JP5235922B2 - Water storage facility operation support system, operation support method and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To support operation of a water storage facility such that power generated by water-power generation can be increased. <P>SOLUTION: A water amount related to an inflow 5 of water into the water storage facility is predicted, generated electric energy and a price thereof are calculated based on the predicted inflow amount, and weather information or the like, a water level of each prescribed unit period is optimized such that a power selling amount becomes maximum, a water amount of ineffective discharges 7, 8 is added to evaluation, and water levels of reservoirs 1, 2 of each unit period constituting a first unit period are optimized while satisfying a variety of restriction. <P>COPYRIGHT: (C)2011,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.

Among the present inventions for solving the above-mentioned problems, the main invention is the first and second reservoirs, the first generator that performs hydroelectric power generation using the water discharged from the first reservoir, and the first And a second generator that performs hydroelectric power generation using water discharged from the second reservoir, and the water used in the first generator supports the operation of the water storage facility that flows into the second reservoir A predicted inflow amount for obtaining a predicted value of an inflow amount that is an amount of water flowing into the first and second reservoirs from other than the first generator in each unit period within a predetermined period An acquisition unit, a reservoir discharge amount model for calculating a discharge amount from the reservoir based on the amount of change in the reservoir amount from the start point to the end point of the unit period and the inflow amount, and the hydroelectric power generation Hoyo water intake amount is the amount of water to be A model storage unit for storing an electric energy model for calculating electric energy generated in the unit period by the hydroelectric power generation based on the water storage amount of the reservoir, and discharged from the water storage facility without being supplied to the generator A penalty coefficient storage unit that stores a penalty coefficient that is a coefficient for evaluating an invalid discharge flow that is an amount of water, and a minimum value and a maximum value of a water intake amount that are amounts of water that the generator accepts from the reservoir for power generation A constraint storage unit that stores a constraint on a water intake amount including a first storage amount that is a storage amount of the first reservoir at the start time of the unit period for each unit period within the predetermined period, and While changing each of the 2nd water storage amount which is the water storage amount of the said 2nd reservoir, calculating the change amount of the said 1st water storage amount and the 2nd water storage amount, the said flow A predicted amount of water and a change amount of the first water storage amount are applied to the reservoir water discharge amount model to calculate a first water discharge amount from the first reservoir, and the predicted value of the inflow amount and the second water discharge amount are calculated. The amount of change in the amount of stored water is applied to the reservoir discharge model to calculate the second discharge amount from the second reservoir, and the maximum amount of the first discharge amount that satisfies the constraint on the intake amount is calculated. A value obtained by subtracting a first water intake amount, which is a water amount, from the first water discharge amount, and a second water intake amount, which is a maximum water amount satisfying a restriction condition for the water intake amount, among the second water discharge amounts, 2 to calculate the reactive discharge flow rate, and apply the first and second intake water amounts and the first and second water storage amounts to the electric energy model. The first and second electric energy are calculated, and the first and second electric energy are calculated. A value obtained by multiplying the invalid discharge flow rate by the penalty coefficient is subtracted from a total value to calculate an evaluation value of the amount of generated power, and the first and second reservoirs that maximize the total of the evaluation values An optimum water storage amount determination unit that determines a combination of amounts as a plan of the optimum water storage amount is provided.

According to the storage facility operation support system of the present invention, simulation is performed by changing each of the storage amounts of the two reservoirs, and the optimal storage amount of the reservoir that satisfies the constraints and maximizes the evaluation value of the electric energy is achieved. Can be planned. Therefore, the operator of the water storage facility can manage the water storage amount of the reservoir according to the output from the water storage facility operation support system of the present invention, and can manage the optimal water storage amount. The amount of water stored based on this can be used by inexperienced ones.
In addition, the amount of generated power can be evaluated in consideration of the invalid discharge flow rate.

The water storage facility operation support system according to the present invention further includes a power amount weight storage unit that stores weights for the power amount for each time zone, and the optimum water storage amount determination unit corresponds to a time zone to which each unit period belongs. The weight to be read from the power amount weight storage unit, and the value obtained by multiplying the total value of the first and second power amounts by the weight is subtracted from the value obtained by multiplying the invalid discharge flow rate by the penalty coefficient. The evaluation value may be calculated.
In this case, the amount of generated power can be weighted and evaluated for each time period. Therefore, for example, in a time zone where power demand is high, it is possible to perform a flexible evaluation according to the time zone, such as increasing the evaluation of the amount of generated power.

  In the water storage facility operation support system of the present invention, the constraint condition storage unit further stores a constraint condition for the invalid discharge amount, and the optimum water storage amount determination unit is a combination of the first and second water storage amounts. Among them, the combination in which the total of the evaluation values becomes the maximum may be determined as the optimum water storage amount plan in which the invalid discharge flow rate satisfies the constraint condition for the invalid discharge flow rate.

Moreover, the other aspect of this invention is the 1st and 2nd reservoir, the 1st generator which performs hydroelectric power generation using the drainage from the said 1st reservoir, and the water discharge from the said 2nd reservoir A second power generator that performs hydroelectric power generation using water, and the water used in the first power generator is a method for supporting the operation of a water storage facility that flows into the second reservoir, The computer obtains a predicted value of the inflow amount that is the amount of water flowing into the first and second reservoirs from other than the first generator in each unit period within a predetermined period, and starts the unit period A reservoir discharge model for calculating a discharge amount from the reservoir based on a change amount of the storage amount in the reservoir from the time point to the end point and the inflow amount, and a water intake amount that is an amount of water used for the hydroelectric power generation and Based on the amount of water stored in the reservoir An electric energy model for calculating the electric energy generated by the hydroelectric power generation in the unit period is stored in a memory, and an ineffective flow rate that is an amount of water discharged from the water storage facility without being supplied to the generator is evaluated. A penalty coefficient that is a coefficient to be taken in, a constraint condition for the water intake including a minimum value and a maximum value of the water intake that is the amount of water that the generator accepts from the reservoir for power generation, For each of the unit periods, the first storage amount that is the storage amount of the first reservoir and the second storage amount that is the storage amount of the second reservoir at the start of the unit period are changed. And calculating a change amount of the first storage amount and a change amount of the second storage amount, and applying the predicted value of the inflow amount and the change amount of the first storage amount to the reservoir discharge model. Said A first discharge amount from one reservoir is calculated, and a predicted value of the inflow amount and a change amount of the second storage amount are applied to the reservoir discharge amount model to calculate a second discharge amount from the second reservoir. A discharge amount is calculated, and a value obtained by subtracting a first intake amount, which is a maximum amount of water satisfying a constraint on the intake amount, from the first discharge amount, from the first discharge amount, and the second discharge amount. A sum of values obtained by subtracting the second water intake amount, which is the maximum water amount satisfying the constraint on the water intake amount from the water discharge amount, from the second water discharge amount, and calculating the invalid discharge flow rate. The first and second power amounts are calculated by applying the two water intake amounts and the first and second stored water amounts to the power amount model, and from the total value of the first and second power amounts. Subtracting the value obtained by multiplying the invalid discharge flow by the penalty coefficient, An evaluation value of the competence is calculated, and a combination of the first and second water storage amounts that maximizes the sum of the evaluation values is determined as an optimal water storage amount plan.

Moreover, in the water storage facility operation support method of the present invention, the computer stores a weight for each power period in the memory, and reads the weight corresponding to the time period to which each unit period belongs from the memory. The evaluation value may be calculated by subtracting a value obtained by multiplying the invalid discharge flow rate by the penalty coefficient from a value obtained by multiplying the total value of the first and second electric energy by the weight.

Moreover, the other aspect of this invention is the 1st and 2nd reservoir, the 1st generator which performs hydroelectric power generation using the drainage from the said 1st reservoir, and the water discharge from the said 2nd reservoir And a second generator that performs hydroelectric power generation using the water, and the water used in the first generator is a program for supporting the operation of a water storage facility that flows into the second reservoir. Obtaining a predicted value of an inflow amount that is an amount of water flowing into the first and second reservoirs from other than the first generator in each unit period within a predetermined period; and A reservoir discharge model that calculates the discharge from the reservoir based on the amount of change in the reservoir in the reservoir from the start to the end of the unit period and the inflow, and the amount of water used for the hydroelectric power generation there intake amount and the Storing in a memory an electric energy model for calculating an electric energy generated in the unit period by the hydroelectric power generation based on an amount of water stored in the water reservoir; and water discharged from the water storage facility without being supplied to the generator A step of storing a penalty coefficient, which is a coefficient for evaluating an ineffective flow rate, and a water intake amount including a minimum value and a maximum value of a water intake amount that the generator accepts from the reservoir for power generation. A first storage amount that is a storage amount of the first reservoir and a storage amount of the second reservoir at the start time of the unit period for each unit period within the predetermined period And changing each of the first and second stored water amounts, and calculating a change amount of the first stored water amount and a changed amount of the second stored water amount. A first water discharge amount from the first reservoir is calculated by applying a change amount of the first water storage amount to the reservoir discharge amount model, and a predicted value of the inflow amount and a change amount of the second water storage amount are calculated. Is applied to the reservoir discharge model to calculate a second discharge amount from the second reservoir, and the first discharge amount is a maximum amount of water that satisfies a restriction condition for the intake amount out of the first discharge amount. A value obtained by subtracting a water intake amount from the first water discharge amount and a second water intake amount that satisfies a constraint condition for the water intake amount out of the second water discharge amount is subtracted from the second water discharge amount. And the first and second water intake amounts and the first and second water storage amounts are applied to the electric energy model to calculate the first and second electric powers. The amount is calculated and the invalidity is calculated from the total value of the first and second electric energy. A value obtained by multiplying the discharge flow rate by the penalty coefficient is subtracted to calculate an evaluation value of the generated electric energy, and an optimal combination of the first and second water storage amounts that maximizes the total of the evaluation values is calculated. And a step of determining as a plan for the amount of stored water.

In the program of the present invention, in the step of further causing the computer to execute a step of storing a weight of the electric energy for each time zone in the memory, and in the step of determining the optimal water storage amount plan in the computer, The weight corresponding to the time zone to which each unit period belongs is read from the memory, and the invalid discharge is multiplied by the penalty coefficient from a value obtained by multiplying the total value of the first and second electric energy by the weight. The evaluation value may be calculated by subtracting the obtained value .

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

  According to the present invention, it is possible to increase the power generated by hydroelectric power generation.

It is a figure for demonstrating the water storage facility assumed by this embodiment. 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 10 of this embodiment. It is a figure which shows the software structure of the inflow amount prediction system 10 of this embodiment. It is a figure which shows the structural example of the meteorological inflow amount performance information memorize | stored in the inflow amount performance database. It is a figure which shows the hardware constitutions of the optimal water level calculation system. It is a figure which shows the software structure of the optimal water storage level calculation system. It is a figure which shows the structural example of the item information memorize | stored in the item memory | storage part. It is a figure which shows an example of the screen 31 which inputs specification information. It is a figure which shows the structural example of the electric power price database. It is a figure which shows the structural example of the daily optimal water level database 255. FIG. It is a figure which shows the structural example of the efficiency test result database. It is a figure which shows the flow of the estimation process by the total conversion efficiency model estimation part 214. FIG. It is a figure which shows the structural example of the river flow volume results database 258. FIG. It is a figure which shows the flow of the estimation process by the river flow model estimation part 216. FIG. It is a figure which shows an example of the screen 60 used for the simulation of the optimal water level of each day. It is a graph which shows the result of having performed simulation about the past performance water level. It is a figure which shows an example of the screen 70 used when simulating the optimal water level for every hour. It is a figure which shows the flow of the process which concerns on the simulation about the optimal water level for every hour. It is a figure which shows the flow of a calculation process of an evaluation value. It is a diagram showing a flow of calculation of the evaluation value E _t. It is a figure which shows the flow of a detection process of a constraint violation. It is a figure which shows the flow of a calculation process of a river flow rate and the amount of discharged water. It is a graph which shows the electric power generation amount in the case where individual optimization is performed and in the case where collective optimization is performed.

  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.

== Composition of water storage facilities ==
FIG. 1 is a diagram for explaining a water storage facility assumed in the present embodiment. The water storage facility includes two reservoirs 1 and 2 and generators 3 and 4. In the water storage facility of this embodiment, the reservoirs 1 and 2 are connected.
The reservoir 1 receives an inflow 5 of water from a river or a forest. Reservoir 1 stores water and discharges the stored water. The generator 3 receives the water discharged from the reservoir 1 (hereinafter, the amount of water that the generator receives from the reservoir is referred to as “amount of water intake”, and is expressed as “Q”). It is assumed that the generator 3 is a water turbine generator. The water used for power generation in the generator 3 flows into the reservoir 2 through the generator 3. Reservoir 2 also receives inflow 6 from river A and the like. Reservoir 2 also stores water and discharges the stored water. The generator 4 receives the water discharged from the reservoir 2 and generates power. The water used for power generation in the generator 4 is discharged into the river through the generator 4. In the present embodiment, the generators 3 and 4 are assumed to have the same performance.

From reservoirs 1 and 2, a constant amount of water (hereinafter referred to as “maintenance flow rate”, expressed as “S0”; the unit is “m ³ / s”) flows directly into rivers A and B. It is. This discharge of the maintenance flow rate is also called a responsible discharge. From the reservoir 1, an ineffective discharge 7 that is not given to the generator 3, is not used for responsible discharge, is not stored in the reservoir 1, and is discharged into the river A can also be generated. The invalid discharge 7 occurs, for example, when the water level of the reservoir 1 exceeds a predetermined maximum water level or when the reservoir 1 discharges water that exceeds the sum of the water intake amount and the maintenance flow rate of the generator 3. Similarly, an invalid discharge 8 can also be generated from the reservoir 2.

  There are various restrictions on water storage facilities. For example, the water levels of the reservoir 1 and the reservoir 2 (hereinafter referred to as “H”, and the water levels of the reservoirs 1 and 2 are also referred to as “H1” and “H2”, respectively, the unit is “m”). There is a restriction that it must be within the range. The details of restrictions in the water storage facility will be described later.

In the water level operation support system of the present embodiment, the amount of water related to the inflow 5 of water into the water storage facility (hereinafter referred to as “inflow amount”, referred to as “R”. In addition, the inflow amount to the reservoirs 1 and 2 is indicated respectively. “R1” and “R2”) are predicted, and the amount of electricity generated and the price (hereinafter referred to as “amount of electricity sold”) are calculated based on the predicted inflow amount and weather information, etc. In addition to optimizing the water level for each predetermined unit period (hereinafter also referred to as “first unit period”. In this embodiment, it is assumed to be one day) so that the amount of electricity sold is maximized, the invalid discharges 7 and 8 The amount of water (hereinafter referred to as “invalid discharge flow rate”, expressed as “S”. The invalid discharge flow from the reservoirs 1 and 2 are expressed as “S1” and “S2”, respectively, and the unit is “m ³ / s. )) And the first unit period in addition to the evaluation Position period the water level of the reservoir 1 and 2 for each (hereinafter, "second unit period" and 1 hour to. In. This embodiment is also referred to), to optimize while satisfying various constraints.
Details will be described below.

== System configuration ==
FIG. 2 is a diagram showing the overall configuration of the water level operation support system of the present embodiment.
The water level operation support system of the present embodiment is configured to include two subsystems, an inflow amount prediction system 10 that predicts the inflow amount, and an optimum water level calculation system 20 that calculates the optimum water level in the water storage facility. Each of the inflow amount prediction system 10 and the optimum water storage level calculation system 20 is a computer such as a personal computer, a workstation, or a PDA (Personal Digital Assistance). The inflow amount prediction system 10 and the optimum water storage level calculation system 20 may be configured by a plurality of computers.
The inflow amount prediction system 10 and the optimum water storage level calculation system 20 are connected to each other via a communication network 30 so as to communicate with each other. The communication network 30 is, for example, the Internet or a LAN (Local Area Network). The communication network 30 is constructed by, for example, Ethernet (registered trademark), a public telephone line network, a wireless communication network, or the like.

== Inflow prediction system 10 ==
In the inflow amount prediction system 10 of the present embodiment, the prediction accuracy is improved by predicting the inflow amount in consideration of the snowmelt amount.
FIG. 3 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 10 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 parameters, and predicted values of temperature obtained by, for example, weather forecasts (hereinafter referred to as “predicted temperature”) and precipitation prediction values (hereinafter referred to as “predicted precipitation”) as regression models. Apply to calculate the predicted value of inflow.

  FIG. 4 is a diagram illustrating a hardware configuration of the inflow amount prediction system 10 of the present embodiment. The inflow amount prediction system 10 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 30. 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, and the like. 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. 5 is a diagram illustrating a software configuration of the inflow amount prediction system 10 of the present embodiment. The inflow prediction system 10 of this 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 10 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 including various actual values of meteorology and actual values of inflow (hereinafter referred to as “meteorological inflow history information”). FIG. 6 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 amount, snowfall amount, and snowfall amount 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 statistical model of precipitation (hereinafter referred to as “precipitation model 1”), a statistical model of snowfall (hereinafter referred to as “snowfall model 2”), and a statistical model of snowfall (hereinafter referred to as “rainfall model 2”). , “Snow accumulation model 3”), a statistical model of snow melting (hereinafter referred to as “snow melting model 4”), and a statistical model relating to the increase in inflow from the previous day (hereinafter referred to as “inflow model 5”). ") Is stored. 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 μ ₁ , the temperature μ _{2 at which} snow melting starts, the regression coefficients α _{1 to} α ₃ , β ₁ , β ₂ 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 advance in the parameter storage unit 152 as predetermined constants.

In the following description, the temperature, precipitation amount, snowfall amount, snowfall amount, snowmelt amount, and inflow amount on the date t are T _t , P _t , _St , D _t , M _t, and R _t , respectively. Let ΔR _{t 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 .

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.

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。 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 R _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 ΔR _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 The 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.

を回帰分析して、回帰係数α_２および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およびα_３をパラメタ記憶部１５２に登録する。 Moreover, the snowfall temperature estimation part 111 reads the meteorological inflow actual result information corresponding to the date t from the meteorological and inflow actual result database 153 for each date t, the precipitation and temperature of the read meteorological inflow actual result information, and the above 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 history information, P2 _t , and the snowfall model 2, and estimates the regression variable γ. The snowfall temperature estimation unit 111 registers the estimated regression variable γ in the parameter storage unit 152.
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, the inflow model estimation unit 113 calculates P1 _t for each date t based on the precipitation in the precipitation data 1, δ and the weather inflow performance information corresponding to the date t for each date t. The inflow amount model 5 is subjected to regression analysis 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 “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 “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 amount predicting unit 117 includes the predicted temperature acquired by the predicted temperature acquiring unit 114, the predicted precipitation acquired by the predicted precipitation acquiring unit 115, the parameters estimated by the snowfall temperature estimating unit 111, the weather inflow actual result information, and the inflow amount. A predicted value of the inflow amount (hereinafter referred to as “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 R _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 predicting unit 117 substitutes α ₁ , μ ₁ , R _t−1 , β ₁ , P1 _t , β ₂ , and M _t into the inflow amount model 5 to calculate a predicted value ΔR _t of the inflow increase amount. , R _t−1 is added to ΔR _t to calculate the predicted inflow amount R _t .

As described above, according to the inflow amount prediction system 10 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 10 of this embodiment, the amount of snowmelt can be calculated from precipitation and temperature. Prediction of precipitation and temperature are easily available because there are various methods for weather forecasting. 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 20 ==
The optimal water level calculation system 20 simulates the water levels of the reservoirs 1 and 2 using the predicted value of the inflow amount predicted by the inflow amount prediction system 10, and calculates an optimal water level plan.

  FIG. 7 is a diagram illustrating a hardware configuration of the optimum water storage level calculation system 20. As shown in the figure, the optimum water storage level calculation system 20 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 30, 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. 8 is a diagram illustrating a software configuration of the optimum water storage level calculation system 20. As shown in the figure, the optimum water level calculation system 20 includes a specification input unit 211, a stored water amount set value input unit 212, a predicted inflow amount acquisition unit 213, an overall conversion efficiency model estimation unit 214, a daily optimal water level plan unit. 215, river flow model estimation unit 216, hourly optimum water level planning unit 217, optimum water level output unit 218, specification storage unit 251, model storage unit 252, constraint storage unit 253, power price database 254, daily optimum water level database 255 The hourly optimum water level database 256 is provided. It should be noted that the specification input unit 211, the stored water amount set value input unit 212, the predicted inflow amount acquisition unit 213, the total conversion efficiency model estimation unit 214, the daily optimal water level planning unit 215, the river flow model estimation unit 216, the hourly optimal water level The planning unit 217 and the optimum water level output unit 218 are realized by the CPU 201 included in the optimum water storage level calculation system 20 reading out the program stored in the storage device 203 to the memory 202 and executing it. In addition, the specification storage unit 251, the model storage unit 252, the constraint storage unit 253, the power price database 254, the daily optimal water level database 255, and the hourly optimal water level database 256 are stored in the memory 202 or the memory included in the optimal water storage level calculation system 20. This is realized as a storage area provided by the device 203. The specification storage unit 251, model storage unit 252, constraint storage unit 253, power price database 254, daily optimal water level database 255, and hourly optimal water level database 256 are all or part of May be managed by different database servers, and the optimum water level calculation system 20 may access the database server.

  The specification storage unit 251 (corresponding to the “penalty coefficient storage unit” of the present invention) is information including set values of various specifications such as a water storage facility, a river, and a power generation facility (hereinafter referred to as “specification information”). .) Is memorized. FIG. 9 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 set value. In the present embodiment, it is assumed that the specification information can include a setting value for each date and time and is set in the format of “day / hour / separate”.

  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. FIG. 10 is a diagram illustrating an example of a screen 31 on which the specification input unit 211 receives input of specification information. The specification input unit 211 receives specification information input on the screen 31 by the user and registers it in the specification storage unit 251.

In addition, the specification input unit 211 previously stores the maximum water level of the reservoir 1 (hereinafter referred to as “H1 _max ”, the unit is “m”), and the minimum water level of the reservoir 1 as specifications relating to the water storage facility. (Hereinafter referred to as “H1 _min ”, the unit is “m”), the maximum water level of the reservoir 2 (hereinafter referred to as “H2 _max ”, the unit is “m”), the reservoir 2 Minimum water level (hereinafter referred to as “H2 _min ”. The unit is “m”) The maximum water volume (hereinafter referred to as “maximum water intake”) that can be given to the maintenance flow rate S0 and the generators 3 and 4; “Q _max ” (unit is “m ³ / s”), the minimum amount of water available for power generation (hereinafter referred to as “minimum water intake” and “Q _min ”). “M ³ / s”), the height of water discharge after power generation (hereinafter “water discharge level”) Called ", referred to as" _{H out} ". In addition, the water discharge position according to the reservoirs 1 and 2, respectively referred to as" _{H1 out} "and" _{H2 out} ". The unit is" m ".), Loss drop ( Hereinafter, it is expressed as “H _los ”, and the loss heads related to the reservoirs 1 and 2 are expressed as “H1 _los ” and “H2 _out ”, respectively, and the unit is “m”. Upper limit value (hereinafter referred to as “upper limit ineffective discharge flow rate”, expressed as “S _max ”. The unit is “m ³ / s”), the amount of water stored in reservoirs 1 and 2 (hereinafter referred to as “V”) In addition, the storage amounts of the reservoirs 1 and 2 are also expressed as “V1” and “V2”, respectively, and the unit is “m ³ ” (hereinafter referred to as “step amount”). m ³ / s · date "in it.), against the disabled discharge amount Negative evaluation coefficient (hereinafter referred to as “penalty coefficient”, expressed as “γ”), and the like, and the specification information about the received specification is created and stored in the specification storage unit 251 It shall be registered. In the present embodiment, the maximum water level H1 _{max of} the reservoir 1, the minimum water level H1 _min of the reservoir 1, the maintenance flow rate S 0, the maximum intake amount Q _max , the minimum intake amount Q _min , the maximum water level H 2 _{max of} the reservoir 2, It is assumed that the specification information for each date and time is included in the specification information regarding the minimum water level H2 _min and the maintenance river flow rate Lc.

  The power price database 254 stores the power price by date and time zone. FIG. 11 is a diagram illustrating a configuration example of the power price database 254. As shown in the figure, the power price database 254 stores an evaluation value of the power price (the unit is “yen / kWh”) in association with the date or the time zone. The electric power price does not need to coincide with the actual electric power price to be sold, and the weight for the electric energy in consideration of the power generation cost and demand fluctuation is set as the price. The power price database 254 may store the power price corresponding to the month or season instead of the date, or store the power price in association with the season, month, date and time zone. You may do it. In the present embodiment, it is assumed that the power price is input from the user in advance and registered in the power price database 254.

  The water storage amount set value input unit 212 includes a period (hereinafter referred to as “operation period”. In this embodiment, the length of the operation period is 6 days) and the reservoirs 1 and 2. For the first day of the operation period at the start of the day (0 o'clock) (hereinafter referred to as “initial water storage”) and at the end of the last day (24 o'clock, that is, the start of the seventh day) )) With the target value of the water storage amount (hereinafter referred to as “final target water storage amount”). For example, the water storage amount set value input unit 212 can accept input of the initial water storage amount and the target water storage amount from the screen 31 of FIG. 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. Further, the water storage amount set 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 10 and acquires a predicted value of the inflow amount R for each day in the operation period predicted by the inflow amount prediction system 10. 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. In the present embodiment, it is assumed that the amount of inflow from the river or the like to the reservoirs 1 and 2 is the same. In the reservoir 2, inflow from the generator 3 exists in addition to inflow from a river or the like, R2 = R1 + Q.

  The daily optimal water level database 255 stores the optimal water level of the reservoirs 1 and 2 on each day during the operation period. FIG. 12 is a diagram illustrating a configuration example of the daily optimal water level database 255. As shown in the figure, the daily optimum water level database 255 stores optimum water levels H1 and H2 for each day (0th to 6th days) during the operation period.

The daily optimal water level planning unit 215 (corresponding to the “optimum water storage amount determination unit” of the present invention) calculates the optimal water levels H1 and H2 of the reservoirs 1 and 2 for each day by simulation, and calculates each calculated The optimum water levels H1 and H2 for the day are registered in the daily optimum water level database 255. The statistical model used by the daily optimal water level planning unit 215 for the simulation is stored in the model storage unit 252. The model storage unit 252 stores the following models A1 to A13. The model A1 is for calculating the water level H based on the water storage amount V (water level calculation model), and is expressed by the following equation. . _{Incidentally, a 1, b 1, c} 1 is a constant specific to the reservoir.

The model A2 is referred to as an upper limit value of water storage amount (hereinafter referred to as “upper limit water storage amount” based on the highest water level H _max (the highest water level H1 _max or the highest water level H2 _max ), and is expressed as V _max . The upper limit water storage amount is expressed as “V1 _max ” and “V2 _max ” (the unit is “m”), and is expressed by the following equation.

The model A3 is referred to as a lower limit value of the stored water amount (hereinafter referred to as “lower limit stored water amount”, based on the lowest water level H _min (the first lowest water level H1 _min or the second lowest water level H2 _min ), and is expressed as “V _min ”. The lower limit water storage amounts for the reservoirs 1 and 2 are respectively expressed as “V1 _min ” and “V2 _min ” (unit is “m”), and are expressed by the following equations.

The model A4 is based on the water storage amount from 0:00 to 24:00 on the first day (that is, 0:00 on the next day). It is expressed as “ΔV”), and is expressed by the following equation, where V _t is a water storage amount at 0:00 on a certain date t.

The model A5 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.

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。 Model A6 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 represented 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.

The model A7 is for calculating the ineffective discharge flow rate S based on R0 and the water intake amount Q, and is represented by the following equation.

The model A8 is for calculating the effective head hn based on the water level at the end of the day, the water discharge level H _out and the loss head H _los, where the water level at date 0:00 is H _t , Is expressed by the following equation, where d is a predetermined constant for converting the height to sea level.

Model A9 (total conversion efficiency model) and model A10 (electric energy calculation model) are generated electric power Pn (hereinafter, generated electric power related to the reservoir 1, that is, electric power generation) generated based on the intake water amount Q and the effective head hn. The amount of power generated by the machine 3 is expressed as “Pn1”, and the generated power related to the reservoir 2, that is, the amount of power generated by the generator 4 is expressed as “Pn2”. The conversion efficiency of hydroelectric power generation in the generators 3 and 4 (hereinafter referred to as “total conversion efficiency”) is ce and the acceleration of gravity is expressed by g. As will be described later, the regression analysis is performed using the model A9, with the total conversion efficiency ce as an objective variable, and the effective head hn and the water intake Q as 1 to n powers as explanatory variables. a ₂ and b _{21 to} b _2n are regression coefficients obtained by the regression analysis, and c ₂ is a constant.

The model A11 is for calculating the amount of generated power En (the unit is “MWh”) generated per day based on the generated power Pn (power amount calculation model), and is represented by the following equation. The

The model A12 is for calculating the amount of power sold (unit is “1000 yen”) based on the amount of generated power En for a certain date and the power price corresponding to that date. Is represented by

Model A13 is for determining the water level of the river based on the river flow rate (river flow model), and is represented by the following equation. As will be described later, using the model A13, the water level of the river B (hereinafter referred to as “river water level”, expressed as “Hr”; the unit is “m”) is the objective variable, and the river B The regression analysis is performed using 1 to the nth power of the flow rate (hereinafter referred to as “L”, the unit is “m ³ / s”) as an explanatory variable. a _{41 to} a _4n are regression coefficients obtained by the regression analysis, and c ₄ is a constant.

  In the constraint storage unit 253 (corresponding to the “constraint condition storage unit” of the present invention), models representing various constraints in the water storage facility (hereinafter referred to as “constraint model”) are stored. The constraint storage unit 253 stores the following constraint models B1 to B9.

なお、水位Ｈ１は貯水量Ｖ１に応じて一意に定まるので、上記制約モデルＢ１およびＢ２は等価である。 Reservoir 1 has the following restrictions. That is, the water storage amount V1 must be not less than the lower limit water storage amount V1 _{min and not} more than the upper limit water storage amount V1 _max . Further, the water level H1 of the reservoir 1 must be not less than the minimum water level H1 _{min and not} more than the maximum water level H1 _max . On the other hand, the invalid discharge flow rate S1 must be smaller than the upper limit invalid discharge flow rate _Smax . These constraints are expressed by the following constraint models B1 to B3.

Since the water level H1 is uniquely determined according to the water storage amount V1, the constraint models B1 and B2 are equivalent.

The generators 3 and 4 have the following restrictions. That is, the water intake amount of the generators 3 and 4 (hereinafter referred to as “Q”) is referred to as a predetermined minimum water amount (hereinafter referred to as “minimum water intake amount” and is expressed as “Q _min ”). ³ / s ”)) and a predetermined maximum water amount (hereinafter referred to as“ maximum water intake ”, expressed as“ Q _max ”. The unit is“ m ³ / s ”). I must. This constraint is expressed by the following constraint model B4.

なお、上述したように貯水池２の水位Ｈ２は、貯水池２の貯水量Ｖ２に基づいて一意に定まるので、上記式Ｂ５およびＢ６は等価である。 Reservoir 2 has the following restrictions. That is, the water storage amount 2 must be not less than the lower limit water storage amount V2 _{min and not} more than the upper limit water storage amount V2 _max . Moreover, the water level H2 of the reservoir 2 must be not less than the minimum water level H2 _{min and not} more than the maximum water level H2 _max . On the other hand, the invalid discharge flow rate S2 must be smaller than the upper limit invalid discharge flow rate _Smax . Furthermore, a predetermined unit time of the amount of water discharged from the reservoir 2 to the river B (hereinafter referred to as “water discharge amount”, expressed as “F”) (in this embodiment, 30 minutes, 10 minutes or An arbitrary time shorter than the second unit time, such as 15 minutes, can be used.) The absolute value of the change amount (hereinafter referred to as “water discharge amount change amount” and expressed as “ΔF”) is predetermined. The threshold value (hereinafter referred to as “water discharge allowable value”, expressed as “ΔF _max ”) must not be exceeded. On the other hand, the flow rate L of the river B is equal to or higher than a predetermined minimum amount (hereinafter referred to as “maintenance river flow rate” and expressed as “Lc”. The unit is “m ³ / s”). Water must be discharged from reservoir 2 to river B. These constraints are expressed by the following constraint models B5 to B9.

As described above, since the water level H2 of the reservoir 2 is uniquely determined based on the water storage amount V2 of the reservoir 2, the above formulas B5 and B6 are equivalent.

In the present embodiment, the amount of water discharged to the river B is limited to the upper limit (hereinafter referred to as “ΔHr”, the unit is “m”) of the river water level increase per 30 minutes (hereinafter referred to as “m”). , “ΔHr _max ”. The unit is “cm / 30 minutes.”). That is, ΔHr _max is set to 10 (cm / 30 minutes), for example, where the increase amount ΔHr of the river water level per 30 minutes does not exceed 10 cm. Therefore, the water discharge allowable value ΔF _max is expressed as the following equation.

したがって、放水許容値ΔＦ_ｍａｘは次式（Ｃ２）となる。なお、式Ｃ１およびＣ２もモデル記憶部１５１に記憶されているものとする。

Here, the water level fluctuation (ΔHr ÷ ΔF) per unit water amount is expressed by the following equation by differentiating the equation A13.

Therefore, the water discharge allowable value ΔF _max is expressed by the following equation (C2). It is assumed that equations C1 and C2 are also stored in the model storage unit 151.

  The total conversion efficiency model estimation unit 214 performs estimation related to the model A9 indicating the relationship between the total conversion efficiency ce, the effective head hn, and the water intake Q. The reason why the effective head hn and the water intake amount Q are selected as explanatory variables of the total conversion efficiency ce is as follows.

  A reaction water turbine such as a Francis turbine has a characteristic that the efficiency changes in response to fluctuations in the effective head hn and the water intake Q. For example, in a Francis turbine, the pressure of water is changed to speed, and the runner rotates by reaction of running water to generate power. When the effective head hn changes, the speed of water also changes. Therefore, when the effective head hn decreases, the runner output in the water turbine generator also decreases, and the overall conversion efficiency ce decreases. In addition, since the turbine generator has a certain loss regardless of the water intake Q, when the water intake Q decreases, this loss increases relative to the energy obtained from the water intake. Will be reduced. On the other hand, even when the water intake amount Q increases, for example, the flowing water in the suction pipe from which the water after rotating the runner is discharged becomes turbulent and the loss may increase. Thus, since the total power generation efficiency ce is affected by the effective head hn and the water intake Q, the effective head hn and the water intake Q are selected as explanatory variables of the total conversion efficiency ce in this embodiment.

  As for the relationship between the total conversion efficiency ce, the effective head hn, and the water intake Q, it is assumed that the reservoir 1 has been previously tested. The experimental results are stored in the efficiency test result database 257. FIG. 13 is a diagram illustrating a configuration example of the efficiency test result database 257. As shown in the figure, the efficiency test result database 257 correlates the effective head corresponding to each of the reservoirs 1 and 2, the intake amount related to the generator using the water discharged from the reservoir, and the total conversion efficiency. I remember it.

The total conversion efficiency model estimation unit 214 applies a record stored in the efficiency test result database 257 to the model A9 to perform regression analysis.
FIG. 14 is a diagram showing a flow of estimation processing by the total conversion efficiency model estimation unit 214. The total conversion efficiency model estimation unit 214 sets 1 to n (S401). If n is greater than 1 (S402: YES), total conversion efficiency model estimation unit 214 calculates the nth power of Q for each record stored in efficiency test result database 257, and records the calculated nth power of Q as a record. Are added as new items (S403). The total conversion efficiency model estimation unit 214 generates a model A9 having the total conversion efficiency ce as an objective variable, hn and Q from the first power to Q n power as explanatory variables, and the generated model A9 is subjected to an efficiency test. The regression coefficients a ₂ , b _{21 to} b _2n and c ₂ are estimated by applying the contents of each record in the result database 257 (S404). The total conversion efficiency model estimation unit 214 performs a statistical test (S405), and if the test result is not significant (S406: NO), increments n (S407) and performs the processing from step S402. In step S406, the total conversion efficiency model estimation unit 214 calculates the efficiency included in the record and the hn and Q raised from the first power of Q to the nth power of Q for each record in the efficiency test result database 257. A difference from the result applied to the model A9 may be calculated, and it may be determined whether or not the total of the calculated differences is equal to or less than a predetermined threshold value. Further, the total conversion efficiency model estimation unit 214 may determine whether or not the regression coefficient determination coefficient is equal to or greater than a predetermined value. If the test result is significant (S406: YES), the total conversion efficiency model estimation unit 214 sets the total conversion efficiency ce as an objective variable, and sets the effective head hn and the intake water Q from the first power to the nth power of Q. A model A9 including the regression coefficient calculated by the regression calculation is created in the regression equation as an explanatory variable and registered in the model storage unit 252 (S408).

  As described above, since the relationship between the effective head hn, the intake water amount Q, and the total conversion efficiency ce can be expressed as a significant model A9, it is possible to accurately simulate the generated power amount. In this embodiment, since it is assumed that the generators 3 and 4 have the same performance, the total conversion efficiency ce is estimated only once.

Daily optimum water level planning unit 215 reads the step amount from the specification storage unit 251, water storage amount set value of the first day of the input unit 212 initial reservoir capacity operational period of reservoirs 1 and 2 accepted water storage V1 ₁ and V2 ₁ and the final target water storage volume of the reservoirs 1 and 2 received by the water storage volume setting value input unit 212 is set as the water storage volumes V1 ₇ and V2 ₇ on the 7th day, for each date t (t = 2 to 6), 0:00 The simulation is performed by changing the stored water volume V1 _t in steps from V1 _{min, t} to V1 _{max, t} and the stored water volume V2 _t in steps from V2 _{min, t} to V2 _{max, t.} The combination of V1 _t and V2 _t is determined so that the total sum of En becomes maximum. The daily optimal water level planning unit 215 uses dynamic programming in this simulation. By using dynamic programming, the optimal combination of V1 _t and V2 _t can be calculated quickly.

  The river flow model estimation unit 216 performs an estimation process related to the model A13 indicating the relationship between the river water level Hr and the river flow L. The process of selecting the river flow rate L as the explanatory variable of the river water level Hr is as follows.

ここでｖ（ｍ／ｓ）は流速、ｎは粗度係数、ｒは径深（ｍ）、ｉは動水勾配である。長方形水路の場合、水路幅Ｂ、水深ｈであれば、流積Ａ＝Ｂ×ｈであり、潤辺長Ｃ＝Ｂ＋２ｈである。径深ｒ＝流積Ａ÷潤辺長Ｃであるから、上記公式は次のように変形できる。

ここで、水路幅Ｂが水深ｈに比して十分広い（ｈ＜＜Ｂ）とすると、ｖは次式に近似できる。

流量Ｌ＝流積Ａ×流速ｖであり、Ａ＝Ｂｈであるから、流量Ｌは次式により表される。 The following Manning formula is used to calculate the flow velocity of rivers and artificial waterways.

Here, v (m / s) is a flow velocity, n is a roughness coefficient, r is a depth (m), and i is a hydraulic gradient. In the case of a rectangular channel, if the channel width is B and the depth is h, the flow product A = B × h and the wet side length C = B + 2h. Since the radial depth r = the flow product A ÷ the wet side length C, the above formula can be modified as follows.

Here, if the water channel width B is sufficiently wider than the water depth h (h << B), v can be approximated by the following equation.

Since the flow rate L = the flow product A × the flow velocity v and A = Bh, the flow rate L is expressed by the following equation.

したがって、水深ｈは、流量Ｌの３／５乗に比例することが分かる。そこで、本実施形態では、河川水位Ｈｒの説明変数として河川流量Ｌを説明変数として選定した。

Therefore, it can be seen that the water depth h is proportional to the third power of the flow rate L. Therefore, in this embodiment, the river flow rate L is selected as the explanatory variable as the explanatory variable of the river water level Hr.

  The actual values of the river water level Hr and the river flow L are assumed to be registered in the river flow performance database 258 in advance, and the river flow model estimation unit 216 applies the record stored in the river flow performance database 258 to the model A13. To do regression analysis. FIG. 15 is a diagram illustrating a configuration example of the river flow rate record database 258. As shown in the figure, the river flow record database 258 stores the river water level and the river flow in association with each other.

FIG. 16 is a diagram showing a flow of estimation processing by the river flow model estimation unit 216. The river flow model estimation unit 216 sets 1 to n (S441). If n is larger than 1 (S442: YES), the river flow model estimation unit 216 calculates the nth power of the river flow L for each record stored in the river flow performance database 258, and calculates the nth power of the calculated L. The record is added as a new item (S443). The river flow model estimation unit 216 generates a model A13 having the river water level Hr as an objective variable and the river flow L from the first power to the nth power of the L as explanatory variables, and the river flow performance database 258 is generated in the generated model A13. by applying the contents of each record of performing estimation of the regression coefficients _a 41 _{~a 4n} and _{c 4} (S444). The river flow model estimation unit 216 performs a statistical test (S445). If the test result is not significant (S446: NO), n is incremented (S447), and the process from step S442 is performed. In step S446, the river flow model estimation unit 216 determines, for each record in the river flow record database 258, the model A13 from the water level Hr included in the record to the Lth power of L included in the record. A difference from the result applied to the above may be calculated, and it may be determined whether or not the total of the calculated differences is equal to or less than a predetermined threshold. Further, the river flow model estimation unit 216 may determine whether or not the determination coefficient of the regression equation is equal to or greater than a predetermined value. If the test result is significant (S446: YES), the river flow model estimation unit 216 uses the river water level Hr as an objective variable, and a regression equation using each of the river flow L from the first power to the nth power as explanatory variables. The model A13 including the regression coefficient calculated by the regression calculation is created and registered in the model storage unit 252 (S448).
As described above, the model A13 indicating the relationship between the river flow rate and the river water level is registered in the model storage unit 252.

  The hourly optimum water level planning unit 217 (corresponding to the “optimum water level determination unit” of the present invention) plans the optimum water level every hour based on the water level of each day stored in the daily optimum water level database 255. To do. The hourly optimum water level planning unit 217 performs a simulation to change the water storage amounts V1 and V2 so as to satisfy the above-described constraints of A1 to A6. The hourly optimum water level planning unit 217 calculates the evaluation value E of the generated power amount by the following model A14, and determines the water level of the reservoir 1 that maximizes the evaluation value E of the generated power amount.

ここで、ｔは時間であり、ｗｆ_ｔは時間帯別の重み係数である。本実施形態において、ｗｆ_ｔは電力価格データベース２５４に登録されている時間帯別の電力価格とする。なお、上記モデルＡ１４は予めモデル記憶部２５２に記憶されているものとする。

Here, t is time, and wf _t is a weighting factor for each time zone. In the present embodiment, wf _t is the electricity price database 254 that are registered in each time zone power prices. It is assumed that the model A14 is stored in the model storage unit 252 in advance.

The hourly optimal water level planning unit 217 calculates the water level H1 (water storage amount V1) of the reservoir 1 and the water level H2 (water storage amount V2) of the reservoir 2 for each time from 0:00 to 24:00 on the target day of the optimization process. Perform a simulation. The hourly optimal water level planning unit 217 receives the target date, reads the water levels H1 and H2 of the reservoirs 1 and 2 corresponding to the target date from the daily optimal water level database 255, and reads the read water levels H1 and H2 respectively at 0:00. The water levels H1 ₀ and H2 ₀ are set, and the water levels H1 and H2 of the reservoirs 1 and 2 corresponding to the next day of the target day are read from the daily optimum water level database 255, and the read water levels H1 and H2 are respectively set to the water level H1 ₂₄ and Let H2 be ₂₄ . Further, the hourly optimum water level planning unit 217 stores the water storage amounts V1 ₀ and V2 at ₀ and 24:00 based on the water levels H1 ₀ and H2 _{0 at 0} and the water levels H1 ₂₄ and H2 _{24 at 24:00} and the model A1. ₀ and V1 ₂₄ and V2 ₂₄ are calculated. The hourly optimal water level planning unit 217 reads the step amount from the specification storage unit 251, reads the power price corresponding to each time from 0:00 to 24:00 from the power price database 254, and each time from 1:00 to 23:00 For t, a simulation is performed by changing each of the water levels H1 _t and H2 _t by a step amount, and a transition (plan) of a combination of H1 _t and H2 _t (t = 0 to 24) that satisfies all the constraints of A1 to A9 Among these, a plan that maximizes the evaluation value E of the model A14 is determined. The hourly optimum water level planning unit 217 registers the determined combination of H1 _t and H2 _{t in} the hourly optimum water level database 256.

  The optimum water level output unit 218 outputs the water level stored in the daily optimum water level database 255 and the optimum water level stored in the hourly optimum water level database 256 to the output device 206.

== Simulation of optimum water level by day ==
FIG. 17 is a diagram illustrating an example of a screen 60 used for the simulation of the water level for each of the first unit periods for the reservoirs 1 and 2. The screen 60 is provided with an operation period input field 611, an initial storage amount input field 612, a final target storage amount input field 613, and various specification display fields 621 to 627.

The daily optimal water level planning unit 215 reads and reads the set values H _min and H _max (H1 _min and H1 _max or H2 _min and H2 _max ) corresponding to the minimum water level and the maximum water level from the specification storage unit 251. H _min and H _max are displayed in the display column 621 and the display column 622. The daily optimum water level planning unit 215 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 daily optimum water level planning unit 215 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 fields 624 and 625 respectively. The daily optimum water level planning unit 215 reads the set value H _out (H1 _out or H2 _out ) corresponding to the water discharge level from the specification storage unit 251, and displays the read H _out in the display field 626. The daily optimal water level planning unit 215 reads the set value H _los (H1 _los or H2 _los ) corresponding to the loss head from the specification storage unit 251, and displays the read H _los in the display field 627.

The daily optimum water level planning unit 215 calculates V _max and V _min using the above-described model A2 and model A3, 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 10, acquires the predicted value of the inflow amount R for each day in the operation period predicted by the inflow amount prediction system 10, and displays the acquired R This is displayed in the column 633. In the case of the simulation of the reservoir 2, since the reservoir 2 accepts water discharged from the generator 3 in addition to the inflow 6 from the river or the like, the daily optimal water level planning unit 215 The amount of water discharged from the generator 3, that is, the water intake amount Q of the generator 3 is added to the inflow amount R acquired from the inflow amount prediction system 10.

  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 daily optimum water level planning unit 215 simulates the water storage amount V for each day during the operation period. The daily optimum water level planning unit 215 performs a simulation of the water storage amount V as follows, for example. In the example of FIG. 17, 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 daily optimal water level planning unit 215 performs the following simulation for each of the reservoirs 1 and 2.

Daily optimum water level planning unit 215, the initial water amount inputted in the input column 612 and the first day of water storage V _1, day 7 of reservoir capacity the final target water amount inputted in the input field 613 and V _7, to simulate a reservoir capacity V _t from the first day until the sixth day, and displays the water volume V in the display column 651.

The daily optimal water level planning unit 215 calculates the water level H _t by applying the water storage amount V _t for each date t to the model A1, and displays the calculated water level H _t in the display field 652. The operational water level planning unit 215 applies the V _t and V _{t + 1} to the model A4 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 215 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 A5, and determines the intake amount Q corresponding to R0 by the model A6. The amount of water taken Q is displayed in the display field 654. The operational water level planning unit 215 calculates the invalid discharge flow rate S by applying R0 and the water intake amount Q to the model A7, and displays the calculated S in the display column 655.

Operation level planning unit 215, for each day t 1-6 days, the water level _{H t + 1} calculated above, by applying the water discharge position _{H out} and loss difference _{H los} model A8, to calculate the effective head hn _t, to display the calculated hn _t to display column 656.

  The operational water level planning unit 215 calculates the generated electric power Pn by applying the water intake amount Q and the effective head hn to the model A9, and calculates the generated electric power En by applying the calculated Pn to the model A11. The generated power Pn is displayed in the display field 657, and the calculated En is displayed in the display field 660.

  The operational water level planning unit 215 reads the power price corresponding to the date of each day during the operation period from the power price database 254 and applies the En and the read power price to the model A12 to calculate the amount of power sold. The read power price is displayed in the display field 659, and the calculated power sale amount is displayed in the display field 658.

The operational water level planning unit 215 performs simulation by changing the water storage amount V _t (t = 2 to 6) from the maximum V _{min, t} to V _{max, t} and repeating the above processing, and sells during the operation period. the total amount of electricity amount is to determine the combination of reservoir capacity V _t with the maximum. Incidentally, operational level planning unit 215, the dynamic programming, to determine the combination of reservoir capacity V _t, can be determined efficiently optimal water volume. Operation level planning unit 215 displays a display area 651 to 660 so as to correspond to a combination of the determined water volume _{V t.}

  The operational water level planning unit 215 performs the above simulation for each of the water storage amount V1 of the reservoir 1 and the water storage amount V2 of the reservoir 2, so that the optimal amount of electricity sold in the reservoirs 1 and 2 is maximized. A plan for daily water storage volumes V1 and V2 can be created.

== Results of daily optimal water level simulation ==
FIG. 18 is a graph showing a result of a simulation of a past actual water level in a certain reservoir.
FIG. 18A is a graph showing changes in water intake and water level in July 2003, which is known as a relatively high water season. In the water storage facility, the water level was 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. 18B is a graph showing changes in water intake and 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 and operating the water level H so as to maximize the amount of electricity sold, the amount of electricity sold can be increased compared to the operation based on the experience of the operator. It was confirmed that

  As described above, according to the water level operation support system of the present embodiment, it is possible to present a water level that maximizes the amount of power sold according to the initial water storage amount and the final target water storage amount. In addition, in the configuration in which the two reservoirs 1 and 2 are connected as in the present embodiment, the amount of power generated by the generator 4 is maximized in consideration of the inflow from the generator 3 to the reservoir 2. In addition, since the water storage amount V of the reservoir 2 can be determined, the combination of the water storage amounts V can be determined so that the amount of power sold as a whole becomes large. 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.

== Simulation of optimal water level by time ==
FIG. 19 is a diagram illustrating an example of a screen 70 used when simulating the optimal water level and the second unit period (1 hour). FIG. 22 is a diagram showing a flow of processing related to the simulation.

The hourly optimal water level planning unit 217 accepts designation of a day to be simulated (hereinafter referred to as “target day”) (S801). The hourly optimum water level planning unit 217 can accept the designation of the target date by clicking on the day displayed in the display column 611 of the screen 60 shown in FIG. 17 described above, for example. When another optimum water level planning unit 217 reads the water level H1 of the reservoir 1 which corresponds to the target day from the daily optimal level database 255, it calculates the water quantity V1 from the read H1 and level calculation model A1 and V1 ₀ (S802 ), The water level H1 of the reservoir 1 corresponding to the next day of the target date is read from the daily optimum water level database 255, and the water storage amount V1 is calculated from the read H1 and the water level calculation model A1 to be V1 ₂₄ (S803). Further, when another optimum water level planning unit 217 reads the water level H2 of reservoir 2 corresponding to the target date from the daily optimal level database 255, calculates the water volume V2 from the read H2 and the water level calculation model A1 and V2 ₀ (S804), the water level H2 of the reservoir 1 corresponding to the next day of the target date is read from the daily optimum water level database 255, and the stored water amount V2 is calculated from the read H2 and the water level calculation model A1 to be V2 ₂₄ (S805). ). When another optimum water level planning unit 217 displays a V1 ₀ to display column 711 of the screen 70 shown in FIG. 19, displays the _{V1 24} to the display section 712. Further, when another optimum water level planning unit 217 displays the V2 ₀ to display column 713 of the screen 70, and displays the _{V2 24} on the display section 714.

The hourly optimum water level planning unit 217 receives from the specification storage unit 251 the maintenance flow rate (S0), the discharge levels of the reservoirs 1 and 2 (H1 _out and H2 _out ), and the loss heads of the reservoirs 1 and 2 (H2 _los and H2 _los). ), The maintenance river flow rate (Lc) is read (S806). The hourly optimum water level planning unit 217 displays the read maintenance flow rate S0 in the display column 715 and the river maintenance flow rate Lc in the display column 716.

The hourly optimal water level planning unit 217 divides the predicted value of the inflow rate R into the reservoir 1 acquired by the predicted inflow amount acquisition unit 213 corresponding to the target date by 24, and the predicted inflow amount into the reservoir 1 at each time R1 _t (t = 0 to 23) is set (S807), and the predicted inflow amount R1 _t into the reservoir 1 is displayed in the display column 721. The hourly optimal water level planning unit 217 may be configured such that the inflow amount acquisition unit 213 receives an input of the predicted inflow amount R1 _{t in} advance and displays the received predicted inflow amount R1 _t in the display column 721.

The hourly optimal water level planning unit 217 sets an extremely small value such as “−99999” to the maximum value E _max of the evaluation value E of the generated electric energy (S808), and the optimal combination of water storage amounts at each time (hereinafter, The “optimum plan” is set to an empty list (S809).

The hourly optimal water level planning unit 217 changes the water storage amount V1 _t (t = 1 to 23) of the reservoir 1 at each time step by step from the lower limit water storage amount V1 _min to the upper limit water storage amount V1 _max, and the water storage in the reservoir 2 The following processing is performed for each of all combinations of V1 _t and V2 _{t in which} the amount V2 _t (t = 1 to 23) is changed step by step from the lower limit water storage amount V2 _min to the upper limit water storage amount V2 _max .

The hourly optimal water level planning unit 217 performs an evaluation value calculation process shown in FIG. 21 (S810).
The hourly optimum water level planning unit 217 displays the storage amount V1 _t of the reservoir 1 in the display column 722, displays the storage amount V2 _t of the reservoir 2 in the display column 723, and each time t (t = 1 to 23). The following processing is performed for each.

The hourly optimum water level planning unit 217 calculates the water level H1 _{t of the} reservoir 1 by applying V1 _t to the water level calculation model A1 (S821), and displays H1 _t in the display column 731. The hourly optimum water level planning unit 217 calculates the water level H2 _{t of the} reservoir 2 by applying V2 _t to the water level calculation model A1 (S822), and displays H2 _t in the display field 732. The hourly optimum water level planning unit 217 acquires the predicted value L _t of the flow rate of the river B at time _t (S823). For example, the hourly optimum water level planning unit 217 may receive an input of the flow rate L _t of the river B from the user, or may make a prediction based on the river flow rate stored in the river flow rate record database 258. Also good. The hourly optimal water level planning unit 217 displays the acquired L _t in the display field 733. When another optimum water level planning unit 217 applies the obtained _{L t} model A13 calculates the river level Hr _t (S824). When another optimum water level planning unit 217, and wf _t reads the electricity price corresponding to t from the power price database 254 (S825). By repeating the above processing, H1 _t , H2 _t , L _t , Hr _t , and wf _t for each time t (t = 1 to 23) are calculated.

Another optimal level planning unit 217 when next for each of the time t (t = 1~23), when another optimum water level planning unit 217, shown in FIG. 22, performs calculation processing of the evaluation value _{E t} (S826 ). The hourly optimum water level planning unit 217 applies the water storage amounts V1 _t and V1 _{t + 1} of the reservoir 1 to the model A4, calculates the storage amount difference ΔV1 _t of the reservoir 1 (S841), and the inflow amount R1 _t into the reservoir 1 Then, the maintenance flow rate S0 _t and the water storage amount difference ΔV1 _t are applied to the models A5 and A6 to calculate the water intake amount Q1 _t of the generator 3 (S842). The hourly optimum water level planning unit 217 displays the water intake amount Q1 _t in the display column 734. The hourly optimum water level planning unit 217 calculates the invalid discharge flow rate S1 _t from the reservoir 1 by applying the inflow amount R1 _t , the maintenance flow rate S0 _t , the storage amount difference ΔV1 _t and the intake amount Q1 _t to the models A5 and A7. and (S843), and displays an invalid discharge amount S1 _t to display column 735. The hourly optimum water level planning unit 217 calculates the effective head hn1 _t of the reservoir 1 by applying the water level H1 _{t + 1} , the discharge level H1 _out, and the loss head H1 _los of the next time to the model A8 (S844). , Hn1 _t and Q1 _t are applied to the model A9 to calculate the total conversion efficiency ce1 _t for the generator 3 (S845). The hourly optimum water level planning unit 217 displays the total conversion efficiency ce1 _t of the generator 3 in the display field 736. When another optimum water level planning unit 217, _ce1 t, Q1 by applying the _t and H1 _t the model A10, calculates the amount of power generation Pn1 _t by the power generator 3 (S846), displays the generated power amount Pn1 _t column 737 To display.

Next, the hourly optimum water level planning unit 217 adds the intake amount Q1 _t of the generator 3 to the inflow amount R2 _t into the reservoir 2 (S847), and displays the inflow amount R2 _t in the display column 738. The hourly optimum water level planning unit 217 applies the water storage amounts V2 _t and V2 _{t + 1} of the reservoir 2 to the model A4 to calculate the water storage amount difference ΔV2 _t of the reservoir 2 (S848), the inflow amount R2 _t , and the maintenance flow rate S0 _t. And the water storage amount difference ΔV2 _t are applied to the models A5 and A6 to calculate the water intake amount Q2 _t of the generator 4 (S849), and the water intake amount Q2 _t is displayed in the display field 739. The hourly optimum water level planning unit 217 calculates the invalid discharge flow rate S2 _t from the reservoir 2 by applying the inflow amount R2 _t , the maintenance flow rate S0 _t , the storage amount difference ΔV2 _t and the intake amount Q2 _t to the models A5 and A7. and (S850), and displays an invalid discharge amount S2 _t to display column 740. The hourly optimum water level planning unit 217 calculates the effective head hn2 _t of the reservoir 2 by applying the water level H2 _{t + 1} , the discharge level H2 _out and the loss head H2 _los of the next time to the model A8 by applying the water level H2 _{t + 1} of the reservoir 2 to the next time (S851). , Hn2 _t and Q2 _t are applied to the model A9 to calculate the total conversion efficiency ce2 _t of the generator 4 (S852), and the total conversion efficiency ce2 _t of the generator 4 is displayed in the display column 741. The hourly optimum water level planning unit 217 applies ce2 _t , Q2 _t, and H2 _t to the model A10 to calculate the generated power amount Pn2 _t by the generator 4 (S853), and displays the generated power amount Pn2 _t in the display column 742. To display. When another optimum water level planning unit 217 _applies the _{wf t, Pn1 t, S1 t} , Pn2 t, S2 t the model A14, it calculates an evaluation value _{E t} of generated power (S854).

Returning to Figure 21, when another optimum water level planning unit 217 calculates the evaluation value E by summing the _{E t} for each time (S829).
Returning to FIG. 19, the hourly optimum water level planning unit 217 performs the constraint violation detection process shown in FIG. 23 (S811).
In the constraint violation detection process of FIG. 23, the hourly optimal water level planning unit 217 performs the following process for each time t (t = 1 to 23).

The hourly optimum water level planning unit 217 first performs a calculation process of the river flow rate and the water discharge amount shown in FIG. 24 (S860). When another optimum water level planning unit 217 adds the water intake Q2 _t of the generator 4 at time t to the water volume V2 _t reservoir 2 at time t, from which drawing the water storage _{V2 t + 1} of the reservoir 2 at time t + 1 Accordingly, to calculate the water discharge amount _{F t} of the reservoir 2 (S871), by taking the absolute value of the value obtained by subtracting the _{F t-1} from the water discharge amount _{F t} to calculate the water discharge amount variation ΔF _t (S872). The hourly optimum water level planning unit 217 displays a value obtained by adding the discharge amount F _t to the flow rate L _{t of the} river B in the display field 743 of the screen 70 shown in FIG. The hourly optimal water level planning unit 217 acquires the predicted value L _t of the flow rate of the river B at time _t and the upper limit value ΔHr _max of the increase amount of the river water level (S873), and the acquired L _t is displayed on the screen 70 shown in FIG. Is displayed in the display column 733. The hourly optimal water level planning unit 217 may receive, for example, an input of the flow rate L _{t of the} river B and the upper limit upper limit value ΔHr _max of the increase amount from the user, or stored in the river flow rate record database 258. it may be predicted upper limit value upper limit value Hr _max flow rate L _t and the increase amount of river B based on river flow which has been. The hourly optimal water level planning unit 217 calculates the Hr (L _t ) ′ by applying the acquired L _t to the model C1 (S874), and the model described above for the calculated Hr (L _t ) ′ and the ΔHr _max. Applying to C2, the water discharge allowable value ΔF _max is calculated (S875). In addition, when the river flow rate is small, the river water level fluctuation accompanying the flow rate change becomes large. Therefore, instead of the river flow rate L _t , the average flow rate before and after discharge (L _t + L _t + F _t ) / 2 is used. A more accurate simulation can be performed.

Next, the hourly optimum water level planning unit 217 returns to FIG. 23, and the reservoir volume V1 _t of the reservoir 1 is not less than the lower limit reservoir volume V1 _{min and not} more than the upper limit reservoir volume V1 _max (S861: YES). 2 water storage amount V2 _t is not less than the lower limit water storage amount V2 _{min and not} more than the upper limit water storage amount V2 _max (S862: YES), and the invalid discharge flow rate S1 _t from the reservoir 1 is less than the upper limit invalid discharge flow rate _Smax . Yes (S863: YES), disabled discharge amount S2 _t from reservoir 2 is less than the upper limit invalid discharge amount _{S max} (S864: YES), water intake Q1 _t of the generator 3, be minimum intake amount _{Q min} or more and is equal to or less than the maximum intake amount _{Q max,} the intake amount Q2 _t of the generator 4 are the minimum intake quantity _{Q min} or more (S865: YES), and is equal to or less than the maximum intake amount _{Q max} (S86 6: YES), water volume difference [Delta] F _t is not greater than the water discharge allowable value ΔF _max (S867: YES), a value obtained by adding the water discharge amount _{F t} on the flow rate _{L t} river B is in the sustain river flows Lc _t or If there is (S868: YES), it is determined that all the constraints are satisfied (S869), and if not, it is determined that the constraints are violated (S870).

Returning to FIG. 22, when the hourly optimal water level planning unit 217 determines that all constraints are satisfied (S812: YES), if the evaluation value E is greater than Emax (S813: YES), E is set to Emax (S814). ), A combination of V1 _t (t = 0 to 24) and V2 _t (t = 0 to 24) is set as the optimum plan (S815).

By repeating the above processing for all combinations of V1 _t and V2 _t , it is possible to determine the combination of the storage amounts of the reservoirs 1 and 2 that maximizes the evaluation value E of the generated power amount in one day.

  As described above, according to the optimum water level calculation system 20 of the present embodiment, the simulation is performed by changing the water storage amounts V1 and V2 of the reservoirs 1 and 2, and the evaluation value E of the generated power amount is satisfied while satisfying various constraints. The water levels of reservoirs 1 and 2 can be planned so that is maximized. In the model A14, the generated power amount can be evaluated by subtracting a value obtained by multiplying the square of the invalid discharge flow rate S by the penalty coefficient from the generated power amount. Therefore, it is possible to plan the water level of the reservoir so as to increase the amount of generated power as much as possible while taking into account the invalid discharge amount. Therefore, it is possible to adjust the water level of the reservoir in consideration of the reduction in expenditures associated with patrols to discharge rivers that need to be addressed before the occurrence of invalid discharge.

  Moreover, since the operator of the water storage facility can secure a high power generation amount by operating the water level of the reservoir with reference to the water level with reference to the output from the optimum water level calculation system 20 of this embodiment, The water level that has been managed based on the experience of the person who is inexperienced can be given to those with less experience.

上記モデルＡ１４’では、無効放流量Ｓの２乗にペナルティ係数を乗じた値を発電電力Ｐｎから減じて評価値Ｅを算出しているので、無効放流量が少なくなるように貯水量Ｖまたは対応する水位Ｈを決定することができる。 In the optimum water storage level calculation system 20 according to the present embodiment, the combination of the daily water storage amounts V is determined so that the amount of power sales is maximized. However, the combination is such that the power generation amount En is maximum. May be determined. In this case, more efficient power generation can be performed regardless of price fluctuations. It is also possible to determine a combination that maximizes the evaluation value E according to the generated power amount En. In this case, the evaluation value E can be calculated by the following model A14 ′, for example.

In the model A14 ′, since the evaluation value E is calculated by subtracting the value obtained by multiplying the square of the invalid discharge flow rate S by the penalty coefficient from the generated power Pn, the stored water amount V or the corresponding value is reduced so that the invalid discharge flow rate is reduced. The water level H to be determined can be determined.

Further, in this embodiment, in the hourly simulation, the total amount of generated power in both the generators 3 and 4 is changed by the model A14 by changing both the water volume V1 of the reservoir 1 and the water volume V2 of the reservoir 2. Although the evaluation is made, the optimum water level for the reservoir 1 and the optimum water level for the reservoir 2 may be obtained separately as in the daily simulation. In this case, the hourly optimal water level planning unit 215 changes the water storage amount V1 _t of the reservoir 1 to obtain the generated power amount En, the power sale amount, and the evaluation obtained by, for example, the model A11, the model A12, and the upper model A14 ′. The combination of the water storage amount V1 _t that maximizes the value E is determined, and then the water storage amount V2 _t of the reservoir 2 is changed so that the generated power amount En, the amount of power sold, and the evaluation value E are similarly maximized. The combination of the quantity V2 _t can be determined to provide an optimal water level plan.

  However, as in the daily simulation described above, the optimum water level for the reservoir 1 and the optimum water level for the reservoir 2 are obtained separately (hereinafter referred to as “individual optimization”), and In the case where the optimum water levels of the reservoir 1 and the reservoir 2 are obtained simultaneously as in the simulation (hereinafter referred to as “batch optimization”), the water storage in which the batch optimization gives a larger evaluation value of the generated electric energy. A combination of quantities can be determined. FIG. 25 is a graph showing the amount of generated power when individual optimization is performed and when batch optimization is performed. As shown in the graph of FIG. 25, for the generator A, the individual optimization is larger in the amount of generated power than the collective optimization, but for the generator B, the collective optimization is more generated. The amount of power generated is large, and the total amount of power generated by the generators A and B is larger for collective optimization. Therefore, it is preferable to perform batch optimization. Thus, in the present embodiment, individual optimization is performed in the daily simulation, but collective optimization may be performed.

  In addition, the optimum water storage level calculation system 20 may perform simulation by providing an upper limit of the invalid discharge flow rate S. In this case, the daily optimal water level planning unit 215 receives the input of the upper limit value from the user, and among the combinations of the water storage amount V for each day that the invalid discharge rate S does not exceed the upper limit value, the amount of power sale is maximized. Try to decide things.

  Moreover, the optimal water storage level calculation system 20 may perform a simulation by providing a lower limit of the minimum operation output. In this case, the daily optimal water level planning unit 215 receives the input of the lower limit value from the user, and determines the one with the largest amount of power sales out of the combinations of the stored water amounts V whose operation output does not fall below the lower limit value. To. In actual power plant operation, it is not possible to operate with extremely small water intake due to mechanical limitations of the water turbine, so that simulations more suitable for the actual situation are possible.

Ｑ０_ｍｉｎ＞Ｑ_ｍｉｎの場合、

When the minimum operation output value is expressed as “Q0 _min ”, the model A6 is for determining the water intake Q based on the minimum water intake Q _min , the maximum water intake Q _max , and R0 (water intake calculation model). And is expressed by the following equation.
If Q0 _min ≤ Q _min ,

If Q0 _min > Q _min ,

  In the present embodiment, the water storage facility includes two reservoirs, but three or more reservoirs connected in multiple stages may be included. In this case, the discharge amount from the upper reservoir (the intake amount of the upper generator) is added to the inflow amount of the second and subsequent reservoirs.

  Further, in the present embodiment, the generators 3 and 4 have the same performance. However, when the generators 3 and 4 have different performances, first, in the process of estimating the total conversion efficiency in FIG. 3 is read and the estimation process is performed to obtain the total conversion efficiency ce1 for the generator 3, and then only the record corresponding to the generator 4 is read and the estimation process is performed. The conversion efficiency ce2 is obtained. Moreover, you may make it set the maximum water intake amount and the minimum water intake amount for every generator.

  In the inflow amount prediction system 10 of the present embodiment, the amount of water flowing from the river to the water storage facility such as a dam is predicted. However, the inflow amount prediction system 10 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 are acquired and recorded.

Further, the inflow prediction system 10 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 A5 may use the inflow amount R _t−n before the predetermined period (n period) when the series correlation disappears instead of the inflow amount R _{t−1 of the} previous period as an explanatory variable. .

  Moreover, in the optimal water storage level calculation system 20 of this embodiment, although the inflow amount R was acquired from the inflow amount prediction system 10, not only this but the input of the inflow amount R is received from a user, for example. 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.

  In the optimum storage level calculation system 20 of the present embodiment, the power price is assumed to be stored in advance in the power price database 254. 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 power price database 254 may be omitted, and the JEPX price may be acquired each time the daily optimal water level planning unit 215 performs the simulation. Also, instead of the power price, the weighting factor according to the power demand and the top power generation unit price (incremental fuel cost) in the power company are stored, and the value obtained by multiplying the power generation amount by this weighting factor or power generation unit cost is the maximum. As such, the water level of the reservoir may be planned.

  Further, in the optimum water storage level calculation system 20 of the present embodiment, the period for calculating the hourly simulation is assumed to be 24 hours. However, the calculation period is not limited to this, and the calculation period has an appropriate length such as 48 hours or 72 hours. You may make it perform a simulation about. During this calculation period, the specification input unit 211 may receive an input and register it in the specification storage unit 251.

  Further, in the optimum water storage level calculation system 20 of the present embodiment, the second unit period is assumed to be 1 hour, and the simulation is performed for the water storage amount V per hour. The simulation may be performed with a period of every 6 hours or every 3 hours.

In the present embodiment, the water storage amount V _t is changed in the hourly simulation, but the water level H _t may be changed.

Further, in the optimum storage level calculation system 20 of the present embodiment, the evaluation value E of the generated power amount is calculated for all combinations of the storage amount V1 _t of the reservoir 1 and the storage amount V2 _t (t = 0 to 23) of the reservoir 2. The combination that maximizes the evaluation value E is determined as the optimal plan, but dynamic programming may be used for the optimization of the combination. By using the dynamic programming method, it is possible to efficiently determine the optimum one from the enormous combination of stored water amounts.

Further, the optimum water level calculation system 20 of the present embodiment, in the model A14, the evaluation of the disabled discharge amount S _t, it is assumed to use a value obtained by multiplying the penalty factor to the square of S _t, the S _t The absolute value | S _t | may be used without squaring. Also, since the disabled discharge amount S _t is not be negative, it may be used S _t itself.

  Further, in the optimum reservoir level calculation system 20 of the present embodiment, the predicted inflow amount at each time is calculated by dividing the daily inflow amount by 24. However, the inflow amount prediction system 10 performs the inflow amount at each time. And the predicted inflow amount acquisition unit 213 of the optimum reservoir level calculation system 20 may acquire the predicted value of the inflow amount every hour. In this case, the inflow prediction system 10 stores, for example, weather inflow actual information in units of one hour, estimates the inflow model 5 using the weather inflow actual information at each time, and makes predictions. The inflow amount can be calculated. Further, the inflow amount prediction system 10 stores weather inflow amount actual information for every arbitrary time (for example, 6 hours), and uses the inflow amount model 5 by using the above-mentioned meteorological inflow amount actual information for each arbitrary time. The estimated value of the inflow rate every arbitrary time is calculated, and the predicted value of the inflow rate every arbitrary time is divided into the second unit time (1 hour) unit to be the hourly inflow rate. You can also.

Further, in the optimum water storage level calculation system 20 of the present embodiment, the total conversion efficiency is a value that varies according to the water amount Q, but it can also be used as a predetermined constant. In this case, instead of the model A9, the total conversion efficiency ce can be a constant, and the model A10 can be the next model A10 ′.

  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 10 and the optimum water storage level calculation system 20 are different computers, but may be realized by a single computer. Further, at least one of the inflow amount prediction system 10 and the optimum water storage level calculation system 20 can be realized by a plurality of computers.

1 reservoir, 2 reservoirs, 3 generators, 4 generators, 5 inflows, 6 inflows,
7 Invalid discharge, 8 Invalid discharge, 10 Inflow prediction system,
20 Optimal reservoir level calculation system, 30 communication network,
101 CPU, 102 memory, 103 storage device,
104 communication interface, 105 input device, 106 output device,
111 Snowfall temperature estimation part, 112 Snow melting amount model estimation part,
113 Inflow model estimation unit, 114 Predicted temperature acquisition unit,
115 Predicted precipitation acquisition unit, 116 Predicted snowmelt acquisition unit,
117 inflow prediction unit, 151 model storage unit, 152 parameter storage unit,
153 Meteorological and inflow history database, 201 CPU,
202 memory, 203 storage device, 204 communication interface,
205 input device, 206 output device, 211 specification input unit,
212 water storage amount set value input unit, 213 predicted inflow amount acquisition unit,
214 Total conversion efficiency model estimation section, 215 Daily optimal water level planning section,
216 River flow model estimation part, 217 Optimum water level planning part by hour,
218 Optimal water level output unit, 251 specification storage unit, 252 model storage unit,
253 Constraint storage unit, 254 Electricity price database,
255 daily optimal water level database, 256 hourly optimal water level database,
257 Efficiency test result database, 258 River flow record database

Claims (9)

First and second reservoirs, a first generator that performs hydroelectric power generation using water discharged from the first reservoir, and a second that performs hydroelectric power generation using water discharged from the second reservoir The water used in the first generator is a system that supports the operation of a water storage facility that flows into the second reservoir,
A predicted inflow amount acquisition unit that acquires a predicted value of an inflow amount that is an amount of water flowing into the first and second reservoirs from other than the first generator in each unit period within a predetermined period;
Reservoir discharge model for calculating discharge from the reservoir based on the amount of change in the reservoir in the reservoir from the start to the end of the unit period and the inflow, and the amount of water used for the hydroelectric power generation a model storage unit that stores electric energy model for calculating the amount of power generated in the unit period by the hydroelectric based on water volume of water intake and the reservoir is,
A penalty coefficient storage unit that stores a penalty coefficient that is a coefficient for evaluating an invalid discharge amount that is an amount of water discharged from the water storage facility without being given to the generator;
A constraint condition storage unit for storing a constraint condition for the intake amount including a minimum value and a maximum value of the intake amount, which is an amount of water received from the reservoir for power generation by the generator;
For each unit period within the predetermined period, a first storage amount that is a storage amount of the first reservoir and a second storage amount that is a storage amount of the second reservoir at the start of the unit period. While changing each, the amount of change of the 1st water storage amount and the amount of change of the 2nd water storage amount are calculated, and the predicted value of the inflow amount and the amount of change of the 1st water storage amount are used as the water discharge model of the reservoir. To calculate the first discharge amount from the first reservoir, and apply the predicted value of the inflow amount and the change amount of the second storage amount to the discharge amount model of the reservoir. A second water discharge amount from the reservoir, and a value obtained by subtracting the first water discharge amount from the first water discharge amount from the first water discharge amount, which is a maximum water amount satisfying a restriction condition for the water intake amount. , Restriction on the water intake out of the second water discharge amount The ineffective discharge flow rate is calculated by adding a value obtained by subtracting the second water intake amount that is the maximum water amount satisfying the condition from the second water discharge amount, and the first and second water intake amounts and the first water intake amount are calculated. The first and second power amounts are calculated by applying the second water storage amount to the power amount model, and the penalty is determined from the total value of the first and second power amounts to the invalid discharge amount. A value multiplied by a coefficient is subtracted to calculate an evaluation value of the generated electric energy, and a combination of the first and second water storage amounts that maximizes the sum of the evaluation values is determined as an optimal storage amount plan. An optimal water storage amount determination unit determined as
A water storage facility operation support system characterized by comprising: The water storage facility operation support system according to claim 1,
A power weight storage unit that stores weights according to time of day for the power,
The optimum water storage amount determination unit reads the weight corresponding to the time zone to which each unit period belongs, from the power amount weight storage unit, and a value obtained by multiplying the total value of the first and second power amounts by the weight From the above, the evaluation value is calculated by subtracting a value obtained by multiplying the invalid discharge flow rate by the penalty coefficient,
Water storage facility operation support system characterized by The water storage facility operation support system according to claim 1,
The constraint condition storage unit further stores a constraint condition for the invalid discharge flow rate,
The optimum water storage amount determination unit is a combination in which the total of the evaluation values is the largest among the combinations of the first and second water storage amounts, in which the invalid discharge amount satisfies a constraint condition for the invalid discharge amount. To determine the optimum water storage plan,
Water storage facility operation support system characterized by First and second reservoirs, a first generator that performs hydroelectric power generation using water discharged from the first reservoir, and a second that performs hydroelectric power generation using water discharged from the second reservoir The water used in the first generator is a method for supporting the operation of a water storage facility that flows into the second reservoir,
Computer
Obtaining a predicted value of the inflow amount that is the amount of water flowing into the first and second reservoirs from other than the first generator in each unit period within a predetermined period;
Reservoir discharge model for calculating discharge from the reservoir based on the amount of change in the reservoir in the reservoir from the start to the end of the unit period and the inflow, and the amount of water used for the hydroelectric power generation the amount of power model for calculating the amount of power generated in the unit period stored in the memory by the hydroelectric based on water volume of water intake and the reservoir is,
Storing a penalty coefficient that is a coefficient for evaluating an invalid discharge amount that is an amount of water discharged from the water storage facility without being given to the generator;
Storing constraints on water intake, including minimum and maximum water intake, which is the amount of water that the generator accepts from the reservoir for power generation,
For each unit period within the predetermined period, a first storage amount that is a storage amount of the first reservoir and a second storage amount that is a storage amount of the second reservoir at the start of the unit period. While changing each, the amount of change of the 1st water storage amount and the amount of change of the 2nd water storage amount are calculated, and the predicted value of the inflow amount and the amount of change of the 1st water storage amount are used as the water discharge model of the reservoir. To calculate the first discharge amount from the first reservoir, and apply the predicted value of the inflow amount and the change amount of the second storage amount to the discharge amount model of the reservoir. A second water discharge amount from the reservoir, and a value obtained by subtracting the first water discharge amount from the first water discharge amount from the first water discharge amount, which is a maximum water amount satisfying a restriction condition for the water intake amount. , Restriction on the water intake out of the second water discharge amount The ineffective discharge flow rate is calculated by adding a value obtained by subtracting the second water intake amount that is the maximum water amount satisfying the condition from the second water discharge amount, and the first and second water intake amounts and the first water intake amount are calculated. The first and second power amounts are calculated by applying the second water storage amount to the power amount model, and the penalty is determined from the total value of the first and second power amounts to the invalid discharge amount. A value multiplied by a coefficient is subtracted to calculate an evaluation value of the generated electric energy, and a combination of the first and second water storage amounts that maximizes the sum of the evaluation values is determined as an optimal storage amount plan. To decide as,
Water storage facility operation support method characterized by The water storage facility operation support method according to claim 4,
The computer
A weight for each time period for the amount of power is stored in the memory;
The weight corresponding to the time zone to which each unit period belongs is read from the memory, and the invalid discharge is multiplied by the penalty coefficient from a value obtained by multiplying the total value of the first and second electric energy by the weight. Subtracting the calculated value to calculate the evaluation value,
Water storage facility operation support method characterized by The water storage facility operation support method according to claim 4,
The computer
Furthermore, the constraint condition for the invalid discharge flow rate is stored,
Among the combinations of the first and second water storage amounts, the combination in which the total of the evaluation values becomes the maximum among the combinations in which the invalid discharge amount satisfies the constraint condition on the invalid discharge amount is the plan for the optimum storage amount To decide as,
Water storage facility operation support method characterized by First and second reservoirs, a first generator that performs hydroelectric power generation using water discharged from the first reservoir, and a second that performs hydroelectric power generation using water discharged from the second reservoir The water used in the first generator is a program for supporting the operation of a water storage facility that flows into the second reservoir,
On the computer,
Obtaining a predicted value of an inflow amount that is an amount of water flowing into the first and second reservoirs from other than the first generator in each unit period within a predetermined period;
Reservoir discharge model for calculating discharge from the reservoir based on the amount of change in the reservoir in the reservoir from the start to the end of the unit period and the inflow, and the amount of water used for the hydroelectric power generation and storing the electric energy model in the memory for calculating the intake amount and the amount of power generated in the unit period by the hydroelectric based on water volume of the reservoir is,
Storing a penalty coefficient that is a coefficient for evaluating an ineffective flow rate that is an amount of water discharged from the water storage facility without being given to the generator;
Storing constraints on water intake including a minimum and maximum value of water intake that is the amount of water that the generator accepts from the reservoir for power generation;
For each unit period within the predetermined period, a first storage amount that is a storage amount of the first reservoir and a second storage amount that is a storage amount of the second reservoir at the start of the unit period. While changing each, the amount of change of the 1st water storage amount and the amount of change of the 2nd water storage amount are calculated, and the predicted value of the inflow amount and the amount of change of the 1st water storage amount are used as the water discharge model of the reservoir. To calculate the first discharge amount from the first reservoir, and apply the predicted value of the inflow amount and the change amount of the second storage amount to the discharge amount model of the reservoir. A second water discharge amount from the reservoir, and a value obtained by subtracting the first water discharge amount from the first water discharge amount from the first water discharge amount, which is a maximum water amount satisfying a restriction condition for the water intake amount. , Restriction on the water intake out of the second water discharge amount The ineffective discharge flow rate is calculated by adding a value obtained by subtracting the second water intake amount that is the maximum water amount satisfying the condition from the second water discharge amount, and the first and second water intake amounts and the first water intake amount are calculated. The first and second power amounts are calculated by applying the second water storage amount to the power amount model, and the penalty is determined from the total value of the first and second power amounts to the invalid discharge amount. A value multiplied by a coefficient is subtracted to calculate an evaluation value of the generated electric energy, and a combination of the first and second water storage amounts that maximizes the sum of the evaluation values is set as an optimal water storage amount plan. The steps to decide;
A program for running The program according to claim 7,
Further causing the computer to execute a step of storing, in the memory, a weight by time for the amount of power,
In the step of determining the optimum water storage amount plan to the computer, the weight corresponding to the time zone to which each unit period belongs is read out from the memory, and the total value of the first and second electric energy is Subtracting a value obtained by multiplying the invalid discharge flow by the penalty coefficient from a value multiplied by a weight, and calculating the evaluation value;
A program characterized by The program according to claim 7,
In the computer,
Furthermore, the constraint condition for the invalid discharge flow is stored,
Among the combinations of the first and second water storage amounts, the combination in which the total of the evaluation values becomes the maximum among the combinations in which the invalid discharge amount satisfies the constraint condition on the invalid discharge amount is the plan for the optimum storage amount Let me decide as,
A program characterized by

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