Power optimization control device and power optimization control method of storage battery

Abstract

To provide a power optimization control device and a power optimization control method that reflect a horizontal surface solar radiation amount and requires no actually measured solar radiation amount.SOLUTION: A power optimization control device 1 of a storage battery includes: a solar radiation amount prediction unit 2 for calculating a prediction horizontal surface solar radiation amount from an overall solar radiation amount outside the atmosphere, and a transmission coefficient; a power generation amount prediction unit 3 for calculating a prediction power generation amount from a prediction horizontal surface solar radiation amount and a relation between the prediction horizontal surface solar radiation amount and a power generation amount; a power use amount prediction unit 4 for calculating a predicted power use amount by using a power use amount for each time in the latest fixed period; and a control unit 5 for predicting time transition of a SOC of the storage battery based on the predicted power generation amount and the predicted power use amount, and obtaining an optimum nighttime charge amount and a discharge time zone.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a power optimization control device and a power optimization control method for a storage battery (hereinafter simply referred to as a "storage battery") equipped with a photovoltaic power generation device. [Background technology]

[0002] Patent Document 1 shows an example of predicting the amount of solar power generation for each hour according to the following steps 1. to 3. 1. Weather coefficients are calculated for each month and each weather type (sunny, cloudy, rainy, snowy) based on statistical processing of time-series weather record information, calculated total solar radiation for each hour on a clear day, and measured solar radiation for each hour. 2. The power generation coefficient is calculated by statistical processing of the actual measured values ​​of solar power generation at each hour and the actual measured values ​​of solar radiation at each hour. 3. Future hourly calculation The predicted power generation amount for each hour is calculated from the solar radiation, weather coefficient for each hour selected from the month and weather, and power generation coefficient for each hour. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-127140 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the solar power generation forecast in Patent Document 1 had a high probability of causing problems such as those described in 4 and 5 below. 4.Weather forecasts classify the degree of sunny, cloudy, rain, and snow, but even when they are both sunny or cloudy, there is a wide range of cloud cover and there is also a large difference in the amount of sunlight on the ground. 5. Actual measurements of solar radiation are required, but this data is not easily available. Even the Japan Meteorological Agency only measures at a few observation stations, so it is difficult to obtain data for the desired location. [Means for solving the problem]

[0005] In order to solve this problem, the present invention provides a power optimization control device for a storage battery, comprising: a solar radiation prediction unit that calculates a predicted horizontal plane solar radiation from extraatmospheric global solar radiation and a transmittance coefficient; a power generation amount prediction unit that calculates a predicted power generation amount from the predicted horizontal plane solar radiation and the relationship between the predicted horizontal plane solar radiation and the power generation amount; a power usage prediction unit that calculates a predicted power usage amount using hourly power usage amount in a recent fixed period of time; and a control unit that predicts the time progression of the SOC of the storage battery based on the predicted power generation amount and the predicted power usage amount, and determines an optimal nighttime charging amount and discharge time period.

[0006] The power optimization control device may calculate the extra-atmospheric global solar radiation from the date and time when the horizontal plane solar radiation is predicted and information on the location where the solar power generation device is installed, calculate the transmittance coefficient from weather forecast information, and calculate the predicted power generation amount from the predicted horizontal plane solar radiation and a slope and intercept calculated by linear approximation between the predicted horizontal plane solar radiation accumulated from the past to the present and the power generation amount not including temperature losses accumulated from the past to the present.

[0007] In the power optimization control device, when calculating the predicted power generation amount, in addition to the predicted horizontal plane solar radiation accumulated from the past to the most recent and the power generation amount not including the temperature loss accumulated from the past to the most recent, temperature information accumulated from the past to the most recent may also be used to determine a first slope for solar radiation prediction, a second slope for temperature prediction, and a multiple regression analysis intercept by multiple regression analysis, and the predicted power generation amount may be calculated from the predicted horizontal plane solar radiation, the first slope, the second slope, and the multiple regression analysis intercept.

[0008] The power optimization control device may determine the transmission coefficient by taking into account a probability of precipitation in addition to a coefficient set for each weather in the weather forecast information.

[0009] In the power optimization control device, in order to add the influence of the preceding and following times, a transmission coefficient of the preceding and following times may be weighted and added to the transmission coefficient.

[0010] The power optimization control device may further include an insolation amount estimation unit that calculates an estimated horizontal plane insolation amount from the power generation amount record, the slope, and the intercept.

[0011] The power optimization control device may further include an insolation estimation unit that calculates an estimated horizontal plane insolation from a power generation result, the first slope, the second slope, and the multiple regression analysis intercept.

[0012] The present invention also provides a power optimization control method for a storage battery, including the steps of: calculating a predicted horizontal plane insolation from extraatmospheric global insolation and a transmittance coefficient; calculating a predicted power generation amount from the predicted horizontal plane insolation and the relationship between the predicted horizontal plane insolation and the power generation amount; calculating a predicted power consumption using hourly power consumption in a recent fixed period of time; and predicting the time progression of the SOC of the storage battery based on the predicted power generation amount and the predicted power consumption, and determining an optimal nighttime charging amount and discharge time period.

[0013] In the power optimization control method, the extraatmospheric global solar radiation may be calculated from a date and time when the horizontal plane solar radiation is predicted and information on a location where a solar power generation device is installed, the transmittance coefficient is calculated from weather forecast information, and the predicted power generation amount is calculated from the predicted horizontal plane solar radiation and a slope and an intercept calculated by linear approximation between the predicted horizontal plane solar radiation accumulated from the past to the most recent and the power generation amount not including temperature losses accumulated from the past to the most recent.

[0014] In the power optimization control method, when calculating the predicted power generation amount, in addition to the predicted horizontal plane solar radiation accumulated from the past to the most recent and the power generation amount not including the temperature loss accumulated from the past to the most recent, temperature information accumulated from the past to the most recent may also be used to determine a first slope for solar radiation prediction, a second slope for temperature prediction, and a multiple regression analysis intercept by multiple regression analysis, and the predicted power generation amount may be calculated from the predicted horizontal plane solar radiation, the first slope, the second slope, and the multiple regression analysis intercept.

[0015] In the power optimization control method, the transmission coefficient may be calculated by taking into account a probability of precipitation in addition to a coefficient set for each weather in the weather forecast information.

[0016] In the power optimization control method, a transmission coefficient of a preceding and succeeding time may be weighted and added to the transmission coefficient in order to add the influence of the preceding and succeeding time.

[0017] In the power optimization control method, an estimated horizontal plane solar radiation may be calculated from an actual power generation amount, the slope, and the intercept.

[0018] In the power optimization control method, an estimated horizontal plane solar radiation may be calculated from an actual power generation amount, the first slope, the second slope, and the multiple regression analysis intercept. Effect of the Invention

[0019] In the present invention, the parameters for calculating the transmission coefficient are determined in advance, and the predicted horizontal solar radiation is used to obtain the relationship between the amount of solar radiation and the amount of power generation, so there is no need to obtain actually measured solar radiation, which is difficult to obtain.

[0020] In addition, in the present invention, when obtaining the transmission coefficient from the weather forecast, the probability of precipitation is used to set a slope for both sunny and cloudy weather. Therefore, even if the weather forecast has a rough weather classification, the transmission coefficient can be predicted more precisely, and a more accurate prediction of horizontal solar radiation can be obtained.

[0021] Furthermore, in this invention, when determining the transmission coefficient from the weather forecast, weather forecasts for the preceding and following hours are also used, and a gradient is also applied in the time direction. Therefore, even if the weather and time divisions in the weather forecast are coarse, the transmission coefficient can be predicted more precisely, and more precise predicted horizontal solar radiation can be obtained. (Weather forecast information from the Japan Meteorological Agency at three-hour intervals is available free of charge, but in this invention, predicted horizontal solar radiation can be obtained at time intervals shorter than three hours based on this information.) [Brief description of the drawings]

[0022] [Figure 1] 1 is a schematic diagram showing an overall overview of the present invention. [Diagram 2] FIG. 2 is a block diagram showing a power optimization controller according to a preferred embodiment of the present invention; [Diagram 3] FIG. 2 is a schematic diagram illustrating solar radiation prediction. [Figure 4] FIG. 1 is a schematic diagram illustrating a solar radiation prediction that also takes into account the influence of temperature. [Diagram 5] FIG. 13 is a schematic diagram illustrating the calculation of the slope and intercept. [Figure 6] FIG. 13 is a schematic diagram for explaining calculation of the slope and intercept taking into account the influence of temperature. [Figure 7] FIG. 2 is a schematic diagram for explaining power generation amount prediction. [Figure 8] FIG. 11 is a schematic diagram for explaining a power generation prediction taking into account the influence of temperature. [Figure 9] FIG. 1 is a schematic diagram illustrating power consumption prediction. [Figure 10] FIG. 11 is a schematic diagram illustrating a power usage prediction that takes into account the influence of the day of the week and the time of day. [Figure 11] FIG. 11 is a schematic diagram illustrating a power usage forecast that takes into account the influence of temperature and daily influences. [Figure 12] FIG. 2 is a schematic diagram illustrating battery power optimization control. [Figure 13] 1 is a graph diagram and an explanation showing an example of power optimization control of a storage battery. [Figure 14] FIG. 1 is a schematic diagram illustrating solar radiation estimation using a slope and intercept by linear approximation. [Figure 15] FIG. 1 is a schematic diagram for explaining solar radiation estimation using slope 1, slope 2, and intercept by multiple regression analysis taking into account the influence of temperature. [Figure 16] FIG. 1 is a schematic diagram showing an example of estimating horizontal surface solar radiation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, a preferred embodiment of the present invention will be described with reference to the drawings. However, the present invention is not limited to the following description, and various modifications and changes are possible for those skilled in the art based on the gist of the invention described in the claims or disclosed in the description for carrying out the invention. Such modifications and changes are also included in the scope of the present invention. In the drawings, the parts surrounded by dashed lines indicate the functions constituting the present invention.

[0024] <1.Overall Overview>

[0025] FIG. 1 is a schematic diagram showing an overview of the present invention.

[0026] In order to optimize the amount of nighttime charging for storage batteries in homes that are equipped with solar power generation equipment, it is necessary to predict the amount of solar power generation and the amount of power consumption for each home.

[0027] In predicting solar power generation, solar radiation is predicted based on weather forecast information from the Japan Meteorological Agency, which is easily available free of charge, and a linear approximation relationship with daily solar power generation can be used (however, there is no restriction on the type of weather forecast information).

[0028] On the other hand, power usage is predicted using the hourly power usage over a recent fixed period (for example, the past week).

[0029] Based on the predicted solar power generation and power consumption, the system predicts the time progression of the battery's SOC and determines the optimal amount of overnight charging and discharge time period.

[0030] In addition, in this invention, the actual amount of solar radiation at a given time can be estimated from the measured amount of solar power generation by using the relationship between the amount of solar radiation and the amount of solar power generation. In other words, the solar panels of each house can be used like solar radiation sensors that are installed in large numbers throughout the country. This estimated amount of solar radiation data can be used to confirm the assumed power generation efficiency by comparing with the same area, verify the degree of deterioration, and find malfunctions.

[0031] <2. Basic embodiment>

[0032] As shown in FIG. 2, the power optimization control device 1 of this embodiment includes a solar radiation prediction unit 2, a power generation prediction unit 3, a power usage prediction unit 4, and a control unit 5. The solar radiation prediction unit 2 calculates a predicted horizontal plane solar radiation from the extratropical global solar radiation and the transmission coefficient. The power generation prediction unit 3 calculates a predicted power generation amount from the predicted horizontal plane solar radiation and the relationship between the predicted horizontal plane solar radiation and the power generation amount. The power usage prediction unit 4 calculates a predicted power usage amount using the power usage amount for each hour in the most recent fixed period. The control unit 5 controls the solar radiation prediction unit 2, the power generation prediction unit 3, and the power usage prediction unit 4, predicts the time transition of the SOC of the storage battery based on the predicted power generation amount calculated by the power generation prediction unit 3 and the predicted power usage calculated by the power usage prediction unit 4, determines the optimal nighttime charging amount and discharge time period, and performs power optimization control of the storage battery based on the predicted power generation amount and the power usage amount.

[0033] Furthermore, in the power optimization control device 1 and power optimization control method for the storage battery of this embodiment, the power generation amount prediction unit 3 calculates the extra-atmospheric global solar radiation from the date and time when the horizontal plane solar radiation is predicted and from information on the location where the solar power generation device is installed, calculates a transmission coefficient from weather forecast information, and calculates the predicted power generation amount from the predicted horizontal plane solar radiation and the slope and intercept calculated by linear approximation between the predicted horizontal plane solar radiation accumulated from the past to the present and the power generation amount not including temperature losses accumulated from the past to the present.

[0034] In addition, when calculating the predicted power generation, the power generation prediction unit 3 uses the predicted horizontal plane solar radiation accumulated from the past to the present and the power generation not including temperature loss accumulated from the past to the present, as well as the temperature information accumulated from the past to the present, to determine a first slope for solar radiation prediction, a second slope for temperature prediction, and a multiple regression analysis intercept by multiple regression analysis, and calculates the predicted power generation from the predicted horizontal plane solar radiation, the first slope, the second slope, and the multiple regression analysis intercept.

[0035] Further, the solar radiation prediction unit 2 calculates the transmission coefficient by taking into account the probability of precipitation in addition to the coefficient set for each weather in the weather forecast information.

[0036] Furthermore, in order to take into account the influence of the time before and after, the solar radiation prediction unit 2 adds the weighted transmission coefficient of the time before and after to the transmission coefficient.

[0037] Moreover, the power optimization control device 1 and the power optimization control method for a storage battery according to the present embodiment further include an insolation amount estimation unit 6 that calculates an estimated horizontal plane insolation amount from the power generation amount record, the slope, and the intercept.

[0038] Moreover, the solar radiation amount estimation unit 6 calculates an estimated horizontal plane solar radiation amount from the actual power generation amount, the first slope, the second slope, and the multiple regression analysis intercept.

[0039] The control unit 5 also controls the solar radiation amount estimation unit 6 to obtain an estimated value of the horizontal plane solar radiation amount using the calculated estimated horizontal plane solar radiation amount. If the estimated values ​​of the horizontal plane solar radiation amount for multiple storage batteries installed over a wide area are obtained, it is possible to obtain the regional distribution of the horizontal plane solar radiation amount by, for example, creating a heat map.

[0040] Hereinafter, a specific example of the power optimization control device 1 and the power optimization control method for a storage battery according to the present embodiment will be described with reference to the drawings and formulas.

[0041] <3. Solar radiation forecast>

[0042] The predicted horizontal solar radiation is calculated using formula (1).

[0043]

number

[0044] Here, the extraterrestrial global solar radiation can be calculated from the date and time when the horizontal surface solar radiation is predicted and the location information (latitude and longitude) of the photovoltaic power generation device using formula (2).

[0045]

number

[0046] Here, 1367 [W / m 2 ] is the solar constant, and r * / r is the reciprocal of the distance from the center of the earth to the sun, and α is the altitude. (For details, see the URL below.) http: / / es.ris.ac.jp / ~nakagawa / met_cal / solar.html

[0047] On the other hand, the transmission coefficient can be calculated from weather forecast information by the method described below and shown in FIG.

[0048] (1) If the current time is sunny with a 0% chance of precipitation and both the preceding and following times are sunny, the weather will be considered "clear."

[0049] (2) The coefficient α is determined, for example, as follows: Clear: 1.0, Sunny: 0.9, Cloudy: 0.8, Other (rain, etc.): 0.1

[0050] (3) If the probability of precipitation is not 0%, a slope is applied between the rain and the probability of precipitation to obtain the coefficient α'. For example, when it is cloudy (α=0.8) and the probability of precipitation is 30%, α'=α-(α-0.1)×30% =0.8-(0.8-0.1)×0.3=0.59 It becomes.

[0051] (4) Furthermore, the coefficient α″ is calculated by taking into account the influence of the time before and after. As an example, the influence of the time before and after is set as follows: current: 1.0, before and after: 0.5. However, clear skies are not influenced by the time before and after. When the weather is clear: α”=α', otherwise: α” = (previous α’ × 0.5 + current α’ × 1.0 + next α’ × 0.5) ÷ 2

[0052] For example, to find α" at 9 o'clock when the weather is not clear, use the weather forecast information for 6:00, 9:00, and 12:00. α” = (α’ at 6 o’clock × 0.5 + α’ at 9 o’clock × 1.0 + α’ at 12 o’clock × 0.5) ÷ 2 Let us assume that.

[0053] (4') Or, as another example, when obtaining the coefficient α″ that includes the influence of the preceding and following times, it is also possible to weight the preceding and following times as follows: As an example, when using weather forecast information for 6:00 and 9:00, 6 o'clock α” = 6 o'clock α' α” at 7 o’clock = α’ at 6 o’clock × 2 / 3 + α’ at 9 o’clock × 1 / 3 α” at 8 o’clock = α’ at 6 o’clock × 1 / 3 + α’ at 9 o’clock × 2 / 3 9 o'clock α” = 9 o'clock α' Let us assume that. Here, 1 / 3 and 2 / 3 are merely examples and are not limiting.

[0054] Furthermore, various variations that combine the ideas of (4) and (4') are possible.

[0055] (5) Multiply α” by 0.7 to obtain the permeability coefficient. Here, 0.7 is a coefficient that represents the amount of attenuation of solar radiation from outside the atmosphere until it reaches the earth's surface on a clear day. This value 0.7 is an example and is not limited to this.

[0056] The above method of calculating the transmission coefficient is merely an example, and various modifications are possible. In the above, as an example, it is assumed that the transmission coefficient is predicted every hour, but the transmission coefficient may be predicted at finer time intervals, such as every half hour or every 10 minutes. In addition, as shown in FIG. 4, for example, a calculation method that further considers the influence of the temperature in the weather forecast information is also possible.

[0057] 4. Calculation of slope and intercept

[0058] Generally, the relationship between the amount of solar radiation and the amount of power generation is approximately linear. Therefore, the slope and intercept are calculated from the relationship between the amount of solar radiation and the amount of power generation.

[0059] First, the amount of power generation not including temperature loss, which eliminates the influence of temperature loss, is calculated using formula (3). As an example, this can be calculated based on the past to recent accumulated power generation amount data and the temperature loss corresponding to the power generation time of each power generation amount data. The data on the amount of power generation not including temperature loss is accumulated.

[0060]

number

[0061] In solar panels, the power generation efficiency decreases as the temperature rises, so this loss is considered to be temperature loss. As an example, a coefficient determined by the type of solar panel and the ambient temperature can be used. More simply, it is also possible to use a fixed value for each month, regardless of the type of solar panel. (Since temperature loss is ultimately included in the slope and intercept, the influence of the type of solar panel is absorbed.)

[0062] As shown in Fig. 5, a linear approximation is performed between the predicted horizontal plane solar radiation accumulated from the past to the present and the power generation amount not including temperature loss accumulated from the past to the present to calculate the slope and intercept. More specifically, for example, the accumulated data is plotted on a graph with the power generation amount not including temperature loss on the vertical axis and the predicted horizontal plane solar radiation on the horizontal axis, and the slope and intercept can be obtained by linear approximation of the result. The period from the past to the present can be a predetermined period set appropriately from a certain point in time to the present, and its length is not particularly limited.

[0063] Furthermore, it is also possible to use a slope and intercept that take temperature into consideration, as shown in Fig. 6. In this case, when determining the slope and intercept, not only the predicted horizontal plane solar radiation accumulated from the past to the present, but also the temperature information accumulated from the past to the present is used to determine a slope 1 (first slope) for solar radiation prediction, a slope 2 (second slope) for temperature prediction, and an intercept (multiple regression analysis intercept) by multiple regression analysis.

[0064] <5. Power generation forecast>

[0065] The predicted power generation amount can be calculated by the following formula (4) as shown in FIG.

[0066]

number

[0067] When using a slope and intercept that take into account the effect of temperature, the predicted power generation amount is calculated using the following formula (5) as shown in FIG.

[0068]

number

[0069] <6. Power consumption forecast>

[0070] As shown in Fig. 9, in predicting power usage, the predicted power usage is calculated from the power usage for each hour in a recent fixed period. As an example of the recent fixed period, the past seven days can be used as the period, and the hourly average in that period can be used as the predicted power usage for that hour.

[0071] As another example, as shown in FIG. 10, the day of the week and the time period may be taken into consideration, and the day of the week / time period ratio may be found based on data for a longer period of time, and the predicted power amount may be calculated by equation (6).

[0072]

number

[0073] As a further example, as shown in FIG. 11, the predicted power usage may be calculated using both temperature and daily influences as shown in equation (7).

[0074]

number

[0075] Here, each of the coefficients 1 to N, the coefficient T, and the coefficient S may be determined by multiple regression analysis.

[0076] <7. Battery power optimization control>

[0077] As shown in FIG. 12, the control unit 5 predicts the time transition of the SOC of the storage battery based on the predicted power generation amount and the predicted power consumption amount, and performs power optimization control of the storage battery by determining the optimal overnight charging amount and discharge time period.

[0078] As an example, the control unit 5 performs the power optimization control of the storage battery as follows, as shown in FIG. (1) The amount of overnight charging is determined so that the accumulated value on the positive side of the energy balance does not exceed the fully charged capacity of the storage battery. (2) The amount of overnight charging is increased so that the accumulated value on the negative side of the energy balance does not fall below the discharge SOC of the storage battery, within the range not exceeding the amount of overnight charging in (1).

[0079] <8. Solar radiation estimation>

[0080] The slope and intercept obtained from the relationship between solar radiation and solar power generation can also be used to estimate horizontal solar radiation.

[0081] When estimating horizontal plane solar radiation using the slope and intercept of linear approximation, the actual solar radiation at that time is estimated from the measured solar power generation amount using the following equation (8), as shown in Figure 14.

[0082]

number

[0083] Although equation (8) and Fig. 14 show examples of the slope and intercept by linear approximation that does not consider the influence of temperature, it is also possible to use slope 1 (first slope), slope 2 (second slope), and intercept (multiple regression analysis intercept) by multiple regression analysis that considers the influence of temperature. In this case, as shown in Fig. 15, the actual amount of solar radiation at that time is estimated from the measured amount of solar power generation by the following equation (9).

[0084]

number

[0085] The estimated horizontal plane solar radiation data obtained in this way can be used, for example, by comparing it with data from neighboring areas to confirm the expected power generation efficiency, verify the degree of deterioration, and detect malfunctions, and can be included as part of the power optimization control of the storage battery.

[0086] An example of an estimation of horizontal solar radiation is shown in Figure 16. The left figure in Figure 16 shows the estimated horizontal solar radiation obtained from data on 100 residential storage batteries as a heat map. The right figure in Figure 16 shows the satellite image and rain cloud data at that time. (Areas where there is no heat map data, such as Hokkaido except for the vicinity of Sapporo, indicate that there are no or only a few storage batteries from which data can be obtained.)

[0087] It can be seen that Ishikawa Prefecture, Toyama Prefecture, and Niigata Prefecture, which are estimated to have high power generation based on the heat map, are covered with thin or no rain clouds. Conversely, it can be seen that the Kyushu region, which is estimated to have low power generation based on the heat map, is covered with rain clouds. In other words, there is a good correlation or agreement between the estimated horizontal solar radiation amount based on the present invention and the actual distribution of rain clouds. [Explanation of symbols]

[0088] 1. Power optimization control device 2. Solar radiation forecast section 3. Power generation forecast section 4. Power Consumption Prediction Unit 5. Control section 6 Solar radiation estimation part

Claims

1. a solar radiation prediction unit that calculates a predicted horizontal solar radiation amount from the extratropical solar radiation amount and the transmittance coefficient; a power generation amount prediction unit that calculates a predicted power generation amount based on the predicted horizontal plane solar radiation amount and a relationship between the predicted horizontal plane solar radiation amount and the power generation amount; a power consumption prediction unit that calculates a predicted power consumption amount by using the hourly power consumption amount in a recent fixed period; a control unit that predicts a time transition of an SOC of a storage battery based on the predicted power generation amount and the predicted power consumption amount, and determines an optimal nighttime charging amount and a discharge time period; A power optimization control device for a storage battery comprising:

2. Calculating the amount of extra-atmospheric global solar radiation from the date and time when the horizontal surface solar radiation amount is predicted and information on the location where the solar power generation device is installed; Calculating the transmission coefficient from weather forecast information; 2. The power optimization control device for a storage battery according to claim 1, wherein the predicted power generation amount is calculated from the predicted horizontal plane solar irradiation and a slope and an intercept calculated by linear approximation between the predicted horizontal plane solar irradiation accumulated from the past to the present and the power generation amount not including temperature loss accumulated from the past to the present.

3. 3. The power optimization control device for a storage battery according to claim 2, wherein when calculating the predicted power generation amount, in addition to the predicted horizontal plane solar radiation accumulated from the past to the most recent and the power generation amount not including the temperature loss accumulated from the past to the most recent, temperature information accumulated from the past to the most recent is also used to determine a first slope for solar radiation prediction, a second slope for temperature prediction, and a multiple regression analysis intercept by multiple regression analysis, and the predicted power generation amount is calculated from the predicted horizontal plane solar radiation, the first slope, the second slope, and the multiple regression analysis intercept.

4. 4. The power optimization control device for a storage battery according to claim 2, wherein the transmission coefficient is calculated by taking into account a probability of precipitation in addition to a coefficient set for each weather in the weather forecast information.

5. The power optimization control device for a storage battery according to claim 4 , wherein a transmission coefficient for a time before and after the power supply is weighted and added to the transmission coefficient in order to add the influence of the time before and after the power supply.

6. The power optimization control device for a storage battery according to claim 2 , further comprising an insolation amount estimation unit that calculates an estimated horizontal plane insolation amount from the power generation amount record, the slope, and the intercept.

7. 4. The power optimization control device for a storage battery according to claim 3, further comprising a solar radiation estimation unit that calculates an estimated horizontal plane solar radiation from a power generation record, the first slope, the second slope, and the multiple regression analysis intercept.

8. Calculating a predicted horizontal solar radiation amount from the extratropical solar radiation amount and the transmittance coefficient; calculating a predicted power generation amount from the predicted horizontal plane solar radiation and a relationship between the predicted horizontal plane solar radiation and the power generation amount; Calculating a predicted amount of power consumption using hourly power consumption in a recent fixed period; predicting a time transition of an SOC of a storage battery based on the predicted power generation amount and the predicted power consumption amount, and determining an optimal nighttime charging amount and a discharge time period; A power optimization control method for a storage battery comprising:

9. Calculating the amount of extra-atmospheric global solar radiation from the date and time when the horizontal surface solar radiation amount is predicted and information on the location where the solar power generation device is installed; Calculating the transmission coefficient from weather forecast information; 9. The power optimization control method for a storage battery according to claim 8, wherein the predicted power generation amount is calculated from the predicted horizontal plane solar irradiation and a slope and an intercept calculated by linear approximation between the predicted horizontal plane solar irradiation accumulated from the past to the most recent and the power generation amount not including temperature loss accumulated from the past to the most recent.

10. 10. The power optimization control method for a storage battery according to claim 9, wherein when calculating the predicted power generation amount, in addition to the predicted horizontal plane solar radiation accumulated from the past to the most recent and the power generation amount not including the temperature loss accumulated from the past to the most recent, temperature information accumulated from the past to the most recent is also used to determine a first slope for solar radiation prediction, a second slope for temperature prediction, and a multiple regression analysis intercept by multiple regression analysis, and the predicted power generation amount is calculated from the predicted horizontal plane solar radiation, the first slope, the second slope, and the multiple regression analysis intercept.

11. 11. The method for optimizing power control of a storage battery according to claim 9, further comprising the step of calculating the transmission coefficient by taking into account a probability of precipitation in addition to a coefficient set for each weather in the weather forecast information.

12. The power optimization control method for a storage battery according to claim 11, further comprising the step of weighting the transmission coefficient and adding a transmission coefficient for a time before and after the power supply to the transmission coefficient in order to add an influence of the time before and after the power supply.

13. The power optimization control method for a storage battery according to claim 9 , further comprising the step of: calculating an estimated horizontal plane solar radiation amount from the actual power generation amount, the slope, and the intercept.

14. The power optimization control method for a storage battery according to claim 10 , further comprising the steps of: calculating an estimated horizontal plane solar radiation amount from a power generation result, the first slope, the second slope, and the multiple regression analysis intercept.

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