WO2015170411A1 - Plan creation device and power management device - Google Patents

Abstract

[Problem] To provide a plan creation device and a power management device for creating a demand response (DR) plan which can reduce a consumer's load. [Solution] A plan creation device of an embodiment of the present invention comprises a creation unit, a calculation unit, and a selection unit. The creation unit creates a plurality of control plans which include the control content of consumer apparatuses. The calculation unit calculates the degree of load of control plans according to the activity state of the consumer and the control content of the apparatuses. From among the plurality of control plans, the selection unit selects a control plan in accordance with the degree of load.

Description

Plan creation device and power management device

Embodiments of the present invention relate to a plan creation device and a power management device.

In recent years, power generation companies, power retailers, DR aggregators, etc. have requested DR (Demand Consumers) to save electricity, and DR (Demand Response) is implemented in each country. Among DRs, those that automatically control devices owned by consumers in accordance with the requested power-saving content are called ADR (Autmatic） DR), which has become widespread mainly in the United States and other countries where power deregulation is progressing.

In ADR, since the consumer's equipment is automatically controlled, the degree of freedom of the consumer is lowered, and there is a tendency for the burden on the consumer to increase, such as a loss of comfort accompanying reduction in power consumption. For this reason, ADR which achieves the reduction of the target power consumption is considered, considering a consumer's burden.

As such ADR, a method has been proposed in which the degree of burden for each control content of the device is set in advance and a DR plan is created so that the degree of burden on the consumer is reduced. However, in this method, the behavioral state of the customer is not considered in setting the burden level. Generally, since the burden on the consumer changes according to the behavioral state, the conventional method has a limit in reducing the burden on the consumer.

JP 2010-166636 A

Provide a plan creation device and a power management device that create a DR plan that can reduce the burden on consumers.

The plan creation device according to an embodiment includes a creation unit, a calculation unit, and a selection unit. The creation unit creates a plurality of control plans including the control contents of the devices owned by the consumer. A calculation part calculates the burden degree of the control plan according to a consumer's action state and the control content of an apparatus. The selection unit selects a control plan from a plurality of control plans according to the degree of burden.

The block diagram which shows the function structure of the plan preparation apparatus which concerns on 1st Embodiment. The figure which shows an example of the reduction amount table. The figure which shows an example of an action state table. The figure which shows an example of a burden degree table. The figure which shows an example of a target reduction amount table. The figure which shows an example of a control plan table. The figure which shows the other example of a control plan table. The figure explaining the calculation method of the burden degree of a control plan. The block diagram which shows the hardware constitutions of the plan preparation apparatus which concerns on 1st Embodiment. The flowchart which shows operation | movement of the plan preparation apparatus which concerns on 1st Embodiment. The figure which shows an example of the action state table in 2nd Embodiment. The figure which shows the other example of the action state table in 2nd Embodiment. The figure which shows an example of a control plan table. The figure which shows an example of the reduction amount table. The figure which shows an example of the burden degree table for air-conditioning equipment. The figure which shows an example of the burden degree table for TV. The block diagram which shows the function structure of the plan preparation apparatus which concerns on 4th Embodiment. The figure which shows an example of an action state table. The figure which shows the action state selected by the burden calculation part. The figure which shows an example of a control plan table. The block diagram which shows the function structure of the power management apparatus which concerns on 7th Embodiment. The figure which shows an example of a simulation result. The figure which shows the other example of a simulation result.

Hereinafter, embodiments of the plan creation device and the power management device will be described with reference to the drawings.

(First embodiment)
First, the plan creation apparatus according to the first embodiment will be described with reference to FIGS. The plan creation device according to the present embodiment is a device that creates a control plan (DR plan) for equipment possessed by a consumer. The control plan defines the control content of the device during a predetermined control target period. The consumer includes a house, an office, a factory, and the like that have devices to be controlled.

The plan creation device calculates the degree of burden of the created control plan according to the behavioral state of the consumer, and selects the control plan according to the degree of burden. The degree of burden of the control plan is the burden felt by the user of the customer (resident of the house, etc.) when the device is controlled according to the control plan, that is, the degree of comfort loss or convenience loss Is a parameter that quantitatively represents. It means that the greater the degree of burden, the greater the burden. The consumer behavior state is a behavior state of a consumer user.

In the present embodiment, a case will be described in which each consumer has one device, or a plurality of devices, which are collectively controlled. The case where each consumer has a plurality of devices and they are individually controlled will be described in the third embodiment.

Here, FIG. 1 is a block diagram showing a functional configuration of the plan creation device according to the present embodiment. As shown in FIG. 1, the plan creation apparatus according to the present embodiment includes a reduction amount DB1, an action state DB2, a burden level DB3, a target reduction amount DB4, a control plan DB5, a control plan creation unit 6, A reduction amount calculation unit 7, a burden level calculation unit 8, and a control plan selection unit 9 are provided.

The reduction amount DB1 is a database in which the reduction amount of power consumption of the device is stored for each customer. FIG. 2 is a diagram illustrating an example of a reduction amount table stored in the reduction amount DB1. In this reduction amount table, the power consumption reduction amount (kW) when the device is controlled from ON to OFF, that is, the maximum reduction amount of the device is set for each consumer.

In this embodiment, since each consumer has one device, only one reduction amount table is prepared. On the other hand, when each customer has a plurality of devices, as many reduction tables as shown in FIG. 2 are prepared.

In FIG. 2, the reduction amount is electric power (kW), but may be electric power (kWh). The reduction amount of each device may be set in advance or may be updated by an external input.

The behavior state DB 2 is a database that stores, for each consumer, the behavior state of the customer in a period (control target period) that is subject to device control. FIG. 3 is a diagram illustrating an example of a behavior state table stored in the behavior state DB 2. In this behavior state table, the control target period is from 13:00 to 19:59, and the behavior state of each consumer is set every hour from 13:00 to 19:59, and the behavior of each consumer is set. The status is classified into two types, “at home” and “absent”. Note that the date and time or the time at which the control target period and the behavior state of the customer are stored can be arbitrarily set.

The behavior state set in the behavior state table is the behavior state of the customer estimated at the date or time. For example, according to this behavior state table, the behavior state of the customer 2 at 15:30 is estimated to be “at home”. However, the behavior state of the consumer obtained by referring to the behavior state table does not necessarily match the actual behavior state of the consumer.

In addition, the behavioral state is not limited to “at home” and “absent”, but can be classified into arbitrary types such as “danran”, “cooking”, “sleep”, “TV watching”, and the like. A case where there are three or more behavior states will be described in a third embodiment.

Furthermore, a plurality of action state tables may be prepared according to the weather, temperature, day of the week, season, and the like. For example, it is conceivable that the consumer's behavioral state changes due to the above factors, such as the possibility of being “at home” is higher on a rainy day than on a sunny day. If an action state table corresponding to these factors is prepared, it is possible to improve the estimation accuracy of the consumer's action state.

If the estimation accuracy of the behavioral state is low, the burden on the consumer may increase. For example, if a control plan for turning off an air conditioner created on the assumption that no customer is present is implemented while the customer is at home, the burden on the customer increases. However, as described above, by improving the estimation accuracy of the behavior state of the consumer, an increase in the burden on the consumer can be suppressed and the burden can be reduced.

In addition, in FIG. 3, although the action state is set for every hour, it is not restricted to this, You may set for every arbitrary periods, such as every 1 minute and every 15 minutes. Moreover, the behavior state of each consumer may be set in advance or may be updated by an external input.

The burden level DB 3 is a database in which the load level set according to the behavior state of the consumer and the control content of the device is stored for each device. The degree of burden is set by estimating the degree of burden felt by the consumer. FIG. 4 is a diagram illustrating an example of a burden degree table stored in the burden degree DB 3. This burden degree table is a burden degree table indicating the burden degree of one consumer, and such a burden degree table is stored for each consumer in the burden degree DB 3. In this burden degree table, there are two types of behavior states, “at home” and “absent”, and there are two types of control contents, “ON” and “OFF”, but this is not restrictive.

In the burden degree table of FIG. 4A, the burden degree is set to 0.0 when “ON” and “absent”, and the burden degree is 1.0 when “at home” and “OFF”. Is set to This assumes a lighting device that burdens the user when turned off while at home.

In the burden degree table of FIG. 4B, when “ON”, the burden degree is set to 0.0, and when “at home” and “OFF”, the burden degree is set to 1.0 and “not present”. When it is “OFF”, the degree of burden is set to 0.1. This assumes an air conditioner or the like in which the room temperature rises or falls when it is turned off when it is absent, and a burden on the user may come home.

The degree of burden on the customer varies depending on the behavioral state even when the device is controlled with the same control content. For example, although the burden is small even if the lighting is turned off when the customer is absent, the burden becomes large if the lighting is turned off while at home.

Thus, by setting the degree of burden according to the behavioral state, the estimation accuracy of the degree of burden can be improved. When the estimation accuracy of the degree of burden is low, selecting the control plan according to the degree of burden does not necessarily reduce the burden on the consumer, but the degree of burden estimated accurately as in this embodiment By selecting a control plan according to the situation, the burden on the consumer can be reduced.

Since the degree of burden is a relative value, it may be set to a value of 1.0 or more or a negative value. It is also possible to set the same degree of burden for a plurality of consumers. Furthermore, the degree of burden may be set in advance or may be updated by an external input.

As a method of setting and updating the burden level, for example, a method of taking a questionnaire to the user of the customer can be mentioned. Specifically, the user of each consumer may evaluate the degree of burden for each set of action state and control content, and set the degree of burden according to the evaluation. Since the degree of burden may change according to the external environment such as the season, it is preferable to update the burden regularly or irregularly.

The target reduction amount DB 4 is a database that stores the target reduction amount of power consumption in the control target period that is reduced by the entire customer through control according to the control plan. FIG. 5 is a diagram illustrating an example of a target reduction amount table stored in the target reduction amount DB 4. In this target reduction amount table, a plurality of hourly target reduction amounts (MW) from 13:00 to 19:59 are held for each time zone.

In FIG. 5, the target reduction amount is power (MW), but may be power amount (MWh). Further, instead of the target reduction amount, a maximum power consumption that is an upper limit value of power consumption for the entire consumer may be set. Furthermore, the target reduction amount for each time slot may be set in advance or may be updated by an external input.

The control plan DB 5 is a database that stores the control plan selected by the control plan selection unit 9. FIG. 6 is a diagram illustrating an example of a control plan table stored in the control plan DB 5. In the control plan shown in the control plan table of FIG. 6, the control target period is from 13:00 to 19:59, and the control content is set every hour. Further, there are two types of control content, “ON” or “OFF”. According to this control plan, the equipment of the customer 1 is turned on at 15:30.

FIG. 7 is a diagram showing another example of the control plan table. In this control plan table, the control target period is from 13:00 to 19:59 as in FIG. 6, and the control content is set every hour. In this control plan table, the control content is not represented by “ON” and “OFF” but by the reduction rate of the power consumption of the device. The reduction rate here is a reduction rate with respect to the maximum amount of power consumption of the device. The reduction rate set for each time zone is the reduction rate totaled in that time zone. For example, the equipment that the customer 1 has has a reduction rate of 20% from 15:00 to 15:59. To be controlled.

Such control of the equipment is realized by intermittent operation or change of operation parameters. For example, when the device of the consumer 1 is a lighting device, the power consumption may be reduced by intermittent operation in which ON and OFF are alternately repeated. Further, the power consumption may be reduced by turning off the first half 20% and turning on the second half 80% in the time period. Furthermore, power consumption may be reduced by reducing the illuminance of the lighting device.

Further, instead of the reduction rate, control content corresponding to the reduction rate may be set. Examples of such control content include operation parameter setting values (such as a set temperature of 23 ° C.) and controls such as “ON” and “OFF”. “ON” and “OFF” of the devices correspond to reduction rates of 0% and 100%, respectively. Therefore, the control plan table of FIG. 6 can be represented by the reduction rate, as in FIG.

In any case, the control content set in the control plan is associated with the reduction rate. In FIG. 7, the control content is set every hour, but is not limited thereto, and may be set every arbitrary period such as every minute or every 15 minutes.

The control plan creation unit 6 (hereinafter referred to as “creation unit 6”) creates a plurality of control plans as shown in FIGS. That is, the creation unit 6 creates a combination of the control contents of the devices that the customer has in the control target period. The control target period is arbitrary. Moreover, in FIG.6 and FIG.7, although the control content is set for every time slot | zone, the control plan may set one control content over a control object period.

In the present embodiment, the creating unit 6 creates one or a plurality of control plans at a time, and when the created control plan does not satisfy a predetermined condition, it is determined that one or two again. Create multiple control plans. By repeating such a loop process a plurality of times, the creating unit 6 creates a plurality of control plans. The creation method of the control plan by the creation unit 6 may be a known combination creation method such as a brute force method, or may be another method according to the search method of the control plan selection unit 9.

The reduction amount calculation unit 7 (hereinafter referred to as “calculation unit 7”) refers to the reduction amount table and calculates the reduction amount of power consumption according to the control plan created by the creation unit 6. As illustrated in FIG. 5, when the target reduction amount is set in each of a plurality of time zones, the creation unit 6 calculates the reduction amount for each time zone in which the target reduction amount is set.

Specifically, the calculation unit 7 first calculates the amount of reduction for each customer in each time zone in which the control content is set in the control plan table. For example, when calculating the reduction amount at the 13:00 level of the customer 1, the calculation unit 7 refers to the control plan table of FIG. 7 and acquires a reduction rate of 20%. If the reduction rate cannot be obtained directly from the control plan table, refer to the reduction rate table (not shown) indicating the correspondence between the control content and the reduction rate, and set the reduction rate of the control content obtained from the control plan table. get.

And the calculation part 7 acquires the maximum reduction amount 1.3 (kW) of the consumer 1 with reference to the reduction amount gable of FIG. From the maximum reduction amount 1.3 (kW) and the reduction rate 20% acquired in this way, the calculation unit 7 calculates the reduction amount in the 13:00 range of the customer 1 to 1.3 (kW) × 20% = 0.26 ( kW). The calculation unit 7 calculates the reduction amount for other customers by the same method, and sums them to obtain the reduction amount in the 13:00 level of the control plan.

In the case of this embodiment, since the time zone in which the target reduction amount is set matches the time zone in which the control content is set, the reduction amount in the time zone in which the control content is set is the reduction amount to be obtained. . On the other hand, if the time zone in which the target reduction amount is set does not match the time zone in which the control content is set, the target reduction amount is calculated from the reduction amount in the time zone in which the control content is set. What is necessary is just to calculate the amount of reduction in the time zone in which is set.

For example, when the target reduction amount is set every 2 hours, the sum of 2 hours of the reduction amount every hour of the control plan is the reduction amount to be obtained. In the following, it is assumed that the time zone in which the target reduction amount is set coincides with the time zone in which the control content is set. In this case, generally, reduction Q _m in time slot m of the control plan can be obtained from the following expression.
Q _m = Σ_n v _nm × q _n (1)

In Expression (1), n is a customer ID (1 ≦ n ≦ N), m is a time zone number (1 ≦ m ≦ M) in which a target reduction amount is set in the control target period, and v _nm is a customer n. The reduction rate in the time zone m, q _n is the maximum reduction amount of the customer n. Σ_n means that the sum of n = 1 to N is taken. The same applies to the following equations.

The burden level calculation unit 8 (hereinafter referred to as “calculation unit 8”) refers to the behavior state table and the load level table, and calculates the load level of the control plan created by the creation unit 6. Specifically, the calculation unit 8 first calculates the degree of burden of each consumer in each time zone. For example, when calculating the degree of burden at the 13:00 level of the customer 1, the calculation unit 8 refers to the control plan table in FIG. 6 to obtain the control content “OFF” and refers to the behavior state table in FIG. Then, the action state “absent” is acquired.

And the calculation part 8 acquires the burden degree 0.0 of "OFF" and "absence" with reference to the burden degree table of FIG. 4 based on the acquired control content and action state. The calculation unit 8 obtains the degree of burden for other customers and other time zones by the same method, and sums them to obtain the degree of burden of the control plan. FIG. 8A is a table showing the degree of burden obtained in this way. In FIG. 8A, only the degree of burden on the customer 1 is shown. In the case of FIG. 8A, the degree of burden on the customer 1 is 3.0.

In the above description, since the control contents set in the control plan and the control contents set in the burden degree table coincide, the burden degree can be acquired directly from the burden degree table. On the other hand, if the control contents set in the control plan and the control contents set in the burden degree table do not match like the control table in FIG. 7 and the burden degree table in FIG. The degree of burden cannot be obtained directly from the degree table. In such a case, the calculation part 8 should just calculate the weighted average of the burden degree set to the burden degree table.

For example, when calculating the degree of burden of the customer 1 at the 16:00 level, the calculation unit 8 refers to the control plan table in FIG. 7 to obtain the control content “20%” and displays the behavior state table in FIG. Referring to, the action state “at home” is acquired. Then, the calculation unit 8 refers to the burden degree table of FIG. 4 based on the acquired control content and action state, and sets the burden degree 1.0 of “OFF (100%)” and “at home” to “ ON (0%) ”and“ at-home ”burden degree of 0.0 are acquired.

The calculation unit 8 calculates a weighted average of the acquired burden levels, and calculates a load ratio of 0.8 (= 0.0 × 20% + 1.0 × (1-20%)). The calculation unit 8 obtains the degree of burden for other customers and other time zones by the same method, and sums them to obtain the degree of burden of the control plan. FIG. 8B is a table showing the degree of burden calculated in this way. In FIG. 8B, only the degree of burden on the customer 1 is shown. In the case of FIG. 8B, the degree of burden on the customer 1 is 2.4.

In this case, the control plan burden B (V) is obtained by the following equation.
B (V) = Σ_n Σ_m (b _n ^(1,1) × p _nm × v _nm ) + {b _n ^(2,1) × (1−p _nm ) × v _nm } + {b _n ^{(1,2 )} × p _nm × (1−v _nm )} + {b _n ^(2,2) × (1−p _nm ) × (1−v _nm )} (2)

In Equation (2), V is a matrix (control plan table) made up of elements v _nm , and b _n ^{(j, i)} is a j-row i-column burden degree table. As shown in FIG. 4, “at home” when i = 1, “absent” when i = 2, “ON” when j = 1, and “OFF” when j = 2. p _nm is a value that is 1 when “at home” and 0 when “not at home”. This expression (2) is also applicable when the control content is represented by “ON” and “OFF”. In this case, v _nm is 0 when the control content is “ON”, and v _nm is 1 when the control content is “OFF”.

Note that the degree of burden of the control plan calculated by the calculation unit 8 is not limited to the above. For example, calculate the degree of burden in each time zone of each customer, calculate the maximum value or average value for each consumer (or time zone), and calculate the total burden degree calculated for each customer (or time zone). It is good also as a burden degree of a control plan.

The control plan selection unit 9 (hereinafter referred to as “selection unit 9”) selects a control plan according to the degree of burden from among control plans in which the power consumption or power consumption of the devices controlled according to the control plan satisfies a predetermined condition. Select. The control plan selected by the selection unit 9 is stored in the control plan DB 5.

First, the selection unit 9 selects, from among a plurality of control plans created by the creation unit 6, a control plan whose power consumption is equal to or less than a predetermined value in all time zones. As in the present embodiment, when the target reduction amount C _m of each time zone is set, the selection unit 9, control reduction Q _m becomes the target reduction amount C _m or more in all time zones m Select a plan. When the upper limit value of the power consumption in each time zone is set instead of the target reduction power C _m , the selection unit 9 selects a control plan in which the power consumption is equal to or lower than the upper limit value in all the time zones m. To do.

The selection unit 9 selects a control plan that minimizes the degree of burden from the control plans thus selected, and stores it in the control plan DB 5. Selection of a control plan by the selection unit 9 can result in an optimization problem. When the above-described B (V) is used as the degree of burden, the control plan selected by the selection unit 9 is a solution to the following optimization problem.
Minimize: B (V)
Subject to
C _m ≤ Q _m , for all m in M
0 ≦ v _nm ≦ 1, for all v _nm in V ... (optimization formula 1)

Needless to say, for example, when the sign of the degree of burden B (V) is negative, it results in the problem of maximizing −B (V). In this case, -B (V) can also be defined as the comfort level. Further, in the above optimization formula 1, only the burden level −B (V) is targeted for minimization. However, for example, both the burden level −B (V) and the total reduction amount Σ_mQ _m can be performed. It is also possible to choose the one that is only optimal. For example, by using Minimize: a * B (V) + b * Σ_m (−Qm) instead of Minimize: B (V), the burden degree −B (V) is small and the total reduction amount Σ_mQ _m is A large control plan can be obtained using the constants a and b as tradeoff coefficients. That is, in this case, the evaluation function is minimized by using the linear sum of the power consumption (here, the total reduction amount Σ_mQ _m ) and the degree of burden −B (V) as an evaluation function. Here, the evaluation function means that the smaller the degree of burden, the smaller the value of the evaluation function, and the smaller the power consumption or power consumption (or the larger the total reduction amount), the evaluation function. Is a function that decreases the value of. Needless to say, for example, when the sign of the evaluation function is negative, the result is a maximization problem.

This optimization problem is a linear optimization problem with v _nm as a variable because each numerical value other than v _nm is a fixed value, and the control plan obtained as a solution has a reduction amount greater than or equal to the target reduction amount, that is, consumption Among control plans in which the electric power is a predetermined value or less, the control plan has the smallest burden. This linear optimization problem can be solved by using known solutions such as metaheuristic algorithms (genetic algorithm (GA), annealing method, etc.) and linear programming (single method (simplex method), interior point method, etc.). Can do.

The plan creation apparatus described above can be realized by using the computer apparatus 100 as basic hardware. As shown in FIG. 9, the computer device 100 includes a CPU 101, an input device 102, a display device 103, a communication device 104, a main storage unit 105, and an external storage unit 106. It is connected to the.

The input device 102 includes input devices such as a keyboard and a mouse, and outputs an operation signal generated by operating the input device to the CPU 101. The display device 103 is a display such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube). The communication device 104 has wireless or wired communication means, and communicates with an external device by a predetermined communication method.

The external storage unit 106 is, for example, a storage medium such as a hard disk, a memory device, a CD-R, a CD-RW, a DVD-RAM, or a DVD-R. The external storage unit 106 stores a program for causing the CPU 101 to execute processing by the plan creation device. Moreover, the data of each database with which a plan preparation apparatus is provided are memorize | stored.

The main storage unit 105 expands a program stored in the external storage unit 106 under the control of the CPU 101, and stores data necessary for executing the program, data generated by the execution of the program, and the like. When the CPU 101 executes the program, the functional configurations of the creation unit 6, the calculation unit 7, the calculation unit 8, and the selection unit 9 are realized. The main storage unit 105 is an arbitrary memory such as a nonvolatile memory.

The above program may be installed in advance in the computer apparatus 100, or may be stored in a storage medium such as a CD-ROM and installed in the computer apparatus 100 as appropriate. A configuration without the input unit 102, the display device 103, and the communication device 104 is also possible.

Next, the operation of the plan creation device according to this embodiment will be described with reference to FIG. FIG. 10 is a flowchart showing a plan creation method according to the present embodiment. Below, the plan creation method using GA is demonstrated.

When using the GA, first, the creating unit 6 creates a plurality of solutions (control plans) that are initial values using the initial steps of the GA, that is, a set of solutions (step S1). When the control content (reduction rate) in the control plan is set to be any of 2 ^L discrete values, the control content set in each time zone of each consumer is represented by L bits. When there are M time slots and N customers, each solution (control plan) is represented by LMN bits.

Next, each control plans created, calculator 7 calculates a reduction amount Q _m of the power consumption of each time slot m, calculator 8 calculates bear degree B of (V) (Step S2).

Selection unit 9, using a reduced amount Q _m and burden of B (V), determines the optimum condition is satisfied or not a set of solutions that have been created (Step S3). The optimal condition here is a condition for completing genetic operation in GA. As the optimum condition, for example, the improvement of the objective function (degree of burden) compared with the set of solutions of the previous generation becomes small, and the genetic operation is repeated a predetermined number of times (number of generations).

When the set of solutions of the current generation does not satisfy the optimal condition (NO in step S3), the process returns to step S1, and the creation unit 6 performs genetic operations such as selection, crossover, and mutation on the bit string of LMN bits. Apply and create a set of next generation solutions.

When the current generation solution set satisfies the optimal condition (YES in step S3), the selection unit 9 selects the solution (control plan) having the smallest objective function (burden) from the current generation solution (control plan) set. ) Is selected and stored in the control plan DB 5 (step S4).

By using GA, it is possible to select an approximate solution, that is, a control plan having the smallest burden among the searched control plans. GA is useful because it can be used not only for linear optimization problems but also when the burden is expressed by a non-linear function or a discontinuous function.

Note that, in step S2 of the GA, the degree of burden may be calculated only for a control plan in which the reduction amount is equal to or greater than the target reduction amount. In this case, in step S2, first, the reduction amount of the control plan is calculated and compared with the target reduction amount. Then, a control plan having a reduction amount less than the target reduction amount is removed, and the calculation unit 8 only has to calculate the degree of burden only for a control plan in which the reduction amount is equal to or greater than the target reduction amount. Thereby, the calculation amount in the plan creation method can be reduced.

As shown in FIG. 6, when the control content in the control plan is only two values, that is, “ON” or “OFF”, v _nm is either 0 or 1. In this case, the optimization problem for selecting the control plan is a problem called a 0-1 integer programming problem, and can be solved by any existing method. For example, a solution space full search using a tree structure route, a branch and bound method, or the like can be used as a solution. In this case, a method for obtaining a suboptimal solution by obtaining a solution (control plan) as a linear optimization problem with relaxed restrictions and rounding off the obtained solution to 0 or 1 can also be used.

As described above, according to the plan creation apparatus according to the present embodiment, a control plan with the smallest burden is selected (in the searched control plan) from among control plans whose reduction amount is equal to or greater than the target reduction amount. be able to. Therefore, it is possible to create a control plan that can reduce the burden on the consumer while achieving the target reduction in power consumption.

Moreover, since the plan creation apparatus according to the present embodiment selects the control plan using the degree of burden set according to the behavior state of the consumer, the burden on the consumer can be reduced.

Here, as another example of the plan creation method according to this embodiment, a plan creation method using the simplex method using the fact that the optimization formula 1 is a linear optimization problem will be described. The simplex method is generally known as a solution method specialized for linear optimization problems.

When using the simplex method, first, the creating unit 6 creates one solution (control plan) that is an initial value using the initial step of the simplex algorithm (step S1). The creation unit 6 also creates a simplex table used as auxiliary information when executing the simplex method.

Next, the created control plan, calculator 7 calculates a reduction amount Q _m of the power consumption of each time slot m, calculator 8 calculates bear degree B of (V) (Step S2). Calculation of reductions Q _m is equivalent to finding an increase limits of modified optimization problem in normal form. Further, since the burden level B (V) is replaced with the value z which is the objective function of the optimization problem transformed into the normal form, the calculation of the burden level B (V) is performed for the row for z in the simplex table. It corresponds to calculating.

Selection unit 9, using a reduced amount Q _m and burden of B (V), and determines whether this control scheme meets the optimization equation 1 (step S3). More specifically, it is determined whether or not the simplex table created for the control plan satisfies the optimum condition of the simplex method according to the optimization formula 1.

When the simplex table does not satisfy the optimum condition (NO in step S3), the selection unit 9 transmits the simplex table to the creation unit 6, and the process returns to step S1. Then, the creating unit 6 creates a new solution (control plan) according to the simplex method using the received simplex table.

If the simplex table satisfies the optimum condition (YES in step S3), the process proceeds to step S4, and the selection unit 9 selects this control plan and stores it in the control plan DB 5 (step S4). At this time, since the control plan that satisfies the optimal condition is represented in the form of a simplex table, the control plan is stored in the control plan DB 5 in place of the original optimization problem reduction rate v _nm .

By using the simplex method, it is possible to select an approximate solution, that is, a control plan having the smallest burden among searched control plans. Since the simplex method is a solution method specialized for the linear optimization problem, an approximate solution of the optimization equation 1 which is a linear optimization problem can be efficiently obtained.

(Second Embodiment)
Next, the plan creation apparatus according to the second embodiment will be described with reference to FIGS. 11 and 12. The plan creation device according to the present embodiment calculates the degree of burden based on the behavior state set as the probability, and selects the control plan so that the degree of burden is reduced.

FIG. 11 is a diagram illustrating an example of a behavior state table stored in the behavior state DB 2. According to this behavior state table, the behavior state at 13:00 is set as “at home” with a probability of 19% and “absent” with a probability of 81%. In the first embodiment, the behavior state is uniquely set for each time zone, but in this embodiment, the behavior state is set as a probability as shown in FIG.

The calculation unit 8 refers to the behavior state table and the burden level table as illustrated in FIG. 11 and calculates the load level of the control plan created by the creation unit 6. For example, in the case of the control plan of FIG. 7, the calculation unit 8 first calculates the degree of burden of each customer in each time zone. For example, when calculating the degree of burden due to the control content of the 16:00 level of the customer 1, the calculation unit 8 refers to the control plan table of FIG. 6 to acquire the control content “OFF”, and the behavior state table of FIG. The action states “48% at home” and “52% absent” are acquired.

Then, the calculation unit 8 refers to the burden level table of FIG. 4A based on the acquired control content and action state, and sets the “OFF” and “at-home” burden levels to 1.0 and “OFF”. ”And“ absent ”burden degree of 0.0 is acquired.

The calculating unit 8 calculates the degree of burden on the 16:00 level of the customer 1 as 1.0 × 48% + 0.0 × 52% = 0.48. The degree of burden calculated in this way is a so-called expected value. The calculation part 8 calculates | requires a burden degree by the same method also about the following time slot | zones and another customer, and calculates those sums as a burden degree of a control plan.

In the present embodiment, the control plan burden degree B (V) is obtained by the following equation.
B (V) = Σ_n Σ_m (b _n ^(1,1) × p _nmi × v _nm ) + {b _n ^(2,1) × p _nmi × (1−v _nm )} + {b _n ^{(1,2 )} × (1−p _nmi ) × v _nm } + {b _n ^(2,2) × (1−p _nmi ) × (1−v _nm )} (3)

In Formula (3), _pnmi is the probability of the behavior state i in the time zone m of the customer n. In Equation (2), p _nm is a value of 1 or 0 depending on the behavioral state, but this is because the behavioral state is uniquely set in the first embodiment. This is because the probability is 100% or 0%.

As in the first embodiment, the selection unit 9 selects a control plan having the smallest degree of burden B (V) calculated by the calculation unit 8 from among control plans whose reduction amount is equal to or greater than the target reduction amount. When the above-described B (V) is used as the degree of burden, the control plan selected by the selection unit 9 is a solution to the following optimization problem.
Minimize: B (V)
Subject to
Σ_n Q _m ≧ C _m , for all m in M
0 ≤ v _nm ≤ 1, for all v _nm in V ... (optimization formula 2)

Moreover, as shown in FIG. 12, an uncertainty may be set to the probability of the action state. As this uncertainty, for example, the fluctuation range of the probability of the action state can be used. This assumes that the probability of the behavioral state fluctuates within a range of ± uncertainty%. For example, the possibility of “at home” at 13:00 in FIG. 12 is assumed to vary within a range of 14% to 24%. In this case, the control plan selected by the selection unit 9 is a solution to the following optimization problem.
Minimize: B (V)
Subject to
Σ_n Q _m ≧ C _m , for all m in M
0 ≤ v _nm ≤ 1, for all v _nm in V
p * _nmi -e _nm <p _nmi <p * _nmi + e _nm ... (optimization formula 3)

However, p * nmi is a home probability in the time zone m of the customer n on FIG. In the above optimization equation 3, e _nm is the uncertainty in the time zone m of the customer n. Since this optimization problem is a quadratic optimization problem, that is, an optimization problem including the product of two independent variables (vnm and pnmi), it cannot be solved by linear programming. However, it can be solved using a solution of the existing quadratic optimization problem such as the above-mentioned GA.

The degree of uncertainty is not limited to the fluctuation range of the probability of the behavior state, and may be a standard error of the probability distribution of the fluctuation range, for example. In this case, the following optimization formula can be used instead of the above optimization formula 3.
Minimize: B (V)
Subject to
Σ_n Q _m ≧ C _m , for all m in M
0 ≤ v _nm ≤ 1, for all v _nm in V
P (B (V)> δ) <ε (optimization formula 4)

The constraint condition P (B (V)> δ) <ε in the optimization formula 4 means that the probability that the degree of burden B (V) is equal to or greater than the constant δ is equal to or less than the constant ε. This constraint can be solved by a quadratic programming method because it assumes a quadratic inequality when the probability distribution of the fluctuation range is assumed to be a normal distribution.

As described above, in this embodiment, since the behavioral state of each consumer is set as a probability, the estimation accuracy of the behavioral state can be improved. By minimizing the degree of burden calculated based on such behavior state, the burden on the consumer can be reduced.

(Third embodiment)
Next, a plan creation apparatus according to the third embodiment will be described with reference to FIGS. In the present embodiment, a case will be described in which each consumer has a plurality of devices, which can be individually controlled.

In the present embodiment, the burden degree table and the reduction amount table are prepared for each device that the customer has. The plan creation device creates a control plan in which the control content of each time zone is set for each device, and calculates the burden degree of the control plan using the burden degree table and the reduction amount table for each device.

In the following, each consumer has two devices, an air conditioner and a TV, and the behavioral state is classified into five categories of “cooking”, “TV viewing”, “group”, “sleep”, and “absence”. Shall be. The above “danran” is an action state that does not apply to any of “cooking”, “TV watching”, and “sleeping” although the consumer is at home.

FIG. 13A is a diagram illustrating an example of a control plan table for air conditioning equipment, and FIG. 13B is a diagram illustrating an example of a control plan table for TV. Thus, in this embodiment, the creation unit 6 creates a control plan for each device.

FIG. 14 is a diagram illustrating an example of a reduction amount table stored in the reduction amount DB 2. FIG. 14A is a reduction amount table for air conditioning equipment, and FIG. 14B is a reduction amount table for TV. . In these reduction amount tables, the maximum reduction amount (kW) of the air conditioner and the TV is set.

The calculation unit 7 calculates a reduction amount Qdm for each device in each time zone based on such a reduction amount table for each device.
Q _dm = Σ_n (q _dn × v _dnm ) (4)

In equation (4), d (1 ≦ d ≦ D) is the number of the device that each customer n has, v _dnm is the reduction rate in the time zone m of the device d of the customer n, and q _dn is the device of the customer n This is the maximum reduction amount of d. Reduction Q _m of the control program in each time period is the sum of reductions Q _dm, obtained by the following equation.
Q _m = Σ_d Q _dm = Σ_d Σ_n (q _dn × v _dnm ) (5)

FIG. 15 is a diagram illustrating an example of a burden degree table for an air conditioner, and the control content is classified into two, “ON” and “OFF”. In this burden degree table, when the air conditioner is “ON” and when the consumer is “absent”, the burden degree of the consumer is set to 0.0. In addition, the degree of burden is set to 1.0 when “OFF” and “TV viewing” and when “OFF” and “Group”.

On the other hand, the burden degree at the time of “OFF” and “cooking” is set to 1.2 which is larger than 1.0. This assumes that when heat is generated during cooking, the burden on the consumer due to turning off the air conditioning equipment increases.

Also, the degree of burden when “OFF” and “sleep” is set to 0.8, which is smaller than 1.0. This generally assumes that air conditioning is often weakened during sleep, so that the burden on the customer due to turning off the air conditioning equipment is reduced.

FIG. 16 is a diagram showing an example of a burden table for TV, and the control contents are classified into two, “ON” and “OFF”. In this burden level table, when the TV is “ON”, and when the customer is “absent”, “sleep”, and “group”, the burden level of the customer is set to 0.0. Further, the degree of burden when “OFF” and “TV viewing” are set to 1.0.

On the other hand, the degree of burden when “OFF” and “cooking” is set to 0.6, which is smaller than 1.0. This is because the frequency of viewing TV during cooking is reduced, and it is assumed that the burden on the consumer is smaller than in “TV viewing”.

Thus, in the present embodiment, the degree of burden corresponding to the characteristics of each device is set for each device. In addition, you may update the burden degree of each apparatus by a questionnaire etc. Moreover, the same burden degree table may be used for every apparatus with respect to a some consumer.

The calculating unit 8 refers to such a burden degree table, and first calculates the burden degree of the control plan for each device. The degree of burden B _d (V _d ) of the control plan for each device is obtained by the following formula obtained by extending the above formula (2) and formula (3).
B _d (V _d ) = Σ_n Σ_m (b _n ^(1,1) × p _{1 nm} × v _{d1 nm} ) + (b _n ^(2,1) × p _{2 nm} × v _d1nm ) + (b _n ^(1,2) × p _1nm × v _d2nm ) + (b _n ^(2,2) × p _2nm × v _d2nm )
= Σ_n Σ_m Σ_ (i, j) (b _n ^{(i, j)} × p _inm × v _djnm ) (6)

In the formula _{(6), v d1nm} the reduction rate in the time zone m equipment d of customers _{n, v d2nm = 1-v} d1nm, V d elements _v consisting _D1nm matrix _{V d1} and element _v consisting _D2nm matrix V A matrix in which _d2 is arranged vertically, b _n ^{(j, i)} is a burden degree table of j rows and i columns. As shown in FIG. 15, “cooking” when i = 1, “TV watching” when i = 2, “cooperation” when i = 3, “sleep” when i = 4, “sleeping” when i = 5 “Not present”, “ON” when j = 1, and “OFF” when j = 2. p _knm is a value that is 1 when the behavior state j = k in the time zone m of the customer n, and 0 otherwise.

Since the burden level B (V) of the control plan P is the sum of the burden levels B _d (V _d ), it is calculated by the following equation.
B (V) = Σ_d B _d (V _d ) (7)

The selection unit 9 selects the control plan with the smallest burden degree B (V) calculated by the calculation unit 8 from among the control plans whose reduction amount is equal to or greater than the target reduction amount. When the above-described B (V) is used as the degree of burden, the control plan selected by the selection unit 9 is a solution to the following optimization problem.
Minimize: B (V)
Subject to
C _m ≤ Q _m , for all m in M
0 ≤ v _d1nm ≤ 1, for all v _d1nm in V _d
v _d2nm = 1−v _d1nm (optimization formula 5)

As described above, according to the plan creation device according to the present embodiment, a control plan can be created even when a customer has a plurality of devices and they can be individually controlled. In addition, since the burden level and reduction amount of the control plan are calculated for each device using the burden level table and reduction amount table prepared for each device, the estimation accuracy of the burden level is improved and the burden on the consumer is reduced. be able to.

In the present embodiment, the control content of the burden level table may be different for each device. For example, the control content of the TV load level table may be “ON” and “OFF”, and the control content of the air conditioner load level table may be 3 or more “set temperatures”.

Also, as in the second embodiment, the degree of burden may be set as a probability. That is, the probability may be set for each of the five action states, the expected value of the degree of burden may be calculated, and the control plan may be selected based on the expected value. Thereby, a burden of a consumer can further be reduced.

(Fourth embodiment)
Next, a plan creation apparatus according to the fourth embodiment will be described with reference to FIGS. FIG. 17 is a block diagram illustrating a functional configuration of the plan creation device according to the present embodiment. As shown in FIG. 17, the plan creation device according to the present embodiment further includes an action state prediction unit 10. Other configurations are the same as those of the plan creation device according to the first embodiment.

The behavior state prediction unit 10 (hereinafter referred to as “prediction unit 10”) predicts the behavior state of each consumer. The predicted behavior state is stored in the behavior state DB2. The prediction unit 10 predicts the behavior state at a predetermined interval or before creating the control plan, and updates the behavior state table.

The prediction unit 10 predicts the behavior state using prediction information such as the actual behavior state of the customer before the control target period, weather, temperature, humidity, and power consumption in the control target period. The prediction unit 10 may include a parameter DB 11 that stores these various parameters, or may acquire each main parameter from an external device when predicting an action state.

As a behavior state prediction method, for example, there is a method using a behavior prediction model. The behavior prediction model is a function or a rule set that outputs a behavior state according to one or more input parameters. As behavior prediction model parameters, actual behavioral state, weather, temperature, and humidity history information can be used.

The behavior prediction model can be created and updated by machine learning of the relationship between these parameters and the actual behavior state using regression analysis such as linear regression or logistic regression.

The actual behavior state can be specified or estimated based on information acquired from a video or sensor installed at the consumer. When the behavior states output by the behavior prediction model are “at home” and “absent”, the behavior prediction model can be created from the power consumption information of the consumer.

FIG. 18 is a diagram illustrating an example of an action state table predicted by the prediction unit 10 and stored in the action state DB 2. In the case of this behavior state table, there are three behavior states that the prediction unit 10 outputs, “going out”, “group”, and “cooking”, and the prediction unit 10 outputs these probabilities for each time zone. Yes. In FIG. 18, “outing”, “cooking”, and “cooking” are not performed at the same time, so the sum of the probabilities of each action state is 1.

The calculation unit 8 may obtain the degree of burden of the control plan P using the expected value of the degree of burden calculated from such an action state table, or the action state having the highest probability as shown in FIG. You may select and obtain | require the burden degree of a control plan.

In each of the above-described embodiments, the behavioral state estimated in each time zone is set in advance, but the behavioral state of the customer may change depending on the season, etc., so when using the same behavioral state table There is a risk that the estimation accuracy of the behavioral state is lowered.

However, according to the present embodiment, the behavior state of each consumer is predicted and updated using the latest behavior prediction model. Therefore, the fall of the estimation accuracy of a behavioral state is suppressed, and a consumer's burden can be reduced.

(Fifth embodiment)
Next, a plan creation device according to a fifth embodiment will be described with reference to FIG. In the present embodiment, the creation unit 6 creates a control plan in which control details are set for each group of a plurality of consumers. The devices of a plurality of consumers included in the same group are controlled according to the same control content set in the control plan.

FIG. 20 is a diagram illustrating an example of a control plan table created by the creation unit 6. As shown in FIG. 20, in this control plan table, the control contents in each time zone of each group are set. A group is created by classifying the entire consumer by a predetermined method.

Classification methods for consumer groups include, for example, classification methods according to demographic attributes such as age, gender, family composition, region, and occupation, and behavioral state trends (for example, “absence of daytime”) ) And a clustering method such as a k-means method can be used. In addition, the consumer group may be classified according to a jurisdiction electric power company classification, a jurisdiction substation classification, and a jurisdiction transformer classification, which are classifications set on the consumer side by an electric power supplier such as an electric power company. Such a group may be created in advance or may be created when a control plan is created.

In the present embodiment, the reduction amount DB 1 stores a reduction amount table in which the maximum reduction amount is set for each group or for each customer. The behavior state DB 2 stores a behavior state table in which a behavior state is set for each group or for each customer. Further, the burden level DB 3 stores a load level table in which the load level is set for each group or for each customer.

The calculation unit 7 refers to the reduction amount table and the control plan table and calculates the reduction amount for each time zone. When the maximum reduction amount is set for each group, the reduction amount for each time period of each group can be calculated from the maximum reduction amount of the group and the control content (reduction rate) of the group. In addition, when the maximum reduction amount is set for each customer, each customer's time zone is calculated from the maximum reduction amount of the customer and the control content (reduction rate) of the group including the customer. The amount of reduction can be calculated. The reduction amount for each time zone of the control plan is calculated by totaling the reduction amounts for each customer or each group calculated for each time zone.

The calculation unit 8 refers to the behavior state table and the burden level table to calculate the load level of the control plan. When the behavior state and the degree of burden are set for each group, the degree of burden for each group according to the behavior state of the group and the control content can be calculated with reference to the burden degree table. In addition, when the behavior state and the degree of burden are set for each consumer, referring to the burden degree table, each consumer according to the behavior state of the consumer and the control content of the group including the consumer The degree of burden can be calculated.

The total degree of burden for each customer or group calculated in this way is calculated to calculate the degree of burden for the control plan. Note that one of the behavioral state and the degree of burden may be set for each group, and the other may be set for each consumer. In any case, the degree of burden for each consumer is first calculated, and the degree of burden on the control plan is calculated as the sum.

The selection unit 9 selects a control plan according to the reduction amount and the degree of burden of the control plan. The method for selecting the control plan is as described in the above embodiments.

As described above, according to the present embodiment, the control plan is created for each group. For this reason, it is possible to reduce the amount of calculation for creating the control plan and shorten the processing time for creating the control plan. In addition, since the devices can be controlled collectively for each group, the control can be simplified compared to the case where the devices are controlled for each consumer.

(Sixth embodiment)
Next, a plan creation device according to the sixth embodiment will be described. In each of the above-described embodiments, it is assumed that the control plan is created in advance and is not changed during the control target period. In the present embodiment, a case will be described in which the control plan is updated at predetermined time intervals during the control target period.

During the control target period of the control plan selected by the selection unit 9, the creation unit 6 updates the control plan at predetermined time intervals. Here, it is assumed that the control plan created by the creation unit 6 and selected by the selection unit 9 is a control plan that defines the control content of M time zones. In the present embodiment, the creation unit 6 updates the control plan after the time zone m + s (1 ≦ s) during the time zone m of the control plan.

For example, when the creation unit 6 updates the control plan of FIG. 7 every hour, the control plan after 14:00 is updated from 13:00 to 13:59, and from 14:00 to 14:59 The control plan after 15:00 is updated in the meantime, and this is repeated in subsequent time zones. The control plan update method is the same as the control plan creation method in which the update target period is the control target period.

The update interval of the control plan by the creation unit 6 is not limited to one hour, but is arbitrary. For example, when the update interval is 2 hours, the creation unit 6 updates the control plan after 14:00 from 13:00 to 13:59, and after 16:00 from 15:00 to 15:59 The control plan is updated and this is repeated in subsequent time zones.

In the above description, s = 1, that is, the creation unit 6 updates the control plan after the time zone m + 1 in the time zone m, but s may be 2 or more. For example, when s = 2, the creation unit 6 updates the control plan after 15:00 between 13:00 and 13:59, and controls after 16:00 between 14:00 and 14:59. Update the plan.

Furthermore, the update target period may be a predetermined period after the time zone m + s. For example, when the update target period is 3 hours, the creation unit 6 updates the control plan from 14:00 to 16:59 from 13:00 to 13:59, and from 14:00 to 14:00. The control plan from 15:00 to 17:59 is updated until 59 minutes.

As described above, in the plan creation device according to the present embodiment, the control plan is updated at predetermined time intervals. During the control target period of the control plan, the behavior state of the consumer may be updated by information such as the prediction unit 10 or a sensor installed in the consumer. In addition, the action state table to be used may change due to changes in weather and temperature.

According to the present embodiment, since the control plan being executed can be updated to a control plan according to the change in the behavior state during the control target period as described above, the estimation accuracy of the behavior state is improved, and the demand The burden on the house can be reduced.

In order to efficiently reflect the change in the behavioral state, for example, the control plan after 14:00 is updated between 13:50 and 13:59, and the update is performed immediately before the update target period. Is preferred.

(Seventh embodiment)
Next, a power management apparatus according to the seventh embodiment will be described with reference to FIGS. The power management apparatus according to the present embodiment operates a device owned by a consumer according to a control plan created by the plan creation apparatus. FIG. 21 is a block diagram illustrating a functional configuration of the power management apparatus according to the present embodiment. As shown in FIG. 21, the power management apparatus according to this embodiment includes a plan creation apparatus according to the first embodiment, a control plan communication unit 12, an output unit 13, an input unit 14, and a device control device Dn. And an input / output terminal Tn. Note that the power management apparatus may include any plan creation apparatus according to the second to sixth embodiments instead of the plan creation apparatus according to the first embodiment.

The control plan communication unit 12 (hereinafter, referred to as “communication unit 12”) is selected by the selection unit 9 of the plan creation device, and the control plan stored in the control plan DB 5 is transferred to the equipment of each customer via the network. Send. The function of the communication unit 12 is realized by the communication device 104.

The control plan transmitted from the communication unit 12 is received by the device control device Dn possessed by each customer n. The device control device Dn is a control device that controls each device that the customer n has, and operates each device of the customer n according to the control plan received from the communication unit 12.

Specifically, the device control device Dn intermittently operates each device or changes the operation parameter so that the reduction rate specified by the control content is achieved. The intermittent operation of the device is possible by, for example, intermittently cutting off the power supply to the device using a smart tap or the like. In addition, a method of directly transmitting a control signal to a device using a wireless communication protocol such as Echonet-Lite (registered trademark) or ZigBee (registered trademark) is also possible. Further, it is possible to intermittently cut off the power supply to some functions of the device.

ADR control according to the control plan is realized by such device control by the device control device Dn. That is, the power management apparatus according to the present embodiment constitutes an ADR system.

Further, the communication unit 12 may transmit a confirmation request to each customer terminal such as a personal computer, a tablet terminal, a mobile phone, and a smartphone via mail, a WEB site, and application software. The confirmation request is for confirming with the consumer whether or not the consumer's equipment may be controlled based on the control plan.

It is conceivable that the device control device Dn performs control only for the customer n who has agreed to perform control according to the control plan. That is, when the device control device Dn receives from the communication unit 12 information indicating consent inputted by an operator (user) of the customer n using the input / output terminal Tn that can communicate with the communication unit 12, the device control device Dn controls each device according to the control plan. Further, when a sufficient reduction amount cannot be obtained only by implementation to the agreed customer, the control plan may be created again by the plan creation device only for the agreed customer. In this case, the power management apparatus includes the input / output terminal Tn of each customer n.

Further, in this case, information indicating consent or disagreement is sent from the input / output terminal Tn of the customer n to the communication unit 12 via the network such as the device control device Dn or the general Internet, for example, the creation unit It may be determined whether 6 performs re-planning, and re-planning may be performed. Here, re-planning means that the selection unit 9 newly selects a control plan. The replanned control plan may be requested for confirmation from each customer n in the same way as the first time, and in order to avoid the replanning from continuing endlessly, confirmation requests are not made or k times (k It is conceivable that only a real number) is requested for confirmation. In addition, when presenting a confirmation request, for example, three types of responses “A. Agree only under the conditions of present presentation”, “B. Agree after re-planning”, and “C. Disagree” are collected from consumers. For the customer group that replied to the current plan, select “C for the customer group that replied A”, which is controlled by the presently presented control conditions. It is also conceivable to perform re-planning under the condition that “there is no control for the customer group that has been made”.

Furthermore, it is also possible to confirm whether or not control is possible for each device or time period by a confirmation request. The power management device performs control according to the control plan only for devices and time zones for which consent has been obtained, and re-creates a control plan for the devices and time zones for which consent has not been obtained using the plan creation device. May be.

The output unit 13 outputs information stored or generated by the power management apparatus as an image or sound, and provides it to the operator of the power management apparatus. The function of the output unit 13 is realized by the display device 103. The input unit 14 enables output or editing of information stored or generated by the power management apparatus by information input from an operator of the power management apparatus. The function of the input unit 14 is realized by the input device 102. The information output by the output unit 13 is preferably editable by information input by the operator via the input unit 14.

For example, the output unit 13 displays a simulation result when the device is controlled according to the control plan. Thereby, the operator can visually confirm the control plan and simulation result created by the plan creation device, and edit the control plan by inputting information using the input unit input device 102. Here, editing means correcting or changing a part of the control plan. FIG. 22 is a diagram illustrating an example of a simulation result. In FIG. 22, the horizontal axis represents time, the vertical axis represents the reduction amount, and the solid line in the graph represents the reduction amount of the control plan. As described above, the reduction amount Q _{m in} each time zone m of the control plan is obtained by the following formula based on the reduction rate v _nm of the customer n in the time zone m and the maximum reduction amount q _n of the customer n. It is done.
Q _m = Σ_n v _nm × q _n

The solid line in the graph, plotting the reduced amount Q _m of each time period m of the thus calculated control plan is created by connecting the reductions Q _m with a solid line. By displaying the simulation result of FIG. 22, the operator can visually confirm the reduction amount based on the control plan and the deviation from the target reduction amount. Note that the method of calculating the reduction amount of the control plan is not limited to this.

FIG. 23 is a diagram showing another example of the simulation result. In FIG. 23, the horizontal axis represents time, the vertical axis represents power consumption, the solid line in the graph represents the predicted power consumption when the control plan is not implemented, and the broken line represents the predicted power consumption when the control plan is implemented. Yes. When the power management apparatus acquires the predicted value of power consumption in advance, a simulation result as shown in FIG. 23 can be displayed.

By displaying such a simulation result, the operator can visually confirm the transition and maximum value of power consumption when the control plan is executed. This is advantageous when the control plan is created so that the maximum value of power consumption is not more than a predetermined value.

In addition, as a simulation result, the amount of reduction and power consumption may be displayed for each consumer and for each time zone, and the burden level of the entire consumer and the burden level for each customer may be displayed for each time series and each time zone. It may be displayed.

In addition, after performing control according to the control plan, it is possible to display the actual power consumption and the power consumption based on the simulation result in comparison with each other, or to display an error thereof.

Further, the output unit 13 is not limited to the simulation result, and may display a control plan table as shown in FIG. 7 on the screen. Thereby, the operator can visually confirm the control plan created by the plan creation device or edit the control plan by inputting information using the input device 102.

As described above, according to the power management apparatus according to the present embodiment, a consumer device can be operated according to a control plan with a small burden on the consumer created by the plan creation apparatus. Can be reduced.

Note that the present invention is not limited to the above-described embodiments as they are, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. Moreover, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the above embodiments. Further, for example, a configuration in which some components are deleted from all the components shown in each embodiment is also conceivable. Furthermore, you may combine suitably the component described in different embodiment.

1: reduction amount DB, 2: behavior state DB, 3: burden level DB, 4: target reduction amount DB, 5: control plan DB, 6: control plan creation unit, 7: reduction amount calculation unit, 8: load level calculation Unit: 9: control plan selection unit, 10: action state prediction unit, 11: parameter DB, 12: control plan communication unit, 13: output unit, 14: input unit, 100: computer device, 101: CPU, 102: input Device 103: display device 104: communication device 105: main storage unit 106: external storage unit 107: bus D: device control unit T: input / output terminal

Claims (20)

1. A creation unit that creates a plurality of control plans including the control content of the equipment that the customer has,
   A calculation unit that calculates the degree of burden of the control plan according to the behavior state of the consumer and the control content of the device;
   A selection unit that selects a control plan according to the degree of burden from a plurality of the control plans,
   A plan creation device comprising:
2. The plan creation device according to claim 1, wherein the selection unit selects a control plan in which power consumption or power consumption of the consumer satisfies a predetermined condition and the degree of burden is minimum.
3. The plan creation device according to claim 1, wherein the selection unit selects a control plan that minimizes an evaluation function including power consumption or power consumption and the degree of burden.
4. The calculation unit according to any one of claims 1 to 3, wherein the calculation unit calculates a degree of burden of the control plan from a degree of burden set in advance according to the behavior state of the consumer and the control content of the device. The planning device described in 1.
5. The predetermined condition is that the power consumption or power consumption reduction amount of the consumer is a predetermined value or more, or the consumer power consumption or power consumption amount is a predetermined value or less. The plan creation device according to any one of claims 4 to 4.
6. 6. The plan creation device according to claim 1, wherein the behavior state of the consumer is preset according to at least one of a time zone, weather, temperature, day of the week, and season.
7. The plan creation device according to any one of claims 1 to 6, wherein the behavior state of the consumer is set as a probability.
8. The plan creation device according to any one of claims 1 to 7, wherein the creation unit creates the control plan for each device of the consumer.
9. The plan creation device according to any one of claims 1 to 8, further comprising a prediction unit that predicts the behavior state of the consumer.
10. The plan creation device according to any one of claims 1 to 9, wherein the creation unit creates the control plan for each group of the plurality of consumers.
11. The plan creation device according to any one of claims 1 to 10, wherein the creation unit updates the control plan at a predetermined time interval during a control target period of the control plan.
12. 12. The plan creation device according to claim 1, wherein the selection unit selects the control plan using a linear programming method.
13. The plan creation apparatus according to claim 12, wherein the linear programming method is a simplex method.
14. 12. The plan creation device according to claim 1, wherein the selection unit selects the control plan using a metaheuristic algorithm.
15. The plan creation device according to claim 14, wherein the metaheuristic algorithm is a genetic algorithm.
16. A plan creation device according to any one of claims 1 to 15,
    A communication unit that transmits the control plan selected by the selection unit of the plan creation device to a device control device that controls a device of the consumer;
    A power management apparatus comprising:
17. The power management device according to claim 16, wherein the device control device controls the device according to the control plan when receiving information indicating consent of the consumer to the control plan.
18. The power management device according to claim 16 or 17, wherein the selection unit newly selects a control plan when the communication unit receives information indicating consent or disagreement of the consumer with respect to the control plan. .
19. The output unit according to any one of claims 16 to 18, further comprising an output unit that outputs a simulation result when the control of the device is performed according to the control plan selected by the selection unit of the plan creation device. Power management device.
20. The power management apparatus according to any one of claims 16 to 19, further comprising an input unit for editing the control plan.

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