JP5842682B2 - Control plan generation method, control plan generation device, and program - Google Patents

Description

  The present technology relates to a technology for optimizing a control plan.

  Regarding energy conservation, the theme is to reduce peak power in addition to reducing total power. In the future, it is expected that a mechanism will be introduced to reduce peak power and level power consumption using storage batteries in communities such as buildings, homes, and local governments.

  However, centrally arranging storage batteries has problems in terms of cost and safety. Therefore, it is required to reduce the peak power by well controlling the storage batteries arranged in a distributed manner.

  It is difficult to plan device control for suppressing peak power and the like while using distributed storage batteries. Even optimization techniques can be difficult.

JP 2001-258176 A JP 11-072262 A

  Accordingly, an object of the present technology is, in one aspect, to efficiently optimize a control plan involving state transition.

  In the control plan generation method according to the present technology, (A) a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices is used as another command. By replacing, a second control plan composed of a plurality of second commands is generated, and (B) a state transition when a plurality of devices are controlled according to the plurality of second commands in the second control plan is simulated (C ) Based on the simulation result, it is determined whether the state transition by the second control plan is closer to the predetermined target than the state transition by the first control plan. (D) If the second control plan is closer to the predetermined target, When the computer repeatedly executes the process of updating one control plan, and (b1) the simulation includes a continuation command that means continuation of the second command in the second control plan, Determining a state transition by performing the same control as the control by the second instruction before one than continue command.

  The control plan can be optimized efficiently.

FIG. 1 is a schematic diagram of a control system. FIG. 2 is a diagram illustrating an example of the power mode. FIG. 3 is a diagram illustrating an example of power usage in each power supply mode. FIG. 4 is a diagram illustrating an example of a screen provided by software for controlling the power supply mode. FIG. 5 is a diagram illustrating the relationship between the actual measurement timing and the saturation state. FIG. 6 is a diagram illustrating an example of a charge curve and a discharge curve. FIG. 7 is a diagram illustrating an example of a relationship between an undefined command value, an executable command value, and a charging rate. FIG. 8A is a diagram illustrating an example of a flow of a main routine of simulation processing. FIG. 8B is a diagram illustrating an example of the flow of the main routine of the simulation process. FIG. 9 is a diagram illustrating an example of a configuration of an area for storing a target index. FIG. 10 is a diagram illustrating an example of a configuration of an area for storing a power demand prediction. FIG. 11 is a diagram illustrating an example of a configuration of an area for storing executable command values. FIG. 12 is a diagram illustrating an example of the configuration of the storage area for the charging rate. FIG. 13 is a diagram showing a subroutine processing flow for performing simulation in units of notebook PCs. FIG. 14 is a diagram illustrating an example of a configuration of an area for storing an indeterminate command value. FIG. 15 is a diagram illustrating a configuration example of a generation unit that performs local search. FIG. 16 is a diagram illustrating an example of a generation process flow based on local search. FIG. 17 is a diagram illustrating a configuration related to control of the notebook PC. FIG. 18 is a diagram showing an overall processing flow. FIG. 19 is a diagram illustrating an example of command values when an executable region is set as a search space. FIG. 20 is a diagram illustrating an example of a power usage state when an executable region is set as a search space. FIG. 21 is a diagram illustrating an example of a control result when an executable region is set as a search space. FIG. 22 is a diagram illustrating an example of an undetermined command value when mapping the search space. FIG. 23 is a diagram showing an example of executable command values when mapping the search space. FIG. 24 is a diagram illustrating an example of the power usage state when mapping the search space. FIG. 25 is a diagram illustrating an example of a control result when mapping the search space. FIG. 26 is a schematic diagram illustrating a change in power consumption over time. FIG. 27 is a schematic diagram illustrating a change over time in the charging rate of the notebook PC. FIG. 28 is a diagram illustrating components additionally provided in the second embodiment. FIG. 29 is a diagram illustrating an example of data stored in the actual measurement value storage unit in the second embodiment. FIG. 30 is a diagram illustrating an example of data stored in the setting data storage unit in the second embodiment. FIG. 31 is a diagram illustrating a processing flow according to the second embodiment. FIG. 32 is a diagram illustrating an example of data stored in the intermediate data storage unit according to the second embodiment. FIG. 33 is a diagram illustrating a processing flow according to the second embodiment. FIG. 34 is a diagram illustrating a processing flow according to the second embodiment. FIG. 35 is a diagram illustrating an example of a charge / discharge number table according to the second embodiment. FIG. 36 is a schematic diagram illustrating a change in power consumption over time. FIG. 37 is a functional block diagram of a control unit according to the third embodiment. FIG. 38 is a diagram illustrating a processing flow according to the third embodiment. FIG. 39 is a diagram illustrating an example of data stored in the fatigue degree data storage unit. FIG. 40 is a functional block diagram of a computer.

[Embodiment 1]
In the present embodiment, it is proposed to use a battery of a notebook PC (Personal Computer) as surplus power in an office based on a power demand forecast for each community such as an office. The battery of the notebook PC is used like a storage battery. This aims to reduce peak power and level power usage.

  For example, a control method is conceivable in which discharging is performed when the amount of power used for a certain time exceeds a threshold value, and charging is performed when the power consumption does not exceed the threshold value.

  Leave a minimum charge on the laptop battery. This is because the notebook PC must operate even when the power source is unavailable during a power failure or business trip. Also, useless battery deterioration due to repeated charging and discharging should be avoided.

  First, an outline of the control system according to the present embodiment will be described. FIG. 1 is a schematic diagram of a control system. The control system is connected to the network. For example, the control system is on a cloud and is connected to an office LAN (Local Area Network) via the Internet. Alternatively, the control system is directly connected to the office LAN.

  A notebook PC is connected to the office LAN. The control system collects information such as the power mode, the charging rate of the battery, and the remaining amount from the notebook PC, and further performs control related to the power of the notebook PC.

  A control system or a prediction system connected to a notebook PC via a network in the same manner as the control system performs power demand prediction. Here, a prediction for one day is assumed. The control system or the prediction system performs prediction based on the current measured value of the amount of power used by the notebook PC and other electric devices, the past measured value, and the like.

  The control system makes a daily charge / discharge plan based on the power demand prediction and the current power usage. The charge / discharge plan is repeatedly optimized throughout the day.

  Next, the notebook PC will be described. The activated notebook PC maintains the battery charge rate and the power mode state. The control system and the prediction system obtain this information via the network.

  The power mode will be described. FIG. 2 shows an example of the power supply mode. The power supply mode is one of an AC power supply mode (hereinafter referred to as an AC power supply mode), a battery mode, and a charge mode.

  In the AC power supply mode, the notebook PC uses power supplied from an AC power supply (hereinafter referred to as AC power supply). However, in the AC power supply mode, the notebook battery is not charged. Therefore, the charging rate of the battery does not change. The AC power is supplied from outside the control target such as an electric power company or a private power generation system.

  In the battery mode, the notebook PC uses the power supplied from the battery. Therefore, the power supplied from the AC power supply is not used, and the charging rate of the battery decreases monotonously.

  In the charge mode, the notebook PC uses the power supplied from the AC power source and further charges the battery of the notebook PC. Therefore, the charging rate of the battery increases.

  FIG. 3 shows a change in power when the notebook PC is used. The horizontal axis indicates time, and the vertical axis indicates power consumption (W). In the figure, Ac indicates an AC power supply mode, Ba indicates a battery mode, and Ch indicates a charge mode. The power used in the charge mode is higher than the power used in the AC power supply mode.

  The notebook PC can change the power mode. For example, the power mode may be controlled by software. FIG. 4 is an example of a screen provided by software that controls the power supply mode. This screen accepts designation of a time zone during which charging is not performed and a time zone during which the battery is operated. The notebook PC automatically operates in the designated power supply mode. It is not necessary to connect or disconnect the power cable for switching the power mode.

  However, when the charge mode of each notebook PC overlaps, the total power consumption increases. Therefore, the overall control of the power supply mode is performed by the control system. The control system switches the power mode of each notebook PC on behalf of the user. The control system remotely controls the notebook PC via the above-described software, for example.

  Next, the battery of the notebook PC will be described. For example, a lithium ion battery is used as the battery. Excessive deterioration must be avoided, so when controlling the power supply mode, consider the deterioration of the lithium ion battery.

The lithium ion battery has the following properties.
1. Deterioration progresses due to charging near 100% (referred to as overcharging) and discharging around 0% (referred to as overdischarging).
2. Degradation does not accelerate by repeating recharging without discharging. (No memory effect)
3. By repeating charge and discharge, the chargeable capacity decreases. (Cycle deterioration)

  Care should be taken to prevent battery deterioration and avoid unnecessary charging and discharging. Also, avoid charging near 100% and discharging near 0%. On the other hand, the number of charge / discharge switching times is not considered due to the above properties.

  The notebook PC is disconnected from the network and is reconnected later. It is difficult to predict the time until reconnection and the charging rate at the time of reconnection. Therefore, it is desirable to review the charge / discharge plan at short intervals. The power demand prediction and the charge / discharge plan optimization are repeated, for example, at 30-minute intervals.

  The state also changes during optimization calculations. Therefore, it is desirable that the calculation time for optimization is shorter. For example, in this embodiment, it is assumed that the control system finishes the optimization calculation in about one minute.

  If charging with nighttime power is not possible, the control ends at a predetermined end time even at night. As the time elapses, the number of sections to be optimized may be reduced. Further, in order to reduce the peak power on the next day, the charge amount at the end time may be maximized.

The charge / discharge plan is derived from an optimization problem with multiple objective functions. For example,
1. The daily peak power of the entire office is calculated as an index, and the goal is to minimize that index.
2. The battery charge amount at the end time is calculated as an index, and the goal is to maximize it.
3. The total power consumption of the office is calculated as an index, and the goal is to minimize that index.

ピーク電力削減を最大目標とすると、ピーク電力P、充電量の総和C、総使用電力量Gの順に優先される。これらは、例えば辞書式順序で単目的化した目的関数を用いることにより、総合的に評価することができる。
辞書式順序とは、以下のように定義される。
ｘ＝（ｘ₁，．．．ｘ_n），ｙ＝（ｙ₁，．．．ｙ_n）∈Ｒに対して、ベクトルに対する辞書式順序≦_lexは以下のように定義される。
ｘ≦_lexｙ ＜＝＞ｘ＝ｙ または ｘ≠ｙ かつ
ｘ_j＜ｙ_j （ｊ＝min（ｊ｜ｘ_j≠ｙ_j） The following objective functions will be described.

If peak power reduction is the maximum target, priority is given in the order of peak power P, total amount C of charge, and total amount of power G used. These can be comprehensively evaluated by using, for example, an objective function that is singly-purposed in a lexicographic order.
The lexicographic order is defined as follows.
For x = (x ₁ ,... x _n ), y = (y ₁ ,... y _n ) εR, the lexicographic order ≦ _lex for the vector is defined as follows:
x ≦ _lex y <=> x = y or x ≠ y and
x _j <y _j (j = min (j | x _j ≠ y _j )

The parameters will be described below.

FIG. 5 shows the relationship of each variable for each time. In general, the command value u _i [k _s −1] and the measured value v _{i, ks} do not match. The reason why the maximum value and the minimum value exist in the charging rate is that the charging rate is saturated. For example, since the charging rate does not exceed 100%, even when operating in the power supply mode Ch at time k _s −1, when it reaches 100%, it changes to the power supply mode Ac, and the measured values v _{i, ks} are The power mode becomes Ac.

In the power supply modes other than Ac, the measured value x _{i, ks} of the charging rate does not coincide with the variable x _i [k _s ] of the charging rate, but is treated as the same value.

The problem at the optimization start time is formulated as a mathematical programming problem with the command value u _i [k] as a decision variable.

  The objective function (1a) is a single objective function that is lexicographically ordered.

  The constraint equations (1b) and (1c) indicate that the peak power is the maximum value of the past actual measured value of power used for the entire office and the amount of power used for the entire office after battery control of the notebook PC. Represents.

Since the power demand prediction D ^ks [k] is predicted based on the actual measurement value s [k] (kεK _p ) of the power consumption, it includes the power consumption of the notebook PC. For this reason, D ^ks [k] is adjusted by the sum of the difference between the power consumption in the power supply mode, which is a command value for the notebook PC, and the power consumption assuming that the notebook PC is operating in the power supply mode Ac. ing.

  In the power mode Ba, the power consumption is 0 because the power from the AC power source is not used. Further, since more power is used in the power mode Ch than in the power mode Ac, the power consumption by the power mode Ch is larger than the power consumption by the power mode Ac.

Constraints (1d) represents that the charged amount C is the sum of the amount of charge of all of the notebook PC in the optimization end time k _e.

The constraint equation (1e) represents that the total power consumption G is the sum of the power used from the AC power supplies of all notebook PCs from time k _s to k _e .

  When energy is lost due to charging and discharging, the battery is charged in the power supply mode Ch after operating in the power supply mode Ba from the state where the charge rate is 50%, and the original charge rate is restored to 50%. The amount of power used becomes greater than the amount of power used when operating in the power supply mode Ac while remaining 50%. Therefore, minimization of the total power consumption G from the AC power supply leads to minimization of the operation in the power supply mode Ch and contributes to prevention of battery cycle deterioration.

The constraint equation (1f) represents that the actually measured value of the power supply mode is set as the power supply mode at time k _s −1.

  The constraint equation (1g) means that it operates in the specified power supply mode at each time of the optimization period.

  The constraint equation (1h) represents that the measured value of the charging rate is set to the charging rate at the optimization start time.

  The constraint equation (1i) is a state equation regarding the charging rate.

Incidentally, f _i to reduce the charging rate when operating in power supplied from the battery _{(x i [k], Ba} ) < a x _i [k], f _i since the time of charging the charging rate increases (x _i [k], Ch)> x _i [k]. Here, it is assumed that there is no spontaneous discharge. Therefore, f _i (x _i [k], Ac) = x _i [k].

The constraint equation (1j) represents the range of the charging rate. (A) indicates that the battery is not discharged when the charging rate is equal to or less than x ^l _i , and (B) indicates that the battery is not charged when the charging rate is equal to or greater than x ^u _i .

  The upper limit value and the lower limit value are values that can be set by the user. These values are used to avoid over-discharge and over-charge that cause battery degradation. For example, the minimum charging rate is set to 10% and the maximum charging rate is set to 80%. Further, the user can arbitrarily set these values so as not to decrease the charge amount, for example.

The constraint equation (1k) represents a constraint for avoiding overcharge. This equation, the charging rate does not start charging when not been lowered by 10% or more than the maximum charging rate x ^u _i being set, it represents that.

  FIG. 6 shows the transition of the charging rate during charging and discharging of the notebook PC. As can be seen from FIG. 6, the time required for charging is shorter than the time spent for discharging. In this way, since the battery is charged in a short time, the amount of power used during charging (Ch) is large as shown in FIG.

Based on this data, a function of the charging rate f _i is set. The amount of power used in each power supply mode depends on a CPU (Central Processing Unit) usage rate and the like, but the function g _i is also approximated by a linear function, assuming a constant value as shown in FIG.

Thus, the given problem is a combinatorial optimization problem in which a variable u _i [k] that gives an optimum value is searched. Since the decision variable u _i [k] takes three states of the power supply modes Ac, Ba, and Ch, there are many combinations, and it is difficult to obtain an exact solution by brute force. Therefore, the approximate solution method based on the local search method is one of the practically effective means.

As a simple local search method, the search neighborhood as shown below can be considered. The search space is defined as follows.

The replacement is an operation of replacing an arbitrary variable included in u∈X (u _i [k] where k∈K _o , i∈M) with an arbitrary element of U. X is closed for the replace operation.

In a simple local search method, the search neighborhood N _m (u) for u∈X is defined as follows.
N _m (u) = {v∈X | v is a substitution of u m times}

  However, such a simple local search method may not be efficiently searched in the feasible region. In that case, a method of preparing a different search space and performing a local search efficiently in that space can be considered. In the following example, a local search algorithm based on an imaginary search space is adopted instead of a simple local search method. The imaginary search space includes an indirect command (in this example, a imaginary power supply mode. For example, a continuation command Du described later). Indirect commands cannot be executed directly, but the fictitious search space is converted into an executable region, so that it can finally be executed.

A new variable u ′ _i [k] (k∈K _o , i∈M) is introduced for the fictional search space. This variable is called an undefined command value.

This variable u ′ _i [k] is a variable representing the command value of the power supply mode, and has a fictitious power supply mode Du (Dummy) in addition to the power supply modes Ac, Ba, and Ch that can be executed as the command value. The power mode Du indicates that the power mode at the previous time is continued, and is also referred to as a continuation command.

The search space by the power mode set U ′ = {Ac, Ba, Ch, Du} with Du added is defined as follows.

Hereinafter, the simulation process by the simulation unit (more specifically, the simulation unit 19 shown in FIG. 15) in the control system (more specifically, one or a plurality of computers that execute charge / discharge control) shown in FIG. 1 will be described. To do. The simulation unit 19 also performs mapping from the space X ′ to the space X. The simulation unit 19 obtains an executable command value u _i [k] and an objective function value, which are decision variables of the original problem, based on the undefined command value u ′ _i [k]. The simulation unit 19 also performs subroutine processing to be described later. In the subroutine process, the simulation unit 19 assumes, for each notebook PC i∈M, the power consumption at each time of the optimization period, the executable command value u _i [k] at each time, and each time. Determine the charge rate x _i [k] to be used.

In the subroutine processing, the simulation unit 19 sets the power supply mode Ac to an executable command value when the undefined command value u ′ _i [k] that violates the constraint is received. Since the power mode Ac can always be executed, this setting allows the simulation unit 19 to obtain a solution that can always be executed for any undefined command value u ′ _i [k].

  Setting the power mode Ac in response to an indeterminate command value that violates the constraint means that the notebook PC itself automatically enters the power mode when the power mode according to the command value received from the control system violates the constraint. Corresponds to the action of changing to Ac. Since it cannot actually be charged to 100% or more, it is reasonable to change the power mode to Ac when the power mode Ch reaches 100% as shown in FIG.

FIG. 7 is a diagram illustrating an example of the relationship between the uncertain command value and the executable command value and the charging rate. The horizontal axis represents the time axis, and the vertical axis represents the charging rate of the notebook PC battery. In this example, the minimum charging rate is 10%. That is, the simulation unit 19 performs control so that the charging rate does not become 10% or less. Since the power mode Du means the continuation of the previous power mode, the simulation unit 19 repeats u _i [1] = Ac for u ′ _i [2] = Du, and u _i [2] = Ac. Set. The simulation unit 19 repeats u _i [3] = Ba for u ′ _i [4] = Du, and sets u _i [4] = Ba. On the other hand, since the charging rate reaches 10% at time 5, the simulation unit 19 sets u _i [6] = Ac without repeating u _i [5] = Ba for u ′ _i [6] = Du. To do. At time 7 after that, the simulation unit 19 does not follow the command u ′ _i [7] = Ba and sets u _i [7] = Ac. Thus, the simulation unit 19 generates an executable solution uεX from arbitrary u′εX ′.

Next, specific simulation processing will be described. 8A and 8B are diagrams illustrating a flow of a main routine of the simulation process. In this simulation process, the simulation unit 19 performs the undetermined command value u ′ _i [k], the actual power supply mode measured value v _{i, ks} and the charging rate actual measured value x _{i, ks} for all notebook PCi, The power demand prediction D ^ks [k] and the actual measurement value s [k] of the used electric energy are input, and executable command values u _i [k] and objective function values for all notebook PCi are output. First, the simulation unit 19 initializes the total power consumption variable G, the total charge amount variable C, and the peak power variable P [k] (S101 to S105). FIG. 9 is a diagram illustrating an example of a configuration of an area for storing a target index. The simulation unit 19 sets the total power consumption variable G to 0, sets the charge amount total variable C to 0, and sets the peak power variable P [k] at each time to the power demand prediction D ^ks [ k]. FIG. 10 is a diagram illustrating an example of a configuration of an area for storing a power demand prediction.

  The simulation unit 19 repeats the loop process of S109 to S121 for each notebook PC. The simulation unit 19 initializes an executable command value in the first line of S111. Specifically, the simulation unit 19 sets the actual measurement value of the power supply mode as a command value variable one unit time before the optimization start time. FIG. 11 is a diagram illustrating an example of a configuration of an area for storing executable command values. Next, the simulation part 19 initializes a charging rate by the 2nd line of S111. Specifically, the simulation unit 19 sets the measured value of the charging rate at the optimization start time as a variable of the charging rate at the optimization start time. FIG. 12 is a diagram illustrating an example of the configuration of the storage area for the charging rate. The simulation unit 19 performs simulation (subroutine processing) in units of notebook PCs in the third line of S111. This process will be described later.

In the fourth line of S111, the simulation unit 19 uses the notebook PCi power consumption G _i (included in the result of simulatePC ()) calculated by the above-mentioned simulation for each notebook PC unit as the total power consumption amount. Add to variable G. When the loop of S109 to S121 is executed for all the notebook PCs, the power used by all the notebook PCs is added up, and the simulation unit 19 calculates the total power consumption G.

In the fifth line of S111, the simulation unit 19 adds the charge amount c _i x _i [k _e ] of the notebook PC at the optimization end time to the charge amount total variable C. Simulation unit 19, by multiplying the charging rate of the notebook PC _i in the optimization end time on the capacity of the notebook PC _i to (in the result of simulatePC ()), the charge amount of the note PC _i in the optimization end time Ask for. The simulation unit 19 acquires information on the capacity of each notebook PC from the PC information table. The PC information table stores information on the capacity of each battery for each notebook PC.

  In addition, the simulation unit 19 uses the above-described notebook PC unit simulation (subroutine processing), which has already calculated the charging rate of the notebook PCi at the optimization end time. By repeating the loop of S109 to S121, the simulation unit 19 adds up the charge amounts at the optimization end times of all the notebook PCs, and thus calculates the charge amount sum C.

Further, the simulation unit 19 calculates a peak power variable P at each time of the optimization period (S113 to S119). The simulation unit 19 subtracts the power consumption when it is assumed that control is performed according to the power mode of the executable command value from the power consumption when it is assumed that each notebook PC is controlled in the AC power mode ( This is equivalent to the amount of power saved. At this time, the simulation unit 19 only calculates the amount of power used from the AC power source. The simulation unit 19 calculates the amount of power used based on the charging rate and the power mode. The simulation unit 19 obtains a peak power variable at each time by adjusting the amount of power to be reduced from D ^ks [k] that has already been set as an initial value for all notebook PCs. The peak power variable obtained by the simulation unit 19 indicates the total power consumption in each time zone.

Finally, the process proceeds to the process of FIG. 8b, and the simulation unit 19 searches for the maximum value from the actual measurement value s [k] and the peak power variable P [k] of the used electric energy at each time. Specifically, the simulation unit 19 compares the actual measurement values s [k] of each used electric energy, and when k is equal to or less than k _s , obtains the maximum value among them (S123 to S131). , K is larger than k _s , each peak power variable is also compared with the maximum value, and the maximum value of the whole is obtained (S133 to S141).

Next, a notebook PC unit simulation by a subroutine process will be described with reference to FIG. In this subroutine processing, the simulation unit 19 inputs an uncertain command value u ′ _i [k] for one notebook PCi, and performs optimization in the case of operating according to the uncertain command value u ′ _i [k]. The executable command value u _i [k] at the target time, the charging rate x _i [k _e ] at the final time of the optimization period, and the power consumption G _i during the optimization period are output. The simulation unit 19 initializes the power consumption variable Gi during the optimization period to 0 (S201).

The simulation unit 19 further performs a loop process of S205 to S227 for each time of the optimization period. In S207, it is determined whether or not the undetermined command value of the notebook PC _i at the time within the optimization period is the continuation command Du. When the undetermined command value of the notebook PC _i at each time is the continuation command Du (S207: Yes route), the simulation unit 19 repeats the previous command value and sets it to the undetermined command value. (S209). If the uncertain command value is not the continuation command Du (S207: No route), that is, if it is an executable power mode command (Ac, Ba, Ch), the simulation unit 19 does not change the command value. . FIG. 14 is a diagram illustrating an example of a configuration of an area for storing an indeterminate command value.

  In S211, it is determined whether the constraint condition (1k) and the expressions (A) and (B) are satisfied. When the restriction (1k) is satisfied, the simulation unit 19 changes the unconfirmed command value to Ac (S213). That is, when the charging rate has not decreased by 10% or more from the maximum value, the simulation unit 19 prevents charging.

Then, the simulation unit 19 obtains the charging rate at the next time based on the charging rate x _i [k] at the time and the uncertain command value u ′ _i [k] (power supply mode) (S215).

  In S217, it is determined whether or not the constraint condition (1j) is satisfied. When the restriction (1j) is satisfied, the simulation unit 19 changes the executable command value to Ac (S217, S219). That is, when the charging rate exceeds the upper limit or the lower limit, the simulation unit 19 prevents overdischarge and overcharge from occurring. Furthermore, the simulation part 19 calculates | requires the charging rate of the next time based on the charging rate of the said time, and executable command value (S221).

  If none of the restrictions are met, the simulation unit 19 sets the undetermined command value to an executable command value (S223).

The simulation unit 19 obtains the power consumption of the notebook PC in the unit time according to the charging rate x _i [k] at the time related to the process and the executable command value u _i [k] (power supply mode). The power amount is added to the variable G _i of the power consumption of the notebook PC so far (S225).

  In this way, the simulation unit 19 specifies an executable command value, sets the value of the variable G as the total power consumption, sets the value of the variable C as the charge amount sum, and sets the value of the variable P as the peak power (1 Obtained as the amount of power in unit time).

Next, local search processing by the replacement unit (specifically, the replacement unit 13 in FIG. 15) of the control system shown in FIG. 1 (more specifically, one or a plurality of computers that perform charge / discharge control) will be described. The replacement unit 13 replaces an arbitrary command value included in u′∈X ′ (k∈K _o , u ′ _i [k] where i∈M) with an arbitrary element of U. That is, the replacement unit 13 replaces some command values with any of the executable power mode commands (Ac, Ba, or Ch) other than Du.

The search neighborhood N ′ _m (u ′) for u′∈X ′ is defined as follows.
N ' _m (u') = {v'∈X '| v' is the replacement of u 'm times}

  FIG. 15 is a diagram illustrating a configuration example of a generating unit that performs local search in the control system (more specifically, one or more computers that perform charge / discharge control) illustrated in FIG. 1. The generation unit 39 includes an initialization unit 11, a setting data storage unit 12, a replacement unit 13, a first control plan storage unit 15, a second control plan storage unit 17, a simulation unit 19, and a third control plan. The storage unit 21, the result storage unit 23, the determination unit 25, the update unit 27, and the output unit 29 are included.

  The first control plan storage unit 15 and the second control plan storage unit 17 are storage units for storing a control plan corresponding to the search space. Therefore, the first control plan stored in the first control plan storage unit 15 and the second control plan stored in the second control plan storage unit 17 include the command value Du.

  The third control plan storage unit 21 is a storage unit for storing a control plan corresponding to an executable area. Therefore, the command value Du is not included in the third control plan stored in the third control plan storage unit 21.

  Next, control plan generation processing by local search shown in FIG. 16 will be described. The initialization unit 11 performs initialization in S301. Set the first command value in the control plan. In the case of this configuration example, the initialization unit 11 sets the same control plan in the first control plan storage unit 15 and the second control plan storage unit 17.

The initialization unit 11 sets the continuation command Du to all the first command values u ′ _i [k]. Setting the continuation command Du to all initial command values u ′ _i [k] by the initialization unit 11 means that a state in which nothing is initially controlled is assumed. In this way, the initialization unit 11 sets a fixed solution instead of a random solution as the initial solution for local search. U in the figure is the solution of the search space.

The simulation unit 19 performs an initial simulation process in S303. The simulation unit 19 reads indefinite command value u ′ _i [k] from the second control plan storage unit 17 and performs the simulation process as described above. The simulation unit 19 stores the simulation result in the result storage unit 23 in a state where the original control plan can be specified. The simulation unit 19 further stores a third control plan including the generated executable command value in the third control plan storage unit 21. In the figure, u _a is an objective function value obtained as a result of the simulation. The simulation unit 19 performs a simulation process using various setting data stored in the setting data storage unit 12. The setting data includes data of the functions f _i and g _i described above. Further, the simulation unit 19 measures actual values (measured values v _{i, ks in the} power supply mode and measured values x _{i, ks in the} charging rate) from each notebook PC and the power demand forecast D ^ks [k] obtained from the prediction system. Is also used.

  If the end condition described below is not satisfied (S305), the replacement unit 13 performs a replacement process corresponding to selection in the vicinity of the search (S307). Thereby, the replacement unit 13 generates a second control plan in which a part of the first control plan in the first control plan storage unit 15 is replaced, and stores the generated second control plan in the second control plan storage unit 17. To do. V in the figure is a solution selected from the vicinity and corresponds to the second control plan. The arrow means selection of a solution from the neighborhood.

Subsequently, the simulation unit 19 performs a simulation process based on the second control plan (S309). The simulation unit 19 stores the simulation result in the result storage unit 23 as described above. Further, the simulation unit 19 stores the third control plan including the generated executable command value u _i [k] in the third control plan storage unit 21 as described above. V _a in the figure, the objective function value obtained as the result of a simulation of the solution selected from the vicinity.

Subsequently, the determination unit 25 performs a determination process (S311). The determination unit 25 determines that an improvement has been made when the simulation result after replacement is closer to the target than the simulation result before replacement. In this example, the determination unit 25 determines superiority or inferiority based on a size comparison based on an index obtained from a function value obtained by singularizing multiple objects. When the goal is to maximize the index, the determination unit 25 determines that the improvement has been made when the index after replacement is larger than the index before replacement, and when the goal is to minimize the index, When the index of is smaller than the index before replacement, it is determined that the index is improved. Comparing the simulation results objective function value v _a of simulation in the vicinity and the objective function values u _a result solutions by the original solution, is meant to determine if the index is decreasing, improved and .

  When the determination unit 25 determines that the improvement has been made, the update unit 27 performs an update process (S313). The update unit 27 writes the second control plan in the second control plan storage unit 17 in the first control plan storage unit 15 as a new first control plan. In addition, the updating unit 27 updates the information in the result storage unit 23 so that the simulation result of the second control plan becomes the simulation result of the new first control plan.

  The generation unit 39 repeats the processing from S305 to S313 until the end condition is satisfied. The generation unit 39 determines the end when the execution time limit for optimization has elapsed. Further, the generation unit 39 determines that the processing has ended by assuming that the solution has fallen into the local optimal solution even when the solution is not improved for all the elements in the vicinity of the search.

  Upon completion of the processing, the output unit 29 outputs the third control plan, which is an executable command value, to the remote control unit that actually controls each notebook PC. A simulation result may be output. Note that the simulation result may include not only the value of the objective function but also data on the peak power P, the battery charge amount C, and the total power consumption G.

  FIG. 17 is a diagram illustrating a configuration related to control of the notebook PC. FIG. 18 is a diagram showing an overall processing flow. FIGS. 17 and 18 also show the configuration and processing related to power demand prediction and actual value measurement, respectively. The control system (more specifically, one or a plurality of computers that perform charge / discharge control) receives information on power demand prediction from an external prediction system or an internal prediction processing unit corresponding to the prediction system in addition to the generation unit 39. A power demand prediction storage unit 33 that is an area for storing the acquired power demand prediction information, and a measurement unit that measures the current charging rate and the current power supply mode in each notebook PC as measured values. 35 and an actual measurement value storage unit 37 that is an area for storing the measured actual measurement values.

When the generation processing time at a predetermined timing is reached (S401), the acquisition unit 31 acquires a power demand prediction and stores it in the power demand prediction storage unit 33 (S403). The measurement unit 35 measures the information held by the notebook PC via the network, and stores the acquired actual measurement value in the actual measurement value storage unit 37 (S405). The actual measurement value includes the actual measurement value v _{i, ks} of the power supply mode and the actual measurement value x _{i, ks of the} charging rate. Then, the generation unit 39 performs the above-described control plan generation process (S407).

  When the remote control processing time at a predetermined timing is reached (S409), the remote control unit 41 remotely controls each notebook PC according to the control plan (third control plan made up of executable command values) obtained from the generation unit 39. (S411). That is, the remote control unit 41 instructs the notebook PC to select the power mode according to the command value that can be executed by each notebook PC at each time of the optimization period. When the predetermined optimization period has elapsed, the process is terminated (S413).

  Through the series of operations described above, a plan for controlling the notebook PC is generated and the notebook PC is controlled based on the plan. However, a situation that cannot be dealt with only by the above-described processing can be considered.

  For example, it is assumed that the measurement unit 35 cannot obtain the actual measurement value from the notebook PC. This is because when the notebook PC is not connected to the LAN, the measurement unit 35 cannot measure the information held by the notebook PC. For example, when the user takes the notebook PC out of the office, the measuring unit 35 cannot measure the information held by the notebook PC. In such a situation, the power consumption does not increase, but the charging rate may decrease. In addition, even when the user uses the notebook PC without connecting to the LAN in the office, the measurement unit 35 cannot measure the information held by the notebook PC. In such situations, AC power may be used.

  It is desirable to avoid a situation where the current situation is worse than assumed by simulation. Therefore, it is assumed that the simulation unit 19 is in an unfavorable state with respect to the target when an actual measurement value cannot be obtained. When the actual measurement value from the notebook PC cannot be obtained, the simulation unit 19 performs the following process.

  The simulation unit 19 assumes that the power mode is Ch from the viewpoint of power consumption, and uses the assumed power mode Ch to calculate power consumption such as S117 in FIG. 8A and S225 in FIG. This is because the power mode Ch consumes the largest amount of power among the power modes assumed in such a situation.

  Further, the simulation unit 19 assumes that the power mode is Ba from the viewpoint of charging, and calculates the charging rate such as S215 in FIG. 13 using the assumed power mode Ba. This is because when the notebook PC is operating in the power supply mode Ba in such a situation, the charging rate decreases and the charging amount decreases.

  Moreover, the case where the power supply of a notebook PC cannot be remotely controlled is also assumed. For example, there may be a case where the notebook PC is not connected to an AC power source. In this case, the simulation unit 19 assumes that the power mode is Ac from the viewpoint of power consumption. Even if the notebook PC is not currently connected to the AC power source, there is a possibility that it will be reconnected to the AC power source immediately. Therefore, the simulation unit 19 uses the power consumption when operating with the AC power source in anticipation of the state. Perform a simulation. Specifically, the simulation unit 19 calculates power consumption such as S117 in FIG. 8A and S225 in FIG. 13 using the assumed power supply mode Ac.

  Further, the simulation unit 19 assumes that the power supply mode is Ba from the viewpoint of charging, and calculates the charging rate such as S215 in FIG. 13 using the assumed power supply mode Ba. This is because the notebook PC may use a battery power source under such a situation.

  Finally, an example of control by a simple local search method is compared with an example of control by this embodiment, that is, an example of control by local search based on a different fictitious search space.

  First, an example of control by a simple local search method is shown. 19, 20, and 21 show examples of command values, examples of power usage states, and examples of control results when an executable region is set as a search space.

  The initial values are all in power mode Ac. In this example, unless the power mode of 15:00 is changed to Ba, the peak power amount in the predicted power demand does not decrease. However, with the simple local search method, the probability that the command value at this time can be directly replaced is low, and high improvement efficiency cannot be expected.

  Next, an example of control by different fictitious search spaces will be shown. 22, 23, 24, and 25 show examples of undefined command values, examples of executable command values, examples of power usage states, and examples of control results when mapping the search space. FIG.

  Assuming that it takes about 2 hours for the charging rate to drop from the maximum value to the minimum value, if any command value at the time of 13:30 to 15:00 is replaced with Ba, it will occur at 15:00 Expected reduction in peak power consumption can be expected. In the control using a different fictitious search space, the probability of effective replacement is higher than in the previous example, and the expectation of improvement is great.

  In this example, the peak power is reduced because the replacement unit 13 replaces a part of the command values at 14:00 with Ba in the initial state in which all the command values are the continuation command Du.

[Embodiment 2]
In the first embodiment, a function f _i is prepared in advance for the charging rate, and a simulation process is performed based on the function f _i . If only the initial setting is used without changing the function f _i , there is a deviation from the actual state depending on the state of the battery. For example, FIG. 26 shows an example of a change in power consumption over time. In the example of FIG. 26, when the control as in the first embodiment is not performed, the peak power is high as shown by the curve a. However, if the control as in the first embodiment is performed, In the simulation, the peak power can be suppressed as shown by the curve b. Here, in the function f _i , the battery charge rate can be charged from 0% to 100% in about 1 hour and 40 minutes in the charge mode Ch, and the battery charge rate is reduced from 100% to 0% in about 3 hours in the battery mode Ba. Is used. However, in actuality, it takes about 2 hours to charge from 0% to 100% of the battery charge rate, and it takes about 2 hours and 10 minutes to reach 0% from the battery charge rate of 100%. In the example, as shown by the curve c, the phenomenon that the simulation does not match in the latter half of the day and the peak power increases occurs, and the effect of the first embodiment is reduced.

  In addition, as schematically shown in FIG. 27, when control is performed such that the battery charge rate that has been reduced so far is increased to a predetermined value such as 80% by the control end time (for example, 20:00). In the simulation, it is possible that the battery can be charged up to a predetermined value, but is not actually charged up to a predetermined value.

Therefore, by performing the following processing and setting an appropriate function f _i , the peak power increases by reducing the difference between the actual charging rate and the charging rate on the simulation. To make the problem less likely to occur.

  For this reason, in this embodiment, a configuration as shown in FIG. 28 is provided in a control system (specifically, a computer that performs charge / discharge control). Note that the same reference numerals are given to components having the same functions as those in the first embodiment. In the present embodiment, a setting data update unit 43 and an intermediate data storage unit 44 are added. When the actual measurement value is newly stored in the actual measurement value storage unit 37 (for example, every 30 minutes), the setting data update unit 43 performs the processing described below using the actual measurement value, and the intermediate data storage unit 44 performs the following processing. Store data as described. Then, the setting data is updated based on the data stored in the intermediate data storage unit 44 at a predetermined cycle such as every week, and the setting data stored in the setting data storage unit 12 in the generation unit 39 is updated. Update.

In the present embodiment, it is assumed that an actual measurement value as shown in FIG. 29 is obtained and stored in the actual measurement value storage unit 37 at each control time (for example, every unit time such as 30 minutes). That is, for each notebook PC, the notebook PC identifier, the charging rate (%) (= x _{i, ks} ), and the drive state (= v _{i, ks} ) are stored. It is assumed that data for each control time is accumulated in the measured value storage unit 37 cumulatively. Note that too old data may be discarded.

In the present embodiment, it is assumed that the setting data storage unit 12 stores data of a function f _i as shown in FIG. In this embodiment, by linearly approximating the curve shown in FIG. 6, the rate of change during charging (the rate of increase of the charging rate in the case of the charge mode Ch) and the rate of change during discharging (the charge in the case of the battery mode Ba). It is assumed that the function f _i is expressed as (rate of decrease in rate). That is, in the example of FIG. 30, for each notebook PC, the identifier of the notebook PC, the change rate at the time of charging, and the change rate at the time of discharge are registered.

  Next, a process performed each time a new actual measurement value is stored in the actual measurement value storage unit 37 will be described with reference to FIG. In the processing flow of FIG. 31, data representing the actual state of how the charging rate changes at the control time interval is generated.

  The setting data updating unit 43 initializes the counter i for the notebook PC to 1 (S501). Then, the setting data updating unit 43 determines whether the driving state of the notebook PCi included in the current actual measurement value stored in the actual measurement value storage unit 37 is the battery mode Ba or the charge mode Ch (S503).

If the notebook PCi is in the AC power supply mode Ac, the process proceeds to S509 without doing anything. On the other hand, when the driving state of the notebook PCi is the battery mode Ba or the charging mode Ch, the setting data updating unit 43 sets the charging rate x _{i, ks of the} notebook PCi included in the actual measurement value and one unit time ago. A difference (x _{i, ks} −x _{i, ks−1} ) from the charging rate x _{i, ks−1} of the notebook PCi included in the actually measured value is calculated (S505). Then, the setting data update unit 43 stores the driving state and the amount of change in the battery charge rate included in the actual measurement value in the intermediate data storage unit 44 in association with the identifier of the notebook PCi (S507). For example, data as shown in FIG. 32 is stored in the intermediate data storage unit 44. In the example of FIG. 32, the notebook PC identifier, the drive state, and the battery charge rate change amount are stored for each notebook PC. Since the entry is not registered in the AC power supply mode Ac, the number of entries registered by the notebook PC varies.

  Then, the setting data update unit 43 determines whether there is an unprocessed notebook PC (S509). For example, the notebook PCs on the setting data storage unit 12 are processed in order, and it is determined whether the last notebook PC has been processed. If there is an unprocessed notebook PC, the setting data update unit 43 increments the counter i by 1 (S511), and the process proceeds to S503. On the other hand, if there is no unprocessed notebook PC, the process for the actual measurement value ends.

Then, after a predetermined period such as one week, it corrects the data (Fig. 30) of the function f _i by carrying out the processing as shown in FIG. 33. That is, the setting data update unit 43 initializes the counter i of the notebook PC to 1 (S521). Then, the setting data update unit 43 reads the data of the driving state Ch for the notebook PCi in the intermediate data storage unit 44 (S523). In the example of FIG. 32, the fifth entry is read for the notebook PC 1. Then, the setting data update unit 43 calculates a charging rate increase average value t _i that is an average value of the charging rate change amounts included in the read data (S525).

Thereafter, the setting data updating unit 43 corrects the rate of change s _i during charging of the notebook PC _{i in} the setting data storage unit 12 with the charging rate increasing average value t _i (S527).

For example, the corrected charge rate of change s _i ^new is calculated using the weights w _{s, i} and w _{t, i} .
s _i ^new = 1 / (w _{s, i} + w _{t, i} ) * (w _{s, i} * s _i + w _{t, i} * t _i )

Fixed values may be set for the weights w _{s, i} and w _{t, i} . For example, w _{s, i} = 9 and w _{t, i} = 1 may be fixedly used.

Further, since it can be determined that the reliability is higher as the number n _{i of} entries read out when calculating the average value is larger _, it is also possible to use w _{t, i} = n _i . At this time, some fixed value is set in w _{s, i} . That is, it is expressed as follows.
s _i ^new = 1 / (w _{s, i} + n _i ) * (w _{s, i} * s _i + n _i * t _i )

Further, the setting data update unit 43 reads the data of the driving state Ba for the notebook PCi in the intermediate data storage unit 44 (S529). In the example of FIG. 32, the second entry is read out for the notebook PC 2. Then, the setting data update unit 43 calculates a charging rate decrease average value c _i that is an average value of the discharge rate change amount included in the read data (S531).

Thereafter, the setting data updating unit 43 corrects the rate of change d _i at the time of discharging the notebook PC _{i in} the setting data storage unit 12 with the charging rate decreasing average value c _i (S533).

For example, the corrected change rate d _i ^new during discharge is calculated using the weights w _{c, i} and w _{d, i} .
^{_{d i new = 1 / (w}} d, i + w c, i) * (w d, i * d i + w c, i * c i)

Similarly to the rate of change during charging, fixed values may be set for the weights w _{c, i} and w _{d, i} . For example, w _{d, i} = 9 and w _{c, i} = 1 may be fixedly used.

Similarly to the rate of change at the time of charging, it can be determined that the reliability is higher as the number n _{i of} the entries read out when calculating the average value is larger, so it can be used as w _{c, i} = n _i. It is. At this time, some fixed value is set in w _{d, i} . That is, it is expressed as follows.
d _i ^new = 1 / (w _{d, i} + n _i ) * (w _{d, i} * d _i + n _i * c _i )

  Thereafter, the setting data update unit 43 determines whether there is an unprocessed notebook PC (S535). For example, the notebook PCs on the setting data storage unit 12 are processed in order, and it is determined whether the last notebook PC has been processed. When there is an unprocessed notebook PC, the setting data update unit 43 increments the counter i by 1 (S537), and the process proceeds to S523. On the other hand, when there is no unprocessed notebook PC, the setting data update unit 43 clears the data (FIG. 32) stored in the intermediate data storage unit 44 (S539). However, instead of clearing, processing such as storing and saving in another storage area or setting a flag so as to be excluded from the next processing target is performed.

  If it does in this way, the change rate at the time of charge and the change rate at the time of discharge can be correct | amended according to the state of the battery of each notebook PC.

Note that the correction method may be other than the method described above. In particular, when a lithium ion battery used in a notebook PC reaches a certain value (for example, about 500 to 600 times), the subsequent capacity reduction is accelerated. That is, it is considered that the battery characteristics are greatly deviated from the model. Therefore, a method of changing the weighting rate w _s of the change rate during charging and the change rate during discharge according to the total number m of charging / discharging is also conceivable.

For example, if m <M (threshold), w _{s1, i} is used, and if m ≧ M, w _{s2, i} (<w _{s1, i} ) is used. Note that j = 1 or 2.
^{_{d i new = 1 / (w}} sj, i + n i) * (w sj, i * d i + n i * c i)

Similarly, if m <M (threshold), w _{d1, i} is used, and if m ≧ M, w _{d2, i} (<w _{d1, i} ) is used.
^{_{d i new = 1 / (w}} dj, i + n i) * (w dj, i * d i + n i * c i)

The total charge / discharge count m is stored in the intermediate data storage unit 44 by the process as shown in FIG. In the present embodiment, the charging direction is set to “+” when the charge mode Ch appears, and is set to “−” when the battery mode Ba appears. Even if the AC power supply mode Ac appears during that time, the charging direction is not changed. For example, when a mode transition of Ac, Ba, Ac, Ac, Ch, Ac occurs,-is set when the battery mode Ba appears, and + is set when the charge mode Ch appears.
First, the setting data update unit 43 initializes the counter i of the notebook PC to 1 (S541). Then, the setting data update unit 43 determines whether the driving state of the notebook PCi included in the current actual measurement value stored in the actual measurement value storage unit 37 is the battery mode Ba (S543). When the driving state of the notebook PCi is the battery mode Ba, the setting data update unit 43 determines whether the charging direction of the notebook PCi in the charge / discharge number table prepared in the intermediate data storage unit 44 is + ( S545). The charge / discharge frequency table is, for example, a table as shown in FIG. In the example of FIG. 35, for each notebook PC, the notebook PC identifier, the charging direction, and the total number of times of charging and discharging are registered. As initial values for the newly introduced notebook PC, the charging direction is set to “+”, and the total number of times of charging / discharging is set to “0”.

  Then, if the charging direction is +, the setting data updating unit 43 changes the charging direction for the notebook PCi to “−” in the charging / discharging frequency table, and increments the total charging / discharging frequency by 1 (S547). Then, the process proceeds to S555. On the other hand, also when the charging direction is “−”, the process proceeds to S555.

  On the other hand, when the driving state of the notebook PC i is not the battery mode Ba, the setting data update unit 43 determines whether the driving state is the charge mode Ch (S549). If the drive state is not the charge mode Ch, the process proceeds to S555. On the other hand, when the drive state is the charge mode Ch, the setting data update unit 43 determines whether the charge direction for the notebook PCi is “−” in the charge / discharge number table (S551). If the charging direction is “+”, the process proceeds to S555.

  On the other hand, if the charging direction is “−”, the setting data updating unit 43 changes the charging direction to “+” for the notebook PCi in the charge / discharge count table, and increments the total charge / discharge count by 1 (S553). Then, the process proceeds to S555.

  In S555, the setting data update unit 43 determines whether there is an unprocessed notebook PC (S555). If there is an unprocessed notebook PC, the setting data update unit 43 increments i by 1 (S557), and the process proceeds to S543. On the other hand, if there is no unprocessed notebook PC, the process ends.

  By performing the processing as described above, the total number of charge / discharge can be accumulated in the charge / discharge number table for each control time.

  If the model is appropriately changed as described above, for example, a change in peak power with time as shown in FIG. 36 can be obtained. In the example of FIG. 36, the curve a represents the case without control, the curve b represents the curve assumed in the simulation, and the curve d represents the curve when the model is appropriately changed as in the second embodiment. In this way, the peak power increases as the battery deteriorates, but the peak power is suppressed more than the curve c as shown in FIG. Specifically, the curve c has a peak power of −4.5% than that without control, whereas the curve d has a peak power of −6.7% than without control.

Note that the data of the function f _i for a notebook PC to be newly introduced into the system is set using a catalog value (for example, the time from when the battery is empty to the completion of charging, the time that can be used after full charging).

In addition, when a new notebook PC is introduced into the system, it is not controlled, and charging and discharging are repeated (for example, 20% or less->charging-> 80% or more->discharge-> 20% or less-> ..) Are stored, and the actual change data is stored, and when the next correction time of the function f _i comes, the initial value of the change rate may be determined from the actual change data.

  In addition, a threshold is set for the total number of times of charging / discharging, the rate of change during charging, and the rate of change during discharging, and when that value is exceeded, the fact that the battery life is approaching is displayed on the screen of the notebook PC to be controlled. You may make it call attention.

  In addition, although the example which correct | amends both the change rate at the time of discharge and the change rate at the time of charge was shown, only one side may be corrected.

[Embodiment 3]
In the first embodiment, since attention is paid only to the value of the objective function, there is a possibility of outputting only a control command that overuses the battery of a certain notebook PC. For example, there is a possibility that control is performed to increase only the total number of charging / discharging of the same notebook PC. This is not a problem in the short term, but increases the power consumption of the entire control target or increases the peak power in the medium to long term. In addition, expediting battery replacement will increase the cost of purchasing goods.

  Therefore, for example, by changing the configuration of the generation unit 39 to the generation unit 39b as shown in FIG. 37 and carrying out the processing as shown in FIG. 38, the battery of the specific notebook PC as described above is abused. Suppresses the output of various control commands.

Since the basic part of the generation unit 39b is the same as that of the generation unit 39, components having the same functions are denoted by the same reference numerals.
Specifically, the generation unit 39b replaces the selection unit 101, the candidate data storage unit 102, the output unit 103, and the fatigue data storage unit 104 with the determination unit 25 and the output unit 29 in the generation unit 39. Have.

  The selection unit 101 selects a control plan to be finally output via the output unit 103 from the simulation results and the third control plan stored in the result storage unit 23 and the third control plan storage unit 21. At this time, the selection unit 101 makes a determination using the data stored in the fatigue degree data storage unit 104. In addition, the selection unit 101 also determines a process end condition. The candidate data storage unit 102 stores simulation results to be output from the output unit 103 and candidate control plan data. The output unit 103 outputs the simulation result and the third control plan selected by the selection unit 101 to the remote control unit 41.

  Next, processing contents of the generation unit 39b shown in FIG. 37 will be described with reference to FIG. This processing is performed instead of the processing flow of FIG.

  The initialization unit 11 performs initialization processing (S601). The initialization process includes a process of setting an initial command value in the control plan. Also in the present embodiment, the initialization unit 11 sets the same control plan in the first control plan storage unit 15 and the second control plan storage unit 17.

For example, the initialization unit 11 sets the continuation command Du to all the first command values u ′ _i [k]. Setting the continuation command Du to all initial command values u ′ _i [k] by the initialization unit 11 means that a state in which nothing is initially controlled is assumed. In this way, the initialization unit 11 sets a fixed solution instead of a random solution as the initial solution for local search. U in the figure represents the solution of the search space.

Next, the simulation unit 19 performs an initial simulation process (S603). Specifically, the simulation unit 19 reads the undefined command value u ′ _i [k] from the second control plan storage unit 17 and performs the simulation process shown in FIGS. 8A and 8B. Then, the simulation unit 19 stores the simulation result in the result storage unit 23 in a state where the corresponding control plan can be specified. Further, the simulation unit 19 stores the generated third control plan including the executable command value in the third control plan storage unit 21. U _a in the figure represents an objective function value obtained as a simulation result.

The simulation unit 19 performs a simulation process using various setting data stored in the setting data storage unit 12. The setting data includes data of the functions f _i and g _i described above. Further, the simulation unit 19 measures actual values (measured values v _{i, ks in the} power supply mode and measured values x _{i, ks in the} charging rate) from each notebook PC and the power demand forecast D ^ks [k] obtained from the prediction system. Is also used.

  Further, in the present embodiment, the selection unit 101 registers an executable control plan (that is, a third control plan) for u in the set S. Specifically, the selection unit 101 reads out executable control plan data for u from the third control plan storage unit 21, stores it in the candidate data storage unit 102, and stores it in the result storage unit 23. The simulation result is also stored in the candidate data storage unit 102 in association with the control plan.

  Then, until it is determined that the termination condition described below is satisfied (S605: No route), the replacement unit 13 performs a replacement process corresponding to selection in the vicinity of the search (S607). Specifically, the replacement unit 13 generates a second control plan in which a part of the first control plan in the first control plan storage unit 15 is replaced, and the generated second control plan is used as the second control plan storage unit 17. To store. In the figure, v represents a solution selected from the vicinity and corresponds to the second control plan. The arrows represent the selection of solutions from the vicinity.

Thereafter, the simulation unit 19 performs a simulation process based on the second control plan stored in the second control plan storage unit 17 (S609). The simulation unit 19 stores the simulation result in the result storage unit 23 as in S603. Further, the simulation unit 19 stores the generated third control plan having the executable command value u _i [k] in the third control plan storage unit 21 as in S603. In the figure, v _a represents an objective function value obtained as a result of simulation for a solution selected from the vicinity.

Then, the selection unit 101 performs a determination process (S611). Specifically, the selection unit 101 determines that the improvement has been made when the simulation result after the replacement process is closer to the target than the simulation result before the replacement process. More specifically, the selection unit 101 determines the superiority or inferiority of the control plan based on a magnitude comparison based on a value of an objective function obtained by converting a plurality of purposes into a single purpose. For example, when the selection unit 101 aims to maximize the objective function value, the objective function value for the control plan after the replacement process is larger than the maximum value of the objective function value of the control plan before the replacement process. Judged as improved. On the other hand, when the objective function value is to be minimized, it is determined that the objective function value of the control plan after the replacement process is improved when it is smaller than the objective function value of the control plan before the replacement process. Determining the original and the objective function values u _a simulation result of the solution, and comparing the objective function value v _a simulation result of the solution v near was improved when u _a> v _a is satisfied. The latter case is shown in the figure.

If the objective function value of the current control schemes is judged to have improved, the selection unit 101, a v _a -w _a ≧ e (predetermined threshold value) and a viable control plan w excluded from the set S, the set An executable control plan for v is additionally registered in S (S613). Specifically, for each control plan w already registered in the set S, it is determined whether to satisfy the relationship that their objective function value w _e is v _a -w _a ≧ e. Data regarding the control plan w satisfying such a relationship is deleted from the candidate data storage unit 102. Further, the executable control plan and simulation result for v are stored in the candidate data storage unit 102.
Furthermore, the update unit 27 performs an update process (S615). Specifically, the updating unit 27 writes the second control plan in the second control plan storage unit 17 in the first control plan storage unit 15 as a new first control plan. In addition, the updating unit 27 updates the information in the result storage unit 23 so that the simulation result of the second control plan becomes the simulation result of the new first control plan. Then, the process returns to S605.

On the other hand, in this embodiment, even when it is determined that the objective function value of the current control plan has not improved, the selection unit 101 determines whether the current simulation result is in the vicinity of the best simulation result so far. Judgment is made (S617). Specifically, it is determined whether the objective function value v _a = u _a or v _a > u _a and v _a ≦ u _a + e. This is a relational expression for minimizing the objective function value. When maximizing, the inequality sign is inverted. When such a condition is satisfied, the selection unit 101 additionally registers an executable control plan for v in the set S (S619). More specifically, an executable control plan and simulation result for v are stored in the candidate data storage unit 102. Then, the process returns to S605. Even when the condition of S617 is not satisfied, the process returns to S605.
The generation unit 39b repeats the processing from S605 to S619 until the end condition is satisfied. For example, the generation unit 39b determines that the end condition is satisfied when the execution limit time for optimization has elapsed. Further, even when the solution is not improved for all elements in the vicinity of the search, it is determined that the termination condition is satisfied by assuming that the solution has fallen into the local optimal solution.

When the termination condition is satisfied, the selection unit 101 identifies a control plan from the data of the set S stored in the candidate data storage unit 102 based on the variation index of the battery fatigue level, and performs the control plan and simulation. The result is output to the output unit 103 (S621). In the process of S621, when only one control plan is included in the set S, that control plan is selected. On the other hand, when the set S includes a plurality of control plans, for example, the following battery fatigue degree variation index df (u) is calculated.

  q (i, u) represents the battery fatigue level of the notebook PCi when the control plan u is executed. q (j, u) represents the battery fatigue level of the notebook PCj when the control plan u is executed. It is assumed that the battery fatigue is higher as the battery fatigue level is higher. That is, the value having the largest absolute value of the difference in the battery fatigue level is set as the variation index df (u).

  Such a variation index df (u) is calculated for each control plan included in the set S, and a control plan for which the smallest variation index df (u) is calculated is selected. That is, as much as possible, by adopting a control plan that reduces the variation in the battery fatigue level, it is possible to prevent the battery of some notebook PCs from being overused.

  The battery fatigue level data is stored in, for example, the fatigue level data storage unit 104. In the example of FIG. 39, the notebook PC identifier and the battery fatigue level are stored for each notebook PC.

  For the battery fatigue level, for example, the total number of times of charging / discharging described in the second embodiment can be used. This is because the battery is consumed as the total number of charge / discharge cycles increases. For example, when the total number of times of charging / discharging so far is stored in the intermediate data storage unit 44, the charge / discharge number of times table (FIG. 35) is read, and when it is assumed that each control plan is actually executed. The temporary charge / discharge total number of times is obtained by temporarily updating the charge / discharge number table in the process of FIG. If this total number of times of charging / discharging is applied to the above-described equation, the variation index df (u) can be obtained. In this case, the control plan is selected so that the difference between the minimum value and the maximum value of the total number of times of charging / discharging becomes small.

  In addition, the battery fatigue level q is considered to be a condition that tends to deteriorate long-term use with a high remaining charge (eg 90% or more), or a moderate charge (eg between 20% and 80%) The battery fatigue level q (i, u) for each may be defined by considering the usage that changes in () as a condition in which deterioration does not easily occur.

Further, the variation index df (u) is not an absolute value of the difference but can be changed to the following formula.

  Upon completion of such processing, the output unit 29 outputs the third control plan, which is an executable command value, to the remote control unit 41 that actually controls each notebook PC. A simulation result may be output. Note that the simulation result may include not only the value of the objective function but also data on the peak power P, the battery charge amount C, and the total power consumption G.

  By performing the processing as described above, it is possible to suppress a situation in which the batteries of some notebook PCs deteriorate more rapidly than others. As a result, it is possible to suppress the peak power and the total power consumption by reducing the actual deviation from the simulation result.

  Although one embodiment of the present technology has been described above, the present technology is not limited to this. For example, the functional block configuration described above does not necessarily correspond to the actual program module configuration.

  The configuration of each storage area described above is an example, and the configuration as described above is not necessarily required. Further, in the processing flow, the processing order can be changed if the processing result does not change. Further, it may be executed in parallel.

  Further, the function of the control system (more specifically, one or a plurality of computers that perform charge / discharge control, which may be considered as a control device) may be realized by a plurality of computers instead of a single computer. .

  The above-described control system (more specifically, one or a plurality of computers that perform charge / discharge control) is a computer device, and as shown in FIG. 40, a memory 501 and a CPU (Central Processing Unit) 503. A hard disk drive (HDD) 505, a display control unit 507 connected to the display device 509, a drive device 513 for the removable disk 511, an input device 515, and a communication control unit 517 for connecting to the network. Are connected by a bus 519. An operating system (OS) and an application program for executing the processing in this embodiment are stored in the HDD 505, and are read from the HDD 505 to the memory 501 when executed by the CPU 503. The CPU 503 controls the display control unit 507, the communication control unit 517, and the drive device 513 in accordance with the processing content of the application program, and performs a predetermined operation. Further, data in the middle of processing is mainly stored in the memory 501, but may be stored in the HDD 505. In the embodiment of the present technology, an application program for performing the above-described processing is stored in a computer-readable removable disk 511 and distributed, and installed from the drive device 513 to the HDD 505. In some cases, the HDD 505 is installed via a network such as the Internet and the communication control unit 517. Such a computer apparatus realizes various functions as described above by organically cooperating hardware such as the CPU 503 and memory 501 described above with programs such as the OS and application programs. .

  The embodiments of the present technology described above are summarized as follows.

  In the control plan generation method according to the present embodiment, (A) a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices is changed to another By replacing with a command, a second control plan consisting of a plurality of second commands is generated, and (B) simulating the state transition when a plurality of devices are controlled according to the plurality of second commands in the second control plan, (C) Based on the simulation result, it is determined whether the state transition based on the second control plan is closer to the predetermined target than the state transition based on the first control plan. (D) The second control plan is closer to the predetermined target. When the computer repeatedly executes the process of updating the first control plan in (b1) and the simulation includes a continuation command that means continuation of the second command in the second control plan, Determining a state transition by performing the same control as the control by the second instruction before one than the continuation command.

  In this way, the effect of the replacement of the command value is likely to extend over a long period of time, so that it is easy to change the state transition. As a result, optimization can be expected to be promoted.

  In addition, the device includes a power storage device, and a power source to be used is selected from an externally supplied power source or a power storage device, and the command includes a choice of the type of power source used by the device and whether or not the power storage device needs to be charged. You may make it. Further, the predetermined target may be a target related to power consumption of the device. If it does in this way, the plan which controls power consumption using the electrical storage device with which equipment was equipped can be generated efficiently.

  Further, the target regarding power consumption may be minimization of the peak of power consumption. Since the control plan is generated based on the transition of the power consumption, temporary excessive power use can be suppressed in advance.

  In addition, the device includes a power storage device, and a power source to be used is selected from an externally supplied power source or a power storage device, and the command includes a choice of the type of power source used by the device and whether or not the power storage device needs to be charged. You may make it. The predetermined target may be a target related to charging of the power storage device. In this way, deterioration of the power storage device can be suppressed, and further, no trouble can occur with respect to the original use request for the power storage device.

  When the type of power source currently used by the device is unknown, (b2) in the simulation, it is assumed that the device currently uses power supplied from the outside, and the device further uses the power storage device. Assuming that the battery is currently charged, the power consumption of the device may be calculated. In this way, even when the type of power source currently used by the device is unknown, if it is assumed that the AC power source is used to the maximum extent, a control plan is generated assuming the worst situation regarding power consumption. be able to.

  If the type of power source currently used by the device is unknown, (b3) In the simulation, assuming that the device is currently using the power storage device as a power source, the charging rate of the power storage device is calculated. You may make it calculate. In this way, even when the type of power source currently used by the device is unknown, it is possible to generate a control plan based on the worst situation regarding power storage, assuming that the charging rate has decreased due to discharging. it can.

  When the power source used by the device cannot be selected at present, the power consumption of the device is calculated in (b4) simulation, assuming that the device currently uses the power supplied from the outside. It may be. Thus, even when the power source used by the device cannot be selected at present, it is possible to generate a control plan on the assumption of the worst situation regarding power consumption by assuming that the use of the external power source is resumed immediately. .

  In addition, when it is assumed that the state of charge of the power storage device does not satisfy a predetermined limit, (b5) the simulation gives a second command in the state of charge for the device including the power storage device to the power supply supplied from the outside. It may be performed by changing to an option. In this way, it is possible to avoid an inappropriate command or an unrealizable command that leads to excessive use of the power storage device, and to generate a control plan that is effective from the viewpoint of maintenance and practicality.

  Note that the control plan generation method according to the present embodiment performs the charging and discharging of the power storage device based on the actual characteristic value of at least one of charging and discharging of the power storage device before the simulation described above. A process for correcting at least one of the models may be further included. In this way, by reducing the error between the simulation and the actual, it is possible to avoid the result that the peak power and the like are largely deviated from the estimation.

  In addition, the control plan generation method according to the present embodiment has a predetermined plan when it is closer to a predetermined target and when it is in the vicinity of the second control plan that is not closer to the predetermined target but is closest to the predetermined target. The second control plan that is closer to the target and the second control plan that is close to the second control plan that is closest to the predetermined target are stored, and after the simulation is completed, the power storage device among the stored second control plans A process of selecting the second control plan that minimizes the variation in the index indicating the performance degradation may be further included. In this way, it is possible to prevent some of the power storage devices from jumping out and deteriorating.

  A program for causing a computer to perform the processing according to the above method can be created. The program can be a computer-readable storage medium such as a flexible disk, a CD-ROM, a magneto-optical disk, a semiconductor memory, a hard disk, or the like. It is stored in a storage device. The intermediate processing result is temporarily stored in a storage device such as a main memory.

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

(Appendix 1)
By replacing a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices with other commands, a second command comprising a plurality of second commands. Generate two control plans,
Simulating state transitions when controlling the plurality of devices according to the plurality of second commands in the second control plan,
Based on the simulation result, determine whether the state transition by the second control plan is closer to a predetermined target than the state transition by the first control plan,
The computer repeatedly executes the process of updating the first control plan with the second control plan when closer to the predetermined target,
In the simulation, when the second control plan includes a continuation command that means continuation of the second command, a state by performing the same control as the control by the second command one time before the continuation command Control plan generation method for obtaining transitions.

(Appendix 2)
The device includes a power storage device, and can select a power source to be used from a power source supplied from the outside or the power storage device,
The command includes options for the type of power source used by the device and whether or not the power storage device needs to be charged,
The control plan generation method according to claim 1, wherein the predetermined target is a target related to power consumption of the device.

(Appendix 3)
The control plan generation method according to attachment 2, wherein the target related to the power consumption of the device is to minimize the peak of the power consumption.

(Appendix 4)
The device includes a power storage device, and can select a power source to be used from a power source supplied from the outside or the power storage device,
The command includes options for the type of power source used by the device and whether or not the power storage device needs to be charged,
The control plan generation method according to claim 1, wherein the predetermined target is a target related to charging of the power storage device.

(Appendix 5)
When the type of power source currently used by the device is unknown, it is assumed in the simulation that the device currently uses the power supplied from the outside, and the device further uses the power storage device. The control plan generation method according to attachment 2, wherein the power consumption of the device is calculated on the assumption that the battery is currently charged.

(Appendix 6)
When the type of power source currently used by the device is unknown, in the simulation, it is assumed that the device is currently using the power storage device as a power source, and the charge rate or charge amount of the power storage device The control plan generation method according to appendix 4.

(Appendix 7)
If the power source used by the device cannot be selected at present, the power consumption of the device is calculated in the simulation assuming that the device is currently using the externally supplied power. Control plan generation method.

(Appendix 8)
When it is assumed that the state of charge of the power storage device does not satisfy a predetermined limit, the simulation selects the second command in the state of charge for the device including the power storage device as an option of the power supplied from the outside The control plan generation method according to appendix 2, which is performed by changing to

(Appendix 9)
By replacing a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices with other commands, a second command comprising a plurality of second commands. A replacement unit for generating a two-control plan;
A simulation unit that simulates a state transition when the plurality of devices are controlled according to the plurality of second commands in the second control plan;
A determination unit that determines whether the state transition based on the second control plan is closer to a predetermined target than the state transition based on the first control plan based on a simulation result;
An updating unit for updating the first control plan with the second control plan when closer to the predetermined target;
Have
When the simulation unit includes a continuation command that means continuation of the second command in the second control plan, the simulation unit performs the same control as the control by the second command immediately before the continuation command. A control plan generator that obtains state transitions.

(Appendix 10)
By replacing a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices with other commands, a second command comprising a plurality of second commands. Generate two control plans,
Simulating state transitions when controlling the plurality of devices according to the plurality of second commands in the second control plan,
Based on the simulation result, determine whether the state transition by the second control plan is closer to a predetermined target than the state transition by the first control plan,
The computer repeatedly executes the process of updating the first control plan with the second control plan when it is closer to the predetermined target. In the simulation, the continuation command means continuation of the second command in the second control plan. Is included, a program for causing the computer to execute a process for obtaining a state transition by performing the same control as the control by the second command one time before the continuation command.

(Appendix 11)
Before the simulation,
The control plan generation according to claim 2, further comprising: correcting a model of at least one of charging and discharging of the power storage device based on an actual characteristic value of at least one of charging and discharging of the power storage device Method.

(Appendix 12)
The second control plan closer to the predetermined target and the predetermined target when closer to the predetermined target and in the vicinity of the second control plan closest to the predetermined target but not closer to the predetermined target Keep the second control plan near the second control plan closest to the target,
The supplementary note 2 further including a process of selecting a second control plan that minimizes a variation in an index representing performance degradation of the power storage device from the held second control plans after the simulation is completed. Control plan generation method.

DESCRIPTION OF SYMBOLS 11 Initialization part 12 Setting data storage part 13 Replacement part 15 1st control plan memory | storage part 17 2nd control plan memory | storage part 19 Simulation part 21 3rd control plan memory | storage part 23 Result memory | storage part 25 Judgment part 27 Update part 29 Output part 31 Acquisition unit 33 Electric power demand prediction storage unit 35 Measurement unit 37 Actual measurement value storage unit 39 Generation unit 41 Remote control unit 43 Setting data update unit 44 Intermediate data storage unit 101 Selection unit 102 Candidate data storage unit 103 Output unit 104 Fatigue degree data storage unit

Claims (11)

By replacing a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices with other commands, a second command comprising a plurality of second commands. Generate two control plans,
Simulating state transitions when controlling the plurality of devices according to the plurality of second commands in the second control plan,
Based on the simulation result, determine whether the state transition by the second control plan is closer to a predetermined target than the state transition by the first control plan,
The computer repeatedly executes the process of updating the first control plan with the second control plan when closer to the predetermined target,
In the simulation, when the second control plan includes a continuation command that means continuation of the second command, a state by performing the same control as the control by the second command one time before the continuation command Control plan generation method for obtaining transitions. The device includes a power storage device, and can select a power source to be used from a power source supplied from the outside or the power storage device,
The command includes information indicating whether or not to charge the power storage device and an option of a type of power source used by the device,
The control plan generation method according to claim 1, wherein the predetermined target is a target related to power consumption of the device. The device includes a power storage device, and can select a power source to be used from a power source supplied from the outside or the power storage device,
The command includes information indicating whether or not to charge the power storage device and an option of a type of power source used by the device,
The control plan generation method according to claim 1, wherein the predetermined target is a target related to charging of the power storage device. When the type of power source currently used by the device is unknown, it is assumed in the simulation that the device currently uses the power supplied from the outside, and the device further uses the power storage device. The control plan generation method according to claim 2, wherein the power consumption of the device is calculated on the assumption that the device is currently charged. When the type of power source currently used by the device is unknown, in the simulation, it is assumed that the device is currently using the power storage device as a power source, and the charge rate or charge amount of the power storage device The control plan generation method according to claim 3. 3. When the power source used by the device cannot be currently selected, the power consumption of the device is calculated in the simulation on the assumption that the device currently uses the power supplied from the outside. The control plan generation method described. When it is assumed that the state of charge of the power storage device does not satisfy a predetermined limit, the simulation selects the second command in the state of charge for the device including the power storage device as an option of the power supplied from the outside The control plan generation method according to claim 2, wherein the control plan generation method is performed by changing to By replacing a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices with other commands, a second command comprising a plurality of second commands. A replacement unit for generating a two-control plan;
A simulation unit that simulates a state transition when the plurality of devices are controlled according to the plurality of second commands in the second control plan;
A determination unit that determines whether the state transition based on the second control plan is closer to a predetermined target than the state transition based on the first control plan based on a simulation result;
An updating unit for updating the first control plan with the second control plan when closer to the predetermined target;
Have
When the simulation unit includes a continuation command that means continuation of the second command in the second control plan, the simulation unit performs the same control as the control by the second command immediately before the continuation command. A control plan generator that obtains state transitions. By replacing a part of the first command included in the first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices with other commands, a second command comprising a plurality of second commands. Generate two control plans,
Simulating state transitions when controlling the plurality of devices according to the plurality of second commands in the second control plan,
Based on the simulation result, determine whether the state transition by the second control plan is closer to a predetermined target than the state transition by the first control plan,
When the second control plan is closer to the predetermined target, the computer repeatedly executes the process of updating the first control plan ,
In the simulation, when the second control plan includes a continuation command that means continuation of the second command, a state by performing the same control as the control by the second command one time before the continuation command A program for causing the computer to execute a process for obtaining a transition. Before the simulation,
Based on the actual property values at least one of the charging and discharging of the electric storage device, correct at least one of a model of the charge and discharge of said power storage device
The control plan generation method according to claim 2, wherein the computer further executes processing . The second control plan closer to the predetermined target and the predetermined target when closer to the predetermined target and in the vicinity of the second control plan closest to the predetermined target but not closer to the predetermined target Keep the second control plan near the second control plan closest to the target,
After the simulation is completed, the computer further executes a process of selecting a second control plan that minimizes the variation in the index indicating the performance deterioration of the power storage device from the held second control plans. The control plan generation method according to claim 2.

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