Power management system, power management server, and power management method

Abstract

To improve prediction accuracy of generated power of a solar power generator.SOLUTION: When a prediction starting condition is established (YES in S100), a CEMS server executes processing including: step (S102) for acquiring a wind direction prediction result ; step (S104) for identifying an upwind side point; step (S106) for identifying a downwind side point; step (S110) for calculating a coefficient A using a prediction value and a measured value of a solar radiation amount at the upwind side point; step (S114) for calculating a coefficient B using a prediction value and a measured value of a solar radiation amount in a prediction target area; step (S118) for calculating a coefficient C using a prediction value and a measured value of a solar radiation amount at the downwind side point; step (S120) for calculating a correction coefficient D; step (S124) for correcting a prediction value of a solar radiation amount at a prediction target time of the prediction target area using the correction coefficient D; and step (S126) for predicting a generated power at the prediction target time using the prediction value of the solar radiation amount after the correction.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to management of power generated using a solar power generation device. [Background technology]

[0002] Examples of power adjustment resources for power grids such as microgrids include generators, naturally variable power sources, power storage systems, charging equipment, and vehicles equipped with power sources. Among these, an example of naturally variable power sources is a photovoltaic power generation device such as a solar panel. When a photovoltaic power generation device is installed in an area covered by a microgrid, it is necessary to accurately predict the power generated by the photovoltaic power generation device in order to perform power adjustment with high accuracy.

[0003] For example, Japanese Patent Laid-Open Publication No. 2011-124287 (Patent Document 1) discloses a technique for predicting the amount of solar radiation and calculating the power generation of a solar power generation device from information on the predicted amount of solar radiation. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2011-124287 A Summary of the Invention [Problem to be solved by the invention]

[0005] The above-mentioned solar radiation forecast information is provided by various organizations such as the Japan Meteorological Agency on a regional basis, but the amount of solar radiation may vary from the predicted value due to the amount of clouds and wind direction in the region that is the target of the forecast (hereinafter referred to as the target forecast region) at the time that is the target of the forecast (hereinafter referred to as the target forecast time). Therefore, if the amount of solar radiation varies significantly from the predicted value, the forecast accuracy of the power generated by the photovoltaic power generation device may also decrease, and it may become difficult to use the power adjustment resource.

[0006] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to provide a power management system, a power management server, and a power management method that improve the prediction accuracy of the power generation of a solar power generation device. [Means for solving the problem]

[0007] A power management system according to an aspect of the present disclosure is a power management system that manages power in a power grid installed in a predetermined area. The power management system includes a photovoltaic power generation device installed in the predetermined area and connected to the power grid, an acquisition device that acquires a wind direction at a reference point where the photovoltaic power generation device is installed in the predetermined area, and a calculation device that calculates a predicted value of the amount of solar radiation at the reference point at a prediction target time and calculates the power generation power of the photovoltaic power generation device using the predicted value. The calculation device corrects the predicted value of the amount of solar radiation at the reference point at the prediction target time by weighting first information about the amount of solar radiation at a point upwind of the reference point before the current time more heavily than second information about the amount of solar radiation at other points other than the reference point before the current time.

[0008] In this way, when the amount of solar radiation fluctuates from the initial predicted value due to the influence of clouds or the like, the first information on the amount of solar radiation at the windward point is weighted more heavily than the second information on the amount of solar radiation at other points, so that the predicted value of the amount of solar radiation at the solar power generation device at the target prediction time can be corrected with high accuracy, thereby improving the prediction accuracy of the power generation power of the solar power generation device.

[0009] In one embodiment, the calculation device calculates a first correction amount using an actual measurement value and a predicted value of the amount of solar radiation at a point on the windward side. The calculation device calculates a second correction amount using an actual measurement value and a predicted value of the amount of solar radiation at another point. The calculation device calculates a correction amount for correcting the predicted value of the amount of solar radiation at the reference point at the prediction target time by setting a weighting coefficient for the first correction amount that is larger than that for the second correction amount.

[0010] In this way, the predicted value of the amount of solar radiation at the reference point at the prediction target time can be corrected with high accuracy, thereby improving the prediction accuracy of the power generation power of the photovoltaic power generation device.

[0011] Furthermore, in an embodiment, the actual solar radiation measurements are obtained using at least one of an actinometer, a camera, a rain sensor, and a temperature sensor.

[0012] In this way, it is possible to acquire the actual measured value with high accuracy, and to use the acquired actual measured value to calculate with high accuracy the predicted value of the amount of solar radiation in the photovoltaic power generation device at the prediction target time.

[0013] Additionally, in some embodiments, the predicted solar radiation amount is calculated using a history of actual measurements.

[0014] In this way, the predicted value of the amount of solar radiation in the photovoltaic power generation device at the prediction target time can be calculated with high accuracy.

[0015] Furthermore, in one embodiment, the computing device obtains at least one of the actual measured values ​​and the predicted values ​​from a server external to the power management system.

[0016] In this way, the predicted value of the amount of solar radiation in the photovoltaic power generation device at the prediction target time can be calculated with high accuracy using the actual measured value and the predicted value obtained from the external server.

[0017] A power management server according to another aspect of the present disclosure is a power management server that manages power of a power grid installed in a predetermined area. A photovoltaic power generation device connected to the power grid is installed in the predetermined area. The power management server acquires the wind direction at a reference point where the photovoltaic power generation device is installed in the predetermined area, calculates a predicted value of the amount of solar radiation at the reference point at a prediction target time, and calculates the power generation power of the photovoltaic power generation device using the predicted value. The power management server corrects the predicted value of the amount of solar radiation at the reference point at the prediction target time by weighting first information about the amount of solar radiation at a point upwind of the reference point before the current time greater than weighting second information about the amount of solar radiation at other points other than the reference point before the current time.

[0018] A power management method according to yet another aspect of the present disclosure is a power management method for managing power of a power grid installed in a predetermined area. A photovoltaic power generation device connected to the power grid is installed in the predetermined area. The power management method includes the steps of acquiring a wind direction at a reference point where the photovoltaic power generation device is installed in the predetermined area, calculating a predicted value of the amount of solar radiation at the reference point at a prediction target time and calculating the power generation power of the photovoltaic power generation device using the predicted value, and correcting the predicted value of the amount of solar radiation at the reference point at the prediction target time with a weighting greater than that of second information about the amount of solar radiation at other points other than the reference point at the prediction target time for first information about the amount of solar radiation at the reference point at the prediction target time. Effect of the Invention

[0019] According to the present disclosure, it is possible to provide a power management system, a power management server, and a power management method that improve the prediction accuracy of power generation by a photovoltaic power generation device. [Brief description of the drawings]

[0020] [Figure 1] 1 is a diagram showing a schematic configuration of a power management system according to an embodiment of the present invention; [Diagram 2]FIG. 1 is a diagram for explaining an example of a configuration for predicting the amount of solar radiation in a prediction target area. [Diagram 3] 10 is a flowchart illustrating an example of a process executed by a CEMS server. [Figure 4] 13 shows an example of a plurality of points set in a plurality of directions centered on a reference point. [Diagram 5] 1 is a diagram showing, in tabular form, actual measured values, predicted values, and directions of solar radiation at a reference point and a plurality of points at the same time. [Figure 6] 13 shows an example of the relationship between the predicted value, the actual measured value, and the coefficient at a windward point at the current time and at a reference point and a downwind point in the prediction target area. [Figure 7] 13 shows an example of the relationship between a predicted value and a corrected predicted value at a reference point in a prediction target area at a prediction target time. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference characters and their description will not be repeated.

[0022] 1 is a diagram showing a schematic configuration of a power management system according to the present embodiment. The power management system 100 includes, for example, a CEMS 1, a CEMS server 2, a power receiving and transforming facility 3, a power system 4, and a power transmission and distribution company server 5. CEMS stands for Community Energy Management System or City Energy Management System.

[0023] The CEMS 1 includes a Factory Energy Management System (FEMS) 11, a Building Energy Management System (BEMS) 12, a Home Energy Management System (HEMS) 13, a generator 14, a naturally variable power source 15, an Energy Storage System (ESS) 16, Electric Vehicle Supply Equipment (EVSE) 17, and a vehicle 18. In the CEMS 1, a microgrid MG is constructed by these components. The microgrid MG corresponds to an example of the "power grid" according to the present disclosure.

[0024] The FEMS 11 is a system that manages the supply and demand of electricity used in a factory. The FEMS 11 includes a factory building (including lighting fixtures, air conditioning equipment, etc.) and industrial equipment (production lines, etc.) that operate with electricity supplied from the microgrid MG. Although not shown, the FEMS 11 may also include power generation equipment (generators, etc.) installed in the factory. Electricity generated by these power generation equipment may be supplied to the microgrid MG. The FEMS 11 further includes a FEMS server 110 that is capable of bidirectional communication with the CEMS server 2.

[0025] The BEMS 12 is a system that manages the supply and demand of electricity used in buildings such as offices or commercial facilities. The BEMS 12 includes lighting fixtures and air conditioning equipment installed in the building. The BEMS 12 may include power generation equipment or a cold heat source system (such as a waste heat recovery system or a heat storage system). The BEMS 12 further includes a BEMS server 120 that is capable of bidirectional communication with the CEMS server 2.

[0026] The HEMS 13 is a system that manages the supply and demand of electricity used in the home. The HEMS 13 includes household appliances (such as lighting equipment, air conditioners, and other electrical appliances) that operate using power supplied from the microgrid MG. The HEMS 13 may also include a household heat pump system, a household cogeneration system, a household storage battery, and the like. The HEMS 11 further includes a HEMS server 130 that is capable of bidirectional communication with the CEMS server 2.

[0027] The generator 14 is a power generation facility that is not dependent on weather conditions, and outputs the generated power to the microgrid MG. The generator 14 may include a steam turbine generator, a gas turbine generator, a diesel engine generator, a gas engine generator, a biomass generator, a stationary fuel cell, etc. The generator 14 may include a cogeneration system that utilizes heat generated during power generation.

[0028] The naturally variable power source 15 is a power generation facility whose power output varies depending on weather conditions, and outputs the generated power to the microgrid MG. Although Fig. 1 illustrates a photovoltaic power generation device 15A (Fig. 2) such as a solar panel, the naturally variable power source 15 may include a wind power generation device in addition to the photovoltaic power generation device 15A.

[0029] The power storage system 16 is a stationary power source that stores power generated by the naturally variable power source 15 or the like. The power storage system 16 is a secondary battery, for example a lithium-ion battery or a nickel-metal hydride battery that is a battery (recycled product) used in a vehicle. However, the power storage system 16 is not limited to a secondary battery, and may be a power-to-gas device that produces gaseous fuel (hydrogen, methane, etc.) using surplus power.

[0030] The charging facility 17 is electrically connected to the microgrid MG and configured to be capable of charging and discharging (power supply) between the charging facility 17 and the microgrid MG.

[0031] Specifically, the vehicle 18 is a plug-in hybrid vehicle (PHV), an electric vehicle (EV), or the like. The vehicle 18 is configured to be capable of one or both of external charging and external power feeding. That is, the vehicle 18 is configured to be capable of supplying power from the microgrid MG to the vehicle 18 when a charging cable is connected to an inlet (not shown) of the vehicle 18 (external charging). The vehicle 18 may also be configured to be capable of supplying power from the vehicle 18 to the microgrid MG when a charging cable is connected to an outlet (not shown) of the vehicle 18 (external power feeding).

[0032] In the example shown in Fig. 1, the CEMS 1 includes one each of the FEMS 11, BEMS 12, HEMS 13, generator 14, naturally variable power source 15, and power storage system 16, but the number of these systems or facilities included is arbitrary. The CEMS 1 may include a plurality of these systems or facilities, or there may be systems or facilities not included in the CEMS 1. Each of the FEMS 11 (factory buildings, industrial facilities, etc.), BEMS 12 (lighting fixtures, air conditioning facilities, etc.), HEM 13 (household appliances, etc.), generator 14, naturally variable power source 15, power storage system 16, charging facilities 17, and vehicles 18 included in the CEMS 1 corresponds to a "power adjustment resource" according to the present disclosure, and therefore, hereinafter, when there is no particular distinction between these systems or facilities, they will also be referred to as "power adjustment resources."

[0033] The CEMS server 2 is a computer that manages power adjustment resources within the CEMS 1. The CEMS server 2 includes a control device 21, a storage device 22, and a communication device 23. The control device 21 includes a processor, and is configured to execute predetermined arithmetic processing. The storage device 22 includes a memory that stores programs executed by the control device 21, and stores various information used by the programs (maps, relational expressions, parameters, etc.). The communication device 23 includes a communication interface, and is configured to communicate with the outside (other servers, etc.).

[0034] The CEMS server 2 may be an aggregator server. An aggregator is an electric utility that provides an energy management service by bundling multiple power adjustment resources. The CEMS server 2 corresponds to the “power management server” according to the present disclosure.

[0035] The power receiving and transforming equipment 3 is provided at an interconnection point (power receiving point) of the microgrid MG, and is configured to be able to switch between parallel (connection) and parallel-off (disconnection) between the microgrid MG and the power system 4. The power receiving and transforming equipment 3 includes a high-voltage side (primary side) switchgear, a transformer, a protective relay, measuring instruments, and a control device, all of which are not shown. When the microgrid MG is interconnected with the power system 4, the power receiving and transforming equipment 3 receives, for example, extra-high voltage AC power (a voltage exceeding 7000 V) from the power system 4, and steps down the received power to supply it to the microgrid MG.

[0036] The power system 4 is a power network constructed by power plants and power transmission and distribution facilities. In this embodiment, an electric power company serves as both a power generation business operator and a power transmission and distribution business operator. The electric power company corresponds to a general power transmission and distribution business operator, and also corresponds to a manager of the power system 4, and maintains and manages the power system 4.

[0037] The electricity transmission and distribution company server 5 is a computer that belongs to an electric power company and manages the supply and demand of electricity in the electric power system 4. The electricity transmission and distribution company server 5 is also configured to be capable of bidirectional communication with the CEMS server 2.

[0038] In the power management system 100 having the above configuration, as described above, the naturally variable power source 15 used as a power adjustment resource of the microgrid MG is exemplified by a photovoltaic power generation device 15A such as a solar panel. When the photovoltaic power generation device 15A is installed in a predetermined area targeted by the microgrid MG, in order to perform power adjustment with high accuracy, it is required to predict the power generated by the photovoltaic power generation device 15A with high accuracy. Therefore, for example, it is considered to predict the amount of solar radiation and calculate the amount of power generated by the photovoltaic power generation device 15A from information on the predicted amount of solar radiation.

[0039] FIG. 2 is a diagram for explaining an example of a configuration for predicting the amount of solar radiation in a prediction target area 102. The prediction target area 102 is an area including a point where a solar power generation device 15A included in a variable natural power source 15 is installed, and may be, for example, a city, town, or village, or may be a circular or rectangular area centered on a reference point. In FIG. 2, a case where the prediction target area 102 is, for example, a rectangular area centered on a reference point is explained as an example. The reference point is the position where the solar power generation device 15A is installed. Note that the reference point and the installation point of the solar power generation device 15A are not limited to being the same point, and may be different points at least within a range in which the difference in the predicted value of the amount of solar radiation is equal to or less than a threshold value.

[0040] 2, the CEMS server 2 acquires, for example, information indicating actual measured values ​​of solar radiation at a reference point set in the prediction target area 102, information indicating predicted values ​​of solar radiation at a time point after the current time, and information indicating predicted wind direction at the reference point at a time point after the current time from an external server 200. The external server 200 includes, for example, a server of the Japan Meteorological Agency or a server of an organization that provides information related to solar radiation to the outside.

[0041] A wind vane 116 and a pyranometer 118 are installed at a reference point in the prediction target area 102. The wind vane 116 detects the wind direction and transmits information indicating the detection result to the external server 200. The pyranometer 118 detects the amount of solar radiation and transmits information indicating the detection result to the external server 200. Furthermore, pyranometers 119.1 to 119.n are installed at a plurality of points set in the periphery of the prediction target area 102. Each of the pyranometers 119.1 to 119.n detects the amount of solar radiation and transmits information indicating the detection result to the external server 200.

[0042] The external server 200 predicts the wind direction at a time later than the current time at a reference point based on, for example, the actual measurement value of the anemometer 116 installed at the reference point, the atmospheric pressure distribution, and the predicted change in the atmospheric pressure distribution. The external server 200 calculates the predicted value of the amount of solar radiation at a time later than the current time using the history of the actual measurement value of the amount of solar radiation acquired from the pyranometers 118, 119.1 to 119.n. As a prediction method, a known technique may be used and a detailed description thereof will not be given. The external server 200 stores the actual measurement value and the predicted value of the amount of solar radiation at each point at the same time in a storage device (not shown) in association with each other. In response to a request for information on the amount of solar radiation in the prediction target area 102 from the CEMS server 2, the external server 200 transmits information on the actual measurement value and the predicted value of the amount of solar radiation to the CEMS server 2.

[0043] The CEMS server 2 can calculate the power generation of the photovoltaic power generation device 15A in the prediction target area 102 at the prediction target time by using the predicted value of the amount of solar radiation. The prediction target time may be, for example, a time after a predetermined time (e.g., 30 minutes or 1 hour) has elapsed from the current time, or may be a time when power adjustment is performed.

[0044] The above-mentioned solar radiation prediction information is provided by the external server 200 for each region, but the amount of solar radiation may vary from the predicted value due to the amount of clouds and wind direction in the prediction target region 102 at the prediction target time. Therefore, if the amount of solar radiation varies significantly from the predicted value, the prediction accuracy of the power generated by the solar power generation device 15A may also decrease, making it difficult to use the power adjustment resource.

[0045] Therefore, in this embodiment, the CEMS server 2, which corresponds to the "computing device" in the power management system 100, corrects the predicted value of the amount of solar radiation at the reference point at the prediction target time by weighting the first information about the amount of solar radiation at a point upwind of the reference point before the current time more heavily than the second information about the amount of solar radiation at other points before the current time.

[0046] In this way, when the amount of solar radiation fluctuates from the initial predicted value due to the influence of clouds or the like, the first information on the amount of solar radiation at the upwind point is weighted more heavily than the second information on the amount of solar radiation at other points, so that the predicted value of the amount of solar radiation at the solar power generation device 15A at the prediction target time can be corrected with high accuracy. Therefore, the prediction accuracy of the power generation power of the solar power generation device 15A can be improved.

[0047] An example of processing executed by the CEMS server 2 will be described below with reference to Fig. 3. Fig. 3 is a flowchart showing an example of processing executed by the CEMS server 2. A series of processing shown in this flowchart is repeatedly executed at predetermined intervals.

[0048] In step (hereinafter, step will be abbreviated as S) 100, the CEMS server 2 determines whether or not a prediction start condition is satisfied. The prediction start condition includes, for example, a condition that a prediction of power generation at a prediction target time is requested from the photovoltaic power generation device 15A. The CEMS server 2 determines, for example, that a condition that a prediction of power generation at a prediction target time is requested when a power adjustment using generated power (for example, supply of power to the power system 4) is requested at the prediction target time from the power transmission and distribution company server 5 is satisfied. If it is determined that the prediction start condition is satisfied (YES in S100), the process proceeds to S102.

[0049] In S102, the CEMS server 2 acquires the wind direction prediction result. For example, the CEMS server 2 acquires information on the wind direction predicted at the reference point at the prediction target time from the external server 200 as the wind direction prediction result.

[0050] In S104, the CEMS server 2 identifies a windward point. A point in the windward direction of the reference point is identified as the windward point.

[0051] The CEMS server 2, for example, sets one or more points in each of a plurality of directions centered on a reference point, and specifies a point in the direction corresponding to the upwind side of the wind direction obtained from the wind direction prediction result as an upwind point. In this embodiment, for example, a total of eight points A1 to A8 are set in each of the directions set in eight directions.

[0052] Fig. 4 shows an example of a plurality of points A1 to A8 set in a plurality of directions centered on a reference point. The plurality of points A1 to A8 are points set in advance in each direction of the north, northwest, west, southwest, south, southeast, east, and northeast centered on a reference point set in the prediction target area 102. The points set in each direction may be within a range set for each direction, and are not particularly limited to the positions of the plurality of points A1 to A8 shown in Fig. 4.

[0053] Pyranometers are installed at the multiple points A1 to A8. The above-mentioned pyranometers 119.1 to 119.n include pyranometers installed at the multiple points A1 to A8. That is, the actual measured value and the predicted value of the amount of solar radiation at each point including the reference point can be acquired from the external server 200. The CEMS server 2 stores the actual measured value and the predicted value of the amount of solar radiation at the same time as each point in association with each other, and stores the predicted value of the amount of solar radiation at a prediction target time after the current time in association with each point. After that, when the time reaches the prediction target time, a value indicating the detection result of the amount of solar radiation is acquired from the external server 200 as the actual measured value, and is stored in association with the predicted value at the same time stored in the CEMS server 2. These pieces of information are stored in the CEMS server 2 in a table format (described later), and the CEMS server 2 acquires the actual measured value and the predicted value of the amount of solar radiation corresponding to each point from these pieces of information.

[0054] For example, when the wind direction at the reference point at the prediction target time is southerly, the CEMS server 2 identifies a point A1 on the north side of the reference point as the upwind point.

[0055] In S106, the CEMS server 2 identifies a downwind point. For example, if the wind direction at the prediction target time is southward, the CEMS server 2 identifies a point A5 south of the reference point as the downwind point.

[0056] In S108, the CEMS server 2 acquires the predicted value and the actual measured value of the amount of solar radiation at the windward point at the current time.

[0057] In S110, the CEMS server 2 calculates the coefficient A. The CEMS server 2 calculates the coefficient A as the ratio of the actual measured value of the amount of solar radiation at the current time at the upwind point to the predicted value (=actual measured value / predicted value).

[0058] In S112, the CEMS server 2 acquires the predicted value and the actual measured value of the amount of solar radiation at the current time in the prediction target area 102 (reference point).

[0059] In S114, the CEMS server 2 calculates the coefficient B. The CEMS server 2 calculates the ratio of the actual measured value of the amount of solar radiation at the current time in the prediction target area 102 to the predicted value as the coefficient B.

[0060] In S116, the CEMS server 2 acquires the predicted value and the actual measured value of the amount of solar radiation at the downwind point at the current time.

[0061] In S118, the CEMS server 2 calculates the coefficient C. The CEMS server 2 calculates the coefficient C as the ratio of the actual measured value of the amount of solar radiation at the downwind point at the current time to the predicted value.

[0062] In S120, the CEMS server 2 calculates a correction coefficient D. The correction coefficient D is a coefficient for correcting a predicted value in the prediction target area 102 (reference point) at the prediction target time. The CEMS server 2 calculates the correction coefficient D using coefficients A, B, and C. The CEMS server 2 calculates the correction coefficient D by, for example, a weighted average of coefficients A, B, and C. That is, the CEMS server 2 calculates the correction coefficient D using, for example, the formula D=(w1×A+w2×B+w3×C) / (w1+w2+w3). w1, w2, and w3 indicate weight coefficients set for the coefficients A, B, and C, respectively. At this time, the weight coefficient w1 is set to a value larger than each of the weight coefficients w2 and w3 of the other points. The weight coefficients w1, w2, and w3 may be predetermined values, or may be set based on the date or wind speed indicating the season, etc. For example, when the wind speed is high or when the date indicates a windy season, the weighting coefficient w1 may be set to a larger value than when the wind speed is low.

[0063] In S122, the CEMS server 2 acquires a predicted value of the amount of solar radiation at the prediction target time in the prediction target area 102. The CEMS server 2 acquires a predicted value of the amount of solar radiation at the prediction target time in the prediction target area 102 from the external server 200, for example.

[0064] In S124, the CEMS server 2 corrects the predicted value of the amount of solar radiation at the prediction target time in the prediction target area 102. Specifically, the CEMS server 2 multiplies the predicted value of the amount of solar radiation at the prediction target time in the prediction target area 102 acquired from the external server 200 by a correction coefficient D, and sets the calculated value as the corrected predicted value.

[0065] In S126, the CEMS server 2 predicts the power generation at the prediction target time in the prediction target area 102. Specifically, the CEMS server 2 calculates the power generation at the prediction target time in the prediction target area 102 using the predicted value of the amount of solar radiation after correction and the power generation in the solar power generation device 15A per a predetermined amount of solar radiation.

[0066] An example of the operation of the CEMS server 2 in this embodiment based on the above-described structure and flowchart will be described with reference to FIGS.

[0067] FIG. 5 is a diagram showing in a table format the actual and predicted values ​​of the amount of solar radiation at the reference point and each of the multiple points A1 to A8 at the same time. The CEMS server 2, for example, uses information from the external server 200 to store the actual and predicted values ​​of the amount of solar radiation at the reference point and each of the multiple points A1 to A8 in association with each other as shown in FIG. 5. For example, when the CEMS server 2 acquires the actual value of the amount of solar radiation at each point at the current time from the external server 200, it stores the actual value of the amount of solar radiation at each point at the current time in association with the predicted value of the amount of solar radiation at each point at the current time acquired in advance. FIG. 5 shows, for example, that at the current time, the actual value of the amount of solar radiation at the reference point is Ia(0) and the predicted value of the amount of solar radiation is Ib(0), and the actual values ​​of the amount of solar radiation at the multiple points A1 to A8 are Ia(1) to Ia(8), respectively, and the predicted values ​​of the amount of solar radiation are Ib(1) to Ib(8), respectively. Fig. 6 shows an example of the relationship between the predicted value, the actual measured value, and the coefficient at the current time at a windward point (A1), a reference point, and a leeward point (A5) in the prediction target area 102. Fig. 7 shows an example of the relationship between the predicted value and the corrected predicted value at the reference point in the prediction target area 102 at the prediction target time.

[0068] For example, when a prediction start condition is met by a request for prediction of the power generation power of the photovoltaic power generation device 15A at a prediction target time after a predetermined time has elapsed from the current time (YES in S100), a prediction result of the wind direction at the reference point at the prediction target time is acquired from the external server 200 (S102). Then, a windward point is identified using the wind direction prediction result (S104). For example, if the wind direction prediction result is southward, point A1 in the north direction is identified as the windward point. Furthermore, a downwind point is identified using the wind direction prediction result (S106). For example, if the wind direction prediction result is southward, point A5 in the south direction is identified as the downwind point.

[0069] The actual measured value Ia(1) and the predicted value Ib(1) of the amount of solar radiation at the specified windward point at the current time are acquired (S108). The ratio Ca(1) (=Ia(1) / Ib(1)) of the actual measured value to the predicted value at the acquired point A1 is calculated as the coefficient A (see FIG. 6) (S110).

[0070] Next, the actual measured value Ia(0) and the predicted value Ib(0) of the amount of solar radiation at the current time at the reference point in the prediction target area 102 are acquired (S112). Then, the ratio Ca(0) (=Ia(0) / Ib(0)) of the actual measured value Ia(0) to the predicted value Ib(0) of the amount of solar radiation at the current time in the prediction target area 102 is calculated as the coefficient B (see FIG. 6) (S114).

[0071] Furthermore, the actual measurement value Ia(5) and the predicted value Ib(5) of the amount of solar radiation at the specified downwind point at the current time are acquired (S116). The ratio Ca(5) (=Ia(5) / Ib(5)) of the actual measurement value to the predicted value at the acquired point A5 is calculated as the coefficient C (see FIG. 6) (S118).

[0072] The calculated coefficients A, B, and C are used to calculate the correction coefficient D (S120). Specifically, as described above, the correction coefficient D is calculated by the formula D=(w1×A+w2×B+w3×C) / (w1+w2+w3). At this time, since the weighting coefficient w1 is greater than both of w2 and w3, in the calculation of the correction coefficient D, the coefficient A is weighted more heavily than the coefficients B and C.

[0073] Then, a predicted value Ib of the amount of solar radiation in the prediction target area 102 at the prediction target time is acquired (S122), and the acquired predicted value Ib is multiplied by the calculated correction coefficient D (see FIG. 7) to calculate a corrected predicted value Ib' (=Ib×D) (S124). The calculated corrected predicted value Ib' is used to predict the power generation in the prediction target area 102 at the prediction target time (S126).

[0074] As described above, according to the power management system 100 of this embodiment, when the amount of solar radiation fluctuates from the initial predicted value due to the influence of clouds or the like, the predicted value of the amount of solar radiation in the photovoltaic power generation device 15A at the prediction target time can be corrected with high accuracy by setting the weighting coefficient W1 of the coefficient A at the upwind point to a value larger than the weighting coefficients w2 and w3 of the coefficients B and C at the other points. This makes it possible to improve the prediction accuracy of the power generated by the photovoltaic power generation device 15A. Therefore, it is possible to provide a power management system, a power management server, and a power management method that improve the prediction accuracy of the power generated by the photovoltaic power generation device.

[0075] Furthermore, since the actual measured value of the amount of solar radiation is obtained using a pyranometer installed at each point, the actual measured value of the amount of solar radiation can be obtained with high accuracy. Therefore, the predicted value of the amount of solar radiation in the photovoltaic power generation device 15A at the prediction target time can be calculated with high accuracy using the actual measured value and the history of the actual measured value obtained from the external server 200.

[0076] Modifications will be described below. In the above embodiment, the actual measured value of the amount of solar radiation is detected using an actinometer, but the actual measured value of the amount of solar radiation may be detected using at least one of a camera, a raindrop sensor, and a temperature sensor instead of or in addition to the actinometer. For example, the actual measured value of the amount of solar radiation may be detected based on the amount of light acquired using a camera, or a raindrop sensor may be used to detect the presence or absence of rainfall, or a temperature sensor may be used to predict the amount of clouds, and gradual change processing may be performed to suppress sudden changes in the actual measured value of the amount of solar radiation when the amount of clouds is large based on the prediction result.

[0077] Furthermore, in the above-described embodiment, an example was given of dividing the directions into eight directions and setting a point in each direction where actual measured values ​​and predicted values ​​of solar radiation can be obtained, but it is also possible to divide the directions into four directions (east, west, south, and north) and set a point in each direction where actual measured values ​​and predicted values ​​of solar radiation can be obtained, or to subdivide the directions into 16 or more directions and set a point in each direction where actual measured values ​​and predicted values ​​of solar radiation can be obtained.

[0078] Furthermore, in the above-described embodiment, the upwind point and the downwind point are points outside the prediction target area 102 as an example, but for example, the upwind point and the downwind point may be points on the upwind side and the downwind side, respectively, of the reference point of the prediction target area 102, and may be points within the prediction target area 102.

[0079] Furthermore, in the above embodiment, the CEMS server 2 has been described as acquiring information regarding the amount of solar radiation at each of the reference point and a plurality of points outside the prediction target area 102 from the external server 200. However, the CEMS server 2 may also be configured to directly acquire information regarding the amount of solar radiation using an actinometer or the like installed at each point.

[0080] Furthermore, in the above embodiment, the correction coefficient D is calculated using the coefficients A, B, and C, and the corrected predicted value is calculated by multiplying the predicted value of the amount of solar radiation in the prediction target area 102 at the prediction target time by the calculated correction coefficient D, but the corrected predicted value may be calculated, for example, by calculating the difference between the actual value and the predicted value at each point, calculating a weighted average of the calculated differences at each point as a correction amount, and adding the calculated correction amount to the predicted value of the amount of solar radiation in the prediction target area 102 at the prediction target time. In this case, the weighting coefficient for the difference at the windward point is set to a value larger than the weighting coefficients for the differences at the other points.

[0081] Furthermore, in the above embodiment, it has been described that one point is set for each direction around the prediction target area 102, but multiple points may be set for each direction. In this case, the CEMS server 2 may calculate the correction coefficient D using the average value of the actual measured values ​​of the solar radiation at multiple points in a direction specified as the upwind side or the downwind side and the average value of the predicted values, or may calculate the correction coefficient D using the actual measured value and the predicted value at one point that is closer to the upwind side or the downwind side among multiple points in the same direction.

[0082] Furthermore, in the above-described embodiment, it has been described that the weighting coefficient A at the upwind point is set to a value larger than the coefficients B and C at other points including the reference point and the downwind point, but the other points are not limited to the reference point and the downwind point, and may be, for example, only the reference point or only the downwind point.

[0083] Furthermore, in the above embodiment, it has been described that the power generation at the target prediction time is predicted in the CEMS server 2, but the power generation at the target prediction time may be predicted by a server other than the CEMS server 2, and the prediction result may be transmitted to the CEMS server 2.

[0084] Furthermore, in the above embodiment, the CEMS server 2 has been described as predicting the power generation at the prediction target time, but, for example, the amount of power generation during a planned power adjustment period may be predicted. The CEMS server 2 may, for example, predict the amount of power generation during the planned power adjustment period by multiplying the predicted power generation by the control period and integrating it for the planned power adjustment period (or a predetermined period).

[0085] Furthermore, in the above embodiment, the CEMS server 2 has been described as acquiring a predicted value of the amount of solar radiation at each point from the external server 200, but the CEMS server 2 may calculate a predicted value of the amount of solar radiation at each point using a history of actual measured values ​​of the amount of solar radiation at each point. For example, the CEMS server 2 may acquire an actual measured value of the amount of solar radiation from a pyranometer at each point (the reference point and each of points A1 to A8), and calculate a predicted value of the amount of solar radiation at each point using the history of the acquired actual measured values ​​of the amount of solar radiation.

[0086] Furthermore, in the above embodiment, the CEMS server 2 corrects the predicted value of the amount of solar radiation at the reference point at the prediction target time by setting the weighting coefficient w1 of the coefficient A calculated using the actual value and predicted value of the amount of solar radiation at a point on the windward side of the reference point to a value larger than the weighting coefficients w2 and w3 of the coefficients B and C calculated using the actual value and predicted value of the amount of human traffic at other points. However, at least one of the weighting coefficients w1, w2, and w3 may be a predetermined value, or may be set using the wind speed at the reference point. Alternatively, the weighting coefficient w1 may be set using the wind speed at the windward side, the weighting coefficient w2 may be set using the wind speed at the reference point, and the weighting coefficient w3 may be set using the wind speed at the downwind side. Alternatively, the coefficients A, B, and C may be corrected using the wind speed at the reference point, the coefficient A may be corrected using the wind speed at the upwind side, the coefficient B may be corrected using the wind speed at the reference point, and the coefficient C may be corrected using the wind speed at the downwind side.

[0087] The above-described modified examples may be implemented in whole or in part in appropriate combination. The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present invention is defined by the claims, not the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0088] 1 CEMS, 2 CEMS server, 3 substation equipment, 4 power grid, 5 power transmission and distribution company server, 11 FEMS, 12 BEMS, 13 HEMS, 14 generator, 15 naturally variable power source, 15A solar power generation device, 16 power storage system, 17 charging equipment, 18 vehicle, 21 control device, 22 storage device, 23 communication device. 100 Power management system, 102 forecast target area, 110 FEMS server, 120 BEMS server, 130 HEMS server, 116 wind vane, 118 solar pyranometer, 200 external server.

Claims

1. A power management system that manages power in a power grid installed in a predetermined area, A solar power generation device installed in the predetermined area and connected to the power grid; an acquisition device that acquires a wind direction at a reference point where the solar power generation device is installed within the predetermined area; a calculation device that calculates a predicted value of the amount of solar radiation at the reference point at a prediction target time and calculates a power generation amount of the solar power generation device using the predicted value, The calculation device corrects the predicted value of the amount of solar radiation at the reference point at the prediction time by weighting first information about the amount of solar radiation at a point upwind of the reference point before the current time greater than weighting second information about the amount of solar radiation at a point downwind of the reference point before the current time.

2. The computing device includes: calculating a first correction amount using an actual measurement value and a predicted value of the amount of solar radiation at the windward point; calculating a second correction amount using the actual measured value and the predicted value of the amount of solar radiation at the downwind point; 2 . The power management system according to claim 1 , further comprising: a weighting coefficient for the first correction amount set to be greater than a weighting coefficient for the second correction amount to calculate a correction amount for correcting a predicted value of the amount of solar radiation at the reference point at the prediction target time.

3. The power management system according to claim 2 , wherein the actual measured value of the amount of solar radiation is obtained using at least one of an actinometer, a camera, a raindrop sensor, and a temperature sensor.

4. The power management system according to claim 2 , wherein the predicted value of the amount of solar radiation is calculated using a history of the actual measured values.

5. 5. The power management system according to claim 2, wherein the computing device acquires at least one of the actual measured value and the predicted value from a server external to the power management system.

6. A power management server that manages power of a power grid installed in a predetermined area, the predetermined area having a photovoltaic power generation device connected to the power grid installed therein, The power management server includes: acquiring a wind direction at a reference point where the photovoltaic power generation device is installed within the predetermined area; Calculating a predicted value of the amount of solar radiation at the reference point at a prediction target time, and calculating a power generation amount of the solar power generation device using the predicted value; A power management server corrects a predicted value of the amount of solar radiation at the reference point at the prediction target time by weighting first information about the amount of solar radiation at a point upwind of the reference point before the current time greater than weighting second information about the amount of solar radiation at a point downwind of the reference point before the current time.

7. A power management method for managing power of a power grid installed in a predetermined area, the method comprising: acquiring a wind direction at a reference point where the solar power generation device is installed within the predetermined area; calculating a predicted value of the amount of solar radiation at the reference point at a prediction target time, and calculating a power generation amount of the solar power generation device using the predicted value; and correcting a predicted value of the amount of solar radiation at the reference point at the prediction target time with a greater weighting for first information about the amount of solar radiation at a point upwind of the reference point before the current time than for second information about the amount of solar radiation at a point downwind of the reference point before the current time.

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