JP2015122851A - Control method, control server and control program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To generate a control plan in a small processing time.SOLUTION: Based on each storage battery residual amount included in a plurality of notebook PCs, a classification section 133a in a control server 100 adds a notebook PC having a mutually resembled residual amount of a storage battery to the same group, to classify notebook PCs in each first group into a plurality of first groups. Also, a classification section 133b classifies the plurality of notebook PCs into each second group in a manner to have a uniform residual amount distribution of the storage battery in each notebook PC included in each second group. Then, a generation section 133 calculates, based on power demand prediction data, power to be distributed to the second group for a certain segment, to generate a control plan in which a state related to the charge and the discharge of each notebook PC belonging to the second group is specified using the calculated power as a restriction condition.

Description

  The present invention relates to a control method and the like.

  In recent years, for example, the mechanism of energy supply is changing, such as the introduction of renewable energy and power trading. In the office, there is a conventional technology that uses each battery of a plurality of notebook PCs (Personal Computers) in the office in order to use a storage battery in the office and suppress peak power.

  This prior art predicts a power demand curve and notebook battery remaining amount data from information such as past power consumption transitions and weather forecasts, and regulates charge / discharge of the notebook PC battery based on the demand curve. Create a control plan. Then, the conventional technology performs control to switch the driving mode of the notebook PC to any one of battery driving, AC (Alternate Current) driving, and battery charging while AC driving based on the control plan.

JP2013-132195A

  However, the above-described conventional technology has a problem that a control plan cannot be created in a short processing time.

  For example, when the system scale is large, it takes a lot of processing to plan and simulate the charging and discharging of every laptop PC battery in the company and to create an optimal control plan by simulation. Resulting in.

  An object of one aspect is to provide a control method, a control server, and a control program capable of creating a control plan with a small processing time.

  In the first plan, the computer executes the following processing. The computer classifies the plurality of devices into a plurality of first groups by adding devices having similar storage battery amounts to the same group based on the remaining amount of storage batteries included in the plurality of devices. The computer classifies the plurality of devices into the second group in units of the first group so that the remaining amount distribution of the storage batteries of the plurality of devices included in each second group is uniform. The computer calculates the power to be distributed to the second group in a certain section based on the power demand prediction data, and uses the calculated power as a constraint condition to indicate the state related to charging / discharging of the devices belonging to the second group for each time. Create a prescribed control plan.

  According to one embodiment of the present invention, there is an effect that a control plan can be created in a short processing time.

FIG. 1 is a diagram illustrating a configuration of a system according to the present embodiment. FIG. 2 is a diagram for explaining a state relating to charging / discharging of the storage battery of the notebook PC. FIG. 3 is a diagram illustrating the configuration of the control server according to the present embodiment. FIG. 4 is a diagram illustrating an example of demand forecast data. FIG. 5 is a diagram illustrating an example of the PC information table. FIG. 6 is a diagram illustrating an example of charging data. FIG. 7 is a diagram illustrating an example of discharge data. FIG. 8A is a diagram illustrating an example of a first control plan table. FIG. 8B is a diagram illustrating an example of the second control plan table. FIG. 9 is a diagram for explaining the processing of the classification unit. FIG. 10 is a diagram illustrating an example of the remaining amount distribution. FIG. 11 is a diagram illustrating an example of a hierarchical node PC. FIG. 12 is a diagram illustrating an example of the classification result according to the present embodiment. FIG. 13 is a diagram for explaining the processing of the power calculation unit according to the present embodiment. FIG. 14 is a diagram (1) for explaining the process of the generation unit according to the present embodiment. FIG. 15 is a diagram (2) for explaining the process of the generation unit according to the present embodiment. FIG. 16 is a diagram (3) for explaining the process of the generation unit according to the embodiment. FIG. 17 is a diagram for explaining the process of the simulation unit according to the present embodiment. FIG. 18 is a flowchart illustrating the processing procedure of the control server according to the present embodiment. FIG. 19 is a flowchart illustrating the processing procedure of the control plan generation process. FIG. 20 is a diagram illustrating an example of control in three layers by multi-time scheduling. FIG. 21 is a diagram illustrating an example of a computer that executes a control program.

  Hereinafter, embodiments of a control method, a control server, and a control program disclosed in the present application will be described in detail with reference to the drawings. Note that the present invention is not limited to the embodiments.

  The configuration of the system according to the present embodiment will be described. FIG. 1 is a diagram illustrating a configuration of a system according to the present embodiment. As shown in FIG. 1, the system includes a distribution board 20, notebook PCs (Personal Computers) 30 a, 30 b, 30 c, an illumination 50 a, a multi-function device 50 b, and a control server 100. Distribution board 20, notebook PCs 30 a, 30 b, 30 c and control server 100 are connected to each other via network 10. The distribution board 20, the notebook PCs 30 a, 30 b, 30 c, the illumination 50 a, and the multifunction device 50 b are connected to the power line 40.

  The network 10 corresponds to, for example, an in-house LAN (Local Area Network). As the in-house LAN, an arbitrary type of communication network such as a wired LAN or a wireless LAN may be adopted and connected to another network such as the Internet or a LAN.

  In the example illustrated in FIG. 1, the case where the notebook PCs 30 a, 30 b, and 30 c are connected to the control server 100 is illustrated, but the configuration is not limited to the illustrated configuration. For example, an arbitrary number of notebook PCs may be connected to the control server 100.

  In the example illustrated in FIG. 1, a case where the notebook PCs 30 a, 30 b, 30 c, the illumination 50 a, and the multifunction device 50 b are connected to the power line 40 is illustrated, but the configuration is not limited to the illustrated configuration. That is, any electrical product may be connected to the power line 40. For example, electrical products such as a television, a refrigerator, and a microwave oven are connected to the power line 40. Hereinafter, when the illumination 50a, the multi-function device 50b, and other electric products are collectively referred to without distinction, they are referred to as an electric product 50. The electrical product 50 includes, for example, any product that consumes electric power in the company.

  The control server 100 is a server device installed in the company, and creates a control plan that defines charging / discharging of batteries of a plurality of notebook PCs.

  The distribution board 20 supplies power to the notebook PCs 30a, 30b, 30c, the illumination 50a, and the multi-function device 50b through the power line 40.

  The notebook PCs 30a, 30b, and 30c are notebook personal computers used by users in the company. In the following description, the notebook PCs 30a, 30b, and 30c are appropriately described as “notebook PC30” or simply “PC”.

  The notebook PC 30 is installed with a client application that controls charging / discharging of a storage battery mounted on the notebook PC 30. For example, the notebook PC 30 receives a control plan that defines a state related to charging / discharging of the storage battery of the own device from the control server 100, and switches the state related to charging / discharging of the storage battery of the own device according to the received control policy. The notebook PC 30 is an example of a device. Further, the storage battery of the notebook PC is also referred to as “battery” as appropriate.

  Here, the state regarding charge / discharge of the storage battery of the notebook PC 30 will be described. FIG. 2 is a diagram for explaining a state relating to charging / discharging of the storage battery of the notebook PC. In FIG. 2, the horizontal axis indicates time, and the vertical axis indicates power value [W]. For example, in the time zones 2a and 2d, the storage battery is not charged / discharged, and the notebook PC 30 is operating with an AC (Alternating Current) power source. This state is also referred to as “AC”. Further, for example, in the time zone 2b, a state is shown in which the notebook PC 30 is operating by discharging the storage battery. This state is also referred to as “BA”. Further, for example, in the time zone 2c, a state in which the notebook PC 30 is operating with an AC power supply while the storage battery is being charged is shown. This state is also referred to as “CH”. As shown in FIG. 2, the notebook PC 30 operates in a state of “AC”, “BA”, or “CH”. For example, when the notebook PC 30 receives from the control server 100 a control plan indicating that it operates at “BA” during the time zone “9:00 to 9:30”, it operates at “BA” during the designated time zone.

  Next, the configuration of the control server 100 shown in FIG. 1 will be described. FIG. 3 is a diagram illustrating the configuration of the control server according to the present embodiment. As illustrated in FIG. 3, the control server 100 includes a communication control unit 110, a storage unit 120, and a control unit 130.

  The communication control unit 110 is a processing unit that transmits and receives data between the distribution board 20 and the notebook PC 30. The communication control unit 110 corresponds to, for example, a network interface card (NIC). The control unit 130 described later exchanges data with the distribution board 20 and the notebook PC 30 via the communication control unit 110.

  The storage unit 120 includes demand prediction data 121, a PC information table 122, charge data 123, discharge data 124, a first control plan table 125, and a second control plan table 126. The storage unit 120 corresponds to a storage device such as a semiconductor memory element such as a random access memory (RAM), a read only memory (ROM), and a flash memory.

  The demand forecast data 121 is time series data of demand power predicted in the system. For example, the demand prediction data 121 is data in which each time zone in a day is associated with a demand power value. This power demand value is calculated from statistical data of past power consumption values, for example.

  FIG. 4 is a diagram illustrating an example of demand forecast data. The horizontal axis in FIG. 4 indicates time, and the vertical axis indicates power value [kW]. FIG. 4 illustrates daily demand forecast data 121 in the company. For example, the demand prediction data 121 is calculated from statistical data of past power consumption values consumed by any product that consumes power in the company. Although FIG. 4 shows the case where the demand forecast data 121 is one pattern, the present invention is not limited to this. For example, the demand forecast data 121 may have a plurality of patterns when there is a difference in day of the week and time, and a plurality of transition methods are predicted.

  The PC information table 122 holds, for example, various types of information regarding the notebook PC 30. FIG. 5 is a diagram illustrating an example of the PC information table. As shown in FIG. 5, the PC information table 122 associates “ID”, “observability”, “control availability”, “state”, “battery capacity”, and “charge rate”. Remember.

  In FIG. 5, ID indicates an ID (Identification) that uniquely identifies the in-house notebook PC 30. The observation availability indicates whether or not the control server 100 can observe the corresponding notebook PC 30. For example, the observation availability “o” indicates that the control server 100 can observe the corresponding notebook PC 30, that is, the corresponding notebook PC 30 is connected to the in-house LAN 10. Further, for example, the observation availability “x” indicates that the control server 100 has not observed the corresponding notebook PC 30, that is, the corresponding notebook PC 30 is not connected to the in-house LAN 10.

  In FIG. 5, controllability indicates whether or not the corresponding notebook PC 30 is connected to the power line 40. For example, control availability “o” indicates that the corresponding notebook PC 30 is connected to the power line 40. Further, for example, the control availability “x” indicates that the corresponding notebook PC 30 is not connected to the power line 40.

  The state indicates the current state of the corresponding notebook PC 30. For example, the state “AC” indicates a state in which the storage battery is not charged / discharged and the notebook PC is operating with an AC power source. Further, for example, the state “BA” indicates a state in which the notebook PC is operating due to the storage battery being discharged. Further, for example, the state “CH” indicates a state in which the storage battery is charged and the notebook PC is operating with an AC power source. The battery capacity indicates a power capacity [Wh] determined as a battery specification of the corresponding notebook PC 30. The charging rate indicates the current charging rate [%] of the corresponding notebook PC 30. In the PC information table 122, for example, a notebook PC 30 used in the company is registered in advance. In addition, “-” in the figure indicates that there is no corresponding data.

  As shown in FIG. 5, for example, the PC information table 122 includes an ID “PC1”, an observation availability “o”, a control availability “o”, a state “AC”, a battery capacity “65”, and a charging rate. “80” is stored in association with each other. That is, it shows that the PC 1 is connected to the network 10 and the power line 40 and is operating with an AC power source, the battery capacity is 65 [Wh], and the current charging rate is 80%. Similarly, the PC information table 122 stores information for other notebook PCs 30.

  Returning to the description of FIG. The charging data 123 is data indicating a change in charging rate when charging a battery, for example. For example, the charging data 123 is data in which a charging rate and time when charging the battery of the notebook PC 30 are associated with each other. The charging data 123 is determined as battery specifications. The charging data 123 may be calculated from the stored data by storing the charging rate for each time when the battery of the notebook PC 30 is charged.

  FIG. 6 is a diagram illustrating an example of charging data. The horizontal axis of FIG. 6 shows time [second], and a vertical axis | shaft shows charging rate [%]. FIG. 6 illustrates the charging rate when charging the battery of the notebook PC 30. Here, for convenience of explanation, the charging data 123 of the battery mounted on a certain notebook PC 30 is shown, but the charging data 123 is stored for each notebook PC 30. For example, the charging data 123 is stored in association with each ID of the notebook PC 30.

  Returning to the description of FIG. For example, the discharge data 124 is data indicating a change in the charging rate when the battery is discharged. For example, the discharge data 124 is data in which the charging rate and time when discharging the battery of the notebook PC 30 are associated with each other. The discharge data 124 is determined as battery specifications. The discharge data 124 may be calculated from the stored data by storing the charging rate for each time when the battery of the notebook PC 30 is discharged.

  FIG. 7 is a diagram illustrating an example of discharge data. The horizontal axis of FIG. 7 shows time [second], and a vertical axis | shaft shows charging rate [%]. FIG. 7 illustrates the charging rate when discharging the battery of the notebook PC 30. Here, for convenience of explanation, the discharge data 124 of the battery mounted on a certain notebook PC 30 is shown, but the discharge data 124 is stored for each notebook PC 30. For example, the discharge data 124 is stored in association with each ID of the notebook PC 30.

  When the plurality of notebook PCs 30 included in the same first group are regarded as a single notebook PC, the first control plan table 125 includes information on a control plan that defines charging / discharging of each storage battery for each time zone. Hold. Detailed description regarding the first group will be described later. FIG. 8A is a diagram illustrating an example of a first control plan table. As shown in FIG. 8, the first control plan table 125 stores a group ID and a time zone every 30 minutes in association with each other. For example, the time zone “9:00” corresponds to the time zone from 9 o'clock to 9:30. The group ID is information that uniquely identifies a group.

  The second control plan table 126 holds information on a control plan that defines charge / discharge of each storage battery for each notebook PC for each time zone. FIG. 8B is a diagram illustrating an example of the second control plan table. As shown in FIG. 8B, the second control plan table 126 stores the ID and the time zone every 30 minutes in association with each other. For example, the time zone “9:00” corresponds to the time zone from 9 o'clock to 9:30. One record of the second control plan table 126 corresponds to the control policy of the corresponding notebook PC 30.

  The control unit 130 includes an acquisition unit 131, a measurement unit 132, a creation unit 133, a control plan specification unit 134, and an output unit 135. The control unit 130 corresponds to an integrated device such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 130 corresponds to an electronic circuit such as a CPU or MPU (Micro Processing Unit).

  The acquisition unit 131 is a processing unit that acquires various types of information of the notebook PC 30 and registers the acquired information in the PC information table 122. In addition, the timing which the acquisition part 131 acquires information may set arbitrary timings by the person using the control server 100. For example, the acquisition unit 131 may acquire information immediately before the creation unit 133 described later creates a control plan.

  The process of the acquisition part 131 is demonstrated using FIG. For example, the acquisition unit 131 acquires information indicating that the state of the PC 2 is “CH”, the charging rate is “50%”, and is connected to the power line 40 from the PC 2. The acquisition unit 131 records the acquired information in the PC information table 122 of FIG. For example, the acquisition unit 131 records control availability “o”, state “CH”, and charge rate “50” in the PC information table 122 in association with the PC 2. Further, since the acquisition unit 131 has acquired information from the PC 2, the acquisition unit 131 determines that the PC 2 is connected to the in-house LAN 10 and records the observation availability “◯” in the PC information table 122. Further, for example, the acquisition unit 131 determines that a PC from which information cannot be acquired is not connected to the in-house LAN 10, and records the observation availability “x” in the PC information table 122. For example, the acquisition unit 131 determines that the PC 3 is not connected to the in-house LAN 10 when the information of the PC 3 cannot be acquired at the timing of acquiring information about other PCs, and sets the observation availability “x” to the PC information table. 122.

  The measuring unit 132 measures the power consumed in the system of FIG. The measurement unit 132 outputs the measured power consumption information to the creation unit 133.

  For example, the measurement unit 132 measures the total amount of power consumed in the company by the electrical product connected to the power line 40. The measurement unit 132 records information on the measured power amount in the storage unit 120. Illustration of power information stored in the storage unit 120 is omitted. Any conventional technique can be applied to the method in which the measuring unit 132 measures the power consumed in the company. For example, the measuring unit 132 may measure the amount of power supplied by the distribution board 20 via the power line 40 and acquire the measured power amount from the distribution board 20. For example, the measurement unit 132 may measure the amount of power supplied from all the outlets in the company and calculate the sum.

  The creation unit 133 is a processing unit that classifies each notebook PC 30 into a plurality of groups based on the remaining capacity of the storage battery, and then executes a local search method for each group to create a control plan. The creation unit 133 includes a classification unit 133a, a power calculation unit 133b, a generation unit 133c, a simulation unit 133d, an update unit 133e, and an execution unit 133f.

  The classification unit 133a is a processing unit that classifies the notebook PCs 30 into a plurality of groups based on the remaining amount of the storage battery. First, the classification unit 133a classifies the plurality of notebook PCs 30 into a plurality of first groups by adding devices having similar storage battery levels to the same group based on the remaining battery levels of the plurality of notebook PCs 30. To do. Then, the classification unit 133a classifies the plurality of notebook PCs 30 into a plurality of second groups in units of the first group so that the remaining amount distribution of the storage batteries of the plurality of notebook PCs included in each second group is uniform.

  First, an example of processing in which the classification unit 133a classifies the notebook PCs 30 into a plurality of first groups will be described. FIG. 9 is a diagram (1) for explaining the processing of the classification unit. In FIG. 9, only the storage battery built in each notebook PC 30 is shown for convenience of explanation, and the illustration of the notebook PC 30 is omitted. For example, let the storage batteries 1a-1x be the storage batteries incorporated in the notebook PCs 30a-30x, respectively. It is assumed that the storage battery has more remaining capacity as the storage battery has more shaded portions. The classification unit 133a rearranges the storage batteries 1a to 1x in order from the one with the smallest remaining amount. For storage batteries having the same remaining amount, either may be arranged first.

  For example, when the classification unit 133a rearranges the storage batteries 1a to 1x, the storage batteries are in the order of decreasing remaining capacity, 1h, 1l, 1e, 1q, 1s, 1m, 1u, 1p, 1w, 1j, 1o, 1x, 1g. , 1d, 1i, 1b, 1t, 1n, 1v, 1r, 1a, 1c, 1k, 1f. The classification unit 133a classifies the first to fourth storage batteries 1h, 1q, 1l, and 1e into the first group 3A. That is, the first group 3A includes notebook PCs 30h, 30q, 30l, and 30e.

  The classification unit 133a classifies the fifth to eighth storage batteries 1s, 1m, 1u, and 1p into the first group 3B. That is, the first group 3B includes notebook PCs 30s, 30m, 30u, and 30p.

  The classification unit 133a classifies the ninth to twelfth storage batteries 1w, 1j, 1o, and 1x into the first group 3C. That is, the first group 3C includes notebook PCs 30w, 30j, 30o, and 30x.

  The classification unit 133a classifies the thirteenth to sixteenth storage batteries 1g, 1d, 1i, and 1b into the first group 3D. That is, the first group 3D includes notebook PCs 30g, 30d, 30i, and 30b.

  The classification unit 133a classifies the 17th to 20th storage batteries 1t, 1n, 1v, and 1r into the first group 3E. That is, the first group 3E includes the notebook PCs 30t, 30n, 30v, and 30r.

  The classification unit 133a classifies the 21st to 24th storage batteries 1a, 1c, 1k, and 1f into the first group 3F. That is, the first group 3F includes the notebook PCs 30a, 30c, 30k, and 30f.

  As described above, the classification unit 133a can group the notebook PCs 30a to 30x into the first groups 3A to 3F, thereby grouping the notebook PCs having similar storage battery remaining amounts.

  Subsequently, after classifying each notebook PC 30 into a plurality of first groups, the classification unit 133a first group unit so that the remaining amount distribution of the storage batteries of the plurality of notebook PCs included in each second group is uniform. Thus, the plurality of notebook PCs 30 are classified into a plurality of second groups. Here, the remaining amount distribution is a distribution indicating the relationship between the number of notebook PCs 30 and the remaining amount of the storage battery. FIG. 10 is a diagram illustrating an example of the remaining amount distribution. In FIG. 10, the vertical axis indicates the number of notebook PCs 30, and the horizontal axis indicates the remaining amount (%) of the storage battery.

  The classification unit 133a generates M second groups by grouping n first groups from the first group. For example, the classification unit 133a generates two second groups by combining three first groups from the first group. The classification unit 133a obtains the remaining amount distribution based on the number of notebook PCs included in the second group and the remaining amount of the storage battery, and calculates the similarity of the remaining amount distribution of each second group. The classification unit 133a repeatedly executes the above processing to identify the first group combination having the maximum similarity, and classifies each notebook PC 30 into the second group based on the identified combination.

  For example, the similarity between the remaining amount distribution of the second group that combines the first groups 3A, 3C, and 3F and the remaining amount distribution of the second group that combines the first groups 3B, 3D, and 3E is higher than other combinations. Suppose it is large. In this case, the classification unit 133a collectively sets the first groups 3A, 3C, and 3F as the second group 4A, and collectively sets the first groups 3B, 3D, and 3E as the second group 4B. The classification unit 133a may calculate the similarity using any conventional technique.

  For example, as described above, the classification unit 133a classifies the notebook PCs 30a to 30x into the first groups 3A to 3F, and classifies the first groups 3A to 3F into the second groups 4A and 4B. As the classification unit 133a performs such classification, the notebook PCs 30 are hierarchized as shown in FIG.

  FIG. 11 is a diagram illustrating an example of a hierarchical node PC. As shown in FIG. 11, a layer before grouping is set as a lower layer 5A. Let the layer classified into the 1st group be the middle layer 5B. The layer classified into the second group is defined as an upper layer 5C. The classification unit 133a outputs the information on the classification result to the power calculation unit 133b, the generation unit 133c, the simulation unit 133d, the update unit 133e, and the control plan specification unit 134.

  FIG. 12 is a diagram illustrating an example of the classification result according to the present embodiment. As shown in FIG. 12, this classification result includes the second group identification information for uniquely identifying the second group, the first group identification information for uniquely identifying the first group, and the ID for uniquely identifying each notebook PC. Is associated. For example, IDs corresponding to the notebook PCs 30a to 30x are 30a to 30x, respectively. In the example shown in FIG. 12, the first group 3A includes notebook PCs 30a, 30h, 30l, and 30q. The first group 3A is included in the second group 4A.

  The power calculation unit 133b is a processing unit that calculates the power allocated to each group classified by the classification unit 133a. For example, the power calculation unit 133b calculates the value of power allocated to each second group based on the formula (1). The peak power predicted value shown in Expression (1) is the peak power predicted value of the entire system excluding the power consumption of the notebook PC 30. The current power consumption is the current power consumption of the entire system excluding the power consumption of the notebook PC 30. The power calculation unit 133b outputs information on the power allocated to each second group to the simulation unit 133d.

  Power allocated to each second group = (peak power predicted value−current power consumption) / second group number (1)

Next, the process of the power calculation unit 133b will be described in more detail. More specifically, the power calculation unit 133b calculates the power that can be used by the notebook PC 30 in each time period by solving the optimization problem shown in Expression (2). However, the conditions of formulas (3), (4), and (5) shall be satisfied. In Expressions (2) to (5), “k” is a variable indicating each time zone. FIG. 13 is a diagram for explaining the processing of the power calculation unit according to the present embodiment. In FIG. 13, the horizontal axis indicates time, and the vertical axis indicates power value. The line segment 6a shows the predicted value of the demand power value in each time zone excluding the power of the notebook PC 30. The line segment 6b is the maximum value of the predicted value of the demand power value excluding the power of the notebook PC 30, and corresponds to _Dmax . u [k] is a total value of the power allocated to all the notebook PCs 30 in the time zone k. D [k] is a predicted value of the demand power value excluding the power of all the notebook PCs 30 in the time zone k. Note that a time zone in which the predicted value of the demand power value is the maximum value is k ′.

  minΣu [k] (2)

u [k] ≦ D _max −D [k] (3)

  D [k−1] + u [k−1] + g [k−1] ≦ D [k] + u [k] + g [k] (4)

Here, equation (2) is an optimization problem of minimizing the area of the shaded portion in FIG. Expression (3) is a conditional expression for preventing the power allocated to the notebook PC 30 in the time zone immediately before a certain time zone k from exceeding the maximum value of the demand power. Equation (4) is a condition that the sum of u [k], D [k], and g [k] gradually increases. Here, g [k] is g [k] = 0 (D _max −D [k] ≦ power when all notebook PCs are CHed), g [k] = D _max −D [k] (D _max -D [k]> power when all notebook PCs are CHed). In addition, Formula (5) shows that the average remaining amount of the storage battery of the notebook PC 30 is not less than the predetermined value τ in the time zone immediately before the time zone k ′. Thereby, before it becomes the maximum value of the predicted value of the demand power value excluding the power of the notebook PC 30, the average remaining amount of the storage battery of the notebook PC 30 can be made equal to or greater than the predetermined value τ.

  The power calculation unit 133b solves the optimization problem of Equation (2), calculates u [k] for each time slot, and divides u [k] by the second group number, thereby The power allocated to the second group is calculated. The power calculation unit 133b outputs information on the power allocated to each second group to the simulation unit 133d.

  The section in which the power calculation unit 133b calculates the power allocated to each second group is a section that is larger than the section of the first control plan table. For example, the section of the first control plan table shown in FIG. 8A is divided into 30 minutes, but the power calculation unit 133b calculates the power allocated to each second group in 90 minutes. The power calculation unit 133b determines the power that can be used in each section based on a rough trend including the maximum value of the predicted value of the demand power value.

  The generation unit 133c is a processing unit that generates a control plan by regarding a plurality of notebook PCs included in the first group as a single notebook PC 30. For example, when the notebook PCs 30 are classified as shown in FIG. 12, the notebook PCs 30e, 30h, 30l, and 30q are regarded as a single notebook PC “first group 3A”.

  It is assumed that the generation unit 133c collects the storage batteries of each first group regarded as a single notebook PC and the storage batteries of each notebook PC included in the first group. For example, let the storage battery of 1st group 3A be the storage battery which put together each storage battery of notebook PC30e, 30h, 30l, and 30q. For example, the remaining amount of the storage batteries of the first group is a value obtained by averaging the remaining amounts of the notebook PCs 30 included in the first group.

  The generation unit 133c generates an initial first control plan by setting a state for each first group of the first control table 125 for each time period. 14 to 16 are diagrams for explaining the processing of the generation unit according to the present embodiment. In FIG. 14, as an example, the description will be made using the first groups 3A and 3B regarded as a single notebook PC. As illustrated in FIG. 14, for example, the generation unit 133 c refers to the PC information table 122 and sets the state “AC” for all time zones of the first group including the controllable notebook PCs. Although explanation is omitted here, when the uncontrollable notebook PC is included in the first group, the state of each time zone corresponding to the group may be set to “UN1” or “UN2”. .

  The generation unit 133c selects an arbitrary time zone of the generated control plan and switches to any state of “AC”, “BA”, and “CH”. This is referred to as a “switching instruction”. FIG. 15 (1) shows an example of the first control plan table 125 before switching, and FIG. 15 (2) shows an example of the first control plan table 125 after switching. As illustrated in FIG. 15, for example, the generation unit 133c selects “9:30” of the first group 3B. The generation unit 133c switches the state of the selected time zone and the subsequent time zones to “BA”. The shaded area in FIG. 15 indicates the time zone of the group for which a switching instruction has been issued. In addition, the generation unit 133c records the first group time zone in which the switching instruction is issued in the first control plan table 125.

  In addition, when the state is switched, the generation unit 133c switches the state until a switching instruction is issued after the next time zone. FIG. 16 (1) shows an example of the first control plan table 125 before switching, and FIG. 16 (2) shows an example of the first control plan table 125 after switching. FIG. 16 illustrates a case where a switched state exists. FIG. 16A illustrates a case where there is an instruction to switch the state “BA” of “12:30” of the first group 3A. As illustrated in FIG. 16, for example, the generation unit 133c selects “11:30” of the first group 3B and issues a “switching instruction” for switching to “CH”. The generation unit 133c switches the state of the selected subsequent time zone to “CH”. In this case, since there is an instruction to switch the state “BA” of “12:30” of the first group 3A, the generation unit 133c switches the state up to “12:00” to “CH”.

  The generation unit 133c regards the plurality of notebook PCs included in the first group as a single notebook PC, executes the above-described processing, and outputs information on the first control plan of each first group to the simulation unit 133d. To do. In addition, the production | generation part 133c produces | generates the information of a 1st control plan for every 2nd group. For example, when the second groups 4A and 4B exist, the first groups 3A, 3C, and 3F included in the second group 4A are regarded as a single notebook PC, and the first control plan is generated. The controller 133c generates the first control plan by regarding each of the first groups 3B, 3D, and 3E included in the second group 4B as a single notebook PC.

  Returning to the description of FIG. The simulating unit 133d is a processing unit that simulates the power demand in each time period using the first control plan for each first group generated by the generating unit 133c. For example, the simulating unit 133d simulates the demand power by subtracting the power usage amount by the first group from the demand prediction data 121 and adding the power usage amount when the group operates according to the first control plan. To do. The simulating unit 133d outputs the simulation result to the updating unit 133e.

FIG. 17 is a diagram for explaining the process of the simulation unit according to the present embodiment. The horizontal axis in FIG. 17 indicates time, and the vertical axis indicates power value [kW]. FIG. 17 shows a simulation result when a control plan is created at intervals of 10 minutes from 8:00 to 20:00. As shown in FIG. 17, for example, the simulation unit 133d simulates demand power every 10 minutes based on the first control plan, and calculates a post-control peak 11a every 10 minutes. For example, the simulating unit 133d calculates the post-control peak max _j for each time period using the following equation (6). 11b corresponds to the demand forecast data 121 in FIG.

max _j (demand forecast [j] −Σ _i E _Ai + Σ _i E _{si [j]} ) (6)

In Expression (6), i represents an index of the first group regarded as a single notebook PC. j represents a time zone index. For example, j = 1 corresponds to the time zone from 9 o'clock to 9:30. The demand forecast [j] indicates a demand forecast value in the j-th time zone, and is a value given from the demand forecast data 121, for example. E _{si [j]} indicates the power value of each state in the j-th time zone of the i-th first group.

For example, power usage E _A state "AC" is, for example, a value obtained by multiplying the number of the notebook PC included in the first group to the 10 [W]. The power values E _B state "BA" is, for example, a value obtained by multiplying the number of the notebook PC included in the first group to 0 [W]. The power value E _C in the state “CH” is a value obtained by multiplying, for example, 60 [W] by the number of notebook PCs included in the first group.

In addition, the power consumption E _U1 state "UN1" is a value obtained by multiplying the number of the notebook PC included in the first group to the E _{C [W]} because it uses power when the state "CH". In addition, the power consumption E _U2 state "UN2" is a value obtained by multiplying the number of the notebook PC included in the first group E _{A [W]} because it uses power when the state "AC". E _Ai indicates the amount of power used in the state “AC” of the i-th first group. In addition, said Formula (6) is an example, and is not limited to this. For example, Σ _i E _Ai does not have to be subtracted when controlling with more margin.

  Further, the simulation unit 133d simulates demand power for each time zone by adding a constraint condition to the first control plan. For example, the simulation unit 133d calculates the charging rate of the storage battery in each time zone for each first group. For example, the simulating unit 133d refers to the PC information table 122 and acquires the charging rate of the first group. When charging the first group of storage batteries for a certain period of time, the simulating unit 133d refers to the charging data 123 of FIG. 6 and estimates the charging rate after the lapse of time. When discharging for a certain period of time from the first group of storage batteries, the simulation unit 133d refers to the discharge data 124 of FIG. 7 and estimates the charge rate after the lapse of time.

And the simulation part 133d determines whether the estimated charging rate satisfy | fills the conditions of Formula (7), and satisfy | fills Formula (8). The constraint condition of Expression (7) is “the charge amount becomes maximum at the final time k ″”. C _{i in} equation (7) represents the capacitance of the i-th first group. N in Expression (8) is the number of second groups classified by the classification unit 133a. Note that the constraint conditions and numerical values described here are merely examples, and the present invention is not limited to these. For example, a person using the control server 100 may arbitrarily set the constraint condition and the numerical value in consideration of the characteristics of the storage battery.

maxΣC _i [k ″] (7)

  Power consumption within group in time zone k ≦ u [k] / N (8)

  If the simulation unit 133d determines that Expressions (7) and (8) are not satisfied, the simulation unit 133d continues the state of the first group as the state of the first group. The simulation unit 133d uses the changed first control plan to simulate again until the constraint condition is satisfied. The simulation unit 133d simulates the first control plan for each second group. For example, when the second group 4A and the second group 4B exist, the first control plan for the second group 4A is simulated, and the first control plan for the second group 4B is simulated.

  The updating unit 133e updates the first control plan to the first control plan after switching when the simulated result is improved from the simulation result of the first control plan before switching. For example, if the simulated result is improved from the simulation result of the first control plan before switching, the updating unit 133e switches the first control plan for the first control plan table 225. The first control plan is updated.

  For example, the update unit 133e obtains the peak power from the simulation result. The updating unit 133e acquires the power usage amount in each time zone up to the current time as an actual measurement value within one day. The updating unit 133e acquires the power usage amount in each time zone after the current time from the simulation result within the day. The update unit 133e calculates the maximum value of the acquired power consumption as peak power. The update unit 133e compares the calculated peak power with the peak power calculated from the simulation result of the first control plan before switching. The update unit 133e updates the first control plan to the first control plan after switching when the peak power is lower than the peak power calculated from the simulation result of the first control plan before switching.

  The execution unit 133f determines whether or not a predetermined end condition is satisfied. For example, the execution unit 133f determines whether five minutes have elapsed since the creation unit 133 started the process. If five minutes have not elapsed, the execution unit 133f repeatedly executes the processes of the power calculation unit 133b, the generation unit 133c, the simulation unit 133d, and the update unit 133e. In addition, although the case where the end condition is 5 minutes has been described here, the present invention is not limited to this. For example, the termination condition may be an arbitrary time or an arbitrary number of repetitions.

  On the other hand, when 5 minutes have elapsed, the execution unit 133f outputs the updated information of the first control plan table 125 to the control plan specifying unit 134.

  The control plan specifying unit 134 generates a second control plan for each notebook PC 30 based on the first control plan table 125, and generates information on the generated second control plan for each notebook PC 30 as the second control plan table 126. Is a processing unit to be registered. The control plan specifying unit 134 outputs the information in the second control plan table 126 to the output unit 136.

  The control plan specifying unit 134 sets the state of each time zone set in the first group as the maximum value that can be consumed by each notebook PC in the first group. For example, the number of notebook PCs included in each first group is four. In this case, the amount of power used in the state “AC” is, for example, 10 × 4 [W]. Further, the power value of the state “BA” is, for example, 0 × 4 [W]. Further, the power value of the state “CH” is, for example, 60 × 4 [W]. The control plan specifying unit 134 adds the maximum value of power that can be consumed in each time zone to the constraint condition, and solves the optimization problem in the same manner as the creating unit 133, so that the first of the notebook PCs included in the group 2 Generate a control plan.

  For example, when the first control plan table 125 is as shown in FIG. 8A, the constraint conditions applied to each notebook PC 30 included in the first group 3A are as follows. That is, the maximum value of power that can be consumed between “9: 00 to 11:30” is “40 W”, and the maximum value of power that can be consumed between “11:30 to 11:30” is “240 W”. Further, the maximum value of the power that can be consumed between “12:30 to 13:30” is “0 W”.

  The output unit 135 outputs the data of the second control plan table 126 to the corresponding notebook PC 30. The output unit 135 receives data of the second control plan table 126 from the control plan specifying unit 134.

  Each notebook PC 30 that has acquired the second control plan data from the output unit 135 applies the state corresponding to the first section of the second control plan data. For example, when the notebook PC 30a acquires the data of the second control plan shown in FIG. 8A and the current time is “8:30 to 9:00”, the time becomes “9:00”. The notebook PC 30a is driven with the state set to “AC”.

By the way, the classification unit 133a performs optimization calculation, calculates the optimal number of second groups, the number of first groups, and the number of notebook PCs included in the first group, and based on the calculation results, Note PCs may be hierarchized. Hereinafter, an example of the optimization calculation performed by the classification unit 133a will be described. Here, as an example, the total number of notebook PCs is defined as s, the number of notebook PCs included in the first group is defined as s ₁ , the number of first groups is defined as s ₂ , and the number of second groups is defined as s ₃ . For this reason, the condition of Formula (9) is satisfied.

s = s ₁ × s ₂ × s ₃ (9)

The classification unit 133a changes the values of s ₁ , s ₂ , s ₃ , and h in Expression (10), and searches for the values of s ₁ , s ₂ , s ₃ , and h that minimize becomes the minimum value. The classification unit 133a may classify the notebook PCs 30 into the first group and the second group based on s ₁ , s ₂ , and s ₃ searched by the expression (10).

Here, h included in Expression (10) is the number of sections of the control plan. For example, h varies from h _required to h _max . T (s ₂ , h) included in the expression (10) is a function that outputs a predetermined value depending on the values of s ₂ and h, and for example, calculates the calculation time of the control plan. The classification unit 133a is assumed to hold a table that defines the relationship between the values of s ₂ and h and the value of t (s ₂ , h).

Minimize = t (s ₂ , h) × s ₁ × s ₃ (10)

Note that, for example, the classification unit 133a searches for values of s ₁ , s ₂ , s ₃ , and h at which Minimize is the minimum value under the constraints of the expressions (11), (12), and (13). And The administrator may change the constraint conditions as appropriate.

s ₁ ≦ s (11)

s ₁ = s ₃ (12)

600 ≦ t (s ₂ , h) ≦ 900 (13)

  Next, processing of the control server 100 according to the present embodiment will be described. FIG. 18 is a flowchart illustrating the processing procedure of the control server according to the present embodiment. For example, the process shown in FIG. 18 is executed at predetermined time intervals.

  As shown in FIG. 18, the control server 100 acquires various data related to the PC information table 122 from each notebook PC (step S101). The control server 100 counts the number of each notebook PC 30 (step S102).

The classification unit 133a of the control server 100 executes a hierarchical structure calculation process (step S103). For example, in step S103, the classification unit 133a searches for values of s ₁ , s ₂ , s ₃ , and h in which Minimize in the above equation (10) has a minimum value. The classification unit 133a of the control server 100 classifies and stratifies each notebook PC 30 (step S104). The control server 100 calculates the power allocated to each upper second group (step S105).

  The control server 100 executes a control plan generation process (step S106), and controls the driving state of each notebook PC 30 (step S107).

  Next, an example of the processing procedure of the control plan generation process shown in step S106 of FIG. 18 will be described. FIG. 19 is a flowchart illustrating the processing procedure of the control plan generation process. As illustrated in FIG. 19, the generation unit 133c of the control server 100 regards the plurality of notebook PCs 30 included in the first group as a single notebook PC, and generates a first control plan (step S201).

  The generation unit 133d of the control server 100 switches the state of the first control plan (step S202), and the simulation unit 133d simulates demand power (step S203). The simulating unit 133d determines whether the simulated result satisfies the constraint condition (Step S204). If the simulation unit 133d does not satisfy the constraint condition (No at Step S204), the simulation unit 133d proceeds to Step S207.

  On the other hand, the simulation unit 133d of the control server 100 proceeds to Step S205 when the constraint condition is satisfied (Yes in Step S204). The update unit 133e of the control server 100 determines whether or not the simulation result is improved from the first control plan before switching (step S205). If the simulation result is not improved (No at Step S205), the updating unit 133e proceeds to Step S207.

  On the other hand, when the simulation result is improved (Yes in step S205), the update unit 133e of the control server 100 updates the first control plan after switching (step S206). The execution unit 133f of the control server 100 determines whether it is the end timing (step S207). When the execution unit 133f determines that it is not the end timing (No at Step S207), the process proceeds to Step S202.

  On the other hand, when the control server 100 determines that the execution unit 133f is the end timing (step S207, Yes), the control plan specifying unit 134 of the control server 100 sets the second control plan based on the first control plan. A control plan is generated (step S208).

  Next, effects of the control server 100 according to the present embodiment will be described. The control server 100 according to the embodiment 100 generates a control plan by classifying the notebook PCs into a first group and a second group based on the remaining amount of the storage battery that each notebook PC 30 has. For this reason, a control plan can be created with a small processing time.

  In addition, since the control server 100 according to the present embodiment regards a plurality of notebook PCs included in the first group as a single notebook PC 30 and generates a control plan, the time for creating the control plan can be reduced. it can.

  In addition, the control server 100 according to the present embodiment can perform control using a time resolution and an objective function suitable for the hierarchy. FIG. 20 is a diagram illustrating an example of control in three layers by multi-time scheduling. The vertical axis in FIG. 20 represents the power value, and the horizontal axis represents time. A line segment 7A is a power demand prediction curve, and a point 7B is a point indicating a peak of the power demand prediction curve. As shown in FIG. 20, in the upper layer 5C, it is possible to control from a global viewpoint by looking at a rough movement of the power demand prediction curve including the peak point 7B for a long period of time. Further, in the middle layer 5B, it becomes easy to formulate constraints and objective functions from a short-term and local viewpoint, and the objective functions can be set in consideration of different characteristics for each first group. In the lower layer 5A, each notebook PC 30 can be controlled in each section.

  Next, an example of a computer that executes a control program that realizes the same function as the control server 100 described in the above embodiment will be described. FIG. 21 is a diagram illustrating an example of a computer that executes a control program.

  As illustrated in FIG. 21, the computer 300 includes a CPU 301 that executes various arithmetic processes, an input device 302 that receives input of data from a user, and a display 303. The computer 300 also includes a reading device 304 that reads a program or the like from a storage medium, and an interface device 305 that exchanges data with other computers via a network. The computer 300 also includes a RAM 306 that temporarily stores various types of information and a hard disk device 307. The devices 301 to 307 are connected to the bus 308.

  The hard disk device 307 includes, for example, a classification program 307a, a power calculation program 307b, a generation program 307c, a simulation program 307d, an update program 307e, and an execution program 307f. The CPU 301 reads each program 307 a to 307 f and develops it in the RAM 306.

  The classification program 307a functions as a classification process 306a. The power calculation program 307b functions as a power calculation process 306b. The generation program 307c functions as a generation process 306c. The simulation program 307d functions as a simulation process 306d. The update program 307e functions as an update process 306e. The execution program 307f functions as an execution process 306f.

  For example, the classification process 306a corresponds to the classification unit 133a. The power calculation process 306b corresponds to the power calculation unit 133b. The generation process 306c corresponds to the generation unit 133c. The simulation process 306d corresponds to the simulation unit 133d. The update program 306e corresponds to the update unit 133e. The execution process 306f corresponds to the execution unit 133f.

  Note that the programs 307a to 307f are not necessarily stored in the hard disk device 307 from the beginning. For example, each program is stored in a “portable physical medium” such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, and an IC card inserted into the computer 300. Then, the computer 300 may read the programs 307a to 307f from these and execute them.

  The following supplementary notes are further disclosed with respect to the embodiments including the above examples.

(Supplementary note 1) A control method executed by a computer,
Based on the remaining amount of storage battery that a plurality of devices have, add devices with similar remaining amount of storage battery to the same group, classify the plurality of devices into a plurality of first groups,
A plurality of devices are classified into a second group in units of the first group so that the remaining amount distribution of storage batteries of a plurality of devices included in each second group is uniform.
Based on the power demand forecast data, calculate the power to be allocated to the second group in a certain section, and define the state related to charging / discharging of the devices belonging to the second group for each time using the calculated power as a constraint condition A control method characterized by executing a process for creating a control plan.

(Supplementary note 2) The control method according to supplementary note 1, wherein the process of creating the control plan regards a plurality of devices included in the first group as a single device and creates the control plan. .

(Appendix 3) Create a temporary control plan by changing a part of the control plan,
Simulating the power demand for each time zone using the temporary control plan,
For each first group, when the simulated result is improved from the simulation result of the control plan, a process of repeatedly executing the process of updating the control plan to the temporary control plan is further performed. The control method according to attachment 1, wherein the control method is executed.

(Additional remark 4) Based on the remaining amount of the storage battery which a some apparatus has, the apparatus with the remaining amount of a storage battery is added to the same group, The said several apparatus is classified into a some 1st group, A classification unit for classifying the plurality of devices into the second group in units of the first group so that the remaining amount distribution of the storage batteries of the plurality of devices included in the two groups is uniform;
Based on power demand forecast data, calculate the power to be allocated to the second group in a certain section, and define the state related to charging / discharging of the devices belonging to the second group for each time using the calculated power as a constraint. And a creation unit for creating a control plan.

(Supplementary note 5) The control server according to supplementary note 4, wherein the creation unit creates the control plan by regarding a plurality of devices included in the first group as a single device.

(Additional remark 6) The said preparation part changes a part of the said control plan, produces | generates a temporary control plan, simulates the demand power for every said time slot | zone using the said temporary control plan, for every 1st group In addition, when the simulated result is improved from the simulation result of the control plan, the process of updating the control plan to the temporary control plan is repeatedly executed. Control server described in.

(Additional remark 7) Based on the remaining amount of the storage battery which a some apparatus has in a computer, the apparatus with which the remaining amount of a storage battery is similar is added to the same group, The said several apparatus is classified into several 1st group,
A plurality of devices are classified into a second group in units of the first group so that the remaining amount distribution of storage batteries of a plurality of devices included in each second group is uniform.
Based on power demand forecast data, calculate the power to be allocated to the second group in a certain section, and define the state related to charging / discharging of the devices belonging to the second group for each time using the calculated power as a constraint. A control program characterized by causing a process to create a control plan to be executed.

(Supplementary note 8) The control program according to supplementary note 7, wherein the process of creating the control plan regards a plurality of devices included in the first group as a single device and generates the control plan. .

(Supplementary Note 9) A temporary control plan is generated by changing a part of the control plan,
Simulating the power demand for each time zone using the temporary control plan,
For each first group, when the simulated result is improved from the simulation result of the control plan, a process of repeatedly executing the process of updating the control plan to the temporary control plan is further performed. The control program according to appendix 7, wherein the control program is executed.

DESCRIPTION OF SYMBOLS 100 Control server 110 Communication control part 120 Storage part 130 Control part

Claims (5)

A control method executed by a computer,
Based on the remaining amount of storage battery that a plurality of devices have, add devices with similar remaining amount of storage battery to the same group, classify the plurality of devices into a plurality of first groups,
A plurality of devices are classified into a second group in units of the first group so that the remaining amount distribution of storage batteries of a plurality of devices included in each second group is uniform.
Based on power demand forecast data, calculate the power to be allocated to the second group in a certain section, and define the state related to charging / discharging of the devices belonging to the second group for each time using the calculated power as a constraint. A control method characterized by executing a process for creating a control plan.   The control method according to claim 1, wherein the process of creating the control plan creates the control plan by regarding a plurality of devices included in the first group as a single device. Create a temporary control plan by changing a part of the control plan,
Simulating the power demand for each time zone using the temporary control plan,
For each first group, when the simulated result is improved from the simulation result of the control plan, a process of repeatedly executing the process of updating the control plan to the temporary control plan is further performed. The control method according to claim 1, wherein the control method is executed. Based on the remaining amount of storage battery of a plurality of devices, devices having similar storage battery amount are added to the same group, the plurality of devices are classified into a plurality of first groups, and included in each second group A classification unit for classifying the plurality of devices into the second group in units of the first group so that the remaining amount distribution of the storage batteries of the plurality of devices is uniform;
Based on power demand forecast data, calculate the power to be allocated to the second group in a certain section, and define the state related to charging / discharging of the devices belonging to the second group for each time using the calculated power as a constraint. And a creation unit for creating a control plan. Based on the remaining amount of storage batteries of a plurality of devices in the computer, add devices having similar storage battery amounts to the same group, classify the plurality of devices into a plurality of first groups,
A plurality of devices are classified into a second group in units of the first group so that the remaining amount distribution of storage batteries of a plurality of devices included in each second group is uniform.
Based on power demand forecast data, calculate the power to be allocated to the second group in a certain section, and define the state related to charging / discharging of the devices belonging to the second group for each time using the calculated power as a constraint. A control program characterized by causing a process to create a control plan to be executed.

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