Operation plan generation device, and control method and program for operation plan generation device

Abstract

To create an operation plan for an electric water heater that can more reliably suppress electricity charges.SOLUTION: An operation plan generation device for generating an operation plan for an electric water heater, is configured to: store power consumptions of the electric water heater; store daytime and nighttime electricity unit prices and power selling price of a consumer; calculate a predicted value of consumer's surplus power for each frame and a predicted value of a total daily power consumption of the electric water heater on the basis of expected values of weather forecast data for operation plan generation target days; generate a plurality of operation plan candidates that are determined such that the total daily power consumption of the electric water heater becomes this predicted value; calculate a power surplus or shortage amount for each frame by subtracting the power consumption of the electric water heater from the predicted value of the surplus power amount for each frame; calculate the consumer's daily electricity charge for each of the candidates on the basis of the power surplus or shortage amount for each frame, the electricity unit price and the power selling unit price; and select one of the candidates as the operation plan on the basis of the result.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an operation plan generating device, a control method for an operation plan generating device, and a program. [Background technology]

[0002] For consumers who have solar power generation facilities, they can reduce the amount of electricity they purchase from power companies by using electricity generated by solar power generation. However, if the amount of electricity generated by solar power exceeds the consumer's electricity consumption, surplus electricity will result.

[0003] To utilize this surplus electricity, technology has been developed that predicts the surplus electricity that will be generated during the daytime the following day, and if this surplus electricity can cover the power needed to heat water in an electric water heater, the water will be heated during the daytime the following day, but if it cannot, the water will be heated in two periods, day and night (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2022-066217 Summary of the Invention [Problem to be solved by the invention]

[0005] However, even if the electricity needed to heat water in an electric hot water heater can be covered by surplus electricity generated during the day, there are cases in which consumers can reduce their electricity bills by heating water at night and selling the surplus electricity generated during the day.

[0006] Furthermore, if the prediction of surplus electricity is incorrect and the surplus electricity is not enough to cover the amount of electricity needed to heat the electric water heater, the electric water heater will have to be operated using more expensive daytime electricity, which will result in higher electricity bills.

[0007] Therefore, there is a demand for technology that can create an operation plan for an electric water heater that can more reliably reduce electricity bill payments by taking into account daytime and nighttime electricity rates and the unit price of selling surplus electricity.

[0008] The present invention has been made in consideration of the above-mentioned problems, and aims to provide an operation plan generation device that creates an operation plan for an electric water heater that can more reliably reduce electricity bills, and a control method and program for the operation plan generation device. [Means for solving the problem]

[0009] The operation plan generating device for solving the above problem is an operation plan generating device that generates an operation plan for an electric water heater in which an operating state of whether or not to operate the electric water heater is defined for each frame that is formed by dividing a day into predetermined time periods, and includes an electric water heater power consumption storage unit that stores the power consumption per unit time of the electric water heater, a rate storage unit that stores the electricity rate unit price for each time period defined as daytime and nighttime of a consumer having the electric water heater and the unit price of selling surplus electricity, a surplus power forecast value calculation unit that calculates a forecast value for each frame of the surplus power amount obtained by subtracting the power consumption of electrical equipment other than the electric water heater of the consumer from the power generation amount of a photovoltaic power generation facility of the consumer based on the forecast value of each frame of a predetermined type of weather forecast data on a target day for generating the operation plan, and the first power consumption predicted value calculation unit calculating a predicted value of a first power consumption, which is the total daily power consumption of the electric water heater in the electric water heater; a candidate generation unit generating a plurality of candidates for the operation plan in which the operating state of the electric water heater in each frame is determined so that the total daily power consumption of the electric water heater becomes the predicted value of the first power consumption; a power surplus / deficiency calculation unit calculating, for each candidate for the operation plan, an amount of power surplus / deficiency of each frame by subtracting an amount of power consumption corresponding to the operating state of the electric water heater from the predicted value of the surplus power of each frame; and an operation plan generation unit calculating a daily electricity charge of the consumer for each candidate based on the amount of power surplus / deficiency of each frame calculated for each candidate, the electricity rate unit price, and the electricity selling price, and selecting one candidate from the plurality of candidates as the operation plan based on the calculation result of the electricity rate.

[0010] In addition, the problems and solutions disclosed in this application will be made clear by the description in the section of the preferred embodiment of the invention and the descriptions in the drawings. Effect of the Invention

[0011] It is possible to create an operation plan for the electric water heater that can more reliably reduce electricity bill payments. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram showing an overall configuration. [Diagram 2] FIG. 2 is a hardware configuration diagram of the operation plan generating device. [Diagram 3] FIG. 2 is a diagram illustrating a storage device of the operation plan generating apparatus. [Figure 4] FIG. 13 is a diagram showing a weather data management table. [Diagram 5] FIG. 13 is a diagram showing a fee table. [Figure 6] FIG. 13 is a diagram showing an electric water heater management table. [Figure 7] FIG. 13 is a diagram showing a facility table. [Figure 8] FIG. 13 illustrates a power management table. [Figure 9] FIG. 2 is a functional block diagram of an operation plan generating device. [Figure 10] FIG. 4 is a diagram showing candidates for an operation plan. [Figure 11] FIG. 4 is a diagram showing candidates for an operation plan. [Figure 12] FIG. 4 is a diagram showing candidates for an operation plan. [Figure 13] 4 is a flowchart showing a process flow of the operation plan generating device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] At least the following matters will become apparent from the description of this specification and the accompanying drawings. Hereinafter, the present invention will be described in detail based on an embodiment thereof with reference to the accompanying drawings. ==Overall Configuration== FIG. 1 shows an overall configuration including an operation plan generating device 100 according to an embodiment of the present invention.

[0014] The operation plan generating device 100 is configured to be communicably connected to a weather information providing device 200 and a Home Energy Management System (HEMS) controller 300 via a network 500 such as the Internet, a Local Area Network (LAN), or a telephone network.

[0015] The operation plan generating device 100 is an information processing device such as a computer that generates an operation plan that determines whether or not to operate the electric hot water heater 410 of the consumer 1000 for each time frame (frame) that is configured by dividing a day into predetermined time periods (30 minutes in this embodiment). In other words, the operation plan generating device 100 determines whether or not to operate the electric hot water heater 410 for each frame that divides a day into 48 30-minute segments.

[0016] This operation plan is transmitted from the operation plan generating device 100 to the HEMS controller 300, and the HEMS controller 300 controls the operation of the electric water heater 410 according to this operation plan. The operation state of the electric water heater 410 is determined in units of frames, and if it is operated, it continues to operate for 30 minutes, and if it is not operated, it continues to be stopped. Note that, as an example in this embodiment, the operation plan generating device 100 generates an operation plan for the electric water heater 410 from midnight of the next day to 24 hours in the future at 9 p.m. every day and transmits it to the HEMS controller 300.

[0017] The weather information providing device 200 is an information processing device such as a computer that provides a predetermined type of weather data including at least one of solar radiation, temperature, humidity, and wind speed. The weather information providing device 200 provides, for example, MSM (Meso-Scale Model) and MEPS (Meso-scale Ensemble Prediction System) provided by the Japan Meteorological Agency. The MSM is a numerical calculation of a predetermined type of weather forecast value for each area divided into a grid shape with each side of 5 km. The Japan Meteorological Agency distributes MSM every three hours every day (i.e., eight times a day) at one-hour intervals for up to 39 hours ahead. In two of these times, MSM is provided at one-hour intervals for up to 78 hours ahead.

[0018] MEPS calculates weather forecast values ​​for multiple members of a given type for each area divided into a grid of 5 km on a side, with slight variations corresponding to error factors. The Japan Meteorological Agency distributes MEPS every six hours every day (i.e. four times a day), at three-hour intervals, for up to 39 hours in advance. MSM and MEPS contain forecast values ​​for various meteorological data such as solar radiation, air pressure, temperature, relative humidity, precipitation, wind, and cloud cover.

[0019] The weather forecast data is generated every six hours each day (i.e., four times a day) by combining MSM and MEPS to calculate forecast values ​​for multiple members at hourly intervals up to 39 hours ahead.

[0020] The HEMS controller 300 is an information processing device such as a computer owned by the consumer 1000, and is communicatively connected to the electric water heater 410, electric appliances 420 other than the electric water heater 410 (hereinafter simply referred to as electric appliances 420), and the distribution board 400 used by the consumer 1000 via a consumer network 510 such as a LAN. The HEMS controller 300 controls the operation of the electric water heater 410 and the electric appliances 420. For example, the HEMS controller 300 controls the operation of the electric water heater 410 as well as the electric appliances 420 such as air conditioners, televisions, refrigerators, and lighting equipment.

[0021] The distribution board 400 distributes the power supplied from the power system 1100 and the solar power generation facility 900 to the electric water heater 410 and the electric appliances 420, and transmits data indicating the amount of power thereof and data indicating the amount of power distributed to the electric water heater 410 and the electric appliances 420 to the HEMS controller 300. Note that the power consumption of the electric appliances 420 other than the electric water heater 410 can be calculated by subtracting the power consumption of the electric water heater 410 from the total power consumption of the consumer 1000. Therefore, the distribution board 400 may transmit the power consumption of the electric water heater 410 and the total power consumption of the consumer 1000 to the HEMS controller 300.

[0022] The solar power generation facility 900 is a power generation facility that generates power by utilizing solar energy (natural energy). The solar power generation facility 900 includes a power conditioner 910 and a solar cell panel 920. The solar cell panel 920 is installed on the rooftop of a building of the consumer 1000, and converts solar energy into DC power. The power conditioner 910 converts the DC power generated by the solar cell panel 920 into AC power.

[0023] Then, at 9 p.m. every day, the operation plan generating device 100 predicts the amount of solar power generation and the amount of power consumption of the electric hot water heater 410 and the electric equipment 420 for each frame from midnight of the next day until 24 hours from now based on the above-mentioned weather forecast data, calculates the amount of power surplus or shortage for each frame from these predicted values, and generates an operation plan for the electric hot water heater 410 that will make the electricity bill for the consumer 1000 cheaper.

[0024] According to this embodiment, it is possible to create an operation plan for the electric hot water heater 410 that can more reliably reduce the amount of electricity bills paid.

[0025] In addition, the operation plan generating device 100 generates an operation plan for the electric water heater 410 by using members (projected values) of weather forecast data that minimize the amount of surplus power obtained by subtracting the amount of power consumption of the electric equipment 420 other than the electric water heater 410 from the amount of solar power generation.

[0026] With this aspect, it is possible to create an operation plan for the electric hot water heater 410 that can more reliably reduce the amount of electricity bills paid, even if the predicted values ​​of the weather data deviate from the actual values.

[0027] More details will be provided below. ==Operation plan generation device== Next, the operation plan generating device 100 will be described in detail.

[0028] 2 shows an example of a hardware configuration of the operation plan generating device 100. The operation plan generating device 100 includes a CPU (Central Processing Unit) 110, a memory 120, a communication device 130, a storage device 140, an input device 150, an output device 160, and a recording medium reading device 170.

[0029] The CPU 110 is responsible for the overall control of the operation plan generating device 100, and realizes various functions of the operation plan generating device 100 by reading out an operation plan generating device control program 700, which is composed of codes for performing various operations related to this embodiment stored in the storage device 140, and various data into the memory 120 and executing or processing them.

[0030] For example, the CPU 110 executes or processes the operation plan generation device control program 700 and various data, and by cooperating with hardware devices such as the memory 120, the communication device 130, and the storage device 140, functions such as the electric water heater power consumption memory unit 101, the fee memory unit 102, the surplus power prediction value calculation unit 103, the first power consumption prediction value calculation unit 104, the candidate generation unit 105, the power surplus / deficiency calculation unit 106, the operation plan generation unit 107, and the weather data acquisition unit 108, which will be described later, are realized.

[0031] The operation plan generation device control program 700 is a general term for programs for realizing the functions of the operation plan generation device 100, and includes, for example, application programs and an OS (Operating System) that run on the operation plan generation device 100, various libraries, etc.

[0032] The memory 120 can be configured, for example, by a semiconductor memory device.

[0033] The storage device 140 is a device that provides a non-temporary storage area for storing various programs, data, tables, etc., such as a hard disk drive, a solid state drive (SSD), a flash memory, etc. In this embodiment, as shown in Fig. 3, the storage device 140 stores various data such as a weather data management table 600, a fee table 610, an electric hot water heater management table 620, a relational expression 630, an equipment table 640, and a power management table 650 in addition to an operation plan generating device control program 700.

[0034] An example of the weather data management table 600 is shown in FIG.

[0035] The operation plan generating device 100 acquires a predetermined type of weather data including the amount of solar radiation from the weather information providing device 200 at a predetermined time every day, and stores the weather data in a weather data management table 600.

[0036] As shown in FIG. 4, the weather data management table 600 has a "date and time" column, a "temperature" column, a "humidity" column, a "wind speed" column, and a "solar radiation" column.

[0037] In the "Date and Time" column, the start time of each frame that arrives every 30 minutes is entered as the forecast target time point of the weather forecast data. Since the forecast value of the weather forecast data is every hour, the expected value of the weather forecast data every 30 minutes is generated by calculating the average value of two chronologically adjacent weather forecast data. In addition, an expected value is generated from the forecast values ​​of multiple members of the weather forecast data. Since the weather information providing device 200 provides the MEPS every six hours, four times a day, the operation plan generating device 100 may update the weather data management table 600 to the latest weather forecast data every time the latest MEPS is obtained.

[0038] The "Temperature" column lists the expected temperature at each prediction time point. The "Humidity" column lists the expected humidity at each prediction time point. The "Wind Speed" column lists the expected wind speed at each prediction time point. The "Solar Radiation" column lists the expected solar radiation at each prediction time point. Although only one expected value is listed in each column of FIG. 4, as described above, the expected value is generated from the predicted values ​​of multiple members of the weather forecast data, so that multiple expected values ​​corresponding to multiple members of the weather forecast data are stored in each column. Although not shown in FIG. 4, the weather data management table 600 may also store expected values ​​of other types of weather data, such as atmospheric pressure and cloud cover.

[0039] Next, an example of the fee table 610 is shown in FIG.

[0040] The rate table 610 records the electricity rate for each time period defined as daytime and nighttime, and the selling price of surplus electricity for the consumer 1000 having the electric hot water heater 410. For example, the daytime time period is from 7:00 to 23:00, and the nighttime time period is from 23:00 to 7:00 the next day.

[0041] In the example shown in FIG. 5, the daytime electricity unit price is 45 yen per kW, the nighttime electricity unit price is 30 yen per kW, and the selling price of surplus electricity is 7.5 yen per kW.

[0042] Next, an example of the electric water heater management table 620 is shown in FIG.

[0043] The electric water heater management table 620 records the power consumption per unit time (for example, per hour) of the electric water heater 410. In the example shown in Fig. 6, the power consumption when the electric water heater 410 is operating is 5 kW per hour. In this case, when the electric water heater 410 is operated for one period (30 minutes), 2.5 kW of power is consumed. Note that, since the power consumption when the electric water heater 410 is not operating is very small, it is assumed to be 0 kW in this embodiment.

[0044] Next, an example of the equipment table 640 is shown in FIG.

[0045] The equipment table 640 records information about the photovoltaic power generation equipment 900 that is used when predicting the amount of power generated by the photovoltaic power generation equipment 900 from weather forecast data. As shown in Fig. 7, for example, the equipment table 640 has fields indicating a "location" indicating the installation location of the photovoltaic power generation equipment 900, an "inclination angle" indicating the installation angle of the solar cell panel 920, an "installation orientation angle" indicating the direction of the solar cell panel 920, a "type" indicating the type of the photovoltaic power generation equipment 900, a "panel capacity" indicating the capacity of the solar cell panel 920, a "power conditioner capacity" indicating the capacity of the power conditioner 910, and a coefficient (hereinafter, "fixed coefficient") that is set according to the equipment type and the like to take into account changes over time, losses, and the like.

[0046] Next, an example of the power management table 650 is shown in FIG.

[0047] The power management table 650 is a table that records the power consumption of each piece of the electric water heater 410 owned by the consumer 1000, the power consumption of each piece of the electric appliances 420 other than the electric water heater 410, and the amount of solar power generation of each piece.

[0048] In addition, the power consumption of each piece of electrical equipment 420 other than the electric water heater 410 may be calculated for each piece by subtracting the power consumption of the electric water heater 410 (which can be obtained from the distribution board 400) from the total power consumption consumed by the consumer 1000 (which can be obtained from the distribution board 400).

[0049] The operation plan generating device 100 constantly acquires the power consumption amount and the solar power generation amount through the HEMS controller 300 and records them in the power management table 650.

[0050] In addition, the operation plan generating device 100 stores a relational expression 630 in the storage device 140.

[0051] The relational expression 630 is, for example, a first relational expression 631, a second relational expression 632, a third relational expression 633, a fourth relational expression 634, a fifth relational expression 635, etc., as shown below.

[0052] A first relational expression 631, as shown in the following formula (1), represents the relationship between the weather forecast data in frame i (i=0 to 47) and the amount of surplus power in frame i.

[0053] Surplus power (i) = a + b × temperature (i) + c × humidity (i) + d × wind speed (i) + e × solar radiation (i) … Equation (1) The amount of surplus power is obtained by subtracting the amount of power consumed by all electric appliances 420 other than the electric hot water heater 410 of the consumer 1000 from the amount of power generated by the photovoltaic power generation facility 900 owned by the consumer 1000 .

[0054] Furthermore, the values ​​of the constants a, b, c, d, and e in equation (1) are different for each frame, and can be determined by referring to the weather data management table 600 and the power management table 650 and performing a regression analysis of the past weather forecast data and surplus power amount for each frame.

[0055] Moreover, first relational expression 631 may be composed of second relational expression 632 to fourth relational expression 634 below, instead of the above formula (1).

[0056] A second relational expression 632, as shown in the following equations (2) to (5), represents the relationship between the weather forecast data in the frame i and the power generation amount of the photovoltaic power generation facility in the frame i.

[0057] Tpa = Ta + (A / (B × V 0.8 +1) +2) × Ga-2 … (2) (Tpa: solar panel temperature (℃), Ta: outside temperature (℃), A: coefficient (e.g., rooftop type "50"), B: coefficient (e.g., rooftop type "0.38"), V: wind speed (m / s), Ga: inclined surface solar radiation (kW / m2)) Kpt = 1 + αmax × (Tpa-25) … (3) (Kpt: temperature correction coefficient, αmax: maximum output temperature coefficient (1 / ℃), Tpa: solar panel temperature (℃)) System output coefficient = Kloss × Kpt …(4) (Kloss: A fixed coefficient that is used universally depending on the type of equipment to take into account changes over time (dirt, deterioration), wiring resistance loss, inverter loss, etc., Kpt: Temperature correction coefficient) Power generation (i) = inclined surface solar radiation (Ga) × system output coefficient × panel capacity … (5) Each of the coefficients described in the formulas (2) to (5) may be obtained by referring to the equipment table 640 or the like.

[0058] A third relational expression 633, as shown in the following formula (6), represents the relationship between the weather forecast data in the frame i and the power consumption of the electric appliance 420 other than the electric water heater 410 of the consumer 1000 in the frame i.

[0059] Electrical equipment power consumption (i) = f + g × temperature (i) + h × humidity (i) + j × wind speed (i) + k × solar radiation (i) … Equation (6) The values ​​of the constants f, g, h, j, and k in equation (6) are different for each frame, and can be determined by referring to the weather data management table 600 and the power management table 650 and performing a regression analysis for each frame on past weather forecast data and power consumption other than that of the electric water heater.

[0060] A fourth relational expression 634 is an expression for calculating the amount of surplus power in frame i from the calculation results of the second relational expression 632 and the third relational expression 633, as shown in the following expression (7).

[0061] Surplus power (i) = power generation (i) - power consumption of electrical equipment (i) ... Equation (7) A fifth relational expression 635, as shown in the following formula (8), represents the relationship between the weather forecast data and the first power consumption, which is the daily power consumption of the electric hot water heater 410, on the target day for generating the operation plan.

[0062] First power consumption = m + n × temperature + p × humidity + q × wind speed + r × solar radiation … Equation (8) The weather forecast data for the target day for generating the operation plan may be a representative value of the weather forecast data for all frames for that day (for example, the average value, maximum value, or minimum value of the weather forecast data for all frames).

[0063] In addition, the values ​​of the constants m, n, p, q, and r in equation (8) can be determined by referring to the weather data management table 600 and the power management table 650 and performing a regression analysis on the representative value of the past weather forecast data for each day and the total daily power consumption of the electric water heater 410.

[0064] Returning to FIG. 2, the storage device 140 may be incorporated in the operation plan generating device 100 or may be externally attached.

[0065] The recording medium reader 170 reads the programs and data recorded on a recording medium 800 such as a CD-ROM or a DVD, and stores them in the storage device 140 .

[0066] The communication device 130 transmits and receives data and programs to and from other computers such as the weather information providing device 200 and the HEMS controller 300 via a network 500 such as the Internet or a LAN (Local Area Network). For example, the above-described operation plan generating device control program 700 can be stored in another computer (not shown), and the operation plan generating device 100 can download and execute the operation plan generating device control program 700 from this computer.

[0067] Alternatively, the communication device 130 periodically receives various weather data such as the actual value of the amount of photovoltaic power generation, the amount of power consumption of the consumer 1000, the temperature, and the amount of solar radiation from the weather information providing device 200 or the HEMS controller 300. In addition, the operation plan generating device 100 may not include the storage device 140, and may realize the functions of the operation plan generating device 100 by using various data such as the above programs and tables stored in another computer (not shown) communicatively connected via the network 500.

[0068] The input device 150 is a device used by an operator or the like to input data to the operation plan generating device 100, and functions as a user interface. As the input device 150, for example, a keyboard, a mouse, a microphone, or the like can be used.

[0069] The output device 160 is a device for outputting information to the outside and functions as a user interface. The output device 160 may be, for example, a display, a printer, a speaker, or the like. <Functional configuration> 9 shows a functional block diagram of the operation plan generating device 100 according to this embodiment. The operation plan generating device 100 includes an electric hot water heater power consumption storage unit 101, a fee storage unit 102, a surplus power amount predicted value calculation unit 103, a first power consumption predicted value calculation unit 104, a candidate generation unit 105, a power surplus / deficiency calculation unit 106, an operation plan generating unit 107, and a weather data acquisition unit 108. These functions are realized by executing or processing an operation plan generating device control program 700 according to this embodiment and various data by the hardware shown in FIG. 2.

[0070] The electric water heater power consumption storage unit 101 stores the power consumption per unit time of the electric water heater 410. In this embodiment, the electric water heater power consumption storage unit 101 is embodied as an electric water heater management table 620 shown in FIG.

[0071] The price storage unit 102 stores the daytime and nighttime electricity unit prices and the selling price of surplus electricity of the consumer 1000 having the electric hot water heater 410. In this embodiment, the price storage unit 102 is embodied as a price table 610 shown in FIG.

[0072] The weather data acquisition unit 108 acquires forecast values ​​(assumed values) of multiple members for each frame of the weather forecast data on the target day for generating the operation plan. In this embodiment, the weather data acquisition unit 108 generates hourly weather forecast data based on the MSM and MEPS transmitted from the weather information providing device 200, and then generates assumed values ​​for the weather forecast data for every 30 minutes, thereby acquiring forecast values ​​for multiple members for each frame. The forecast values ​​for each frame are stored in the weather data management table 600 described above.

[0073] The surplus energy prediction value calculation unit 103 calculates a prediction value for each frame of surplus energy obtained by subtracting the power consumption of the electrical equipment 420 other than the electric water heater 410 of the consumer 1000 from the power generation amount of the solar power generation equipment 900 owned by the consumer 1000 based on the expected value for each frame of a specified type of weather forecast data on the target date for generating the operation plan.

[0074] For example, the surplus power amount prediction value calculation unit 103 can calculate the amount of surplus power by substituting an expected value of weather forecast data into the above-mentioned first relational expression 631. Alternatively, the surplus power amount prediction value calculation unit 103 can calculate the amount of surplus power by using the above-mentioned second relational expression 632 to fourth relational expression 634.

[0075] The expected value for each frame of weather forecast data is a value selected from the forecast values ​​(assumed values) of multiple members stored in the weather data management table 600, and is a value that minimizes the amount of surplus power for the consumer 1000.

[0076] The surplus power prediction value calculation unit 103 selects an expected value from the forecast values ​​of the multiple members. Specifically, the surplus power prediction value calculation unit 103 first calculates multiple expected values ​​of the surplus power by inputting the forecast value of each member into the first relational expression 631 or the second relational expression 632 to the fourth relational expression 634 that represent the relationship between the weather forecast data and the surplus power for each frame. The surplus power prediction value calculation unit 103 then specifies the forecast value of the member that has the smallest expected value of the surplus power as the expected value of the weather forecast data.

[0077] That is, when calculating the amount of surplus power using the forecast values ​​of multiple members of the weather forecast data, the surplus power forecast value calculation unit 103 specifies a forecast value that minimizes the forecast value of the amount of surplus power as the expected value of the weather forecast data. Then, the operation plan generating device 100 creates an operation plan for the electric hot water heater 410 under weather conditions that minimize the amount of surplus power of the consumer 1000, so that the electricity fee paid by the consumer 1000 to the electric power company is suppressed as much as possible.

[0078] The first power consumption predicted value calculation unit 104 calculates a predicted value of the first power consumption, which is the total daily power consumption of the electric hot water heater 410 on the target day for generating the operation plan, based on the expected value of the weather forecast data. For example, the first power consumption predicted value calculation unit 104 can calculate the first power consumption by inputting a representative value of the weather forecast data on the target day for generating the operation plan (for example, the average value, maximum value, or minimum value of each value from frame 0 to frame 47) into the above-mentioned fifth relational expression 635.

[0079] The candidate generating unit 105 generates a plurality of candidates for the operation plan in which the operating state of the electric water heater 410 of each frame is determined so that the total of the daily power consumption of the electric water heater 410 becomes the predicted value of the first power consumption.

[0080] Examples of operation plan candidates are shown in Figs. 10 to 12. Fig. 10 is an example in which the electric water heater 410 is operated continuously for each frame from 1 am to 6 am. Fig. 11 is an example in which the electric water heater 410 is operated continuously for each frame from 1 am to 5:30 am and from 12:00 to 12:30 pm in the daytime. Fig. 12 is an example in which the electric water heater 410 is operated continuously for each frame from 10:00 to 15:00 in the daytime. In all of the candidates, the total operation time of the electric water heater 410 is 5 hours, and the first power consumption is the same (5 hours' worth of power consumption of the electric water heater 410).

[0081] The candidate generation unit 105 may generate candidates by randomly deciding in which frame the electric water heater 410 will be operated, or may decide in which frame the electric water heater 410 will be operated according to an algorithm such as the one below.

[0082] (1) The first (first) candidate is set to start operation at a specified time during the night (for example, 1:00 a.m.) and to operate continuously in each frame until the total power consumption of the electric water heater 410 reaches the first power consumption (for example, FIG. 10 shows the first candidate).

[0083] (2) For the nth candidate (n is a natural number equal to or greater than 2), the operation of the electric water heater 410 is moved for one frame from the frame with the n-1 highest nighttime power consumption (the sum of the power consumption of the electric water heater 410 and the power consumption of the electrical equipment 420) in the n-1th candidate to the frame with the n-1 highest daytime solar power generation (for example, FIG. 11 represents the n(2)th candidate, and FIG. 10 represents the n-1(1)th candidate).

[0084] (3) Hereafter, the process of (2) is repeated while increasing n by 1.

[0085] By using such a method, it is possible to prevent the number of candidates generated by the candidate generating unit 105 from increasing exponentially, and it is possible to improve the processing efficiency of the operation plan generating device 100.

[0086] The power surplus / deficiency calculation unit 106 calculates the power surplus / deficiency of each frame by subtracting the power consumption according to the operating state of the electric hot water heater 410 from the predicted value of the surplus power amount of each frame for each operation plan candidate.

[0087] For each candidate shown in FIG. 10 to FIG. 12, the amount of power surplus or shortage for each frame is shown by a line graph.

[0088] The operation plan generation unit 107 calculates the daily electricity charge for the consumer 1000 for each candidate based on the amount of power surplus or shortage for each frame calculated for each candidate, the electricity rate unit price, and the electricity selling price, and selects one candidate from the multiple candidates as the operation plan based on the calculated electricity rate.

[0089] Specifically, the operation plan generating unit 107 calculates the electricity fee for one day by referring to the power surplus or shortage amount for each frame and the fee table 610 in FIG.

[0090] Then, the operation plan generating unit 107 selects, as an operation plan, a candidate that minimizes the daily electricity fee for the consumer 1000.

[0091] According to this embodiment, it is possible to create an operation plan for the electric hot water heater 410 that can more reliably reduce electricity charges.

[0092] In addition, by calculating the electricity bill based on the result of determining the amount of power surplus or shortage of consumer 1000 using the predicted values ​​(projected values) of members of the weather forecast data that minimize the amount of surplus power of consumer 1000, it is possible to reduce the possibility of an increase in actual electricity bills even if the predictions of the weather forecast data are incorrect.

[0093] In addition, each time the candidate generation unit 105 generates a candidate for an operation plan, the power surplus / deficiency calculation unit 106 and the operation plan generation unit 107 may repeat the process of calculating the power surplus / deficiency and the electricity fee for that candidate, thereby finding a candidate with the lowest electricity fee.

[0094] In this case, the candidate with the lowest electricity cost can be found using an existing method such as the hill climbing method. The hill climbing method is a method in which the electricity cost calculated for the kth (a natural number equal to or greater than 1) candidate is compared with the electricity cost calculated for the k+1th candidate, and the one with the smaller electricity cost is left as a provisional solution. This process is repeated a predetermined number of times while incrementing k by 1, and the final provisional solution that remains is taken as the solution. ==Process flow== Next, with reference to FIG. 13, a process flow when the operation plan generating device 100 according to this embodiment generates an operation plan for the electric hot water heater 410 will be described.

[0095] First, the operation plan generating device 100 acquires forecast values ​​(estimated values) of each of a plurality of members of each frame of the weather forecast data (S1000). Specifically, the operation plan generating device 100 acquires the estimated values ​​of each frame by calculating estimated values ​​every 30 minutes from the hourly weather forecast data generated using the MSM and MEPS transmitted from the weather information providing device 200.

[0096] Then, the operation plan generation device 100 calculates multiple predicted values ​​of the amount of surplus energy by inputting the forecast values ​​of the above-mentioned multiple members into a first relational equation 631 that represents the relationship between weather forecast data and the amount of surplus energy for each frame (S1010).

[0097] Then, the operation plan generating device 100 specifies the forecast value of the member that has the smallest predicted value of the amount of surplus power as the expected value of the weather forecast data (S1020).

[0098] The operation plan generation device 100 calculates a predicted value of the first power consumption, which is the total daily power consumption of the electric water heater 410 on the target day for generating the operation plan, for example by inputting the forecast values ​​(prospect values) of the above members into the fifth relational equation 635 (S1030).

[0099] Then, the operation plan generating device 100 generates a plurality of operation plan candidates in which the operating states of the electric water heaters 410 of each unit are determined so that the total daily power consumption of the electric water heaters 410 becomes the predicted value of the first power consumption (S1040).

[0100] The operation plan generating device 100 calculates the power surplus or shortage of each frame by subtracting the power consumption amount according to the operating state of the electric hot water heater 410 from the predicted value of the surplus power amount of each frame for each operation plan candidate (S1050).

[0101] The operation plan generation device 100 calculates the daily electricity charge of the consumer 1000 for each candidate based on the power surplus or shortage of each frame calculated for each candidate, the electricity rate unit price, and the electricity selling price, and selects one candidate from the multiple candidates as an operation plan based on the calculated electricity charge, for example, so that the daily electricity charge of the consumer 1000 is minimized (S1060).

[0102] According to this embodiment, it is possible to create an operation plan for the electric hot water heater 410 that can more reliably reduce electricity charges.

[0103] In addition, by calculating the electricity bill based on the result of determining the power surplus or shortage of consumer 1000 using weather forecast data that minimizes the amount of surplus power of consumer 1000, it is possible to reduce the possibility of an increase in electricity bill even if the weather forecast deviates from the actual value.

[0104] The above-described embodiment is for the purpose of facilitating understanding of the present invention, and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit of the present invention, and equivalents thereof are also included in the present invention.

[0105] For example, in the above embodiment, a member of the weather forecast data that minimizes the amount of surplus power of the consumer 1000, obtained by subtracting the amount of power consumption of the electrical appliance 420 from the amount of solar power generation, is set as an expected value, and the operation plan generation device 100 generates an operation plan for the electric water heater 410 using the member (expected value) of the weather forecast data that minimizes the amount of surplus power. However, an operation plan for the electric water heater 410 may be generated using a member of the weather forecast data that maximizes the daily power consumption of the electric water heater 410 as an expected value.

[0106] In this case, the first power consumption predicted value calculation unit 104 calculates multiple predicted values ​​of the first power consumption by inputting the forecast values ​​of multiple members of the weather forecast data into the fifth relational equation 635, and identifies the forecast value of the member when the predicted value of the first power consumption is maximum as the expected value of the weather forecast data.

[0107] The surplus power prediction value calculation unit 103 may then calculate a prediction value for each frame of the surplus power amount obtained by subtracting the power consumption of the electrical appliances 420 from the amount of photovoltaic power generation based on the expected value for each frame of the weather forecast data.

[0108] With this embodiment as well, it is possible to create an operation plan for the electric hot water heater 410 that can more reliably reduce the amount of electricity bills paid, even if the predicted values ​​of the weather data deviate from the actual values. [Explanation of symbols]

[0109] 100 Operation plan generating device 101 Electric water heater power consumption memory unit 102 Fee storage unit 103 Surplus energy amount prediction value calculation unit 104 First power consumption prediction value calculation unit 105 Candidate generation section 106 Power surplus / deficiency calculation unit 107 Operation plan generation unit 108 Weather Data Acquisition Unit 110 CPU 120 Memory 130 Communication Equipment 140 Storage device 150 Input Device 160 Output Device 170 Recording medium reader 200 Weather information providing device 300 HEMS Controller 400 Distribution Board 410 Electric water heater 420 Electrical Equipment 500 Network 510 Consumer Network 600 Weather data management table 610 Price Table 620 Electric water heater management table 630 Relational Expressions 631 First Relation 632 Second Relation 633 Third Relation 634 Fourth Relation 635 Fifth Relation 640 Equipment Table 650 Power Management Table 700 Operation plan generation device control program 800 Recording media 900 Solar power generation equipment 910 Power Conditioner 920 Solar Panel 1000 consumers 1100 Power system

Claims

1. An operation plan generating device that generates an operation plan for an electric water heater in which an operation state of the electric water heater is determined for each frame that is formed by dividing a day into predetermined time periods, an electric water heater power consumption storage unit that stores the power consumption per unit time of the electric water heater; a price storage unit that stores electricity rates for each time period defined as daytime and nighttime for the consumer having the electric hot water heater, and a selling price of surplus electricity; a surplus energy forecast value calculation unit that calculates a forecast value for each frame of surplus energy obtained by subtracting the amount of power consumed by electrical appliances other than the electric water heater of the consumer from the amount of power generated by the solar power generation facility of the consumer based on an expected value for each frame of a predetermined type of weather forecast data on a target date for generating the operation plan; a first power consumption predicted value calculation unit that calculates a first power consumption predicted value, which is a total of a daily power consumption of the electric water heater on the target generation date, based on the estimated value of the weather forecast data; a candidate generation unit that generates a plurality of candidates for the operation plan in which an operation state of the electric water heater in each frame is determined so that a total of a daily power consumption of the electric water heater becomes the predicted value of the first power consumption; a power surplus / deficiency calculation unit that calculates a power surplus / deficiency of each unit by subtracting a power consumption amount corresponding to an operating state of the electric hot water heater from a predicted value of the surplus power amount of each unit for each of the operation plan candidates; an operation plan generation unit that calculates a daily electricity charge for the consumer for each candidate based on the amount of power surplus or shortage for each frame calculated for each candidate, the electricity rate unit price, and the electricity selling price, and selects one candidate from the plurality of candidates as the operation plan based on the calculation result of the electricity rate; An operation plan generating device comprising:

2. The operation plan generating device according to claim 1, A weather data acquisition unit that acquires forecast values ​​of a plurality of members of each frame of the weather forecast data on the target generation date Equipped with the surplus power amount prediction value calculation unit calculates a plurality of prediction values ​​of the surplus power amount by inputting the forecast values ​​of the plurality of members into a first relational expression representing a relationship between the weather forecast data and the surplus power amount for each frame, and identifies the forecast value of the member having the smallest predicted value of the surplus power amount as an expected value of the weather forecast data. Operation plan generation device.

3. The operation plan generating device according to claim 1, A weather data acquisition unit that acquires forecast values ​​of a plurality of members of each frame of the weather forecast data on the target generation date Equipped with the first power consumption predicted value calculation unit calculates a plurality of predicted values ​​of the first power consumption by inputting the predicted values ​​of the plurality of members into a fifth relational expression that represents a relationship between the weather forecast data and the first power consumption, and identifies the predicted value of the member having the largest predicted value of the first power consumption as an expected value of the weather forecast data. Operation plan generation device.

4. The operation plan generating device according to claim 1, The predetermined type of weather forecast data includes at least one of temperature, humidity, wind speed, and solar radiation. Operation plan generation device.

5. The operation plan generating device according to claim 1, The operation plan generation unit selects, as the operation plan, a candidate that minimizes the consumer's daily electricity rate. Operation plan generation device.

6. The operation plan generating device according to claim 2, The first relational expression is A second relational expression expressing a relationship between the weather forecast data and the amount of power generation of the solar power generation facility; a third relational expression expressing a relationship between the weather forecast data and the power consumption of an electric appliance other than the electric water heater of the consumer; comprising: Operation plan generation device.

7. A control method for an operation plan generating device that generates an operation plan for an electric water heater, in which an operation state of the electric water heater is determined for each frame that is formed by dividing a day into predetermined time periods, comprising: The operation plan generation device, storing the power consumption per unit time of the electric water heater; storing the electricity rate unit price for each time period defined as daytime and nighttime for the consumer having the electric water heater, and the selling price of surplus electricity; calculating a predicted value for each frame of a surplus power amount obtained by subtracting the power consumption of the electric appliances other than the electric water heater of the consumer from the power generation amount of the photovoltaic power generation equipment owned by the consumer based on an expected value for each frame of a predetermined type of weather forecast data on a target date for generating the operation plan; calculating a first predicted value of power consumption, which is a total of a daily power consumption of the electric water heater on the target generation date, based on the estimated value of the weather forecast data; generating a plurality of candidates for the operation plan in which an operation state of the electric water heater in each frame is determined so that a total of a daily power consumption of the electric water heater becomes equal to the predicted value of the first power consumption; calculating an amount of power surplus or shortage for each unit by subtracting an amount of power consumption corresponding to an operating state of the electric water heater from a predicted value of the amount of surplus power for each unit for each of the operation plans; Calculating a daily electricity charge for the consumer for each candidate based on the amount of power surplus or shortage for each frame calculated for each candidate, the electricity rate unit price, and the electricity selling price, and selecting one candidate from the plurality of candidates as the operation plan based on the calculation result of the electricity rate. A method for controlling an operation plan generating device.

8. A program for causing a computer to generate an operation plan for an electric water heater, the operation plan defining whether or not to operate the electric water heater for each frame that is formed by dividing a day into predetermined time periods, The computer includes: A function of storing the power consumption per unit time of the electric water heater; A function of storing the electricity rate unit price for each time period defined as daytime and nighttime of the consumer having the electric water heater, and the selling price of surplus electricity; A function for calculating a predicted value for each frame of surplus power obtained by subtracting the power consumption of electrical appliances other than the electric water heater of the consumer from the power generation amount of the solar power generation equipment of the consumer based on the predicted value for each frame of a predetermined type of weather forecast data on the target date for generating the operation plan; a function of calculating a predicted value of a first power consumption amount, which is a total of a daily power consumption amount of the electric water heater on the target generation date, based on the estimated value of the weather forecast data; A function of generating a plurality of candidates for the operation plan in which the operation state of the electric water heater in each frame is determined so that the total of the daily power consumption of the electric water heater becomes the predicted value of the first power consumption; A function of calculating the amount of power surplus or shortage of each unit by subtracting the amount of power consumption according to the operating state of the electric water heater from the predicted value of the amount of surplus power of each unit for each of the operation plan candidates; a function of calculating a daily electricity charge for the consumer for each candidate based on the amount of power surplus or shortage for each frame calculated for each candidate, the electricity rate unit price, and the electricity selling price, and selecting one candidate from the plurality of candidates as the operation plan based on the calculation result of the electricity rate; A program to achieve this.

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