JP2009109178A - Storage water heater, operation plan device and operation plan method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To supply hot water to a consumer at a more inexpensive rate, by further reducing the possibility of causing a shortage of the hot water. <P>SOLUTION: A storage water heater 100 is provided for supplying the hot water to the consumer by storing the hot water boiled by electric power in a tank, and includes: a prediction load calculating part 104 calculating a plurality of prediction loads and a probability generated by the respective predication loads by taking similarity between the date of stored load data and the date being a prediction object; an arithmetic operation part 106 calculating a rate expected value by using a plurality of prediction rates and the probability corresponding to the respective prediction rates by calculating the plurality of prediction rates imposed on the consumer by simulating a calorific value required when respectively generating the plurality of prediction loads on a plurality of respective operation patterns; and an optimal operation pattern specifying part 107 specifying an operation pattern lowest in the rate expected value as an optimal operation pattern with every rate expected value corresponding to the plurality of respective operation patterns. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

The present invention relates to a hot water storage type hot water supply apparatus provided with a hot water supply unit using electric power, such as a CO ₂ heat pump water heater or an electric water heater, an operation planning apparatus and an operation planning method thereof.

A hot water storage type hot water supply device is installed in a consumer, generates heat using electric power, and stores the heat to cover the hot water supply load of the consumer. In particular, the CO ₂ heat pump water heater has high energy efficiency, which is the amount of heat stored when using per unit power, and can store a large amount of heat with a small amount of power, and is expected to spread as an energy-saving device. .

  Electricity charges used in hot water storage hot water supply devices vary greatly depending on the time of day, and in particular, the late-night charge is cheaper. Therefore, it is necessary to store as much heat as possible in the late-night charge zone in order to reduce the charge.

  In addition, a hot water storage type hot water supply apparatus generally has a smaller amount of heat that can be stored per hour than an instantaneous water heater. When a hot water supply load is generated that is insufficient for the amount of heat stored in the midnight charge zone, the hot water runs out, so it is necessary to store heat so that there is no hot water.

  Therefore, there is a need for a system that plans operation that stores heat more frequently than the assumed hot water supply load of the day, using cheap late-night price power, so that it can be operated cheaply without running out of hot water. .

  An example of such a system is shown in Patent Document 1.

  In the invention of Patent Document 1, an average daily heat amount used and a variation heat amount that is a standard deviation of the heat amount used are calculated from the heat amount used within the unit period. Next, the value obtained by adding the average amount of heat used and the amount of variation heat is compared with the amount of heat used for one day on the previous day, and the larger value is defined as the amount of heat generated by boiling. The amount of heat for this amount of boiling heat is stored in the late-night charge zone.

By this process, most of the price is raised in the late-night charge zone, where the charge is low, the charge unit price, which is the charge per unit of electricity, is reduced, and the heat is burned out by storing more heat than the average heat used per day. It is possible to reduce the possibility of occurrence.
JP 2004-324905 A

  However, according to the above prior art, there are the following two problems.

  The first problem is that the heat is stored more than the average daily heat consumption, but this is not enough to prevent the hot water from running out.

  For example, there is a certain time lag before the stored heat quantity is actually used by the consumer, and during this time, the stored heat quantity is reduced by heat radiation from the hot water storage tank. As a result, the hot water may run out especially at night when the amount of hot water used is large. In the case where a hot water supply runs out in the actual hot water supply even though the amount of heat is stored at midnight, the amount of heat must be accumulated in a time zone other than midnight. Since the unit price of charges is high outside the late-night charge zone, the purpose of reducing the charge cannot be achieved.

  The second problem is that although the unit price of the charge is low, there is a risk that the amount of electric power will increase, and as a result, the total amount of charges that will be borne by the consumer will be high.

  According to the embodiment of Patent Document 1, an operation is performed in which most of the heat amount per day is stored in the late-night charge zone. Since the amount of water entering the hot water storage tank for storing heat of the hot water storage type hot water supply device is fixed, when storing a large amount of heat at a time, it is necessary to store heat at a high temperature per unit of water. However, as a characteristic of a general hot water supply device, when making hot water, the higher the temperature of the hot water to be made, the lower the energy efficiency. Therefore, if the same amount of heat is stored, comparing the case where energy efficiency is reduced by making hot water with high temperature and the case where energy efficiency is high by making hot water with low temperature, If you make it, you need more power.

  In addition, as a characteristic of a general hot water supply apparatus, energy efficiency is greatly influenced by the temperature of the outside air temperature when boiling hot water. The higher the outside temperature, the higher the energy efficiency. Since the midnight rate zone has a lower outside air temperature than the daytime and evening hours, energy efficiency is low in the midnight rate zone. Therefore, comparing the case of boiling in the late-night charge zone with the case of boiling in the daytime / evening requires a larger amount of power when boiling in the late-night charge zone.

  In general households, the stored hot water is used not from daytime but from evening to night. In this case, as described above, when most of the heat is stored in the late-night charge zone, heat is generated from the hot water storage tank for a long time until the hot water is used from evening to night. For this reason, in order to prevent running out of hot water, it is necessary to boil more than the actual amount of heat used. This compares the case where most of the heat of the day is boiled in the midnight charge zone with the case of boiling just before the hot water supply load is generated. More power is required.

  Therefore, in the operation that stores most of the heat amount of the day in the midnight charge zone, the unit price of the charge is low, but a large amount of electric power is required and there is a risk that the energy saving performance deteriorates.

  Therefore, the present invention has been made to solve the above two problems, a hot water storage type hot water supply device that can reduce the possibility of running out of hot water and can supply hot water to consumers at a lower price, An object is to provide an operation planning apparatus and an operation planning method.

  In order to achieve the above object, a hot water storage type hot water supply apparatus of the present invention is a hot water storage type hot water supply apparatus for storing hot water boiled with electric power in a hot water storage tank and supplying hot water to a consumer, Storage means for accumulating load data including the amount of heat and date and time used in the operation, and by taking similarity between the date and time when the load data was stored and the date and time to be predicted, a plurality of predicted loads and each predicted load A predicted load calculating means for calculating the probability of occurrence of a load, and for each of a plurality of operation patterns, it is applied to the consumer by simulating the amount of heat required when the plurality of predicted loads are generated. Calculating means for calculating a plurality of predicted charges and calculating an expected charge value using the plurality of predicted charges and the probability corresponding to each predicted charge; and for each of the plurality of driving patterns. An optimum operation pattern specifying means for specifying the operation pattern having the lowest expected charge value as an optimum operation pattern, and boiling water so as to store hot water in the hot water storage tank to supply hot water to the consumer according to the operation of the optimum operation pattern. An operation command calculating means for calculating a heating temperature and a boiling command that is an operation start command or a stop command, the hot water storage tank, and storing heat in the hot water storage tank according to the boiling temperature and the boiling command, Hot water supply means for supplying hot water to the consumer in accordance with instructions from the consumer.

  According to this configuration, the possibility of running out of hot water can be further reduced by using a plurality of predicted loads. Furthermore, it becomes possible to select the driving | operation which makes the charge which becomes a burden of a consumer cheap by performing the simulation with respect to a charge.

  INDUSTRIAL APPLICABILITY The present invention can provide a hot water storage type hot water supply apparatus, an operation planning apparatus, and an operation planning method that can reduce the possibility of running out of hot water and supply hot water to consumers at a lower price.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a configuration diagram of a hot water storage type hot water supply apparatus according to the present embodiment.

  The hot water storage type hot water supply apparatus 100 in the figure predicts a plurality of hot water supply loads that are predicted to be generated during an operation plan period that is a target of the operation of the hot water supply apparatus, and performs an operation simulation for each of a plurality of operation patterns. Thus, an operation planning unit 101 that plans an operation that is optimal with respect to a charge and power consumption, and a hot water supply unit 102 that supplies hot water to a consumer by storing heat in a hot water storage tank according to the planned operation are provided.

  The operation planning unit 101 executes an operation simulation for planning an operation in the operation plan period at the start time of the operation plan period. In the present embodiment, 23:00 is regarded as the beginning of the day, and at the same time the time becomes 23:00, an operation simulation is performed for a one-day operation plan from 23:00 on the current day to 23:00 on the next day. In addition, the operation planning unit 101 includes an accumulation unit 103, a predicted load calculation unit 104, a predicted temperature calculation unit 105, a calculation unit 106, an optimum operation pattern specifying unit 107, and an operation command calculation unit 108.

  The accumulating unit 103 is a processing unit that accumulates load data such as the amount of heat used for hot water supply of a customer for each predetermined period and temperature data such as the outside air temperature around the hot water tank and the temperature of water supplied to the hot water tank. It is. In the present embodiment, the accumulating unit 103 includes a hot water supply load measured by the hot water supply unit 102, a hot water temperature that is the temperature of the hot water supplied to the customer, and the surroundings of the hot water storage tank 22 shown in FIG. The outside air temperature, which is the temperature of the outside air, and the water temperature supplied to the hot water storage tank 22 are acquired every predetermined period, and stored as hot water supply load data, tapping temperature data, outside air temperature data, and water temperature data for each predetermined period. . In the present embodiment, data is accumulated as data every hour.

  The predicted load calculation unit 104 is a processing unit that calculates a plurality of predicted loads and their occurrence probabilities by taking the similarity between the load data and the date and time to be predicted. Specifically, the hot water supply load generated in the same time zone on the same day is calculated as the load in the same time zone to be predicted. In the present embodiment, first, predicted load calculation unit 104 reads hot water supply load data and hot water temperature data from storage unit 103. By taking similarities between these data and the date and time, a plurality of predicted loads, which are predicted to occur at each time in 24 hours from 23:00, and hot water supply to customers at each time of each predicted load. The predicted hot water temperature, which is the predicted hot water temperature, and the probability of occurrence of the predicted load are calculated.

  The predicted temperature calculation unit 105 is a processing unit that calculates predicted temperature data by taking the similarity between the date and time when the temperature data is stored and the date and time to be predicted. Specifically, an average value of temperatures accumulated in the same time zone is calculated as a predicted temperature in the same time zone to be predicted. In the present embodiment, first, the predicted temperature calculation unit 105 reads outside air temperature data and water temperature data from the storage unit 103. By taking the similarity between these data and the date and time, the predicted outside air temperature and the predicted water temperature predicted at 24 hours from 23:00 are calculated.

  The calculation unit 106 calculates the amount of electric power applied to the consumer and the predicted charge by simulating the amount of heat required when a plurality of predicted loads are generated, and uses the predicted charge and the probability It is a process part which calculates the charge expected value and electric energy expected value corresponding to each of these driving patterns. In the present embodiment, the calculation unit 106 first acquires the predicted load, the predicted hot water temperature, and the predicted occurrence probability from the predicted load calculation unit 104, and the predicted outside air temperature and the predicted water temperature from the predicted temperature calculation unit 105. Furthermore, a current heat amount that is the amount of heat currently stored in the hot water storage tank 22 from the hot water supply unit 102, a current hot water amount that indicates a water amount that is equal to or higher than a predetermined temperature in the hot water storage tank, and a current hot water temperature that is an average temperature of the current hot water amount And the device efficiency indicating what energy efficiency the hot water supply device has with respect to the outside air temperature, the water temperature, and the boiling temperature. For example, as will be described later, the device efficiency is acquired by using a table indicating the device efficiency with respect to the outside air temperature, the water temperature, and the boiling temperature. Moreover, the calculating part 106 is provided with the driving pattern calculation part 111, the simulation part 112, and the expected value calculation part 113, and calculates a charge expected value and an electric energy expected value based on each acquired information.

  The operation pattern calculation unit 111 calculates a plurality of operation patterns including the amount of heat to be stored in the hot water storage tank 22 by a predetermined boiling time. Specifically, the time at which the amount of heat at each time of the plurality of predicted loads is equal to or greater than a predetermined threshold is set as the boiling time, and the amount of heat to be heated at each heating time is set for each predetermined stage. Thus, a plurality of operation patterns are calculated.

  Based on each of the plurality of operation patterns, the simulation unit 112 calculates the required boiling amount required for each predetermined period so that the boiling target heat amount is stored in the hot water storage tank 22 by the boiling time. The simulation of the predicted charge and the predicted power amount is performed. In the present embodiment, first, the simulation unit 112 sets the current heat amount, the current hot water amount, and the current hot water temperature as the initial state of the hot water storage tank 22. Then, when the hot water supply load is generated according to the predicted load, the predicted hot water temperature, and the predicted occurrence probability, and the outside air temperature and the water temperature are the predicted outside air temperature and the predicted water temperature, the operation pattern calculation unit 111 calculates. Based on each operation pattern, the amount of heat that needs to be heated is calculated. Calculate the predicted power required to boil the calculated amount of heat, multiply by the hourly unit price corresponding to the predicted power for each time, and add them together to calculate the predicted daily charge for each predicted load To do.

  The expected value calculation unit 113 calculates an expected charge value and an expected electric energy amount as calculation results from the predicted charge and predicted electric energy and the occurrence probabilities corresponding thereto. In the present embodiment, the expected charge value corresponding to the driving pattern used in the simulation unit 112 is obtained by multiplying the predicted charge and the predicted power amount calculated for each of the plurality of predicted loads by the corresponding occurrence probability. And the expected electric energy is calculated.

  The optimum driving pattern specifying unit 107 specifies the optimum driving pattern that maximizes the driving evaluation value by calculating the driving evaluation value for each expected charge value and expected electric energy corresponding to each of the plurality of driving patterns. . In the present embodiment, first, the optimum operation pattern specifying unit 107 acquires the expected charge value and the expected electric energy from the calculation unit 106 as calculation results. Further, an operation evaluation method showing the relationship between the desired amount of power consumption of the hot water supply device and the charge from the hot water supply unit 102, and an operation evaluation history that is the amount of power consumption and charge as a result of operating the past hot water supply device are acquired. Then, an operation evaluation value is calculated for the operation evaluation method, and an optimum operation pattern that is an operation that maximizes the operation evaluation value is calculated.

  The operation command calculation unit 108 acquires the optimal operation pattern from the optimal operation pattern specifying unit 107, the current heat amount and the current hot water amount from the hot water supply unit 102, and the current heat amount and the current hot water amount are calculated from the optimal operation pattern. When the value is reached, a boiling command that is information for instructing the hot water supply unit 102 to “run” or “stop” and a boiling temperature that is the temperature of the hot water heated by the hot water supply unit 102 are calculated.

  The hot water supply unit 102 obtains the boiling command and the boiling temperature from the operation command calculation unit 108. When the boiling is started, the boiling water is heated at the boiling temperature, and when the boiling is stopped, the operation is performed. Do not do. Heat storage is performed in the hot water storage tank 22 by the above processing. Moreover, the hot water supply part 102 supplies hot water to a consumer according to a customer's instruction | indication, and measures a hot water supply load of a consumer, a tapping temperature, external temperature, water temperature, and driving | operation evaluation log | history. Furthermore, the hot water supply unit 102 determines an operation evaluation method.

  FIG. 2 is a schematic diagram of a hot water storage type hot water supply apparatus 100 according to the present embodiment. The hot water storage type hot water supply apparatus 100 of FIG. 1 includes a control unit 21, a hot water storage tank 22, a heat pump 23, and a thermometer 24. The control unit 21 corresponds to the operation planning unit 101 in FIG. The hot water storage tank 22, the heat pump 23, and the thermometer 24 correspond to the hot water supply unit 102.

  The hot water storage tank 22 acquires water from the lower part of the hot water storage tank 22 and supplies water to the heat pump 23 from the lower part. And the hot water boiled by the heat pump 23 is acquired from the upper part of the hot water storage tank 22, and hot water is supplied from the upper part to a hot water tap of a consumer. In the hot water storage type hot water supply apparatus of the present embodiment, the hot water storage tank 22 has a capacity of 400L.

  As shown in FIG. 3A, the temperature distribution in the hot water storage tank 22 is normally high on the upper side and low on the lower side. When determining the hot water temperature and the amount of hot water in the hot water storage tank 22, as shown in FIG. Water is regarded as hot water, and water below this temperature is regarded as water having a temperature equal to the temperature of the water supplied to the hot water storage tank 22.

  The heat pump 23 boils the water acquired from the hot water storage tank 22 according to the operation command sent from the control unit 21. In the hot water storage type hot water supply apparatus of this embodiment, it is assumed that the boiling temperature can be set in increments of 5 ° C. from 65 ° C. to 85 ° C., and the amount of heat of 4000 kcal can be boiled per hour.

  The thermometer 24 measures the outside air temperature.

  Hereinafter, an example of the operation in the hot water storage type hot water supply apparatus 100 will be described with reference to the flowchart of FIG.

  As a premise, the hot water storage type hot water supply device 100 has been operated for 28 days or more, and the storage unit 103 is configured to store the hot water supply load used by the customer in that hour, the tapping temperature, the outside temperature, and the water temperature. The average value for each hour is accumulated as hot water supply load data, tapping temperature data, outside air temperature data, and water temperature data for the last 28 days. FIG. 5 shows an example of hot water supply load data, tapping temperature data, outside air temperature data, and water temperature data. As described above, in this embodiment, the period from 23:00 on the current day to 23:00 on the next day is regarded as one day.

  First, at 23 o'clock of the day, the operation planning unit 101 of the hot water storage type hot water supply apparatus 100 issues an instruction to start the operation planning process (S201).

  Next, the predicted load calculation unit 104 predicts a hot water supply load for 24 hours, a tapping temperature, and an occurrence probability thereof (S202). Specifically, the predicted load calculation unit 104 acquires hot water supply load data, which is an hourly hot water supply load for the past 28 days, from the storage unit 103. Of the hot water supply load data, the load data of the same day of the week as the day of the week corresponding to 24 hours to be executed in the future simulation is extracted for four days. For example, as shown in FIG. 6, it is assumed that the day of the week to be simulated is Friday the 30th. The data to be extracted in this case is Friday load data, and is load data on the 2nd, 9th, 16th, and 23rd. The predicted load calculation unit 104 sets, as the predicted load 1, a 24-hour load of the latest day (23 days in the example of FIG. 6) among the hot water supply load data of the same day of the week. Similarly, the load on the day one week before the predicted load 1 (16 days in the example of FIG. 6) is predicted as the predicted load 2, and the load on the day one week before the predicted load 2 (9 days in the example of FIG. 6) is predicted. The load on the day one week before the load 3 and the predicted load 3 (2 days in the example of FIG. 6) is set as the predicted load 4. Moreover, the predicted load calculation part 104 makes the predicted hot water temperature 1 the 24 hour 1 hour hot water temperature of the hot water temperature data on the same day as the predicted load 1 (23 days in the example of FIG. 6). Similarly, the hot water temperature on the same day as the predicted load 2 (16 days in the example of FIG. 6) is the predicted hot water temperature 2, and the hot water temperature on the same day as the predicted load 3 (9 days in the example of FIG. 6) is the predicted hot water temperature 3 and the predicted load. 4 is the predicted hot water temperature 4 on the same day (2 days in the example of FIG. 6). Furthermore, the predicted load calculation unit 104 has an occurrence probability 1 that is the occurrence probability of the predicted load 1 of 40%, an occurrence probability 2 of the predicted load 2 of 30%, an occurrence probability 3 of the predicted load 3 of 20%, and the predicted load 4 of The occurrence probability 4 is 10%. This is because it is considered that the probability of occurrence is higher as the load is closer to the simulation target day.

  Next, the predicted temperature calculation unit 105 predicts the outside air temperature and water temperature for 24 hours (S203). Specifically, the predicted temperature calculation unit 105 is the average of the outside air temperature and the water temperature in the hour for the past 7 days (7 days from 23rd to 29th in the example of FIG. 6) from the storage unit 103. Acquire data and water temperature data. Among the outside air temperature data, an average value for 7 days of the outside air temperature value at 23:00 is calculated and set as the predicted outside air temperature at 23:00. Here, the value at 23:00 means an average value from 23:00 to 00:00 (hereinafter the same). Similarly, the same processing is performed for each time from the next 0:00 to 22:00 to calculate the predicted outside air temperature. Further, the predicted temperature calculation unit 105 calculates the predicted water temperature by calculating an average value of the water temperature value for 7 days at the same time for the water temperature.

  Next, the calculating part 106 performs a driving simulation (S204). The details of the driving simulation (S204) are as shown in the flowchart of FIG.

  First, an instruction to start a driving simulation (S204) is issued (S301).

  Next, the calculating part 106 acquires driving | operation initial information (S302). Specifically, the calculation unit 106 acquires the current heat amount, the current hot water temperature, the current hot water temperature, and the device efficiency from the hot water supply unit 102. Further, the predicted load, predicted hot water temperature and occurrence probability are acquired from the predicted load calculation unit 104, and the predicted outside air temperature and predicted water temperature are acquired from the predicted temperature calculation unit 105. In the following description, it is assumed that a predicted load as shown in FIG. 8 has been acquired. FIGS. 8A to 8D are diagrams illustrating examples of the predicted loads 1 to 4, respectively.

  Next, the operation pattern calculation unit 111 calculates the boiling time (S303). Specifically, the driving pattern calculation unit 111 calculates the total daily load for each of the four types of predicted loads. Further, a time when a predicted load of 20% or more of the total daily load is generated is calculated. For example, the total daily load of the predicted load 1 shown in FIG. 8A is 20000 kcal, and the times when the total load is 4000 kcal, which is 20% of the total daily load, are 4 o'clock and 18:00. Similarly, the daily load total amount of the predicted load 2 shown in FIG. 8B is 18000 kcal, and the times when it becomes 3600 kcal or more, which is 20% of the daily load amount, are 4 o'clock and 22:00. The daily load total amount of the predicted load 3 shown in FIG. 8C is 20000 kcal, and the times when it becomes 4000 kcal or more, which is 20% of the daily load amount, are 14:00 and 22:00. The daily load total amount of the predicted load 4 shown in FIG. 8D is 22000 kcal, and the time when it becomes 4400 kcal or more, which is 20% of the daily load amount, is 18:00. FIG. 9 is a diagram showing the above results. As described above, the driving pattern calculation unit 111 obtains the time when a predicted load of 20% or more of the total daily load is generated for all four types of predicted loads, and obtains all the obtained times and the midnight fee zone. The last time is set as the boiling time. For example, when the last time of the late-night toll zone is 7 o'clock, the boiling time is 4 o'clock, 7 o'clock, 14 o'clock, 18 o'clock, 22 o'clock as shown in FIG. Hereinafter, as an example of the operation, it is assumed that five times of 4 o'clock, 7 o'clock, 14 o'clock, 18 o'clock and 22 o'clock are calculated as the boiling time.

  Next, the driving pattern calculation unit 111 calculates a driving pattern (S304). The operation pattern calculation unit 111 sets an operation pattern by setting a boiling target level calculated based on the amount of heat that can be stored in the hot water storage tank 22 with respect to the boiling time. The maximum amount of heat that can be stored in the hot water storage tank 22 is 34000 kcal, which is a value obtained by multiplying the maximum temperature of 85 ° C. at which the hot water supply unit 102 can boil by 400 L of the capacity of the hot water storage tank 22. The minimum amount of heat that can be stored in the hot water storage tank 22 is divided into 10 steps equally, and the amount of heat corresponding to each step is set to the target level 0, 1, 2,. To do. The amount of heat at the boiling target level 0 is 0 kcal, and the amount of heat at the boiling target level 9 is 34000 kcal. FIG. 10 is a diagram showing each boiling target level and the corresponding target heat amount. Originally, it should have increased by 3778 kcal for each step, but to simplify the explanation below, one step was set to 3800 kcal. The operation pattern is represented by each boiling target level from the order of the five boiling times in the early order. For example, the operation pattern when the boiling target level at all times is 0 is [00000]. [01234] In the case where the boiling target level at 4 o'clock is 0, the target level at 7 o'clock is 1, the target level at 14:00 is 2, the target level at 18:00 is 3, and the target level at 22:00 is 4, is there. The driving pattern calculation unit 111 first calculates [00000] as the initial value of the driving pattern. Each time the operation pattern calculation process (S304) is executed, an operation pattern in which 1 is added from a low-order value such as [00001] is output and increased in decimal, and [99999] is the last operation pattern. It becomes.

  Next, the simulation unit 112 calculates an expected charge value and an expected power amount (S305). Details of the expected charge value and electric energy expected value calculation processing (S305) are as shown in the flowchart of FIG.

  First, an instruction to start calculating expected charge value and expected electric energy (S305) is issued (S401).

  Next, the simulation unit 112 initializes the state of the driving simulation (S402). Specifically, the simulation unit 112 sets the time of interest indicating the current state of the driving simulation to be 23 o'clock, and sets a hot water out flag to be described later to OFF. Also, the current hot water volume as the hot water volume at the time of interest, the current hot water volume as the hot water volume at the time of interest, the current hot water temperature as the hot water temperature at the time of interest as the average temperature of the hot water volume at the time of interest, and the amount of heat stored in the hot water storage tank at the time of interest. The current heat quantity is set as a certain time-of-interest heat quantity. Further, the simulation unit 112 sets that a 24-hour hot water supply load of the predicted load 1 among the four types of predicted loads occurs at each time, and sets the predicted charge 1 and predicted power consumption 1 corresponding to the predicted load 1 to zero, respectively. . Each time the operation simulation state initialization (S402) is processed, the predicted load 2, the predicted load 3, and the predicted load are changed.

  Next, the simulation unit 112 calculates power consumption and a charge (S403). The simulation unit 112 determines whether it is necessary to boil the driving pattern at the time of interest, and calculates the amount of heat to be boiled, which is the amount of heat to be boiled in one hour from the time of interest when it is determined to be necessary. Here, since the time of interest is 23:00, the amount of boiling heat between 23:00 and 0 is calculated.

  The details of the power consumption and fee calculation process (S403) are as shown in the flowchart of FIG.

  First, an instruction to start power consumption and fee calculation processing (S403) is issued (S501).

  Next, the simulation unit 112 calculates a heat amount required for the time of attention, which is a heat amount required for the time of interest (S502). Specifically, first, the simulation unit 112 calculates the nearest boiling time and target boiling level after 23:00, which is the time of interest. Since the nearest boiling time later than 23:00 is 4 o'clock and the operation pattern is [00000], the boiling target level is 0, which is a value corresponding to 4 o'clock in the operation pattern. Therefore, the target heat amount corresponding to the target level 0 is 0 kcal. And as above-mentioned, since the hot water supply part 102 has a boiling capacity of 4000 kcal per hour, the target calorie | heat amount of -4000 kcal is needed 1 hour before the boiling time. Therefore, the heat required for the time of interest is calculated by the following formula 1.

  Amount of heat required for time of interest = target heat amount-(boiling time-time of focus) x 4000 (Equation 1)

  When the time of interest is 23:00, the calculation is performed as -1 o'clock.

  Next, the simulation unit 112 determines whether boiling is necessary (S503). That is, in order for the hot water supply unit 102 to store the amount of heat at the boiling target level at the boiling time, it is determined whether boiling is necessary at the time of interest. Specifically, when the target time heat quantity is smaller than the target time required heat quantity + 4000 kcal, which is the heat quantity that must be stored after one hour, it is determined that boiling is necessary. When the simulation unit 112 determines that boiling is necessary (YES in S503), the simulation unit 112 calculates the amount of heating and the boiling temperature (S504). If the simulation unit 112 determines that boiling is not necessary (NO in S503), the power consumption and fee calculation process (S403) ends (S506).

  When the simulation unit 112 determines that boiling is necessary (YES in S503), the simulation unit 112 calculates the amount of heating heat and the boiling temperature using Equation 2 below (S504).

  Heating amount of boiling = necessary amount of heat at the time of interest + 4000−heat amount of time of interest (Expression 2)

  However, when the calculated amount of heating heat is larger than 4000 kcal, the amount of heating heat is set to 4000 kcal. The boiling temperature is calculated based on the assumed boiling temperature calculated using the following Equation 3.

  Nominal boiling temperature = amount of heat to be heated ÷ (400-amount of hot water at time of interest) (Equation 3)

  Since the boiling temperature of the hot water supply unit 102 can be set only in increments of 5 ° C. between 65 ° C. and 85 ° C. as described above, the simulation unit 112 raises the closest settable temperature above the nominal boiling temperature. Let it be temperature. If the assumed boiling temperature is higher than 85 ° C, the boiling temperature is set to 85 ° C. In this case, the amount of heating heat is calculated by the following equation 4.

  Boiling heat amount = 85 × (400−time of interest hot water amount) (Formula 4)

  Next, the simulation unit 112 calculates power consumption and a charge (S505). First, the simulation part 112 calculates the energy efficiency at the time of boiling at the simulation present time from the boiling temperature, the predicted water temperature, the predicted outside air temperature, and the equipment efficiency. For example, based on the relationship between the outside air temperature, water temperature and boiling temperature shown in FIGS. 13 (a), 13 (b), and 13 (c) and the equipment efficiency, the energy efficiency is calculated using the following equation (5). calculate.

  Energy efficiency = Rated energy efficiency x Outside air temperature equipment efficiency ratio x Water temperature equipment efficiency ratio x Boiling temperature equipment efficiency ratio (Formula 5)

  Here, the outside air temperature equipment efficiency ratio shown in FIG. 13A indicates the equipment efficiency relative to the outside air temperature when the water temperature is the same and the boiling temperature is the same. That is, when the rating is set to 100% at the same water temperature and the same boiling temperature, the outside air temperature equipment efficiency ratio indicates how much the rating becomes depending on the outside air temperature. Similarly, the water temperature equipment efficiency ratio shown in FIG. 13 (b) indicates the equipment efficiency relative to the water temperature when the outside temperature is the same and the boiling temperature is the same. The boiling temperature device efficiency ratio shown in FIG. 13C indicates the device efficiency with respect to the boiling temperature in the case of the same outside air temperature and the same water temperature. In addition, when the outside air temperature, the water temperature, and the boiling temperature are values not in the table, a prorated value is used.

  And the simulation part 112 calculates power consumption using the calculated energy efficiency and the following formula | equation 6, and adds it to the prediction power 1 which shows the power consumption of the prediction load 1. FIG.

  Power consumption = amount of heating heat ÷ (energy efficiency x 3600 x 0.24) (Equation 6)

  Note that × 3600 × 0.24 in Equation 6 is a coefficient used when converting from a unit of heat kcal to a unit of power kWh. The simulation unit 112 calculates a charge using the calculated power consumption and the following formula 7, and adds the calculated charge to the predicted charge 1 indicating the power charge of the predicted load 1. The power unit price for each charge band is a value uniquely determined by the power charge band.

  Charge = Power consumption x Electricity unit price by charge zone (Formula 7)

  With the above processing, the power consumption and fee calculation processing (S403) is completed (S506).

  Next, the simulation unit 112 calculates the state of the hot water storage tank 22 at the next time (S404). That is, the simulation unit 112 calculates the state of the hot water storage tank 22 one hour after the time of interest. Here, since the time of interest is 23:00, the state of the hot water storage tank 22 at 0 is calculated. First, the simulation unit 112 calculates the time-of-interest heat quantity at the next time (0 o'clock) using the following formula 8.

  Attention time calorie at the next time = (Attention time calorie-Prediction load calorie + Boiling calorie) x Heat dissipation loss coefficient (Equation 8)

  However, the minimum value of the time-of-interest heat quantity is the water temperature at that time × 400 L, and the maximum value is 34000 kcal as described above. Here, when the value obtained by subtracting the heat amount of the predicted load from the target time heat amount is below the minimum value, or when the predicted hot water temperature is higher than the target time hot water temperature, the hot water run flag is set to ON. If the hot water runout flag is ON, it indicates that there is a possibility of hot water running out in the operation pattern. The heat dissipation loss coefficient is a function of the outside air temperature, and the function is determined by the performance of the heat storage tank, and the simulation unit 112 calculates the heat dissipation loss coefficient at each time by inputting the predicted outside air temperature. For example, the heat dissipation loss coefficient is calculated based on the relationship between the outside air temperature and the heat dissipation loss coefficient shown in FIG. Next, the simulation unit 112 calculates the predicted load hot water amount, which is the amount of hot water used by the customer due to the occurrence of the predicted load, using Equation 9 below.

  Predicted hot water volume = Predictive load ÷ Predicted hot water temperature (Formula 9)

  Furthermore, the amount of hot water of interest at the next time (0 o'clock) is calculated by the following equations 10 and 11.

  Boiling water amount = boiling heat amount ÷ boiling temperature (Formula 10)

  Attention time hot water volume at the next time = Attention time hot water quantity-Predicted load hot water quantity + Boiling hot water quantity (Formula 11)

  Furthermore, the simulation unit 112 calculates the hot water temperature at the next time according to the following expression 12.

Attention time hot water temperature at the next time = Attention time heat at the next time ÷ Attention time hot water at the next time
(Formula 12)

  When the state of the hot water storage tank 22 at the next time is calculated, 1 hour is added to the time of interest. That is, here, the time of interest is assumed to be 0:00.

  Next, the simulation unit 112 determines whether charges and power consumption are calculated for 24 hours (S405). That is, it is determined whether or not the time of interest is 23:00. If it is 23:00 (YES in S405), it is determined whether the predicted charge and the predicted power consumption have been calculated for all predicted loads (S406). When the time of interest is not 23:00 (NO in S405), power consumption and a charge are calculated (S403). Here, since the time of interest is 0 o'clock, power consumption and charge are calculated (S403). And the simulation part 112 repeats the calculation process (S403) of power consumption and a charge, the calculation process (S404) of the state of the hot water storage tank of the next time, and the determination process (S405) of time of interest.

  If the time of interest is 23:00, the simulation unit 112 determines whether the predicted charge and the predicted power consumption have been calculated for all predicted loads (S406). That is, the simulation unit 112 determines whether the predicted load set in the simulation state initialization process (S402) is the predicted load 4. When the predicted load is the predicted load 4 (YES in S406), the expected value calculation unit 113 calculates the expected charge value and the expected power amount (S407). When the predicted load is not the predicted load 4 (NO in S406), the simulation unit 112 initializes the simulation state (S402). Here, since the predicted load set in the simulation state initialization process (S402) is the predicted load 1, the simulation unit 112 initializes the simulation state (S402). Of the four types of predicted loads, the predicted load 2 is set to generate a hot water supply load for 24 hours at each time, the predicted charge 2 and the predicted power consumption 2 become zero, and the other processes are the same as described above. Thereafter, similarly, the simulation initialization process (S402) to the predicted load determination process (S406) are repeated.

  If the predicted load set in the simulation state initialization process (S402) is the predicted load 4, the expected value calculation unit 113 calculates the expected charge value and the expected electric energy (S407). The expected value calculation unit 113 calculates the expected value of the charge and power consumption for the four types of predicted loads when hot water does not run out of the operation pattern (the hot water out flag is OFF). Since the operation pattern for which the operation simulation has been performed is [00000], the expected charge value and the expected electric energy of the operation pattern [00000] are calculated. However, if the hot water runout flag of the operation pattern [00000] is ON, the predicted fee is not evaluated. Only when the hot water out flag of the operation pattern [00000] is OFF, the expected charge value is calculated by the following equation 13.

  Expected charge value = predicted charge 1 x occurrence probability 1 + predicted charge 2 x occurrence probability 2 + predicted charge 3 x occurrence probability 3 + predicted charge 4 x occurrence probability 4 (Formula 13)

  In addition, the expected electric energy is calculated by the following equation (14).

  Expected electric energy = predicted power 1 × occurrence probability 1 + predicted power 2 × occurrence probability 2 + predicted power 3 × occurrence probability 3 + predicted power 4 × occurrence probability 4 (Formula 14)

  The expected value calculation unit 113 associates the operation pattern, the expected charge value, and the expected electric energy value, and outputs the result as calculation result data to the optimum operation pattern specifying unit 107 for accumulation.

  With the above processing, the expected value calculation processing (S305) is completed (S408).

  Next, it is determined whether the expected charge value and the expected power amount are calculated for all operation patterns (S306). Since the last driving pattern is [99999], when the driving pattern is [99999] (YES in S306), the driving simulation (S204) is ended (S307). If the operation pattern is not [99999] (NO in S306), the operation pattern is calculated (S304). Here, since the operation pattern is [00000], the operation pattern is calculated (S304). The driving pattern calculation unit 111 calculates the driving pattern [00001] (S304). Thereafter, the driving pattern calculation process (S304) and the expected value calculation process (S305) are similarly repeated. When the operation pattern [99999] is obtained and the calculation result data is calculated for all of the operation patterns [00000] to [99999] (YES in S306), the operation simulation (S204) is terminated (S307).

  Next, the optimum operation pattern specifying unit 107 specifies the optimum operation pattern (S205). The optimum driving pattern specifying unit 107 calculates a driving evaluation value according to the driving evaluation method, and calculates an optimum driving pattern that is a driving pattern having the maximum driving evaluation value. First, the optimum operation pattern specifying unit 107 calculates the operation result data from the operation unit 106, the operation evaluation method that is the relationship between the desired amount of power consumption of the hot water supply device and the total power charge from the hot water supply unit 102, and the past operation result of the hot water supply device The driving evaluation history is acquired. Next, the optimum operation pattern specifying unit 107 calculates a charge index and an electric energy index.

  First, the minimum charge and maximum charge are calculated for the expected charge value in the calculation result data, and the value of each charge expected value when the minimum charge is 100 and the maximum charge is 0 is distributed. A certain rate index is calculated for all expected rate values. In addition, the minimum power consumption and the maximum power consumption are calculated with respect to the expected power value in the calculation result data, and each power amount when the minimum power consumption is 100 and the maximum charge is 0 is distributed. An electric energy index that is an expected value is calculated for all expected electric energy values.

  Next, the optimum driving pattern specifying unit 107 calculates a driving evaluation value for the driving evaluation method. The operation evaluation method is a value directly input by the user from the remote controller of the hot water supply unit 102 or a fixed value input in advance, and includes two values [fee coefficient: power coefficient]. The charge coefficient and the power coefficient are set in the range of 0 to 1 so that the sum is 1. Based on the driving evaluation method, the driving evaluation value is calculated by the following Expression 15.

  Operation evaluation value = (Charge index × Charge coefficient) + (Electric energy index × Power coefficient) (Formula 15)

  The driving evaluation value takes a value in the range of 0 to 100, and is calculated for all calculation result data.

  Next, the optimum driving pattern specifying unit 107 calculates the driving pattern having the maximum driving evaluation value as the optimum driving pattern.

  FIG. 14 is a diagram illustrating processing related to the calculation of the driving evaluation value using specific numerical values. In FIG. 14, the charge index is obtained from the expected charge value, the power quantity index is obtained from the expected electric energy value, and the operation evaluation value is obtained according to Equation 15. Here, [1.0: 0.0], [0.8: 0.2], [0.5: 0.5], [0.2: 0.8], [0. 0: 1.0]. As a result, the driving pattern having the maximum driving evaluation value was different from C, C, A, E, and E in order.

  As described above, by specifying the driving evaluation method, it is possible to specify whether to place importance on the charge or on the amount of electric power, and accordingly, the optimum driving pattern can also have different results. In other words, it is possible to select a driving more suited to the user's intention.

  Next, the driving command calculation unit 108 calculates a driving command according to the optimal driving pattern specified by the optimal driving pattern specifying unit 107 (S206). First, the operation command calculation unit 108 acquires an optimal operation pattern from the optimal operation pattern specifying unit 107, and determines whether boiling is necessary every minute. First, in the optimum operation pattern, the boiling target level at the nearest boiling time and the target heat amount corresponding to the latest boiling time are calculated, and the current required heat amount is calculated by the following equation (16).

  Currently required heat amount = target heat amount− (boiling time−current time) × 4000 (formula 16)

  Then, when the current heat quantity is smaller than the current required heat quantity, it is determined that boiling is necessary. When it is determined that boiling is necessary, the amount of heating heat and the boiling temperature are calculated. The amount of heating heat is calculated by the following equation 17, and the boiling temperature is calculated by the following equation 18.

  Boiling heat amount = current required heat amount + 4000−current heat amount (Expression 17)

  Nominal boiling temperature = boiling heat amount / (400−current hot water amount) (Formula 18)

  Since the boiling temperature of the hot water supply unit 102 can be set in increments of 5 ° C. between 65 ° C. and 85 ° C. as described above, the closest settable temperature is set as the boiling temperature that is higher than the assumed boiling temperature. If the assumed boiling temperature is higher than 85 ° C, the boiling temperature is set to 85 ° C. In this case, the amount of heating heat is calculated by the following equation 19.

  Boiling heat amount = 85 × (400−current hot water amount) (Formula 19)

  When it is determined that boiling is necessary, the above-described boiling temperature and “operation” are calculated as a boiling command. When the boiling command is “operation”, the boiling command is “stopped” when the heating heat amount is boiled.

  In addition to the operation according to the optimum operation pattern, the operation command calculation unit 108 performs boiling up to a predetermined heat amount when the current heat amount is equal to or less than a predetermined heat amount or the current hot water amount is equal to or less than a predetermined hot water amount. Thereby, boiling can be performed so that the lower part of the hot water storage tank 22 does not become higher than a predetermined temperature (42 ° C., which is a temperature regarded as hot water in the present embodiment).

  Thus, the operation planning process of the hot water storage type hot water supply apparatus in the present embodiment is completed (S207). When the operation plan process is completed, the hot water supply unit 102 measures the hot water supply load, the tapping temperature, the outside air temperature, and the water temperature of the consumer, and performs the operation based on the boiling command and the boiling temperature acquired from the operation command calculation unit 108.

  FIG. 15 shows an example of the current heat amount, the hot water supply load, and the presence or absence of boiling using specific numerical values when the hot water storage type hot water supply apparatus 100 is operated in an operation pattern in the range of 23:00 to 5 o'clock. FIG. The operation pattern used here was the operation pattern of [5, 6] with the boiling time being 2 o'clock and 7 o'clock.

  Specific numerical values used in FIG. 15 are as follows. First, the hot water supply load is 2000 kcal at 0, 1000 kcal at 1 o'clock, and 5000 kcal at 2 o'clock. The corresponding tapping temperature is 55 ° C. at 0 °, 50 ° C. at 1 ° C., and 45 ° C. at 2 ° C. The current heat quantity at 23:00 is 21000 kcal, the current hot water quantity is 300 L, and the current hot water temperature is 70 ° C. The outside air temperature and water temperature were always 10 ° C. The heat dissipation loss coefficient obtained from the outside air temperature and water temperature was 10%, and the energy efficiency was 50%.

  By using the above numerical values and applying and using Equations 8 to 12 and Equations 16 to 19 at each time, a graph showing the change in heat quantity as shown in FIG. 15 is obtained. As a result of calculation, it was calculated that boiling was necessary at 1 o'clock, 3 o'clock and 4 o'clock.

  Hereinafter, effects in the present embodiment will be described.

  In the present embodiment, the predicted load calculation unit 104 calculates a plurality of predicted loads and their predicted occurrence probabilities, and the calculation unit 106 performs the operation with the maximum operation evaluation value from among the operations that do not run out of hot water. A pattern is selected. As a result, when there is only one predicted load, hot water runs out when a hot water supply load is generated earlier than the predicted load. However, it is possible to select an operation in which hot water does not run out even when the load varies by predicting for a plurality of days such as days when the hot water supply load occurs in different time zones.

  Also, by calculating the expected load occurrence probability and calculating the expected charge value, the operation pattern that minimizes the charge is not selected when there is a large hot water supply load so that the hot water does not run out. It is possible to select an operation that reduces the hot water supply load and does not run out of hot water.

  In the present embodiment, the hot water supply load is predicted, and the predicted temperature calculation unit 105 predicts the outside air temperature and the water temperature. The maximum driving pattern is selected. As a result, it is possible to select an operation that minimizes the charge while avoiding that the charge total is not minimized as a result of the operation considering only the charge unit price.

  Further, by calculating the power consumption amount as a calculation result in addition to the charge, it is possible to select how much of the power consumption amount or the charge is to be emphasized when specifying the optimum operation pattern. For example, when the operation evaluation method indicated by [charge coefficient: power coefficient] is [1.0: 0.0], an operation pattern that minimizes the charge is selected, and [0.0: 1.0 ], It is possible to select an operation pattern that minimizes power consumption. In the case of an operation evaluation method such as [0.7: 0.3], a charge is given priority, but an operation pattern with a certain amount of power consumption is selected.

  In addition, as an operation evaluation method, when specifying an optimal operation pattern only by either one of a charge expectation value or an electric power expectation value like [1.0: 0.0] or [0.0: 1.0] Of course, the optimum driving pattern may be set when the expected charge value or the expected power value is the smallest without calculating the driving evaluation value.

  In the present embodiment, the similarity with the past load used for the load prediction may be not only the day of the week but also the similarity of the outside air temperature and the city water temperature.

  When judging the similarity using the outside air temperature or the city water temperature, the similarity between the method for judging the similarity to the temperature at the start of the operation plan and the one for which the predicted outside temperature / predicted water temperature is calculated. Two methods of determining are conceivable.

  First, the case where similarity is judged with respect to the temperature at the start of the operation plan will be described.

  At the start of the operation plan, the outside air temperature and water temperature are measured. Next, the predicted load calculation unit 104 predicts a hot water supply load for 24 hours, a tapping temperature, and an occurrence probability thereof (S202). Specifically, the predicted load calculation unit 104 acquires hot water supply load data, which is an hourly hot water supply load for the past 28 days, from the storage unit 103. Among hot water supply load data, load data is extracted for four days in descending order of similarity between the outside air temperature and water temperature at the start of the operation plan and the outside air temperature and water temperature at the corresponding time. It should be noted that in determining similarity, both the difference between the outside air temperature and the water temperature may be used, or the similarity may be high if one of the differences is small. If both differences are used, it may be possible to select one with a small sum of differences. The four days extracted in this way are assumed to be predicted loads 1 to 4, respectively, the probability of occurrence is set higher in order of increasing temperature, and a plurality of predicted loads and the probability of occurrence of each predicted load are calculated.

  Next, the case where similarity is judged with respect to what calculated predicted outside temperature and predicted water temperature is demonstrated.

  In this case, instead of the flowchart showing the method of planning the operation of the hot water storage type hot water supply apparatus in FIG. 4, the operation plan is started according to the flowchart showing the method of planning the operation of the hot water storage type hot water supply apparatus in FIG.

  The flowchart shown in FIG. 16 is the same as the flowchart shown in FIG. 4 except that the prediction of the outside air temperature / water temperature (S203) and the prediction of the hot water supply load, tapping temperature and the probability of occurrence thereof for 24 hours (S202). It is the same.

  After predicting the outside air temperature and water temperature (S203), the prediction (S202) of the hot water supply load for 24 hours, the tapping temperature, and the probability of occurrence thereof will be specifically described.

  The predicted load calculation unit 104 acquires hot water supply load data, which is an hourly hot water supply load for the past 28 days, from the storage unit 103. The method for predicting the outside air temperature and water temperature is the same as in the case of FIG. Next, among the hot water supply load data, load data having an outside air temperature variation similar to the predicted outside air temperature and the predicted water temperature is extracted for four days. In this case, for example, a method is used in which an error for every hour is obtained for 24 hours, and the similarity is increased as the sum of the errors is reduced. At this time, both the outside air temperature and the water temperature are calculated, and the similarity may be high in the order of the smallest sum, or the similarity may be determined for either one. The four days extracted in this way are set as the predicted loads 1 to 4, respectively, the probability of occurrence is set higher in descending order of similarity, and a plurality of predicted loads and the probability of occurrence of each predicted load are calculated.

  Moreover, the calculation method of the predicted load may use not only the load on a day with similarities as the predicted load but also a neural network, average processing, or multiple regression analysis based on the similarity.

  In addition, the calculation method of the probability of occurrence of multiple predicted loads is not only a method of increasing the probability of the day close to the present, but also a method of equalizing the probability of occurrence of all predicted loads, and the outside air temperature and city water temperature. There are also a method of increasing the probability of a load on a day with a high degree of similarity, and a method of calculating the probability by performing learning from past similarities of household load history.

  In addition, when a day with high similarity is calculated numerically, such as when the predicted outside air temperature or predicted water temperature error is used for similarity determination, the probability is distributed according to the numerical value indicating the similarity. Thus, the expected charge expected value can be obtained more accurately.

  In other words, for example, in addition to increasing the probability of the day close to the present time, when judging similarity to the calculated predicted outside air temperature / predicted water temperature, the probability is divided by the ratio of the reciprocal of the sum of each error I think that.

  Further, in the present embodiment, the calculation unit 106 simulates the state of the hot water storage tank 22 separately for the amount of heat and the amount of hot water, and when boiling is necessary, the capacity is equal to or lower than a predetermined temperature in the tank. The minimum boiling temperature is calculated so that the amount of heat generated by heating is within the range. That is, the lowermost part of the hot water storage tank 22 is always water at 42 ° C. or lower.

  In a general hot water supply apparatus, the energy efficiency of the hot water supply apparatus has a characteristic that the lower the temperature of the warming water, the higher the temperature. For this reason, since the water supplied to the heat pump 23 is always water of 42 ° C. or less, it can be boiled without lowering the energy efficiency.

  In addition, as described above, since the hot water supply device has the characteristic that the energy efficiency is higher when the boiling temperature is lower, when boiling is performed at the boiling temperature according to the above-described calculation method, boiling is performed at the lowest possible temperature. It can be boiled with high energy efficiency. Due to the characteristics of these hot water supply apparatuses, the setting of the boiling temperature in the calculation unit 106 is the highest energy efficiency for storing the target amount of heat, and the power consumption is minimized.

  According to the configuration of the present invention, the possibility of running out of hot water can be further reduced, and it is possible to arbitrarily select which of the power consumption and the charge is to be performed.

  As mentioned above, although the hot water storage type hot-water supply apparatus of the present invention has been described based on the embodiment, the present invention is not limited to this embodiment. Unless it deviates from the meaning of this invention, the form which carried out the various deformation | transformation which those skilled in the art can think to this embodiment, and the structure constructed | assembled combining the component in different embodiment is also contained in the scope of the present invention. .

  For example, in the configuration of the present embodiment, the time for starting the operation plan may be any time such as 0:00 or 7:00 instead of 23:00. Further, the period for performing the operation plan may be an arbitrary period such as 12 hours or 48 hours instead of 24 hours.

  Further, the accumulation period of the accumulation unit 103 is not limited to 28 days, and data for one month or one year may be accumulated.

  Further, the method of calculating the predicted load and the occurrence probability by the predicted load calculation unit 104 may be any method such as a neural network or regression analysis instead of the method based on the day of the week.

  In addition, the present invention can be realized not only as an apparatus, but also as a method using steps of processing means constituting the apparatus, as a program that causes a computer to execute the steps, or to read a computer that records the program It can also be realized as a possible recording medium such as a CD-ROM, or as information, data or a signal indicating the program. These programs, information, data, and signals may be distributed via a communication network such as the Internet.

The present invention relates to a hot water storage type hot water supply apparatus that stores heat in a hot water storage tank by performing simulation, for example, a hot water storage type hot water supply apparatus provided with a hot water supply unit using electric power, such as a CO ₂ heat pump water heater or an electric water heater, and an operation plan thereof It can be used for equipment and operation planning methods.

It is a lineblock diagram of the hot water storage type hot-water supply apparatus concerning an embodiment of the invention. 1 is a schematic diagram of a hot water storage type hot water supply apparatus according to an embodiment of the present invention. (A) It is a figure which shows the temperature distribution of the hot water storage tank of a hot water storage type hot water supply apparatus. (B) It is a figure which shows the temperature of the hot water storage tank at the time of simulation for convenience. It is a flowchart which shows the method of planning the driving | operation of the hot water storage type hot-water supply apparatus concerning embodiment of this invention. It is a figure which shows an example of the data accumulate | stored in an accumulation | storage part. It is a figure which shows an example of the day used for calculation of prediction load and prediction temperature. It is a flowchart which shows the detail of a driving simulation. It is a figure which shows an example of several prediction load. It is a figure which shows an example of calculation of boiling time. It is a figure which shows the target level and target heat amount of a driving | running pattern. It is a flowchart which shows the detail of calculation of a charge expected value and electric energy expected value. It is a flowchart which shows the detail of calculation of a charge and power consumption. (A) It is a figure which shows an example of the relationship between external temperature and apparatus efficiency ratio. (B) It is a figure which shows an example of the relationship between water temperature and an apparatus efficiency ratio. (C) It is a figure which shows an example of the relationship between boiling temperature and apparatus efficiency ratio. (D) It is a figure which shows an example of the relationship between external temperature and a heat dissipation loss coefficient. It is a figure which shows an example of calculation of an optimal driving | operation pattern. It is a figure which shows an example which operated the hot water storage type hot-water supply apparatus according to the optimal operation pattern. It is a flowchart which shows the method of planning the driving | operation of the other hot water storage type hot-water supply apparatus in connection with embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 21 Control part 22 Hot water storage tank 23 Heat pump 24 Thermometer 100 Hot water storage type hot water supply apparatus 101 Operation planning part 102 Hot water supply part 103 Accumulation part 104 Predictive load calculation part 105 Predictive temperature calculation part 106 Calculation part 107 Optimal operation pattern specification part 108 Operation command calculation part 111 Driving pattern calculation unit 112 Simulation unit 113 Expected value calculation unit

Claims (12)

A hot water storage hot water supply device that stores hot water boiled by electric power in a hot water storage tank and supplies hot water to consumers,
Accumulation means for accumulating load data including the amount of heat and date and time used in hot water supply of the customer for each predetermined period;
Predictive load calculating means for calculating a plurality of predictive loads and the probability of occurrence of each predictive load by taking the similarity between the date and time when the load data is stored and the date and time to be predicted;
For each of a plurality of operation patterns, by calculating the amount of heat required when the plurality of predicted loads are generated, a plurality of predicted charges for the consumer are calculated, and the plurality of predicted charges and A computing means for calculating a charge expected value using the probability corresponding to each predicted charge;
Optimal driving pattern specifying means for specifying the driving pattern with the lowest expected charge corresponding to each of the plurality of driving patterns as the optimal driving pattern;
An operation command calculating means for calculating a boiling temperature and a boiling command that is an operation start command or a stop command so as to store hot water in the hot water storage tank to supply hot water to the consumer according to the operation of the optimum operation pattern;
A hot water storage type hot water supply apparatus comprising the hot water storage tank, comprising: hot water storage means for storing heat in the hot water storage tank in accordance with the boiling temperature and the boiling instruction, and supplying hot water to the consumer in accordance with instructions from the consumer. The predicted load calculation means is used by the consumer for each predetermined period on the second day, with the amount of heat used by the consumer for each predetermined period on the first day as the first predicted load. Calculating the amount of heat as the second predicted load,
The hot water storage type hot water supply apparatus according to claim 1, wherein the first day and the second day are the same day of the week as the day to be simulated, and are different from each other. The computing means is
As the plurality of operation patterns, an operation pattern calculation unit that calculates a plurality of operation patterns including a boiling target heat amount that is a target value of heat amount to be stored in the hot water storage tank by a predetermined boiling time;
Based on each of the plurality of operation patterns, by calculating the required boiling amount required for each predetermined period so that the boiling target heat amount is stored in the hot water storage tank by the boiling time. A simulation unit for calculating the predicted fee for each of the plurality of predicted loads;
The hot water storage type hot water supply apparatus according to claim 1, further comprising: an expected value calculation unit that calculates the expected charge value corresponding to each of the plurality of operation patterns by multiplying each of the predicted charges by the probability. The operation pattern calculation unit calculates the operation pattern by setting, as the boiling time, a time indicating a period in which the amount of heat for each predetermined period included in the plurality of predicted loads is equal to or greater than a predetermined threshold. 3. The hot water storage type hot water supply apparatus according to 3. The hot water storage type hot water supply apparatus according to claim 3, wherein the operation pattern calculation unit equally divides the maximum amount of heat that can be stored in the hot water storage tank into a predetermined number of stages, and uses the amount of heat at each stage as the boiling target heat quantity. The accumulating means further accumulates temperature data including an outside air temperature for each predetermined period around the hot water storage tank and a water temperature for the predetermined period of water supplied to the hot water storage tank,
The hot water storage type hot water supply device further includes:
Predicted temperature calculation means for calculating predicted temperature data including predicted outside air temperature and predicted water temperature by taking similarity between the temperature data and the date and time to be predicted,
The said calculation means calculates the said prediction charge by calculating energy efficiency that a value becomes small when the said prediction outside temperature is high, and a value becomes large when the said prediction water temperature is low. Hot water storage water heater. The calculation means further calculates a plurality of predicted electric energy applied to the consumer by simulating the amount of heat required when the plurality of predicted loads are generated for each of the plurality of operation patterns. And using the plurality of predicted power amounts and the probability corresponding to each predicted power amount to calculate an expected power amount,
The optimum operation pattern specifying unit further decreases the expected charge value and the expected power amount for each of the expected charge value and the expected power amount corresponding to each of the plurality of operation patterns. The hot water storage type hot water supply apparatus according to claim 1, wherein an operation pattern having the maximum operation evaluation value is specified as an optimum operation pattern by calculating an operation evaluation value indicating high evaluation. The optimum operation pattern specifying means calculates a charge index that increases as the expected charge value decreases and a power index that increases as the expected power value decreases, so that the sum becomes a constant value. The hot water storage type hot water supply apparatus according to claim 7, wherein a value obtained by multiplying and adding each of a charge coefficient and an electric energy coefficient defined in the item is calculated as the operation evaluation value. The hot water supply means boils water in the lower part of the hot water storage tank, and supplies the heated water to the upper part of the hot water storage tank,
The hot water storage type hot water supply apparatus according to claim 1, wherein the operation command calculation means calculates a boiling command so that a lower part of the hot water storage tank does not become a predetermined temperature or higher. An operation planning device for determining an optimum operation pattern of a hot water storage hot water supply device that stores hot water boiled by electric power in a hot water storage tank and supplies hot water to a consumer,
Accumulation means for accumulating load data including the amount of heat used in hot water supply of the customer for each predetermined period;
Predictive load calculating means for calculating a plurality of predictive loads and the probability of occurrence of each predictive load by taking the similarity between the date and time when the load data is stored and the date and time to be predicted;
For each of a plurality of operation patterns, by calculating the amount of heat required when the plurality of predicted loads are generated, a plurality of predicted charges for the consumer are calculated, and the plurality of predicted charges and A computing means for calculating a charge expected value using the probability corresponding to each predicted charge;
For each of the expected charge values corresponding to each of the plurality of driving patterns, by calculating a driving evaluation value indicating a higher evaluation as the expected charge value is lower, the driving pattern having the maximum driving evaluation value is determined as the optimal driving pattern. A specific means to identify as,
An operation command calculating means for calculating a boiling temperature and a boiling command that is an operation start command or a stop command so as to store hot water in the hot water storage tank in order to supply hot water to the consumer according to the operation of the optimum operation pattern. Equipped with an operation planning device. An operation planning method for determining an optimal operation pattern of a hot water storage hot water supply device that stores hot water boiled by electric power in a hot water storage tank and supplies hot water to a consumer,
An accumulation step for accumulating load data including the amount of heat used in hot water supply of the customer for each predetermined period;
A predictive load calculating step of calculating a plurality of predictive loads and the probability of occurrence of each predictive load by taking the similarity between the date and time when the load data is stored and the date and time to be predicted;
For each of a plurality of operation patterns, by calculating the amount of heat required when the plurality of predicted loads are generated, a plurality of predicted charges for the consumer are calculated, and the plurality of predicted charges and A calculation step for calculating a charge expected value using the probability corresponding to each predicted charge;
A specifying step for specifying as an optimal driving pattern that specifies the driving pattern with the lowest expected charge value corresponding to each of the plurality of driving patterns as an optimal driving pattern;
An operation command calculating step for calculating a boiling temperature and a boiling command that is an operation start command or a stop command so as to store hot water in the hot water storage tank in order to supply hot water to the consumer according to the operation of the optimum operation pattern. Including operation planning methods. A program for an operation planning device that determines an optimum operation pattern of a hot water storage hot water supply device that stores hot water boiled by electric power in a hot water storage tank and supplies hot water to a consumer.
The program which makes a computer perform the step contained in the driving | operation plan method of Claim 11.

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