JP6020297B2 - Control plan generation method, control plan generation device, and control plan generation program - Google Patents

Description

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

  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. For example, in the prior art 1, there is a technology that controls a plurality of storage batteries as one large storage battery and controls the power uniformly to suppress peak power. In addition, the conventional technique 2 based on regression prediction that charges at a time when the predicted power consumption is small and discharges at a large time, or the conventional technique that charges in a time zone where a peak does not occur and discharges in a time zone where a peak comes due to past trends There is technology 3.

JP 2007-228676 A JP-A-2005-143218 JP 2005-328673 A JP 2011-229238 A

  However, the above-described conventional technique has a problem that the storage battery control plan for reducing the peak power may not be sufficiently optimized.

  For example, in the prior art 1, since a plurality of storage batteries are regarded as one large storage battery, the capacity of each storage battery is not fully used. Further, in the related art 2, when the predicted power consumption is different from the actual power consumption, the peak power cannot be reduced. Moreover, in the prior art 3, since the discharge amount of a storage battery is disperse | distributed, the reduction effect of peak power will become small.

  In one aspect, an object is to provide a control plan generation method, a control plan generation device, and a control plan generation program that can optimize a control plan for a storage battery that reduces peak power.

  In the first plan, the computer executes the following processes. The computer generates a second control plan in which a part of a 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 replaced with another command. The computer sets the maximum value for each time period of demand forecast of multiple power for a given system including multiple devices as the peak power value of each time zone, and controls multiple devices according to multiple commands in the second control plan Simulate the state transitions when Based on the simulation result, the computer 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. The computer updates the first control plan with the second control plan when closer to a predetermined target. The computer repeatedly executes the above processing, and if the second control plan includes a continuation command that means continuation of the command, the computer performs the same control as the command immediately before the continuation command. Find state transitions.

  According to one embodiment of the present invention, it is possible to optimize a storage battery control plan that reduces peak power.

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 a change in power when the notebook PC is used. 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 of each variable for each time. FIG. 6 is a diagram showing the transition of the charging rate during charging and discharging of the notebook PC. 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. FIG. 8A is a diagram (1) showing the flow of the main routine of the simulation process. FIG. 8B is a diagram (2) illustrating 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 flow of subroutine processing. 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 in the control system illustrated in FIG. 1. FIG. 16 is a diagram illustrating a flow of control plan generation processing by 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 diagram illustrating an example of a computer that executes a control plan generation program.

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

  In the first embodiment, as an example, it is proposed to use the battery of a notebook PC (Personal Computer) as surplus power in the office based on the power demand prediction in units of communities such as offices. The battery of the notebook PC is used as 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.

  Note that a minimum charge amount is left in the notebook PC 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, the outline of the control system according to the first embodiment will be described. FIG. 1 is a schematic diagram of a control system. The control system is connected to the network 50. For example, the control system is on the cloud 10 and is connected to a LAN (Local Area Network) of the office 20 via the network 50. Alternatively, the control system is directly connected to the LAN of the office 20. Illustration of the LAN of the office 20 is omitted.

  A plurality of notebook PCs are connected to the LAN of the office 20. The control system collects information such as the power mode and the battery charge rate 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 as in the control system performs power demand prediction with a plurality of probabilities. Here, a prediction for one day is assumed. The control system or the prediction system performs power demand prediction with probability on the basis of 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. For example, the prediction system may perform power demand prediction with probability using any conventional technique. In the case where a plurality of power demand predictions are made, the prediction system associates each power demand prediction with a probability that the power demand prediction is made. For example, a plurality of actual power demands on different dates are compared with the power demand prediction, and the probability of the power demand prediction is calculated from the ratio of the actual power demand that substantially matches the power demand prediction.

  The control system makes a charge / discharge plan for one day based on the power demand forecast with a plurality of probabilities and the current power usage state. 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 is a diagram illustrating an example of the power mode. The power mode is any one of an AC power mode, a battery mode, and a charge mode. Hereinafter, the AC power supply mode is referred to as an AC (Alternating Current) power supply mode.

  In the AC power supply mode, the notebook PC uses power supplied from an 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. For example, the power consumption in the AC power supply mode is 13W. Hereinafter, the AC power source is referred to as an AC power source.

  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. The power consumption in the battery mode is 0W.

  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. For example, the power consumption in the charge mode is 65W.

  FIG. 3 is a diagram illustrating an example of a change in power when the notebook PC is used. The horizontal axis in FIG. 3 indicates time, and the vertical axis indicates power consumption (W). In FIG. 3, 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 a diagram illustrating an example of a screen provided by software for controlling the power supply mode. The screen shown in FIG. 4 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 characteristics. Lithium ion batteries are greatly degraded by charging near 100% and discharging near 0%. Charging near 100% is called overcharging, and discharging around 0% is called overdischarging. Deterioration of the lithium ion battery is not accelerated by repeated recharging in a state where it is not fully discharged. That is, the lithium ion battery has no memory effect. Lithium ion batteries have a rechargeable capacity that is reduced by repeated charge and discharge.

  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 is not considered from the above characteristics.

  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. Each time, 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, the peak power is calculated as an index for one day in the entire office, and the target is to minimize the index. In addition, the battery charge amount at the end time is calculated as an index, and the index is maximized. The total power used in the office is calculated as an index, and the target is to minimize the index.

  Each objective function will be described below.

In the following description, the peak power P (Wh) is a real number variable indicating the peak power from time 0 at the office to the time k _e. Charge amount of the total sum C (Wh) is a real number variable indicating the sum of the battery charge at time k _e. The total electric power consumption G (Wh), the total notebook PC from time k _s to k _e is a variable indicating the total use amount of power used from the AC power source.

  For example, if peak power reduction is the maximum target, priority is given in the order of peak power P, charge amount sum C, and total power consumption G. 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 )

  Hereinafter, various parameters will be described.

A set M representing a notebook PC observed via the network at time k _s is defined by the following equation.

  A set K representing time is defined by the following equation.

A set K _o of time to be optimized is defined as follows.

A set K _p of time before optimization is defined as follows.

  A set U representing the power mode is defined as follows. Ac indicates an AC power supply mode, Ba indicates a battery mode, and Ch indicates a charge mode.

A variable u _i [k] representing a command value (power supply mode) for the notebook PCiεM at time kεK _o is defined as follows.

A variable representing the power mode of the notebook PCiεM at time kεK _o is defined as follows.

Constants v _{i, ks} representing the measured value of the power mode of the notebook PCiεM at time k _s are defined as follows.

と表記する。 A real variable indicating the charging rate of the notebook PCiεM at time kεK _o

Is written.

と表記する。 A constant indicating the measured value of the charging rate of the notebook PCiεM at time k _s

Is written.

と表記する。 A constant indicating the minimum charge of the notebook PCi∈M

Is written.

と表記する。 A constant indicating the maximum charging rate of notebook PCi∈M

Is written.

と表記する。 A constant representing the battery capacity (Wh) of the notebook PCi∈M

Is written.

と表記する。 Measure actual power consumption from time k∈K _p to k + 1 in the entire office

Is written.

と表記する。本実施例１では、電力需要予測Ｄ_ｍ ^ｋｓ［ｋ]は、使用電力量の実測値ｓ[ｋ](ｋ∈Ｋ_ｐ)に基づき時刻毎に、複数種類予測される。ここでは、同一時間帯の各電力需要予測Ｄ^ｋｓを区別するべく、添え字「ｍ」を用いる。 A constant (Wh) indicating the power demand forecast from time kεK _o to k + 1 in the office, obtained at time k _s

Is written. In the first embodiment, a plurality of types of power demand predictions D _m ^ks [k] are predicted for each time based on the actual measured power consumption value s [k] (kεK _p ). Here, the subscript “m” is used to distinguish each power demand forecast ^Dks in the same time zone.

と表記する。 A function representing the charging rate x _i [k + 1] at time k + 1 of the notebook PCi ∈ M in the charging rate x _i [k] and the power supply mode v _i [k].

Is written.

と表記する。 A function indicating the power (Wh) used by the notebook PCiεM in the charging rate x _i [k] and the power mode v _i [k] from the AC power source from time k to k + 1.

Is written.

FIG. 5 is a diagram illustrating the relationship of each variable for each time. The horizontal axis of FIG. 5 indicates time, and the vertical axis indicates the charging rate. In general, the command value u _i [k _s −1] and the actually measured values 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 the time k _s −1, the state changes to the power supply mode Ac when it reaches 100%, and the measured value v _{i , Ks} becomes the power supply mode Ac.

In the power supply modes other than Ac, the measured value x _{i, ks} of the charging rate does not coincide with the charging rate variable x _i [k _s ], 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 an objective function that is unipurposed by lexicographic order.

The constraint equations (1b) and (1c) indicate that the peak power is the maximum value of the past actual measurement value of the power consumption of the entire office and the power consumption of the entire office after battery control of the notebook PC. Represents. A plurality of types of power demand predictions D ^ks [k] in the constraint equation (1c) are predicted based on the actual measurement value s [k] (kεK _p ) of the power consumption, and include the power consumption of the notebook PC. Therefore, D ^ks [k] is adjusted by the sum of the differences 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. Since the power demand forecast ^Dks is generated for each power demand forecast with probability, each power demand forecast ^Dks is distinguished by m here.

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

Constraints (1e) represents the total consumed electric energy G is a sum of the amount of power used from AC power to all of the notebook PC from time k _s to k _e.

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

  The constraint equation (1g) means that it operates in the designated 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. Expression (A) represents that no discharging is performed when the charging rate is equal to or less than x ^l _i , and Expression (B) represents that charging is not performed when the charging rate is equal to or greater than x ^u _i .

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 is a diagram showing the transition of the charging rate during charging and discharging of the notebook PC. A curve 60a in FIG. 6 shows a charge curve, and a curve 60b shows a discharge curve. As can be seen from FIG. 6, the time required for charging is shorter than the time spent for discharging. Thus, since the battery is charged in a short time, as shown in FIG. 2, the amount of power used during charging (Ch) is large.

A function of the charging rate f _i is set based on the data shown in FIG. The amount of electric power used in each power supply mode is dependent on a CPU (Central Processing Unit) utilization, for example on the assumption that takes a constant value as shown in FIG. 2, the function g _i is also approximated by a linear function.

As described above, 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 a large number of combinations, and it is difficult to obtain a rigorous exact solution. Therefore, the approximate solution method based on the local search method is one of the practically effective means.

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

The replacement is an operation of replacing an arbitrary variable included in uεX (u _i [k] where kεK _o , iεM) 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 uεX is defined as follows.

N _m (u) = {vεX | 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] (kεK _o , iεM) 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.

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

  Hereinafter, simulation processing by the simulation unit in the control system shown in FIG. 1 (more specifically, one or more computers that execute charge / discharge control) will be described. The control system includes one or more computers that execute charge / discharge control. Further, the simulation unit is referred to as a simulation unit 19 in association with a diagram described later.

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 that 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 the amount of power used at each time in the optimization period, the executable command value u _i [k] at each time, and the time at each time for i∈M that is each notebook PC. To obtain the charging rate x _i [k].

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

  Setting the power mode Ac in response to an indeterminate command value that violates the constraint means that if the power mode according to the command value received from the control system violates the constraint, the notebook PC itself automatically Is equivalent to the operation 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. Even at time 7 thereafter, 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] for all the notebook PCi, the actual measurement value v _{i, ks of the} power supply mode, the actual measurement value x _{i, ks of the} charging rate _, and the whole Power demand prediction D ^ks [k] and actual measured power consumption value s [k] 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 charge amount total variable C, and the peak power variable P [k] (S101 to S105). In step S105, it is assumed that a plurality of predicted values of D _m ^ks [k] are input to p _m [k], respectively. 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 power demand forecast D _m at the corresponding time to the peak power variable P _m [k] at each time. Set ^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 usage power G _i (included in the result of simulatePC ()) calculated by the above-mentioned notebook PC unit simulation as the total usage power 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. The simulation unit 19 obtains the charge amount of the notebook PCi at the optimization end time by multiplying the capacity of the notebook PCi by the charging rate of the notebook PCi at the optimization end time (included in the result of simulatePC ()). 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 notebook PC unit simulation (subroutine process) described above, and 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 the 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 _m ^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.

In step S117, the simulation unit 19 executes the calculation on the right side of the arrow in step S117 while changing m of P _m [k], and among the executed calculation results, the value that maximizes the calculation result is Store in P [k].

Finally, the process proceeds to the process of FIG. 8b, and the simulation unit 19 searches for the maximum value from the measured value s [k] of the used electric energy at each time and the peak power variable P [k]. Specifically, the simulation unit 19 compares the measured value s [k] of the respective power usage, when k is less than k _s is the maximum value of them (S123~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. FIG. 13 is a diagram showing a flow of subroutine processing. In this subroutine processing, the simulation unit 19 inputs an undefined command value u ′ _i [k] for one notebook PCi, and performs optimization in the case of operating according to the undefined 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. Simulation unit 19 initializes, to 0, the power consumption variable _{G i} of the optimization period (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), the simulation unit 19 repeats the previous command value and sets it to the undetermined command value. (S209). When the uncertain command value is not the continuation command Du (S207, No), that is, when the command is an executable power supply mode (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 (step S211, Yes), 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.

And the simulation part 19 calculates | requires the charging rate of the next time based on the charging rate x _i [k] of the said time, and undecided 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 (S217, Yes), the simulation unit 19 changes the executable command value to Ac (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 apply (S217, No), the simulation unit 19 sets an unconfirmed command value to an executable command value (S223).

The simulation unit 19 obtains the use power 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). It adds the amount of electric power to the variable _{G i} of the power usage of past the note PC (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 of the control system shown in FIG. 1 will be described. Further, the replacement unit is referred to as a replacement unit 13 in association with a diagram described later. 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 of the command values with any of the executable power supply mode commands (Ac, Ba, or Ch) other than Du.

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

  FIG. 15 is a diagram illustrating a configuration example of a generation unit that performs local search in the control system 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. Accordingly, 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.

  FIG. 16 is a diagram illustrating a flow of control plan generation processing by local search. 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 FIG. 16 is a solution of the search space.

The simulation unit 19 performs an initial simulation process in S303. The simulation unit 19 reads the indeterminate 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. U _a in the figure 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. Furthermore, the simulation unit 19 uses the actual measurement values (measurement values v _{i, ks for the} power supply mode and the actual measurement values x _{i, ks} ) for each notebook PC and a plurality of power demand predictions D _m ^ks obtained from the prediction system. [K] is also used.

  If the end condition described below is not satisfied (S305, No), 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. In FIG. 16, v 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. Furthermore, 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 of FIG. 16, 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 a plurality of purposes. 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 determination unit 25 replaces the simulation result after the replacement with the maximum value for each time period of the demand prediction of the plurality of powers for the plurality of notebook PCs obtained by the constraint equation (1c) as the peak power value of each time period. It is determined whether or not it is closer to the target than the previous simulation result.

  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 includes, in addition to the generation unit 39, an acquisition unit 31 that acquires information on a plurality of power demand predictions from an external prediction system or an internal prediction processing unit corresponding to the prediction system, and acquired power demand prediction information Demand storage unit 33 that is a region for storing current, a measurement unit 35 that measures the current charging rate and current power supply mode in each notebook PC as actual values, and an actual value that is a region for storing measured actual values And a storage unit 37.

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 (S404).

  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, in such a situation, when the notebook PC is operating in the power supply mode Ba, 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 in S117 of FIG. 8A and S225 of FIG. 13 using the assumed power supply mode Ac.

  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 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. FIG. 19 is a diagram illustrating an example of command values when an executable region is set as a search space. In FIG. 19, the instruction value of each time zone of each PC is shown. 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.

  The initial values are all in the power mode Ac. In this example, unless the power mode of 15:00 is changed to Ba, the peak power amount in the predicted value of 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. 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. In FIG. 22 and FIG. 23, the instruction value of each PC for each time zone is shown. 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.

  For example, as shown in FIG. 25, assuming that it takes about 2 hours for the charging rate to drop from the maximum value to the minimum value, any command value at the time from 13:30 to 15:00 is replaced with Ba. If it knows, reduction of the peak electric energy estimated to occur at 15:00 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 where all the command values are the continuation command Du.

  Next, the effect of the control system according to the present embodiment will be described. The control system sets a maximum value for each time zone of demand prediction of a plurality of power for a plurality of notebook PCs as a peak power value for each time zone, and controls a plurality of devices in accordance with a plurality of second commands in the second control plan. Simulate state transitions. Then, the control system updates the first control plan with the second control plan when the target function value is smaller than the state transition according to the first control plan based on the simulation result. For this reason, the control plan of the storage battery which reduces peak electric power can be optimized.

  Next, other processes of the control system according to the present embodiment will be described. The simulation unit 19 of the control system calculates a target function value based on the objective function (2a) shown below, demand prediction of a plurality of electric powers with probability, and the second control plan, and the calculated target function value is When the target function value calculated so far is smaller, the first control plan may be updated with the second control plan. The objective functions C and G shown in the above embodiment may be used in combination.

P _m of the objective function (2a) is a peak value of power before control for each demand prediction. Q _m is a peak value of electric power after control for each demand forecast. For example, the peak value Q _m of the power after the control is after the peak power is suppressed by the second control plan, corresponding to the peak power. For example, the power at each time derived from the second control plan is specified, and the maximum power is calculated as the peak power. w _m is the probability of each demand forecast. The probability of each demand prediction may be specified in advance, or the probability of each demand prediction may be obtained from the tendency of the actual measured value of the past power demand.

  For example, the determination unit 25 acquires information on the target function value from the simulation unit 19 and determines whether the target function value calculated based on the second control plan is smaller than the target function value calculated so far. judge. And the update part 27 updates a 1st control plan by a 2nd control plan, when it determines with the determination part 25 being smaller than the target function value calculated until now. When the control system executes the above processing, the control plan of the storage battery that reduces the peak power can be optimized.

  Next, an example of a computer that executes a control plan generation program that implements the same function as the generation unit 39 of the control system shown in the above embodiment will be described. FIG. 26 is a diagram illustrating an example of a computer that executes a control plan generation program.

  As illustrated in FIG. 26, the computer 200 includes a CPU 201 that executes various arithmetic processes, an input device 202 that receives input of data from a user, and a display 203. The computer 200 also includes a reading device 204 that reads a program and the like from a storage medium, and an interface device 205 that exchanges data with other computers via a network. The computer 200 also includes a RAM 206 that temporarily stores various information and a hard disk device 207. The devices 201 to 207 are connected to the bus 208.

  The hard disk device 207 includes, for example, a replacement program 207a, a simulation program 207b, a determination program 207c, and an update program 207d. The CPU 201 reads out each program 207a, 207b and develops it in the RAM 206.

  The replacement program 207a functions as a replacement process 206a. The simulation program 207b functions as a simulation process 206b. The determination program 207c functions as a determination process 206c. The update program 207d functions as an update process 206d.

  For example, the replacement process 206 a corresponds to the replacement unit 13. The simulation process 206 b corresponds to the simulation unit 19. The determination process 206c corresponds to the determination unit 25. The update process 206 d corresponds to the update unit 27.

  Note that the programs 207a to 207d are not necessarily stored in the hard disk device 207 from the beginning. For example, each program is stored in a “portable physical medium” such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, and an IC card inserted into the computer 200. Then, the computer 200 may read out and execute each of the programs 207a to 207d from these.

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

(Appendix 1) Generate a second control plan in which a part of the 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 replaced with another command,
A maximum value for each time period of demand prediction of a plurality of power for a predetermined system including the plurality of apparatuses is set as a peak power value of each time period, and the plurality of apparatuses are set according to the plurality of instructions in the second control plan. Simulate state transitions when controlled,
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,
When the second control plan includes a continuation command that means continuation of the command, the process to simulate the state transition by performing the same control as the control by the command immediately before the continuation command. Required control plan generation method.

(Supplementary Note 2) Generate a second control plan in which a part of a 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 replaced with another command,
Simulating state transitions when controlling the plurality of devices according to the plurality of instructions in the second control plan,
For each of a plurality of demand forecasts with a probability for a predetermined system including the plurality of devices, a difference between a peak value of power in each demand forecast and a peak value in which power is suppressed by state transition of the second control plan is the probability. Calculate the target function value that is the sum of the values corrected by
When the target function value calculated based on the second control plan is smaller than the previously calculated target function value, the computer repeatedly executes the process of updating the first control plan with the second control plan,
When the second control plan includes a continuation command that means continuation of the command, the process to simulate the state transition by performing the same control as the control by the command immediately before the continuation command. Required control plan generation method.

(Supplementary note 3) Replacement for generating a second control plan by replacing a part of a command included in the first control plan with a plurality of control commands repeated at intervals with respect to a plurality of devices with another command And
A maximum value for each time period of demand prediction of a plurality of power for a predetermined system including the plurality of apparatuses is set as a peak power value of each time period, and the plurality of apparatuses are set according to the plurality of instructions in the second control plan. A simulation unit for simulating state transitions when controlled, and
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 second control plan includes a continuation command that means continuation of the command, the simulation unit obtains a state transition by performing the same control as the control by the command immediately before the continuation command. Control plan generator.

(Supplementary Note 4) A replacement unit that generates a second control plan in which a part of a command included in a first control plan that defines a plurality of control commands repeated at intervals with respect to a plurality of devices is replaced with another command. When,
Simulating state transitions when controlling the plurality of devices according to the plurality of instructions in the second control plan,
For each of a plurality of demand forecasts with a probability for a predetermined system including the plurality of devices, a difference between a peak value of power in each demand forecast and a peak value in which power is suppressed by state transition of the second control plan is the probability. A simulation unit for calculating a target function value obtained by totaling the values corrected by
An update unit that updates the first control plan with the second control plan when the target function value calculated based on the second control plan is smaller than the previously calculated target function value;
When the second control plan includes a continuation command that means continuation of the command, the simulation unit obtains a state transition by performing the same control as the control by the command immediately before the continuation command. Control plan generation method.

(Appendix 5)
Generate a second control plan in which 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 replaced with another command,
A maximum value for each time period of demand prediction of a plurality of power for a predetermined system including the plurality of apparatuses is set as a peak power value of each time period, and the plurality of apparatuses are set according to the plurality of instructions in the second control plan. Simulate state transitions when controlled,
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,
If the second control plan is closer to the predetermined target, the first control plan is updated repeatedly.
When the second control plan includes a continuation command that means continuation of the command, the process to simulate the state transition by performing the same control as the control by the command immediately before the continuation command. What control plan generator you want.

(Appendix 6)
A second control plan is generated by replacing a part of the commands 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,
Simulating state transitions when controlling the plurality of devices according to the plurality of instructions in the second control plan,
For each of a plurality of demand forecasts with a probability for a predetermined system including the plurality of devices, a difference between a peak value of power in each demand forecast and a peak value in which power is suppressed by state transition of the second control plan is the probability. Calculate the target function value that is the sum of the values corrected by
When the target function value calculated based on the second control plan is smaller than the previously calculated target function value, the process of updating the first control plan with the second control plan is repeatedly executed.
When the process to be simulated includes a continuation command meaning continuation of the command in the second control plan, a state by performing the same control as the control by the second command one time before the continuation command Control plan generation program for obtaining transitions.

19 simulation unit 25 determination unit 27 update unit 39 generation unit

Claims (4)

Generate a second control plan in which a part of the command included in the first control plan that defines a plurality of control commands repeated at intervals for a plurality of devices is replaced with other commands,
A maximum value for each time period of demand prediction of a plurality of power for a predetermined system including the plurality of apparatuses is set as a peak power value of each time period, and the plurality of apparatuses are set according to the plurality of instructions in the second control plan. Simulate state transitions when controlled,
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,
When the second control plan includes a continuation command that means continuation of the command, the process to simulate the state transition by performing the same control as the control by the command immediately before the continuation command. Required control plan generation method. Generate a second control plan in which a part of the command included in the first control plan that defines a plurality of control commands repeated at intervals for a plurality of devices is replaced with other commands,
Simulating state transitions when controlling the plurality of devices according to the plurality of instructions in the second control plan,
For each of a plurality of demand forecasts with a probability for a predetermined system including the plurality of devices, a difference between a peak value of power in each demand forecast and a peak value in which power is suppressed by state transition of the second control plan is the probability. Calculate the target function value that is the sum of the values corrected by
When the target function value calculated based on the second control plan is smaller than the previously calculated target function value, the computer repeatedly executes the process of updating the first control plan with the second control plan,
When the second control plan includes a continuation command that means continuation of the command, the process to simulate the state transition by performing the same control as the control by the command immediately before the continuation command. Required control plan generation method. A replacement unit that generates a second control plan by replacing a part of a 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 another command;
A maximum value for each time period of demand prediction of a plurality of power for a predetermined system including the plurality of apparatuses is set as a peak power value of each time period, and the plurality of apparatuses are set according to the plurality of instructions in the second control plan. A simulation unit for simulating state transitions when controlled, and
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 second control plan includes a continuation command that means continuation of the command, the simulation unit obtains a state transition by performing the same control as the control by the command immediately before the continuation command. Control plan generator. On the computer,
Generate a second control plan in which 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 replaced with another command,
A maximum value for each time period of demand prediction of a plurality of power for a predetermined system including the plurality of apparatuses is set as a peak power value of each time period, and the plurality of apparatuses are set according to the plurality of instructions in the second control plan. Simulate state transitions when controlled,
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,
If the second control plan is closer to the predetermined target, the first control plan is updated repeatedly.
When the second control plan includes a continuation command that means continuation of the command, the process to simulate the state transition by performing the same control as the control by the command immediately before the continuation command. What control plan generator you want.

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