Power adjustment device and power adjustment method

Abstract

The objective of the present invention is to reduce electricity charges while taking into consideration comfort. [Solution] The system includes a control optimization unit 100 that performs optimization to calculate a control plan power amount that minimizes an objective function including the sum of the products of the control plan power amount and the electricity rate unit price in accordance with constraint conditions, and a control unit 11 that controls the controlled target equipment, which is the equipment to be controlled, based on the control plan power amount calculated as a result of the optimization, wherein the control plan power amount is the hourly distribution of the power amount within the time period to be predicted, the product in the objective function is the hourly product of the control plan power amount, and the constraint conditions are set so that the sum of the control plan power amounts for a specified range of time is proportional to the sum of the demand forecast values ​​for the specified range of time.

Description

[Technical field]

[0001] The present invention relates to a power adjustment device and a power adjustment method. [Background technology]

[0002] Due to the recent increase in electricity prices, it has become important to reduce the electricity costs of electrical equipment, particularly air conditioners. However, simply reducing electricity costs comes at the expense of comfort.

[0003] Patent Document 1 discloses a generation device, system, and program (see abstract) that is "a control device that generates control information for controlling a consumer's equipment, and includes an information acquisition unit that acquires information on fluctuating electricity rate unit prices and information on the consumer's power usage, and a control information generation unit that generates control information for the consumer's equipment in a target time period based on the electricity rate unit price for a target time period during which the equipment is controlled and information on the consumer's power usage in time periods after the target time period" (see abstract). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2022-31583 A Summary of the Invention [Problem to be solved by the invention]

[0005] The technology described in Patent Document 1 uses a local greedy method, which may result in a local solution and does not necessarily result in an optimal solution (the lowest price in this case). Therefore, although the technology described in Patent Document 1 seeks to find the lowest possible electricity price, the electricity price is not necessarily the lowest.

[0006] The present invention has been made in view of the above background, and an object of the present invention is to reduce electricity bills while taking comfort into consideration. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, the present invention comprises an optimization unit that calculates a control plan power amount that minimizes an objective function including the sum of the products of control plan power amounts and electricity rate units according to constraint conditions, and a control unit that controls a controlled target device, which is an equipment to be controlled, based on the control plan power amount calculated as a result of the optimization, wherein the control plan power amount is an hourly distribution of power amounts within a time period to be predicted, the product in the objective function is an hourly product of the control plan power amounts, and the constraint conditions are set so that the sum of the control plan power amounts for a specified range of time is proportional to the sum of the demand forecast values ​​for the specified range of time. Other solutions will be described in the embodiments as appropriate. Effect of the Invention

[0008] According to the present invention, it is possible to reduce electricity charges while taking comfort into consideration. [Brief description of the drawings]

[0009] [Figure 1] 1 is a conceptual diagram of an air conditioning control system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a functional block diagram of a control device according to the present embodiment. [Diagram 3] FIG. 2 is a diagram showing a configuration of a control optimization unit operating inside the control device. [Figure 4] FIG. 2 is a hardware configuration diagram of a control device according to the present embodiment. [Figure 5A] FIG. 1 is a diagram (part 1) for explaining power budget constraint conditions. [Figure 5B] FIG. 2 is a diagram (part 2) for explaining power budget constraint conditions. [Figure 6A] FIG. 1 is a diagram (part 1) for explaining the effect of introducing a maximum power amount value. [Figure 6B] FIG. 2 is a diagram (part 2) for explaining the effect of introducing the maximum power amount value. [Figure 7A] FIG. 13 is a diagram showing an example of a result of the optimization process (part 1). [Figure 7B] FIG. 2 is a diagram showing an example of a result of the optimization process (part 2). [Figure 8] 4 is a flowchart showing the overall procedure of a power amount adjustment method according to the present embodiment. [Figure 9] 4 is a flowchart showing a detailed procedure of a pre-processing performed in this embodiment. [Figure 10] 4 is a flowchart showing the procedure of a control command generating process performed in the present embodiment. [Figure 11A] FIG. 11 is a diagram showing changes in demand forecast values ​​over time. [Figure 11B] FIG. 11 is a diagram showing a change over time in a control plan power amount. [Figure 12] 6 is a flowchart showing a detailed procedure of an output process performed in the present embodiment. [Figure 13] FIG. 2 is a diagram illustrating an example of demand forecast information. [Figure 14] FIG. 4 is a diagram illustrating an example of electricity rate unit price information. [Figure 15] FIG. 11 is a diagram illustrating an example of priority order information. [Figure 16] FIG. 4 is a diagram showing an example of current value information. [Figure 17] FIG. 11 is a diagram illustrating an example of control plan information. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Next, a mode for carrying out the present invention (referred to as an "embodiment") will be described in detail with reference to the drawings as appropriate.

[0011] <Air conditioning control system Z> FIG. 1 is a conceptual diagram of an air conditioning control system Z in this embodiment.

[0012] The air conditioning control system Z in this embodiment is assumed to be installed in an office or the like, and a plurality of indoor units 3 are connected to one outdoor unit 2. Furthermore, the plurality of outdoor units 2 are controlled by one control device 1. In this embodiment, it is assumed that the air conditioning control system Z as shown in Fig. 1 is installed on each floor, but this is not limiting, and it may be installed on each building, etc.

[0013] The outdoor unit 2 and the indoor unit 3 are collectively referred to as an air conditioner 4. In this embodiment, the outdoor unit 2 is a "control target device." As shown in Fig. 1, there are a plurality of outdoor units 2 that are control target devices.

[0014] <Control device 1> FIG. 2 is a functional block diagram of the control device 1 according to the present embodiment.

[0015] In the control device 1, which is a power amount adjusting device, a control unit 11 and a control optimization unit 100 are executed.

[0016] The control optimization unit 100 optimizes the control plan power amount 602 (see FIG. 7B) and controls the outdoor unit 2 based on the optimization result. The control plan power amount 602 is the hourly distribution of the power amount within the time period to be predicted.

[0017] The control unit 11 controls the control target device (outdoor unit 2), which is the device to be controlled, based on the control plan power amount 602 calculated as a result of the optimization.

[0018] <Control optimization unit 100> FIG. 3 is a diagram showing the configuration of a control optimization unit 100 that operates inside the control device 1. As shown in FIG.

[0019] In the control optimization unit 100 , an optimization processing unit 110 is executed by an execution unit 121 .

[0020] The optimization processing unit 110 is made up of a preprocessing unit 111 , an optimization unit 112 , a control command generating unit 113 , and an output processing unit 114 .

[0021] The execution unit 121 performs main processing that calls the processing performed by each sub-module (optimization processing unit 110). The pre-processing unit 111 performs pre-processing such as data combination on the input data 131 that is input. The optimization unit 112 performs optimization to calculate a control plan power amount 602 that minimizes an objective function including the sum of the products of the control plan power amount 602 (see FIG. 7B) and the power rate unit price in accordance with the constraint conditions (under the constraint conditions). The optimization unit 112 also performs optimization using linear programming.

[0022] The control command generation unit 113 generates a control command for each of the plurality of control target devices based on the control plan power amount 602 calculated as a result of the optimization. The output processing unit 114 post-processes the optimization result and the control command to generate control plan information 132A for the control unit 11 to control the outdoor unit 2, and outputs the generated control plan information 132A to the control unit 11 as output data 132.

[0023] The input data 131 includes floor-specific demand forecast information 131A, power rate unit price information 131B, priority order information 131C, and the like. In the floor-specific demand forecast information 131A, a demand forecast value 601 (see FIG. 7A) is written for each floor. The demand forecast value 601 is a power amount predicted based on information on past actual power demand collected by the control unit 11. For example, the demand forecast information 131A for August of this year is calculated from information on past actual power demand in August. Note that in a system that does not have "power rate unit price information 131B", input is not essential. In that case, the power rate unit price information 131B is replaced with appropriate dummy data, for example, "power reduction priority data for each time period".

[0024] In this embodiment, the demand forecast value 601 and the control plan power amount 602 are the power amounts of all the devices such as the outdoor units 2, PCs, lighting, etc. in the floor or building that is the subject of control of the air-conditioning control system Z. However, if the control device 1 can collect only the amount of power used by each outdoor unit 2, it is also possible for the demand forecast value 601 and the control plan power amount 602 to relate to the amount of power used by each outdoor unit 2.

[0025] In addition, the input data 131 may include control prohibition information and the like. The power rate unit price information 131B stores the power rate unit price at each time. The power rate unit price information 131B is information acquired from an electric power company. The priority order information 131C stores the priority order for each outdoor unit 2. The priority order information 131C is set by the user. Note that in a system that does not have "power rate unit price information 131B", input is not required. In that case, the power rate unit price information 131B is replaced with appropriate dummy data, for example, "power reduction priority data for each time period".

[0026] The input data 131 also includes current value information 131D. Note that the current value information 131D stores the current inverter current value in the outdoor unit 2, the inverter frequency, and the like.

[0027] As described above, the output data 132 is composed of the control plan information 132A. In the control plan information 132A, current control information related to each outdoor unit 2 is stored.

[0028] Furthermore, the optimization processor 110 refers to reference data 133 as necessary. The reference data 133 stores information for managing the path name of the file in which the information is stored, constants, loggers, etc. Furthermore, although not shown in Fig. 3, the optimization processor 110 also refers to setting information. The setting information stores information for managing various parameters, etc.

[0029] <Hardware configuration> FIG. 4 is a hardware configuration diagram of the control device 1 in this embodiment.

[0030] The control device 1 includes a memory 141 , a calculation device 142 , a storage device 143 , an input device 144 , an output device 145 , and a communication device 146 .

[0031] The memory 141 is a main storage device and is configured with a RAM (Random Access Memory) etc. The arithmetic device 142 is configured with a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), etc. The storage device 143 is an auxiliary storage device and is configured with a HDD (Hard Disk Drive), an SSD (Solid State Drive), etc.

[0032] The input device 144 is configured with a keyboard, a mouse, etc. The output device 145 is configured with a display, a printer, etc. The input device 144 and the output device 145 may be connected to the control device 1 as necessary. The communication device 146 communicates with the outdoor unit 2.

[0033] Then, the program stored in the storage device 143 is loaded into the memory 141, and the loaded program is executed by the calculation device 142. This embodies the control unit 11 shown in Fig. 2, the execution unit 121 shown in Fig. 3, and the pre-processing unit 111 to the output processing unit 114 constituting the optimization processing unit 110.

[0034] <Optimization process> Next, the optimization process performed by the optimization unit 112 shown in Fig. 3 will be described with reference to Fig. 5A to Fig. 7B. The optimization process aims to optimize the power fee and the power amount while taking comfort into consideration.

[0035] The optimization process is preferably performed by optimizing the amount of power for 24 hours from the next control target time every hour (every 30 minutes, for example).

[0036] (Decision variable) First, the decision variables used in the optimization process will be described. The decision variables used are x[i] and z[j]. x[i] is a variable that represents the amount of power at each time "i" in the control plan power amount 602 (see FIG. 7B). Note that in the following, "i" represents time. For example, when optimizing the control plan power amount 602 every 30 minutes, x[i] is a variable that represents the amount of power optimized for each time "i" every 30 minutes. Note that "i" is not limited to every 30 minutes.

[0037] Additionally, z[j] is an auxiliary variable for linearizing the process of minimizing the maximum value within the peak shift width. The reason for introducing z[j] will be described later in detail. "j" takes one of the values ​​i-psw≦j≦i+psw. The peak shift width and z[j] will be described later. Additionally, x[i] and z[j] follow the following formula (1).

[0038]

number

[0039] In formula (1), "R" represents all real numbers. The optimization unit 112 obtains x[i] and z[j] through optimization. Of these, x[i] becomes the control plan power amount 602, and is actually used for controlling the outdoor unit 2.

[0040] (vector constant) Next, the vector constants used in the optimization process will be described. The vector constants are vectors of constants related to time. p[i] and c[i] are used as vector constants in the optimization process. p[i] is the demand forecast value 601 (see FIG. 7A) for each time "i", and is based on the demand forecast information 131A for each floor included in the input data 131. An example of the value of p[i] is "···, 100, 150, 100,···" (unit: kWh), etc.

[0041] c[i] is the electricity rate unit price at each time “i”, and is based on the electricity rate unit price information 131B at each time included in the input data 131. An example of c[i] is “···, 2.5, 3.0, 2, 2,···” (unit: yen / kWh), etc.

[0042] (constraint expression) Next, the constraint equations used in the optimization calculation will be described.

[0043] The above x[i] and z[j] are subject to the constraints of the following equations (2) and (3).

[0044] x[i] ≥ 0, z[j] ≥ 0 (2) z[j]-x[i]≧0 (3)

[0045] According to the above-mentioned formula (1) and formula (2), x[i] and z[j] are real values ​​equal to or greater than 0. That is, x[i] (control planned power amount 602) is a value equal to or greater than 0. For example, when optimizing the control planned power amount 602 every 30 minutes, a constraint is imposed by formula (2) that x[i] and z[j] are equal to or greater than 0 at each time "i" every 30 minutes.

[0046] Equation (3) indicates that z[j], which is the maximum value within the peak shift width "psw" of x[i] and is an auxiliary variable, does not exceed x[i]. This is called a maximum value minimization constraint. In other words, equation (3) means that the constraint condition is set such that the control plan power amount 602 (x[i]) does not exceed a predetermined value (z[j]) at a predetermined time.

[0047] In addition, there is a constraint between x[i] and p[i] as shown in the following equation (4).

[0048] x[i-psw]+x[i-psw+1]+x[i-psw+2]+···+x[i-2]+x[i-1]+x[i]+x[i+1]+x[i+2]+···+x[i+psw-2]+x[i+psw-1]+x[i+psw] = w3(p[i-psw]+p[p-psw+1]+p[p-psw+2]+···+p[i-2]+p[i-1]+p[i]+p[i+1]+p[i+2]+···+p[i+psw-2]+p[i+psw-1]+p[i+psw])

[0049] Equation (4) can be summarized as equation (4a).

[0050]

number

[0051] Equations (4) and (4a) are referred to as power amount constraint conditions. In this way, in the power amount constraint condition, which is one of the constraint conditions, the sum of the control plan power amount 602 in a predetermined range of time is set to be proportional to the sum of the demand forecast value 601 in a predetermined range of time.

[0052] In addition, in equations (4) and (4a), "psw" is referred to as the peak shift width. A specific example of "psw" is "2", etc. Furthermore, "w3" is a weighting coefficient of the power consumption constraint condition (equation (4) and equation (4a)), and a possible example is "1.0". Note that equations (4) and (4a) are generated as many times as N-2×psw, where N is the number of i (0≦i≦N).

[0053] The left side of formula (4) and formula (4a) is the sum of the control plan power amount 602 within the peak shift width. The right side of formula (4) and formula (4a) is the demand forecast value 601 of the power amount obtained from the past demand within the peak shift width. In other words, formula (4) and formula (4a) show that within the peak shift width, the subtotal (sum) of the control plan power amount 602 obtained by optimization is the same as the weighted subtotal (sum) of the demand forecast value 601 obtained from the past demand. In particular, when w3=1.0, within the peak shift width, the sum of the control plan power amount 602 obtained by optimization is the same as the sum of the demand forecast value 601 obtained from the past demand.

[0054] In formulas (4) and (4a), if the value of "w3" is reduced, for example, the value of "w3" is set to "0.8", the control device 1 will request that the outdoor unit 2 be operated at 80% of the sum of the demand forecast values ​​601. This means that lowering the cost of electricity is given more importance than comfort.

[0055] 5A and 5B are diagrams for explaining the power amount constraint conditions shown in equations (4) and (4a).

[0056] FIG. 5A is a diagram showing a bar graph of a demand forecast value 601, and FIG. 5B is a diagram showing a bar graph of a control plan power amount 602.

[0057] 5A, the shaded portion of the bar graph (reference number 511) indicates a demand forecast value 601 within the peak shift width. Similarly, in FIG. 5B, the shaded portion of the bar graph (reference number 512) indicates a control plan power amount 602 within the peak shift width.

[0058] When w3=1.0 in formulas (4) and (4a), formulas (4) and (4a) indicate the following: That is, the total value of the portion of the bar graph indicated by diagonal lines in Fig. 5A (reference symbol 511) is the same as the total value of the portion of the bar graph indicated by diagonal lines in Fig. 5B (reference symbol 512).

[0059] This means that the total value of the demand forecast values ​​601 calculated from the amount of power consumed in the past is the same as the total value of the control plan power amount 602 calculated from this. In other words, this means that the outdoor units 2 are driven in a similar manner, resulting in a similar room temperature, and the constraint equation (constraint condition) is set so that the comfort of the room temperature is taken into consideration. In this way, equations (4) and (4a) show that, taking into consideration the comfort of the room temperature, the weighted subtotal of the demand forecast values ​​601 and the weighted subtotal of the control plan power amount 602 are the same within the peak shift width. In addition, in Figures 5A and 5B, the solid curve 501 indicates the change over time in the unit price of electricity.

[0060] As described above, in this embodiment, it is assumed that x[i] and p[i] also include the amount of power consumed by devices such as PCs and lighting used on the target floor. In other words, the control plan power amount 602, which is the result of optimization, includes not only the outdoor unit 2, but also devices other than the air conditioner 4, such as PCs and lighting. How to issue commands to the air conditioner 4 (outdoor unit 2 in this embodiment) from such control plan power amount 602 will be explained in the control command generation process described later.

[0061] (Objective function) The optimization unit 112 optimizes the power rate and the control plan power amount 602 by optimizing the objective function shown in the following formula (5) by linear programming.

[0062]

number

[0063] In formula (5), "w1" and "w2" are weighting coefficients of the objective function, and are values ​​such as "1.0" and "1.1", respectively. The optimization unit 112 minimizes the objective function (formula (5)) obtained by weighting and adding the weighted power charge (first term of formula (5)) and the sum of the maximum power amount "z[j]" within the peak shift width (second term of formula (5)). The power charge is the sum of the product of the control plan power amount 602 "x[i]" and the power rate unit price "c[i]" for each time "i". In this way, the objective function includes the sum of the products of the control plan power amount 602 and the power rate unit price. In the objective function, the product is the product for each hour (for each "i") in the control plan power amount 602.

[0064] Here, by introducing a MAX function into the objective function, it is also possible to use the function shown in equation (6) as the objective function.

[0065]

number

[0066] However, the optimization process using formula (6) is a nonlinear optimization process, which may result in a long processing time or a local solution. Therefore, in this embodiment, formula (5) is set as the objective function, in which z[j] is introduced as an auxiliary variable. This makes it possible to linearize the objective function, and to use a linear optimization process, that is, linear programming.

[0067] However, it is also possible to use a nonlinear objective function as in equation (6) and have the optimization unit 112 find the optimal x[i] by performing nonlinear optimization processing.

[0068] However, by using equation (5), making the objective function a linear function, and applying linear programming, it is possible to shorten the processing time and avoid local solutions. In particular, by using linear programming, it is possible to avoid local solutions and shorten the processing time. Although the simplex method is suitable as an algorithm for solving linear programming, other algorithms may be applied.

[0069] In addition, if the term (second term) that minimizes the maximum power consumption is not included in equation (5), power may be concentrated at times when the unit price of electricity is low, which may result in undesirable results. To mitigate this, the second term (sum of z[i]) is introduced.

[0070] 6A and 6B are diagrams illustrating the effect of introducing the maximum power amount value (the second term (the sum of z[j]) in equation (5)).

[0071] FIG. 6A is a bar graph showing a change over time in a demand forecast value 601, and FIG. 6B is a bar graph showing an example of a control plan power amount 602.

[0072] 6A and 6B, the bar graphs indicate a demand forecast value 601 within a certain peak shift width and a control plan power amount 602. Also, in Fig. 6A and 6B, a solid curve 501 indicates a change over time in the electricity rate unit price.

[0073] If the second term (sum of z[j]) is not introduced into equation (5), power will be concentrated at the time when the unit price of electricity is the cheapest within the peak shift width ("psw") shown by reference numeral 521 in FIG. 6A. As a result, as shown by reference numeral 522 in FIG. 6B, a control plan power amount 602 will be calculated in which power is concentrated at the time when the unit price of electricity is the cheapest. The second term (sum of z[j]) is introduced into equation (5) with the intention of avoiding such extreme solutions as much as possible. In other words, a constraint is set such that the control plan power amount 602 does not exceed a predetermined value at a predetermined time.

[0074] In addition, by adjusting "w1" and "w2" in the formula (5), it is possible to balance the reduction of the power charge (the first term of the formula (5)) and the maximum power amount (the second term of the formula (5)). The adjustment of "w1" and "w2" is performed by the user through manual input with reference to the result of the optimization process. Alternatively, the optimization unit 112 may perform the optimization process for each of a plurality of combinations of "w1" and "w2" by grid search and output the result. Then, the user may select a suitable combination of "w1" and "w2" based on the result of the optimization process of the formula (5) using a plurality of combinations of "w1" and "w2". Alternatively, the optimization unit 112 may calculate a suitable combination of "w1" and "w2" based on a method such as Bayesian optimization. "Proper" refers to the form of the control plan power amount 602 desired by the user.

[0075] In addition, in formula (5), if the value of "w1" is made larger than the value of "w2", optimization is performed to make the power fee as cheap as possible. Conversely, if the value of "w2" is made larger than the value of "w1", optimization is performed in the direction of eliminating the time change of the control plan power amount 602.

[0076] 7A and 7B are diagrams showing an example of a result of the optimization process.

[0077] FIG. 7A is a diagram showing a bar graph of the demand forecast value 601, and FIG. 7B is a diagram showing a bar graph of the control plan power amount 602.

[0078] In addition, in FIGS. 7A and 7B, a solid curve 501 indicates the change in the electricity unit price over time.

[0079] 7A, the amount of power in the demand forecast value 601 is large at the time when the power unit price indicated by the solid curve 501 is the highest. In other words, the peak of the power unit price indicated by the solid curve 501 and the peak of the power amount approximately coincide with each other.

[0080] In contrast to this, in the bar graph shown in Fig. 7B, the peak of the solid curve 501 does not coincide with the peak of the power amount indicated by the control plan power amount 602. In other words, a peak shift has been performed in the bar graph shown in Fig. 7B.

[0081] By using such peak shifting, the optimization unit 112 shifts the date and time when the electricity rate is high and the amount of electricity consumed is large to the date and time when the electricity rate is low. At this time, comfort is maintained by performing the shift so as to satisfy the constraint equations shown in equations (4) and (4a). In this way, the optimization unit 112 performs optimization so as to reduce the electricity rate and the maximum amount of electricity consumed. At this time, the optimization can be performed while taking comfort into consideration by the constraint equations (4) and (4a).

[0082] <Flowchart> Next, the procedure of the power amount adjustment method according to this embodiment will be described with reference to Fig. 8 to Fig. 12. Fig. 3 will also be referenced as necessary.

[0083] FIG. 8 is a flowchart showing the overall procedure of the power amount adjustment method according to this embodiment.

[0084] First, the preprocessing unit 111 acquires the input data 131 (S1). The input data 131 has been explained in FIG.

[0085] Next, the preprocessing unit 111 performs preprocessing such as data combination and filtering as necessary on the input data 131 (S2). For example, in step S2, the preprocessing unit 111 combines the priority order data with the current value information 131D, etc. Details of step S2 will be described later.

[0086] Then, the optimization unit 112 optimizes the control plan power amount 602 for each floor by linear programming (S3). In step S3, the optimization unit 112 obtains an optimal solution of the objective function by linear programming using the constraint equations shown in equations (2) to (4) and the objective function shown in equation (5). Details of the process performed in step S3 have been described above, so a description thereof will be omitted. Step S3 is an "optimization step."

[0087] Next, the control command generating unit 113 generates a demand for each of the outdoor units 2 on each floor based on the result of step S3 (S4). A demand is a control command that stores information in the form of a percentage of the commanded output relative to the maximum output of the outdoor unit 2. In step S4, the control command generating unit 113 distributes the control plan power amount 602 to each of the outdoor units 2 on each floor in a ratio in reverse order of the priority order based on the current state, priority order, etc. of the outdoor units 2. Details of step S4 and the "ratio in reverse order of the priority order" will be described later.

[0088] Thereafter, the control command generating unit 113 passes the plan data generated as a result of step S4 to the output processing unit 114 (S5).

[0089] The output processing unit 114 generates control plan information 132A that stores air conditioning optimization plan information for each outdoor unit 2 based on the plan data. The output processing unit 114 then performs output processing (S6) to transmit the generated control plan information 132A to each of the target outdoor units 2. Details of step S6 will be described later.

[0090] 2 controls the outdoor unit 2 based on the control plan information 132A (S7). Step S7 is a "control step".

[0091] <Pretreatment> FIG. 9 is a flowchart showing the detailed procedure of the pre-processing (step S2 in FIG. 8) performed in this embodiment.

[0092] First, the preprocessing unit 111 reads (S201) each of the input files that make up the input data 131. The input files have already been described in FIG.

[0093] Next, the pre-processing unit 111 converts the format of the input file as necessary (S202). For example, the pre-processing unit 111 converts a horizontal data format into a vertical data format.

[0094] Then, the preprocessing unit 111 combines the files as necessary (S203).

[0095] Next, the preprocessing unit 111 converts the data into an input format used in the optimization process, if necessary (S204).

[0096] <Control command generation process> Next, the control command generation process performed by the control command generating unit 113 shown in FIG. 3 will be described with reference to FIG. 10, FIG. 11A, and FIG. 11B.

[0097] In the control command generation process, the control command generation unit 113 determines the allocation of demands (control commands) to each of the outdoor units 2 that are the control targets, based on the result of the optimization process by the optimization unit 112.

[0098] Furthermore, the control command generation process is executed based on the time of the control plan power amount 602. For example, if the control plan power amount 602 is calculated every 30 minutes, the control command generation process is also executed every 30 minutes.

[0099] First, as a premise, the control plan power amount 602 optimized by the optimization unit 112 includes the power amount of the PC, lighting, etc. in addition to the power amount of the outdoor unit 2 on the floor to be controlled. Note that hereinafter, the indoor unit 3 and outdoor unit 2 on the floor to be controlled will be referred to as the indoor unit 3, outdoor unit 2, etc. to be controlled.

[0100] In this way, the control plan power amount 602 calculated as a result of optimization includes the sum of the control plan power amounts 602 for all the controlled devices (outdoor unit 2).

[0101] Fig. 10 is a flowchart showing the procedure of the control command generation process (step S4 in Fig. 8) performed in this embodiment. Fig. 11A is a diagram showing the time change of the demand forecast value 601, and Fig. 11B is a diagram showing the time change of the control plan power amount 602. In Figs. 11A and 11B, a solid curve 501 indicates the time change of the power unit price fee.

[0102] In FIG. 11A and FIG. 11B, the control command generation process for the times indicated by the hatched bars (reference numerals 541 and 542) will be considered.

[0103] First, the control command generating unit 113 calculates (S401) what percentage the control plan power amount 602, which is the result of the optimization process, has increased or decreased from the demand forecast value 601 at the same time (increase or decrease rate). It is assumed that the control plan power amount 602 indicated by reference numeral 542 in Fig. 11B is increased by 20% from the demand forecast value 601 indicated by reference numeral 541 in Fig. 11A.

[0104] Next, the control command generating unit 113 acquires the total current value of the outdoor units 2 to be controlled (S402). In the example shown in this flowchart, the total acquired current value is "5000A". The current current value of the outdoor units 2 to be controlled is acquired from the "inverter current value" of the current value information 131D described later in FIG. 16. As described above, the demand forecast value 601 includes the power amount of the outdoor units 2 on the floor as well as the power amount of the PC, lighting, etc. In contrast, the current current value can be collected for each outdoor unit 2.

[0105] Next, the control command generating unit 113 multiplies the sum of the current values ​​acquired in step S402 by the increase / decrease rate calculated in step S401. In this way, the control command generating unit 113 calculates the increase / decrease in the current values ​​for all outdoor units 2 that are the control targets (S403). In the example shown in this flowchart, the calculation result in step S403 is 5000A×0.2=1000A.

[0106] Next, the control command generating unit 113 calculates the allocation of the increase or decrease in the current value calculated in step S403 to the outdoor units 2 that are the control targets in the order of priority (S404). The priorities are set in advance. The allocation of the current value to the outdoor units 2 that are the control targets is performed, for example, in a ratio in the reverse order of the priority order. For example, it is assumed that priorities "1", "2", "3", and "4" are assigned to four outdoor units 2, respectively. In such a case, the control command generating unit 113 calculates the current value to be distributed in a ratio in the reverse order of the priority order. In other words, the control command generating unit 113 allocates 1000 A to each of the outdoor units 2 with priority order "1", priority order "2", priority order "3", and priority order "4" in a ratio of 4:3:2:1 (ratio in the reverse order of the priority order). That is, the control command generating unit 113 allocates "400 A" to the outdoor unit 2 with priority level "1," and allocates "300 A" to the outdoor unit 2 with priority level "2." Similarly, the control command generating unit 113 allocates "200 A" to the outdoor unit 2 with priority level "3," and allocates "100 A" to the outdoor unit 2 with priority level "1."

[0107] However, the allocation of current values ​​to each outdoor unit 2 does not have to follow the above. In the above explanation, the ratio of current values ​​to be allocated to the outdoor units 2 is determined in the reverse order of priority, but the ratio at which current values ​​are allocated to each outdoor unit 2 may be set in advance, regardless of priority.

[0108] Next, the control command generating unit 113 calculates, for each of the outdoor units 2 to be controlled, what percentage the allocated current value has increased or decreased relative to the current current value. That is, the control command generating unit 113 calculates the rate of increase or decrease of the current value (S405).

[0109] Then, the control command generating unit 113 generates a demand for each outdoor unit 2 based on the result of step S405 (S406). For example, assume that the current current value of the outdoor unit 2 with the priority level "1" is "1200A". In step S406, this outdoor unit 2 is additionally allocated "400A", so the current value is changed to "1600A". In other words, the current value increases by "1600 / 1200=4 / 3 times". Therefore, if this outdoor unit 2 is currently operating at 60%, the demand becomes 80%. In other words, a control command is sent to the outdoor unit 2 to operate at 80% of the maximum output. Note that the upper limit of the demand is 100, and if the demand is 100(%) or more in step S406, the control command generating unit 113 rounds down the demand to 100.

[0110] Then, the control command generating unit 113 generates plan data in which the demand for each outdoor unit 2 is stored, and outputs the plan data for each outdoor unit 2 to be controlled to the control unit 11 (S407). The plan data is data in which, for example, the ID of the outdoor unit 2 is associated with the demand.

[0111] By performing such a control command generation process, it is possible to generate a control command for the outdoor unit 2 and control the outdoor unit 2. In particular, even if the control plan power amount 602 includes information on the power amount related to equipment other than the outdoor unit 2, which is the controlled equipment, it is possible to generate an appropriate control command for the outdoor unit 2.

[0112] <Output processing> FIG. 12 is a flowchart showing the detailed procedure of the output process (step S6 in FIG. 8) performed in this embodiment.

[0113] First, the output processing unit 114 reads the results (plan data) of the control command generation process (S601).

[0114] Next, the output processing unit 114 converts the read result of the control command generation process into plan data in a format for execution by the control unit 11 (S602). As a result of step S602, control plan information 132A is generated.

[0115] Then, the output processing unit 114 outputs the control plan information 132A generated as a result of step S602 to the control unit 11 (S603).

[0116] <Input data 131> 13 to 16, examples of demand forecast information 131A, electricity rate unit price information 131B, priority order information 131C, and current value information 131D constituting the input data 131 will be described.

[0117] (Demand forecast information 131A) FIG. 13 is a diagram showing an example of the demand forecast information 131A.

[0118] 13, the demand forecast information 131A has the items of "building", "floor", "date and time", and "demand forecast value". That is, in the demand forecast information 131A, the demand forecast value 601 is stored in association with the "building" and "floor" for which the demand forecast value 601 is collected, and the collection time of the demand forecast value 601. The unit of the demand forecast value 601 is "kWh". The "building" and "floor" identify a floor.

[0119] (Electricity rate unit price information 131B) 14 is a diagram showing an example of the power rate unit price information 131B. Note that in a system that does not have the "power rate unit price information 131B", input is not essential. In that case, the power rate unit price information 131B is replaced with appropriate dummy data, for example, "power reduction priority data for each time period."

[0120] As shown in FIG. 14, in the electricity rate unit price information 131B, the "electricity rate unit price" is stored in association with the "date and time". For example, the electricity rate unit price on "2023 / 10 / 31 10:30" is 10.55 (yen / kWh). The electricity rate unit price information 131B includes the electricity rate unit price related to the date and time when optimization will be performed. Note that in a system that does not have the "electricity rate unit price information 131B", input is not required. In that case, the electricity rate unit price information 131B is replaced with appropriate dummy data, for example, "power reduction priority data for each time zone".

[0121] (Priority Information 131C) FIG. 15 is a diagram showing an example of the priority order information 131C.

[0122] As shown in FIG. 15, the priority order information 131C has the following items: "building," "floor," "outdoor unit," and "priority order."

[0123] The "outdoor unit" item stores the ID of the outdoor unit 2. That is, the priority order information 131C stores the priority order of the outdoor unit 2 in association with the ID of the outdoor unit 2 and information on the "building" and "floor" in which the outdoor unit 2 is installed.

[0124] (Current Value Information 131D) FIG. 16 is a diagram showing an example of the current value information 131D.

[0125] The current value information 131D has the following items: “Date and time”, “Building”, “Floor”, “Outdoor unit”, “Indoor unit”, “Power consumption”, and “Indoor unit status”. The current value information 131D also has the following items: “Current room temperature”, “Room temperature setting”, “Inverter frequency”, and “Inverter current value”.

[0126] "Date and time" is the date and time when the information stored in the corresponding record was collected. "Building" and "Floor" are information related to the installation locations of the indoor unit 3 and outdoor unit 2 that are the subject of the information stored in the record. The "Outdoor unit" item stores the ID of the outdoor unit 2, and the "Indoor unit" item stores the ID of the indoor unit 3. "Power consumption" is the power consumption of the outdoor unit 2 in the building and floor that are the subject of the record. The unit of power consumption is "kWh." As described above, the demand forecast value 601 includes the power consumption of PCs and lighting equipment installed on the floor. In contrast, the power consumption of the outdoor unit 2 can be collected in the current value information 131D.

[0127] The "Indoor unit status" field stores information indicating the on / off state of the indoor unit 3 corresponding to the record. In the "Indoor unit status" field, "1" indicates an on state, and "0" indicates an off state. The "Current room temperature" field stores the current room temperature. The "Set room temperature" field stores the room temperature set in the indoor unit 3 corresponding to the record.

[0128] The "inverter frequency" item stores the current frequency (unit: Hz) of the inverter in the outdoor unit 2. The "inverter current value" item stores the current current value (unit: A) of the inverter in the outdoor unit 2.

[0129] <Control Plan Information 132A> FIG. 17 is a diagram showing an example of the control plan information 132A.

[0130] As shown in FIG. 17, the control plan information 132A has the items of "building", "floor", "outdoor unit", "date and time", and "demand".

[0131] The "Building" and "Floor" items store information regarding the building and floor in which the outdoor unit 2 that is the target of the demand, which is a control command, is installed. The "Outdoor Unit" item stores the ID of the outdoor unit 2 that is the target of the demand. The "Date and Time" item indicates the date and time at which the demand is transmitted to the outdoor unit 2. The control command generation process is performed shortly before the demand is transmitted. Then, at the date and time stored in the "Date and Time" column, the demand is transmitted from the control unit 11. The "Demand" item stores the content of the demand. If "100" is stored in "Demand", this indicates that the outdoor unit 2 will operate at 100% output (maximum output).

[0132] In this embodiment, the optimization process inputs a predicted value 601 of power demand on the floor where the air conditioner 4 is installed, and outputs a control plan power amount 602 that minimizes the power charge while considering comfort. At this time, the power charge is optimized using equations (4) and (4a) as constraint equations, and the outdoor unit 2 is controlled based on the power plan (control plan information 132A) based on the optimization result. In other words, the power charge is optimized using equations (4) and (4a) as constraint equations, so that the power charge can be minimized without compromising comfort. This makes it possible to reduce the power charge of the outdoor unit 2, that is, the air conditioner 4, while considering comfort. Furthermore, cost savings due to the reduction (minimization) of the power charge of the air conditioner 4 and contribution to GX (Green Transformation) can be expected.

[0133] In particular, according to this embodiment, the linear programming method is used as the optimization method, so that the electricity rate can be minimized within the balance range defined by "w1" and "w2" in formula (5).

[0134] The control unit 11 shown in FIG.

[0135] In this embodiment, the control optimization unit 100 is integrated with the control device 1 as shown in Fig. 2, but this is not limited to the above. For example, the control optimization unit 100 may be executed in a device (not shown) other than the control device 1. In such a case, the control device 1 and the device in which the control optimization unit 100 is executed may be able to communicate with each other, and the result of the optimization process or the result of the control command generation process may be passed from the device in which the control optimization unit 100 is executed to the control device 1.

[0136] In the present embodiment, the device (control target device) controlled by the control device 1 is the outdoor unit 2, but this is not limited to this as long as it is a device controlled by one control device 1. For example, it may be a lighting device, etc.

[0137] The present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to all of the configurations described.

[0138] Further, the above-mentioned configurations, functions, preprocessing unit 111 to output processing unit 114, storage device 143, etc. may be realized in hardware by designing some or all of them as an integrated circuit, for example. As shown in Fig. 4, the above-mentioned configurations, functions, etc. may be realized in software by a processor such as a CPU interpreting and executing a program that realizes each function. Information such as a program, table, file, etc. that realizes each function can be stored in a recording device such as memory 141 or SSD (Solid State Drive), or a recording medium such as an IC (Integrated Circuit) card, SD (Secure Digital) card, or DVD (Digital Versatile Disc), in addition to being stored in a HD (Hard Disk).

[0139] In addition, in each embodiment, the control lines and information lines are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0140] 1. Control device (power regulator) 2. Outdoor unit (controlled equipment) 11 Control section 100 Control Optimization Department 110 Optimization Processing Unit 112 Optimization Department 113 Control command generation unit 501 Real curve (electricity price) 601 Demand forecast value 602 Control Plan Power Quantity Z Air Conditioning Control System S3 Optimize the control plan power amount for each floor using linear programming (optimization step) S7 Control (Control Step)

Claims

1. an optimization unit that performs optimization to calculate the control plan power amount so that an objective function including a sum of products of the control plan power amount and the power rate unit price is minimized according to a constraint condition; A control unit that controls a control target device that is a device to be controlled based on the control plan power amount calculated as a result of the optimization; having The control plan power amount is a distribution of power amounts by time within a time period to be predicted, In the objective function, the product is a product of the control plan power amount for each time period, The constraint condition is set so that the sum of the control plan power amounts for a predetermined time range is proportional to the sum of the demand forecast values ​​for the predetermined time range. Electric energy adjustment device.

2. The optimization unit is Optimize the above using linear programming 2. The power adjustment device according to claim 1 .

3. The constraint condition is set such that the control plan power amount does not exceed a predetermined value at a predetermined time.

2. The power adjustment device according to claim 1 .

4. There are a plurality of control target devices, The control plan power amount calculated as a result of the optimization includes a sum of the control plan power amounts for all of the control target devices, and a control command generating unit that generates a control command for each of the plurality of control target devices based on the control plan power amount calculated as a result of the optimization.

2. The power adjustment device according to claim 1 .

5. The controlled device is an air conditioner.

2. The power adjustment device according to claim 1 .

6. A control device that controls the device, an optimization step of calculating the control plan power amount so that an objective function including a sum of products of the control plan power amount and the power rate unit price for each hour is minimized according to a constraint condition; a control step of controlling a control target device that is a device to be controlled based on the control plan power amount calculated as a result of the optimization; Run The control plan power amount is a distribution of power amounts by time within a time period to be predicted, The constraint condition is set so that the sum of the control plan power amounts for a predetermined time range is proportional to the sum of the demand forecast values ​​for the predetermined time range. How to adjust the amount of electricity.

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