _max and repeating the above processing, and totals the amount of power sold during the operation period. The combination of the stored water amount V that maximizes the amount is determined. The operational water level planning unit 214 can determine the optimum water storage amount efficiently by determining the combination of the water storage amounts V by dynamic programming. The operational water level planning unit 214 displays the display columns 651 to 660 so as to correspond to the determined combination of the stored water amounts V.

FIG. 11 is a graph showing a result of a simulation of a past actual water level in a water storage facility.
FIG. 11A is a graph showing changes in water intake and operation water level in July 2003, which is known as a relatively high water season. In the water storage facility, the operational water level is determined by the experience of the operator, and the actual value of the amount of electricity sold in the July 2003 period was about 258 (million yen). On the other hand, the amount of power sold corresponding to the combination of the water storage amount V as a result of the simulation was about 269 (million yen). In other words, there was an increase in power sales by about 4%.
FIG. 11B is a graph showing changes in water intake and operating water level in April 2007, which is known as a relatively dry season. The actual value of power sales in the April 2007 period was 107 (million yen), and the power sales price corresponding to the combination of the amount of stored water V as a result of the simulation was 114 (million yen). There was a 6% increase.
As described above, by determining the combination of the water storage amount V so as to maximize the amount of electric power sold and operating the operational water level H by the above simulation, the amount of electric power sold is increased compared to the operation based on the experience of the operator. It was confirmed that it would be possible.

  As described above, according to the water level operation support system of the present embodiment, it is possible to present an operation water level that maximizes the amount of power sold according to the initial water storage amount and the final target water storage amount. Therefore, the operator of the water storage facility can perform hydropower generation more efficiently and effectively by operating the water level of the water storage facility with reference to the presentation from the water level operation support system.

  In the optimum water storage level calculation system 200 of the present embodiment, the combination of the water storage amounts V is determined so that the amount of power sales is maximized. However, the combination that maximizes the amount of generated power En is determined. You may do it. In this case, more efficient power generation can be performed regardless of price fluctuations.

Moreover, the optimum water storage level calculation system 200 may perform simulation by providing an upper limit of the normal discharge flow rate S. In this case, the operational water level planning unit 214 receives the input of the upper limit value from the user, and determines the one with the largest amount of power sale out of the combinations of the stored water amounts V in which the normal discharge flow rate S does not exceed the upper limit value. To. FIG. 12 shows the actual value of the discharge flow rate S in the above-mentioned July 2003 period, the theoretical value of the discharge flow rate S when the power sales amount becomes maximum when no upper limit value is set, and the upper limit value of 15 m ³ / It is the graph which compared with the theoretical value of the discharge flow rate S when the amount of power sales becomes the maximum when it sets with s. As shown in FIG. 12, for example, in the July 24, discharge amount S is increased to 25 ^(m 3/2) close. The amount of electricity sold in this case was also about 269 million yen, an increase of about 4%. Therefore, when operating an actual water storage facility, an upper limit is set for the discharge flow rate in consideration of the downstream impact, such as when there are fishermen or camping people. Even if an upper limit value is set, more efficient power generation can be performed by operating the water level of the water storage facility according to the result of the simulation.

  Moreover, the optimal water storage level calculation system 200 may perform a simulation by providing a lower limit of the minimum operation output. In this case, the operational water level planning unit 214 receives the input of the lower limit value from the user, and determines the one with the largest amount of power sale out of the combinations of the stored water amounts V whose operation output does not fall below the lower limit value. . In actual power plant operation, power generation efficiency is often increased by not operating in an output region where efficiency is significantly reduced, and simulation that is more suitable for the actual situation is possible.

  Moreover, in the inflow amount prediction system 100 of the present embodiment, the amount of water flowing into the water storage facility such as a dam is predicted from the river. However, the inflow amount prediction system 100 can be easily applied to a system that predicts the amount of water flowing through the river. Can do. In this case, the forecast value and the actual value of the temperature and precipitation in the upstream area of the river shall be acquired and recorded.

Further, the inflow prediction system 100 of the present embodiment, the temperature mu ₂ to balance inflow mu ₁ and snow melting begins, was assumed to be stored in advance in the parameter storage unit 152 is not limited to this, in the past A good value may be estimated based on the weather inflow history information. In addition, for each regression model of the present embodiment, when there is a series correlation in the error term, the snow temperature estimation unit 111 may estimate the parameters by the Prais-Winstein transformation or the Cochrane ocut method. In this case, the inflow amount model 5 may use the inflow amount F _t−n before the predetermined period (n period) when the series correlation disappears instead of the inflow amount F _{t−1 of the} previous period as an explanatory variable. .

  In the optimum reservoir level calculation system 200 of the present embodiment, the inflow amount R is acquired from the inflow amount prediction system 100. However, the present invention is not limited to this, and for example, an input of the inflow amount R is received from the user. Also good. Alternatively, the past actual value of the inflow amount may be stored in a database, and the past actual value may be read as R and simulated.

  Further, in the optimum storage level calculation system 200 of the present embodiment, the power price is assumed to be stored in advance in the power price database 253. For example, the power price (JEPX price) at the Japan Wholesale Power Exchange is automatically set. You may make it acquire. In this case, the electric power price database 253 may be omitted, and the JEPX price may be acquired each time the operational water level planning unit 214 performs the simulation.

  Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention can be changed and improved without departing from the gist thereof, and the present invention includes equivalents thereof.

  For example, in the present embodiment, the inflow prediction system 100 and the optimum water storage level calculation system 200 are different computers, but may be realized by a single computer. In addition, at least one of the inflow amount prediction system 100 and the optimum water storage level calculation system 200 can be realized by a plurality of computers.

It is a figure showing the whole water level operation support system composition of this embodiment. It is a graph which shows the change of inflow. It is a figure which shows the hardware constitutions of the inflow amount prediction system 100 of this embodiment. It is a figure which shows the software structure of the inflow amount prediction system 100 of this embodiment. It is a figure which shows the structural example of weather inflow amount track record information. It is a figure which shows the hardware constitutions of the optimal water storage level calculation system. It is a figure which shows the software configuration of the optimal water storage level calculation system. It is a figure which shows the structural example of specification information. It is a figure which shows the structural example of the electric power price database 253. FIG. It is a figure which shows an example of the screen 60 used for the simulation of a working water level. It is a graph which shows the result of having performed simulation about the past performance water level. It is the graph which compared the actual value of the discharge flow rate, the theoretical value of the discharge flow rate in the simulation result with the upper limit value, and the discharge flow rate in the simulation result without the upper limit value.

Explanation of symbols

100 Inflow prediction system 101 CPU
DESCRIPTION OF SYMBOLS 102 Memory 103 Memory | storage device 104 Communication interface 105 Input device 106 Output device 111 Snowfall temperature estimation part 112 Snowmelt amount model estimation part 113 Inflow amount model estimation part 114 Predicted temperature acquisition part 115 Predicted precipitation amount acquisition part 116 Predicted snowmelt amount acquisition part 117 Inflow Amount prediction unit 151 Model storage unit 152 Parameter storage unit 153 Weather and inflow result database 200 Optimal reservoir level calculation system 201 CPU
202 Memory 203 Storage Device 204 Communication Interface 205 Input Device 206 Output Device 211 Specification Input Unit 212 Reservoir Amount Setting Value Input Unit 213 Predicted Inflow Acquisition Unit 214 Operation Water Level Planning Unit 251 Specification Storage Unit 252 Model Storage Unit 253 Power Price Database 300 Communication network

Claims (14)

A system for supporting the operation of a water storage facility for hydroelectric power generation,
A predicted inflow amount obtaining unit for obtaining a predicted value of the inflow amount of water into the water storage facility in each unit period within a predetermined period;
In order to calculate a water intake amount that is an amount of water used for the hydroelectric power generation based on a water storage amount difference that is a difference in water storage amount in the water storage facility from a start time to an end time of the unit period and the inflow amount. Water intake amount calculation model, water level calculation model for calculating the water level of the water storage facility based on the water storage amount, and the amount of electric power generated in the unit period by the hydroelectric power generation based on the water intake amount and the water level. A model storage unit for storing a power amount calculation model for calculation;
For each unit period within the predetermined period, the water storage amount of the water storage facility at the start of the unit period is changed , and the intake water is calculated from the changed storage amount and the predicted value of the inflow amount in the unit period. It calculates the intake amount using the amount calculated model, from the water quantity is the change, the calculated water level using the water level calculation model, from said calculated amount of water intake and the water level was above calculated, the electric power calculation Calculating the amount of electric power using a model , and determining an optimal water storage amount determining unit that determines an optimal combination of the water storage amounts according to the calculated electric energy;
Applying each of the determined water storage amounts to the water level calculation model to calculate the water level, and to output the calculated water level, an operational water level output unit,
A water storage facility operation support system characterized by comprising: The water storage facility operation support system according to claim 1,
The optimum water storage amount determination unit determines the combination of the optimal water storage amounts that has the maximum sum of the electric energy calculated for each unit period in the predetermined period from among the combinations of the changed water storage amounts. To decide as,
Water storage facility operation support system characterized by The water storage facility operation support system according to claim 1,
A power price acquisition unit that acquires a price per unit power in each period,
The optimum water storage amount determination unit has a maximum sum of values obtained by multiplying each of the electric energy calculated for each unit period within the predetermined period by the price among the changed combinations of water storage amounts. Determining what is the optimal combination of water storage,
Water storage facility operation support system characterized by A water storage facility operation support system according to any one of claims 1 to 3,
A water storage amount set value storage unit for storing an upper limit value and a lower limit value of the water storage amount;
The optimum water storage amount determination unit changes the water storage amount at the start time of the unit period for each predetermined step from the lower limit value to the upper limit value, and calculates the electric energy for each unit period. ,
Water storage facility operation support system characterized by The water storage facility operation support system according to claim 4,
The optimum water storage amount determination unit determines the optimum water storage amount combination by dynamic programming;
Water storage facility operation support system characterized by The water storage facility operation support system according to claim 1,
A database for storing the actual value of the inflow and the actual value of the rainfall for each predetermined period;
The difference between the rainfall amount in a certain first period and the inflow amount in the second period before the first period from the first period and the equilibrium inflow amount that is a predetermined constant is used as an explanatory variable, and the second period from the second period to the second period. A model storage unit for storing a regression model whose objective variable is an increase amount of the inflow until one period;
Based on the actual value of the inflow and the actual value of the rainfall stored in the database, and the regression model stored in the model storage unit, regression analysis is performed for each of the explanatory variables. An inflow model estimation unit for estimating a coefficient;
A predicted rainfall amount acquisition unit that acquires a predicted value of the rainfall amount in a prediction period that is a prediction target period;
The actual value of the inflow from the prediction period to the period before the predetermined period is acquired from the database, and the predicted value of the rainfall, the actual value of the inflow, and the regression coefficients are applied to the regression model. An increase amount prediction unit that calculates a predicted value of the increase amount in the prediction period,
The predicted inflow amount acquisition unit calculates the predicted value of the inflow amount in the prediction period by adding the predicted value of the increase amount to the actual value of the inflow amount,
Water storage facility operation support system characterized by The water storage facility operation support system according to claim 6,
The database further stores the actual value of the amount of snow melt for each predetermined period,
The regression model stored in the model storage unit further includes the amount of snow melt in the first period as an explanatory variable,
A predicted snowmelt amount acquisition unit for acquiring a predicted value of the snowmelt amount in the prediction period;
The inflow amount model estimation unit includes the actual value of the inflow amount stored in the database, the actual value of the rainfall amount, the actual value of the snowmelt amount, and the regression model stored in the model storage unit. Performing a regression analysis based on the above and estimating the regression coefficient,
Water storage facility operation support system characterized by The water storage facility operation support system according to claim 1,
For each predetermined period, a database that stores the actual value of the inflow, the actual value of the rainfall, the actual value of the temperature, and the actual value of the amount of snow;
The difference between the rainfall amount in a certain first period, the snowmelt amount in the first period, and the inflow amount in the second period before the predetermined period from the first period and the equilibrium inflow amount that is a predetermined constant is used as an explanatory variable. An inflow model that is a regression model having an increase in the inflow from the second period to the first period as a target variable, and a predetermined constant indicating the temperature at which snow melting starts from the temperature in the first period. The value obtained by multiplying the subtracted value or the larger value of zero by the amount of snow in the second period and the amount of rainfall in the first period are explanatory variables, and the amount of snow melt in the first period is the purpose. A model storage unit for storing a snowmelt amount model which is a regression model as a variable;
Performing regression analysis based on the actual temperature value stored in the database, the actual snow accumulation value, the actual rainfall value, and the snowmelt model stored in the model storage unit, A snowmelt amount model estimation unit for estimating a regression coefficient for each of the explanatory variables related to the snowmelt amount model;
Applying the actual temperature value, the actual snow accumulation value, the actual rainfall value, and the estimated regression coefficient stored in the database to the snow melting model, the snow melting for each predetermined period. The theoretical value of the amount is calculated, the actual value of the inflow amount stored in the database, the actual value of the rainfall amount, the theoretical value of the calculated snow melting amount, and the model storage unit An inflow model estimation unit that performs a regression analysis based on the inflow model, and estimates a regression coefficient for each of the explanatory variables related to the inflow model;
A predicted rainfall amount acquisition unit that acquires a predicted value of the rainfall amount in a prediction period that is a prediction target period;
A predicted temperature acquisition unit that acquires a predicted value of the temperature in the prediction period;
The actual value of the snow cover amount in the period before the predetermined period of the prediction period is acquired from the database, the predicted value of the temperature, the acquired actual value of the snow cover amount, the predicted value of the rainfall amount, and the snow melting amount Applying each of the explanatory variables related to the model to the snowmelt amount model, and calculating a predicted value of the snowmelt amount in the prediction period;
The actual value of the inflow amount in the period from the prediction period to the predetermined period is acquired from the database, the predicted value of the rainfall amount, the predicted value of the snowmelt amount, the actual value of the inflow amount, and each regression coefficient And an increase amount prediction unit that calculates a predicted value of the increase amount in the prediction period by applying to the inflow amount model,
The predicted inflow amount acquisition unit calculates the predicted value of the inflow amount in the prediction period by adding the predicted value of the increase amount to the actual value of the inflow amount,
Water storage facility operation support system characterized by A method for supporting the operation of a water storage facility for hydropower generation,
A water intake amount, which is the amount of water used for the hydroelectric power generation, based on the difference in the storage amount that is the difference in the storage amount in the storage facility from the start point to the end point of the unit period and the inflow amount to the storage facility. The water intake calculation model for calculating, the water level calculation model for calculating the water level of the water storage facility based on the water storage amount, and the hydroelectric power generation based on the water intake and the water level are generated in the unit period. A computer including a memory storing a power amount calculation model for calculating a power amount ,
Obtain a predicted value of the amount of water flowing into the water storage facility in each unit period within a predetermined period ,
For each of the unit period before Symbol within a predetermined period, the changing the water volume of the reservoir property at the start of the unit period, from the predicted value of the inflow in the water volume and the unit period is the change, the using intake quantity calculation model calculates the water intake, the water quantity is the change, to calculate the water level using the water level calculation model, from the calculated amount of water intake and the water level was above calculated, the amount of power Calculate the amount of power using a calculation model, determine the optimal combination of the water storage amount according to the calculated amount of power,
Applying each of the determined water storage amounts to the water level calculation model to calculate the water level and outputting the calculated water level;
Water storage facility operation support method characterized by The water storage facility operation support method according to claim 9,
The computer determines, as the optimal combination of stored water amounts, the combination of the changed stored water amounts that maximizes the total amount of power calculated for each unit period within the predetermined period. ,
Water storage facility operation support method characterized by The water storage facility operation support method according to claim 9,
The computer further obtains a price per unit power in each period,
The computer has a maximum sum of values obtained by multiplying each of the electric energy calculated for each of the unit periods within the predetermined period by the price, among the changed combinations of stored water amounts, To determine the optimal water storage combination,
Water storage facility operation support method characterized by A program for supporting the operation of a water storage facility for hydropower generation,
A water intake amount, which is the amount of water used for the hydroelectric power generation, based on the difference in the storage amount that is the difference in the storage amount in the storage facility from the start point to the end point of the unit period and the inflow amount to the storage facility. The water intake calculation model for calculating, the water level calculation model for calculating the water level of the water storage facility based on the water storage amount, and the hydroelectric power generation based on the water intake and the water level are generated in the unit period. In a computer comprising a memory storing a power amount calculation model for calculating the power amount ,
Obtaining a predicted value of the amount of water flowing into the water storage facility in each unit period within a predetermined period ;
For each of the unit period before Symbol within a predetermined period, the changing the water volume of the reservoir property at the start of the unit period, from the predicted value of the inflow in the water volume and the unit period is the change, the using intake quantity calculation model calculates the water intake, the water quantity is the change, to calculate the water level using the water level calculation model, from the calculated amount of water intake and the water level was above calculated, the amount of power Calculating the amount of power using a calculation model , and determining an optimal combination of the amount of stored water according to the calculated amount of power;
Applying each of the determined water storage amounts to the water level calculation model to calculate the water level, and outputting the calculated water level;
A program for running A program according to claim 12,
In the step of determining the optimum combination of stored water amounts, the computer causes the computer to maximize the total amount of electric power calculated for each unit period within the predetermined period among the changed combinations of stored water amounts. Is determined as a combination of the optimum water storage amount,
A program characterized by A program according to claim 12,
Further causing the computer to execute a step of obtaining a price per unit power in each period,
In the step of determining the optimum water storage amount combination, the computer multiplies the electric power amount calculated for each unit period within the predetermined period by the price from the changed water storage amount combination. The maximum sum of the values obtained is determined as the optimum combination of water storage amount,
A program characterized by

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