JP5407875B2 - Electricity supply and demand prediction method and prediction system in microgrid - Google Patents

Description

  The present invention relates to a power supply and demand prediction method and a prediction system in a microgrid.

  Recently, power consumers such as individual houses, apartment houses, large stores, factories, etc. have their own charging equipment and power generation equipment to cover the supply and demand of power without using commercial power as much as possible. To be increased. Since there is a limit to the power supply and demand of only individual power consumers, as described in Patent Document 1, a power network is formed between a plurality of power consumers, and surplus power of a certain power consumer It is disclosed that power is supplied as a shortage of electricity consumers. A system that has a generator and a power storage device and that can supply and receive power as much as possible through self-sustaining operation is called a microgrid (or microgrid system). In this microgrid, Self-sustained operation that minimizes power transfer in the country is strongly desired.

  Patent Document 2 discloses the use of weather information obtained from another power supply system located in an area where weather information is necessary in order to predict the amount of power generated by a solar power generation device installed in the self-power supply. ing.

WO2004 / 073136 A1 JP 2008-136259 A

  By the way, in the microgrid, it is important to predict the amount of power supply and demand in order to improve the chance of autonomous operation. In other words, if power shortage is predicted ahead of a predetermined time, power storage to the commercial power source is suppressed and stored in the power storage device. Conversely, if power surplus is predicted ahead of the predetermined time, It becomes possible to take measures in advance such as positive discharge from the device. In addition, taking into account fluctuations in the price of electricity purchased from commercial power sources, taking into account the supply and demand of electricity ahead of a predetermined time, the timing of buying and selling power from commercial power sources to improve profitability Can also be adjusted.

  On the other hand, when the power supply / demand amount in the microgrid is predicted, it is conceivable to use data on the past power supply / demand amount in the own microgrid. However, since the electric power supply / demand varies depending on various factors, it is difficult to accurately predict the electric power supply / demand in a predetermined time just by relying on past data in its own microgrid.

  The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a power supply and demand prediction method in a microgrid capable of accurately predicting power supply and demand.

In order to achieve the object, the following solution is adopted in the prediction method of the present invention. That is, as described in claim 1 in the claims,
A method for predicting power supply and demand in a microgrid equipped with a power storage device, a power generation device, and a power consumption load,
A first step of storing and updating data of past and present power supply and demand fluctuations in the own microgrid;
A second step of obtaining data on past and present power supply / demand fluctuations in a plurality of other microgrids;
Based on the data obtained in the first step and the second step, among the plurality of other microgrids, the power supply / demand fluctuation state correlates with the power supply / demand fluctuation state in the own microgrid. And a third step of selecting, as a preceding microgrid, another microgrid in which the power supply / demand fluctuation state is generated for a predetermined time before the power supply / demand fluctuation state in its own microgrid;
A fourth step of predicting a power supply and demand ahead of a predetermined time in its own microgrid based on at least the fluctuation state of the power supply and demand of the preceding microgrid selected in the third step;
It is supposed to be equipped with.

  If a large number of microgrids are installed, there will be microgrids in which the fluctuations in power supply and demand are correlated (similar) to each other. Therefore, by looking at fluctuations in the power supply and demand of other microgrids that precede in time, it becomes possible to predict the power supply and demand for a predetermined time ahead of the own microgrid having a correlation therewith. The fluctuations in power supply and demand in other microgrids include all fluctuations in power generation and fluctuations in power consumption, so power supply and demand in terms of power generation and power consumption of many electrical loads is considered individually. As compared with the case of predicting, the accuracy is improved and the predictive control is also simplified.

Preferred embodiments based on the above solution are as set forth in claims 2 to 8 in the claims. That is,
A fifth step of confirming that the power supply / demand situation for a predetermined past period from the present in the own microgrid has a correlation with the fluctuation situation of the power supply / demand in the preceding microgrid in the corresponding period;
In the fourth step, on the condition that the correlation is confirmed in the fifth step, a predetermined time in the own microgrid based on the power supply / demand fluctuation state in the preceding microgrid Predict power supply and demand ahead,
In the fifth step, when it is not confirmed that there is a correlation, the preceding microgrid is newly selected in the third step.
(Corresponding to claim 2). In this case, it is possible to predict power supply and demand more accurately by verifying again whether or not the fluctuation state of power supply and demand in the period close to the current time has a correlation.

  In the fourth step, based on the fluctuation state of the power supply / demand of the own microgrid obtained in the first step, the power supply / demand of a predetermined time ahead is predicted as the basic prediction, and the basic prediction is performed in the second step. The final prediction of power supply and demand ahead of a predetermined time is made by correcting according to the fluctuation state of power supply and demand of the preceding microgrid obtained in step 3 (corresponding to claim 3). In this case, while predicting power supply and demand based on the fluctuations in power supply and demand in its own microgrid, this forecast is corrected with the fluctuations in power supply and demand in other microgrids that have a correlation. Can be predicted.

In the third step, a plurality of the preceding microgrids are selected,
In the prediction in the fourth step, the data of the plurality of preceding microgrids is used.
(Corresponding to claim 4). In this case, since power supply and demand is predicted using data of a plurality of preceding microgrids, it is preferable for further improving the prediction accuracy.

A sixth step of verifying whether or not the prediction of power supply and demand ahead of a predetermined time in the microgrid of the self matches the actual power supply and demand;
When it is determined in the verification in the sixth step that they do not match, the preceding microgrid determined not to match is selected from the selection target of the preceding microgrid in the subsequent third step. Excluded,
(Corresponding to claim 5). In this case, since the prediction result is verified and the subsequent micro grid is selected thereafter, the subsequent prediction of power supply and demand can be made more accurate.

  The selection of the preceding microgrid in the third step is performed in consideration of the similarity of the type of power supply / demand behavior that affects the power supply / demand in the own microgrid (corresponding to claim 6). . In this case, the power supply and demand behavior that affects power supply and demand may vary greatly depending on the microgrid. However, by selecting the preceding microgrid in consideration of the similarity of this power supply and demand behavior, the power supply and demand can be more accurate. Can be predicted well.

  Upon selection of the preceding microgrid in the third step, the distance from the own microgrid, the number of residents, the age composition, the residence form, the geographical situation, the type of power generation equipment, the number of vehicles equipped with a vehicle battery for traveling, This is performed in consideration of at least one of the direction from the own microgrid and the capacity of the power storage device (corresponding to claim 7). In this case, as in the case of claim 6, the power supply and demand can be predicted with higher accuracy.

  The power supply / demand prediction in the fourth step is performed in consideration of the weather information obtained from the weather observation device installed in the preceding microgrid selected in the third step (claim). 8). In this case, it is possible to predict power supply and demand more accurately by using weather information at the position of the preceding microgrid.

In order to achieve the above object, the following solution is adopted in the prediction system of the present invention. That is, as described in claim 9 in the claims,
A power supply and demand prediction system in a microgrid equipped with a power storage device, a power generation device, and a power consumption load,
Storage means for storing and updating data on past and present power supply / demand fluctuations in the own microgrid;
Data acquisition means for acquiring past and current power supply / demand fluctuation data in a plurality of other microgrids via a communication network;
Based on the data stored in the storage means and the data acquired by the data acquisition means, among the other microgrids, the power supply / demand fluctuation status correlates with the power supply / demand fluctuation status in its own microgrid A preceding microgrid selection means for selecting, as a preceding microgrid, other microgrids that have a relationship and the power supply / demand fluctuation state is generated for a predetermined time before the power supply / demand fluctuation situation in its own microgrid When,
A power supply and demand prediction means for predicting a power supply and demand ahead of a predetermined time in its own microgrid based on the fluctuation state of the power supply and demand of the selected preceding microgrid;
It is supposed to be equipped with. According to the above solution, a power supply and demand prediction system capable of obtaining the effect corresponding to claim 1 is provided.

  According to the present invention, it is possible to accurately predict power supply and demand.

The figure which shows an example of the electric power grid | system to which multiple microgrids were connected. The figure which shows the correlation example of the electric power supply-and-demand of an own microgrid and another microgrid. The figure which shows the correlation example in the past long term. The figure which shows the correlation example in the present time vicinity. The flowchart which shows the example of control of this invention. Explanatory drawing in the case of estimating electric power supply-and-demand based on an own microgrid and two preceding microgrids.

  In FIG. 1, reference numeral 1 denotes a commercial power source, which is owned and managed by an electric power company, and is a normal AC power source. A plurality of microgrids MG1 to MG5 are connected to the commercial power source 1. In FIG. 1, MG1 is its own microgrid, and MG2 to MG5 are positioned as other microgrids. In the following description, when it is not necessary to distinguish each microgrid, the description will be made simply using MG codes. In FIG. 1, the microgrid MG is shown in a form in which a large number of detached houses are gathered, and in a condominium form in which a large number of houses are occupying. A microgrid can be constructed with the body.

  Each microgrid MG has one common power network 10, and this common power network 10 includes a common power generation device (a common power generation facility, for example, a power generation device using fuel such as solar power generation, wind power generation, and cogeneration). G that can be used), a common power storage device (a common storage battery, for example, a large-capacity battery) B, and a plurality of individual power consumers (electric loads that consume power in the) D are connected. Yes. For some of the common power grid 10 and some of the individual consumers D, a charging facility (also serving as a discharging facility) C for the traveling battery-equipped vehicle EV is connected. Each microgrid MG is connected to the commercial power source 1 via a gateway (not shown), and frequency adjustment, voltage adjustment, and phase adjustment are performed when power is exchanged with the commercial power source. In FIG. 1, only four other microgrids MG2 to MG5 are shown, but in reality, more microgrids are assumed (for example, several tens of microgrids).

  A management device U configured using a computer is connected to the own microgrid MG1. The management device U predicts power supply and demand as will be described later. Since the own microgrid MG1 is a detached group and has a charging facility C for the vehicle EV owned by a part of the individual electric power consumer D, the other microgrid MG3 has a similar microgrid configuration. Yes. Also, the other microgrid MG3 is located more east than its own microgrid MG1 (sunrise is early), and the fluctuation situation of power supply and demand from the morning is the fluctuation situation of power supply and demand in its own microgrid MG1. The microgrid is highly likely to have a correlation.

  The management device U exchanges various information with other microgrids MG2 to MG5 via a wireless or information communication line arranged along the commercial power source 1 (via a network). . The information includes the fluctuation situation of power supply and demand over the past long period continuously from the present time of the other microgrids MG2 to MG5, and the fluctuation situation of this power supply and demand is provided in the management device U ( Stored in the storage means). That is, the management apparatus U holds the power supply / demand fluctuation data of not only its own microgrid MG1 but also other microgrids MG2 to MG5.

  Here, regarding fluctuations in power supply and demand, there is a correlation with its own microgrid MG1, and the relationship with other microgrids that fluctuate in time prior to its own microgrid MG1, that is, with the preceding microgrid. This will be described with reference to FIG. As described above, the other microgrid MG3 is a microgrid that is likely to be a preceding microgrid.

  In FIG. 2, a first predetermined period that is a continuous long period (for example, one year) from the present time is set for its own microgrid MG1. It is assumed that there is a correlation between the fluctuation state of the power supply and demand of another microgrid having a correlation with the fluctuation situation of the power supply and demand of its own microgrid MG1 during this first predetermined period. In addition, the fluctuation state of the power supply and demand occurs ahead of a certain time (for example, 2 hours, shown as a corresponding time difference in FIG. 2) in the other microgrids. In this way, fluctuations in power supply and demand occur ahead of time, and another microgrid having a correlation with fluctuations in power supply and demand is used as the preceding microgrid, and the power supply and demand ahead of the predetermined time of its own microgrid MG1. Used for prediction.

  Specifically, the time point t1 in FIG. 2 is the current time point, for example, one hour after the time point t1 is the time point t2, and two hours later is the time point t3. One hour before the current time t1 is the time point α1, and two hours before the time point is the time α2. When predicting the power supply / demand in its own microgrid MG1 at time t2 one hour after the current time t1, the other microgrid (preceding microgrid) precedes its own microgrid MG1 by 2 hours before power supply / demand Therefore, it is only necessary to see the state of power supply and demand in other microgrids at the time α1 that is one hour before the current time t1. Similarly, when predicting power supply / demand in its own microgrid MG1 at time t3 two hours after the current time t1, it is only necessary to look at the power supply / demand situation in other microgrids at the current time t1. It will be.

  FIG. 3 shows an example of the case where there is a correlation. The thick line in the figure is the case of its own microgrid, and the thin line in the figure is the case of another microgrid. FIG. 3 (a) focuses on the fluctuation of power supply and demand over a relatively long time. FIG. 3B is an enlarged view of a short time period in FIG. In FIG. 2, the power demand (power demand and supply) fluctuates one hour ahead of its own microgrid MG1, and the other microgrid MG1 has the power demand in its own microgrid MG1. It can be seen that the power demand amount is “1 / 2.3”. In other words, when the other microgrid shown in FIG. 2 is selected as the preceding microgrid, the power demand amount “2.3 times” of the current power demand amount in the other microgrid is one hour ahead. It is predicted as the amount of power demand in its own microgrid MG1.

  Here, prior to predicting the power supply and demand for a predetermined time ahead of the own microgrid MG1, whether or not the selected preceding microgrid is appropriate is determined again based on the past power supply and demand data from the present time. It is preferable to verify. That is, it is determined based on the past long-term power supply and demand data that precedes a predetermined time and has a correlation, but as shown in the second predetermined period in FIG. After confirming that there is a correlation again, it is preferable to predict power supply and demand based on the preceding microgrid only when it is confirmed that the correlation is still maintained. When the past power supply and demand data immediately after the present time have no correlation, the preceding microgrid may be selected again from other microgrids.

  FIG. 4 shows an example of verifying the correlation in the second predetermined period, which is the short period shown in FIG. 2, as the same microgrid as that selected in FIG. 3 described above as another microgrid. It is. That is, it is confirmed whether or not the correlation is maintained by looking at the state of fluctuation in the power demand from the present time to one hour ago. FIG. 4 (a) shows the fluctuation of power demand at the current time, for example, when the time is from 12:00 to the previous time, and FIG. 4 (b) is an enlarged view of FIG. 4 (a) around the time of 12:00. Is. As can be seen from FIG. 4 (b), the power demand amount fluctuates one hour ahead and the power demand amount is 1 / 2.3. Therefore, in another microgrid shown in FIG. Can be predicted based on the fluctuation of the power demand of the current (based on the fluctuation of the power demand in the short second predetermined period) (FIG. 3). Forecast based on short-term data with more current data closer to the current time than long-term data with more time-sequential data).

  FIG. 5 is a flowchart illustrating an example of control for predicting power supply and demand at a predetermined time ahead in the self-microgrid MG1 described above. This flowchart will be described below. In the following description, S indicates a step. First, in S1, data indicating fluctuations in power supply and demand between the own microgrid MG1 and the other microgrid MG2 is stored and updated (the data for the first predetermined period that is a long period of time is a short period of time). Data for the second predetermined period is also included). Then, in S2, one other microgrid is selected. Thereafter, in S3, whether or not the data related to the power supply / demand fluctuation status selected in S2 has a correlation with the data related to the power supply / demand fluctuation status in its own microgrid MG1 (whether they are similar). Determined. If the determination in S3 is NO, in S4, the other microgrid previously selected in S2 is excluded from the selection target.

  If the determination in S3 is YES, the other microgrid selected in S2 immediately before is determined as the preceding microgrid. Thereafter, in S6, whether or not the fluctuation state of power supply and demand in the preceding microgrid has a correlation with the fluctuation situation of power supply and demand in its own microgrid MG1 in the second predetermined period (whether or not they are similar). ) Is determined. The determination in S6 is a determination as to whether or not there is a correlation in the short period from the present time, and is a re-verification as to whether or not the fluctuation state of the power supply and demand of the preceding microgrid can be used. If the determination in S6 is NO, the process proceeds to S4 (reselection of the preceding microgrid).

  If the determination in S6 is YES, in S7, a predetermined time ahead of the own microgrid MG1 based on the data indicating the fluctuation status of the power supply and demand of the preceding microgrid (data for the second predetermined period that is a short period) Power supply and demand is predicted (prediction of power supply and demand at time t2 and time t3 in FIG. 2).

  After S7, in S8, the prediction result in S7 is verified (verification after time t2 or time t3 in FIG. 2). Thereafter, in S9, it is determined whether or not the prediction result is OK. If the determination in S9 is NO, the process proceeds to S4. If the determination in S9 is YES, the preceding microgrid selected this time is appropriate, and therefore processing is performed indicating that the preceding microgrid is OK (for example, when the next microgrid is selected next time). It is used for processing such as priority selection).

Next, various examples in the case of predicting the power supply / demand of a predetermined time ahead of its own microgrid MG1 based on the fluctuation state of the power supply / demand in the preceding microgrid serving as another microgrid will be described.
(1) As described with reference to FIGS. 2, 5, and the like, prediction can be made based only on data indicating a fluctuation state of power supply and demand in one other microgrid.
(2) As the preceding microgrid, two or more other microgrids can be selected, and prediction can be made based on the data on the fluctuation status of each power supply and demand in the plurality of preceding microgrids. In this case, an arithmetic average value of fluctuations in power supply and demand of a plurality of preceding microgrids may be used as a predicted value, but data having a higher correlation with fluctuations in power supply and demand of its own microgrid MG1 is more reflected. Thus, it is preferable to perform weighting based on the degree of correlation.

  (3) A basic prediction value is determined on the basis of the data on the power supply / demand fluctuation state of the own microgrid MG1, and the basic prediction value is corrected based on the data on the power supply / demand fluctuation state of the preceding microgrid. A final predicted value may be determined. In this case, the number of preceding microgrids to be used may be two or more than three, and the correction weight may be changed according to the degree of correlation.

  Specifically, in FIG. 6 showing the case where there are two preceding microgrids, when the power supply and demand at time t10, which is a predetermined time ahead from the present time, is predicted, the power demand amount based on the data of its own microgrid MG1 (of (Predicted value) is XB, the power demand amount of the first preceding microgrid is X1, the power demand amount of the second preceding microgrid is X2, and the difference between XB and X1 is β1 (more than XB) Therefore, the difference between XB and X2 is β2 (minus value because it is smaller than XB). At this time, for example, based on the following equation (1), the power supply and demand at the time t10 of its own microgrid MG1 can be set as the predicted value Dt. However, KB, K1, and K2 are weighting coefficients, 0 <KB <1, 0 <K1, K2 <1, and K1 + K2 = 1. The basic weighting coefficient KB indicates the overall reflection degree of the preceding microgrid, and K1 and K2 are weighting coefficients corresponding to the degree of correlation of each preceding microgrid with respect to its own microgrid. The power supply / demand value of the preceding microgrid is a corrected value corresponding to the own microgrid. For example, the power demand of the first preceding microgrid is three times the power demand of the own microgrid MG1. Then, a value obtained by multiplying the power demand of the first microgrid by 1/3 is shown in FIG.

  Dt = XB + KB (K1, β1 + K2, β2) (1)

  The data indicating the fluctuation status of the power supply / demand indicates, for example, the power supply / demand for each day of the week for each month from January to December, and for each weather difference (clear, cloudy, rainy) It is preferable to store and update the data. By repeating many predictions of power supply and demand using the preceding microgrid, which weather on a certain day of a month has a certain time, which microgrid should be selected as the leading microgrid It will be judged appropriately (for example, the selection of the preceding microgrid may be performed every hour).

  In order to appropriately select the preceding microgrid, it is preferable to select the preceding microgrid in consideration of the following viewpoints. In other words, it is preferable to take into account the similarity of the type of power supply / demand behavior that affects the power supply / demand in its own microgrid MG1. In addition, the distance from your own microgrid (distance that causes a corresponding time difference of a certain time or more), number of residents, age composition, form of residence, geographical situation, type of power generation equipment (especially solar power generation, wind power generation) , Difference in power generation using fuel, etc.), presence / absence and number of vehicles equipped with in-vehicle batteries for driving such as electric vehicles, direction from own microgrid, capacity of power storage device (at least one of these) It is preferable to do so. In addition, when forecasting power supply and demand in the morning, it is preferable to select another microgrid with a fast sunrise as the preceding microgrid (the time zone when power switches for power consumption devices such as TVs and air conditioners are turned on is their own) Faster than microgrid). In the prediction of power supply and demand from evening to night, it is preferable to select another microgrid that is earlier than the own microgrid MG1 or close to the urban area as the preceding microgrid (power for the nighttime). Fluctuations in supply and demand, and fluctuations in power supply and demand due to commuting. When forecasting power supply and demand ahead of a predetermined time using at least data on fluctuations in power supply and demand in your own microgrid, take into account the weather information obtained from the weather observation equipment installed in the preceding microgrid. (Especially, when natural energy such as sunlight or wind power is used as a power generation device, or when data on fluctuations in power supply and demand is stored and updated for each weather difference).

  Although the embodiment has been described above, the present invention is not limited to the embodiment, and can be appropriately changed within the scope described in the scope of claims. For example, the invention includes the following cases. . An individual power consumer D is connected to the common power network 10 and has an individual power network having individual generators and individual power storage devices, and is a microgrid of a type that operates as independent as possible within the individual power consumer D. There may be. In this case, the power supply / demand amount for a predetermined time as the entire microgrid MG1 can be obtained by summing the power supply / demand amounts for the predetermined time in individual power consumers D. The other microgrids MG2 to MG5 can also function as their own microgrids (many microgrids become their own microgrids in some cases, others become other microgrids, Many of the microgrids that make up the grid group can predict their power supply and demand individually). As the data on the fluctuation of power supply and demand, both the self and other microgrids may use either one of the long period and the short period from the present to the shortest period. In selecting the preceding microgrid, long-term data can be used, and in the prediction of power supply and demand for a predetermined time ahead of the own microgrid MG1, data in the shortest period from the present time can be used.

  The present invention is preferable for accurately predicting power supply and demand for a predetermined time ahead in the microgrid and increasing the chance of autonomous operation.

MG1: Microgrid (self microgrid)
MG2 to MG5: Microgrid (other microgrids)
EV: Vehicle equipped with battery for running U: Management device G: Power generation equipment B: Power storage equipment C: Charging equipment D: Individual power consumer 1: Commercial power source 10: Common power network

Claims (9)

A method for predicting power supply and demand in a microgrid equipped with a power storage device, a power generation device, and a power consumption load,
A first step of storing and updating data of past and present power supply and demand fluctuations in the own microgrid;
A second step of obtaining data on past and present power supply / demand fluctuations in a plurality of other microgrids;
Based on the data obtained in the first step and the second step, among the plurality of other microgrids, the power supply / demand fluctuation state correlates with the power supply / demand fluctuation state in the own microgrid. And a third step of selecting, as a preceding microgrid, another microgrid in which the power supply / demand fluctuation state is generated for a predetermined time before the power supply / demand fluctuation state in its own microgrid;
A fourth step of predicting a power supply and demand ahead of a predetermined time in its own microgrid based on at least the fluctuation state of the power supply and demand of the preceding microgrid selected in the third step;
A power supply and demand prediction method in a microgrid characterized by comprising: In claim 1,
A fifth step of confirming that the power supply / demand situation for a predetermined past period from the present in the own microgrid has a correlation with the fluctuation situation of the power supply / demand in the preceding microgrid in the corresponding period;
In the fourth step, on the condition that the correlation is confirmed in the fifth step, a predetermined time in the own microgrid based on the power supply / demand fluctuation state in the preceding microgrid Predict power supply and demand ahead,
In the fifth step, when it is not confirmed that there is a correlation, the preceding microgrid is newly selected in the third step.
A power supply and demand prediction method in a microgrid characterized by the above. In claim 1 or claim 2,
In the fourth step, based on the fluctuation state of the power supply / demand of the own microgrid obtained in the first step, the power supply / demand of a predetermined time ahead is predicted as the basic prediction, and the basic prediction is performed in the second step. A power supply / demand prediction method for a microgrid characterized in that a final prediction of power supply / demand for a predetermined time ahead is made by correcting according to a fluctuation state of power supply / demand of the preceding microgrid obtained in (1). In any one of Claims 1 thru | or 3,
In the third step, a plurality of the preceding microgrids are selected,
In the prediction in the fourth step, the data of the plurality of preceding microgrids is used.
A power supply and demand prediction method in a microgrid characterized by the above. In any one of Claims 1 thru | or 4,
A sixth step of verifying whether or not the prediction of power supply and demand ahead of a predetermined time in the microgrid of the self matches the actual power supply and demand;
When it is determined in the verification in the sixth step that they do not match, the preceding microgrid determined not to match is selected from the selection target of the preceding microgrid in the subsequent third step. Excluded,
A power supply and demand prediction method in a microgrid characterized by the above. In any one of Claims 1 thru | or 5,
In selecting the preceding microgrid in the third step, the power supply / demand in the microgrid is performed in consideration of the similarity of the type of power supply / demand behavior that affects the power supply / demand in the microgrid. Prediction method. In any one of Claims 1 thru | or 6,
Upon selection of the preceding microgrid in the third step, the distance from the own microgrid, the number of residents, the age composition, the residence form, the geographical situation, the type of power generation equipment, the number of vehicles equipped with a vehicle battery for traveling, A method for predicting power supply and demand in a microgrid, characterized in that the method is performed in consideration of at least one of a direction from its own microgrid and a capacity of a power storage device. In any one of Claims 1 thru | or 7,
The microgrid characterized in that, in the prediction of power supply and demand in the fourth step, it is performed in consideration of weather information obtained from a weather observation apparatus installed in the preceding microgrid selected in the third step Power supply and demand forecast method in Japan. A power supply and demand prediction system in a microgrid equipped with a power storage device, a power generation device, and a power consumption load,
Storage means for storing and updating data on past and present power supply / demand fluctuations in the own microgrid;
Data acquisition means for acquiring past and current power supply / demand fluctuation data in a plurality of other microgrids via a communication network;
Based on the data stored in the storage means and the data acquired by the data acquisition means, among the other microgrids, the power supply / demand fluctuation status correlates with the power supply / demand fluctuation status in its own microgrid A preceding microgrid selection means for selecting, as a preceding microgrid, other microgrids that have a relationship and the power supply / demand fluctuation state is generated for a predetermined time before the power supply / demand fluctuation situation in its own microgrid When,
A power supply and demand prediction means for predicting a power supply and demand ahead of a predetermined time in its own microgrid based on the fluctuation state of the power supply and demand of the selected preceding microgrid;
A power supply and demand prediction system in a microgrid characterized by comprising:

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