Power consumption adjustment device and power consumption adjustment method

Abstract

The challenge is to reduce electricity costs while maintaining comfort. [Solution] The system includes a control optimization unit 100 that performs optimization to calculate the control plan power amount that minimizes an objective function that includes the sum of the products of the control plan power amount and the electricity rate unit price, according to constraints, and a control unit 11 that controls the controlled 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 time-by-time distribution of power amount within the time to be predicted, the product in the objective function is the time-by-time product of the control plan power amount, and the constraints are set so that the sum of the control plan power amounts for a predetermined range of time is proportional to the sum of the demand forecast values ​​for a predetermined range of time.

Description

Technical Field

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

Background Art

[0002] In recent years, affected by the rising electricity prices, it has become important to suppress the electricity cost of electrical equipment, especially air conditioners. However, simply suppressing the electricity cost sacrifices comfort.

[0003] Patent Document 1 discloses a control device for generating control information for controlling a consumer's equipment, including an information acquisition unit for acquiring information on a fluctuating electricity price unit price and the consumer's power consumption, and a control information generation unit for generating control information for the consumer's equipment at a target time limit based on the electricity price unit price at the target time limit, which is the time limit when the equipment control is performed, and the consumer's power consumption information in the time limits after the target time limit (see the abstract).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In the technology described in Patent Document 1, a local greedy method is used, which may fall into a local solution and generally does not necessarily reach the optimal solution (the cheapest value in this case). Therefore, although the technology described in Patent Document 1 seeks to obtain a slightly cheaper electricity price, the electricity price is not necessarily the cheapest value.

[0006] In view of such a background, the present invention has been made, and an object of the present invention is to reduce the electricity price while considering comfort. [Means for solving the problem]

[0007] To solve the aforementioned problems, the present invention includes an optimization unit that performs optimization to calculate the 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, according to constraints, and a control unit that controls a controlled device, which is a device 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 time-by-time distribution of power amount within the time to be predicted, the product in the objective function is the time-by-time product of the control plan power amount, and the constraints are set such that the sum of the control plan power amounts for a predetermined range of time is proportional to the sum of the demand forecast values ​​for the predetermined range of time. Other solutions will be described as appropriate in the embodiments. [Effects of the Invention]

[0008] According to the present invention, electricity costs can be reduced while taking comfort into consideration. [Brief explanation of the drawing]

[0009] [Figure 1] This is a conceptual diagram of the air conditioning control system in this embodiment. [Figure 2] This is a functional block diagram of the control device according to this embodiment. [Figure 3] This diagram shows the configuration of the control optimization unit operating inside the control device. [Figure 4] This is a hardware configuration diagram of the control device in this embodiment. [Figure 5A] This is a diagram (part 1) illustrating the power consumption constraints. [Figure 5B] This is a diagram (part 2) illustrating the power consumption constraints. [Figure 6A] This is a diagram (part 1) illustrating the effect of introducing a maximum power consumption limit. [Figure 6B] This is a diagram (part 2) illustrating the effects of introducing a maximum power consumption limit. [Figure 7A] It is a diagram (part 1) showing an example of the result of the optimization process. [Figure 7B] It is a diagram (part 2) showing an example of the result of the optimization process. [Figure 8] It is a flowchart showing the overall procedure of the power amount adjustment method according to this embodiment. [Figure 9] It is a flowchart showing the detailed procedure of the preprocessing performed in this embodiment. [Figure 10] It is a flowchart showing the procedure of the control command generation process performed in this embodiment. [Figure 11A] It is a diagram showing the time change of the demand prediction value. [Figure 11B] It is a diagram showing the time change of the controlled planned power amount. [Figure 12] It is a flowchart showing the detailed procedure of the output process performed in this embodiment. [Figure 13] It is a diagram showing an example of demand prediction information. [Figure 14] It is a diagram showing an example of electricity price unit information. [Figure 15] It is a diagram showing an example of priority information. [Figure 16] It is a diagram showing an example of current value information. [Figure 17] It is a diagram showing an example of controlled planned information.

Mode for Carrying Out the Invention

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

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

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

[0013] The outdoor unit 2 and indoor unit 3 will be collectively referred to as the air conditioner 4 as appropriate. In this embodiment, the outdoor unit 2 is the "controlled device". As shown in Figure 1, there are multiple outdoor units 2 that are controlled devices.

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

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

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

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

[0018] <Control Optimization Unit 100> Figure 3 shows the configuration of the control optimization unit 100 operating inside the control device 1.

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

[0020] The optimization processing unit 110 consists of a pre-processing unit 111, an optimization unit 112, a control command generation unit 113, and an output processing unit 114.

[0021] The execution unit 121 performs the main processing, calling the processing performed in each submodule (optimization processing unit 110). The preprocessing unit 111 performs preprocessing on the input data 131, such as data merging. The optimization unit 112 performs optimization to calculate the control plan energy amount 602 that minimizes the objective function, which includes the sum of the products of the control plan energy amount 602 (see Figure 7B) and the unit price of electricity, according to the constraints (under the constraints). The optimization unit 112 also performs optimization using linear programming.

[0022] The control command generation unit 113 generates control commands for each of the multiple controlled 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 results and control commands 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 as output data 132 to the control unit 11.

[0023] The input data 131 includes floor-specific demand forecast information 131A, electricity rate information 131B, priority information 131C, etc. The floor-specific demand forecast information 131A contains a demand forecast value 601 (see Figure 7A) for each floor. The demand forecast value 601 is the amount of electricity predicted based on past actual demand power information collected by the control unit 11. For example, the demand forecast information 131A for August of this year is calculated from past actual demand power information for August. Note that input is not required in systems that do not have "electricity rate information 131B". In that case, the electricity rate information 131B is replaced with appropriate dummy data, such as "electricity reduction priority data by time of day".

[0024] In this embodiment, the demand forecast value 601 and the control plan power amount 602 represent the total power consumption of the floor controlled by the air conditioning control system Z, as well as the outdoor units 2, PCs, lighting, and other equipment in the building. However, if the control device 1 can collect only the power consumption used by each outdoor unit 2, the demand forecast value 601 and the control plan power amount 602 can also be based on the power consumption used by each outdoor unit 2.

[0025] In addition, input data 131 may include control prohibition information, etc. Electricity rate unit price information 131B stores the electricity rate unit price for each time. Electricity rate unit price information 131B is information obtained from the power company. Priority information 131C stores the priority for each outdoor unit 2. Priority information 131C is set by the user. Note that input is not required in systems that do not have "electricity rate unit price information 131B". In that case, electricity rate unit price information 131B is replaced with appropriate dummy data, for example, "power reduction priority data for each time period".

[0026] Furthermore, the input data 131 includes current value information 131D. This current value information 131D contains information such as the current inverter current value and inverter frequency of the outdoor unit 2.

[0027] As described above, the output data 132 consists of control plan information 132A. The control plan information 132A stores current control information for each outdoor unit 2.

[0028] Furthermore, the optimization processing unit 110 refers to the reference data 133 as needed. The reference data 133 contains information for managing the file path name where the information is stored, constants, loggers, etc. In addition, although not shown in Figure 3, the optimization processing unit 110 also refers to configuration information. The configuration information contains information for managing various parameters, etc.

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

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

[0031] Memory 141 is the main memory and is composed of RAM (Random Access Memory), etc. The arithmetic unit 142 is composed of CPU (Central Processing Unit), GPU (Graphics Processing Unit), etc. Storage device 143 is the auxiliary storage device and is composed of HDD (Hard Disk Drive), SSD (Solid State Drive), etc.

[0032] The input device 144 consists of a keyboard, mouse, etc. The output device 145 consists of a display, printer, etc. The input device 144 and the output device 145 may be connected to the control device 1 as needed. 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 arithmetic unit 142. This brings into reality the pre-processing unit 111 to the output processing unit 114 that make up the control unit 11 shown in Figure 2, the execution unit 121 shown in Figure 3, and the optimization processing unit 110.

[0034] <Optimization Process> Next, with reference to Figures 5A to 7B, the optimization process performed by the optimization unit 112 shown in Figure 3 will be explained. The purpose of the optimization process is to optimize electricity charges and electricity consumption while taking comfort into consideration.

[0035] The optimization process preferably involves optimizing the amount of power consumed for 24 hours from the next controlled time, hourly (for example, every 30 minutes).

[0036] (Decision variable) First, let's explain the decision variables used in the optimization process. The decision variables used are x[i] and z[j]. x[i] is a variable that represents the amount of energy at each time "i" in the control plan energy 602 (see Figure 7B). In the following, "i" will represent time. For example, when optimizing the control plan energy 602 every 30 minutes, x[i] is a variable that represents the optimized amount of energy for each time "i" in each 30-minute interval. Note that "i" is not limited to every 30 minutes.

[0037] Furthermore, z[j] is an auxiliary variable used to linearize the minimization process of the maximum value within the peak shift width. The details of the meaning of introducing z[j] will be explained later. "j" takes any value between i-psw≦j≦i+psw. The peak shift width and z[j] will be explained later. Also, x[i] and z[j] follow equation (1) below.

[0038]

number

[0039] In equation (1), "R" represents the set of all real numbers. The optimization unit 112 finds x[i] and z[j] through optimization. Of these, x[i] becomes the control plan power amount 602, which is actually used to control the outdoor unit 2.

[0040] (Vector constant) Next, we will explain the vector constants used in the optimization process. A vector constant is a vector 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 for each time "i" (see Figure 7A), and is based on the floor-specific demand forecast information 131A included in the input data 131. Examples of p[i] values ​​include "..., 100, 150, 100,..." (unit: kWh).

[0041] c[i] is the electricity rate per unit for each time point "i", and is based on the electricity rate per unit for each time point 131B contained in the input data 131. An example of c[i] is "···,2.5,3.0,2,2,···" (unit: yen / kWh).

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

[0043] The aforementioned x[i] and z[j] are subject to the constraints shown in equations (2) and (3) below.

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

[0045] From equation (1) and equation (2) described above, x[i] and z[j] are real values ​​greater than or equal to 0. In other words, x[i] (control plan energy 602) is a value greater than or equal to 0. For example, when optimizing the control plan energy 602 every 30 minutes, equation (2) imposes the constraint that x[i] and z[j] must be greater than or equal to 0 at each time point "i" in each 30-minute interval.

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

[0047] Furthermore, there is a constraint between x[i] and p[i] as shown in equation (4) below.

[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] Combining equation (4) gives equation (4a).

[0050]

number

[0051] Equations (4) and (4a) are referred to as the energy constraints. In this way, the energy constraint, which is one of the constraints, is set so that the sum of the planned control energy 602 over a predetermined time range is proportional to the sum of the predicted demand 601 over a predetermined time range.

[0052] Furthermore, in equations (4) and (4a), "psw" is referred to as the peak shift width. A specific example of "psw" is "2". Also, "w3" is the weighting coefficient of the power constraint condition (equations (4) and (4a)), and a value such as "1.0" is possible. Note that equations (4) and (4a) are generated N-2 × psw, where N is the number of i (0 ≤ i ≤ N).

[0053] The left-hand side of equations (4) and (4a) is the sum of the control plan energy amounts 602 within the peak shift range. The right-hand side of equations (4) and (4a) is the demand forecast value 601, which is the energy amount obtained from past demand within the peak shift range. In other words, equations (4) and (4a) show that within the peak shift range, the subtotal (sum) of the control plan energy amounts 602 obtained by optimization is the same as the weighted subtotal (sum) of the demand forecast value 601 obtained from past demand. In particular, if w3 = 1.0, then within the peak shift range, the sum of the control plan energy amounts 602 obtained by optimization is the same as the sum of the demand forecast value 601 obtained from past demand.

[0054] In equations (4) and (4a), if the value of "w3" is reduced, for example to "0.8", the control device 1 will request that the outdoor unit 2 be operated at 80% of the sum of the predicted demand values ​​601. This means prioritizing reducing electricity costs over comfort.

[0055] Figures 5A and 5B illustrate the power constraint conditions shown in equations (4) and (4a).

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

[0057] In Figure 5A, the shaded portion of the bar graph (reference numeral 511) represents the forecast demand value 601 within the peak shift range. Similarly, in Figure 5B, the shaded portion of the bar graph (reference numeral 512) represents the planned control power amount 602 within the peak shift range.

[0058] When w3 = 1.0 in equations (4) and (4a), equations (4) and (4a) indicate the following: that the sum of the portion of the bar graph indicated by the shaded area in Figure 5A (symbol 511) is the same as the sum of the portion of the bar graph indicated by the shaded area in Figure 5B (symbol 512).

[0059] This means that the sum of the demand forecast values ​​601, which are calculated from the amount of electricity consumed in the past, will be the same as the sum of the control plan electricity values ​​602, which will be calculated from these values. In other words, it means that the outdoor units 2 will be driven in a similar manner, resulting in similar room temperatures, and that the constraint equations (constraint conditions) will be set to take into account comfort regarding room temperature. Thus, equations (4) and (4a) show that, taking into account room temperature comfort, the weighted subtotal of the demand forecast values ​​601 and the weighted subtotal of the control plan electricity values ​​602 are the same within the peak shift range. Note that in Figures 5A and 5B, the actual curve 501 shows the change in electricity unit price over time.

[0060] As mentioned above, in this embodiment, it is assumed that x[i] and p[i] include the amount of power consumed by PCs and other equipment such as 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 equipment other than the air conditioner 4, such as PCs and lighting. How commands are issued to the air conditioner 4 (outdoor unit 2 in this embodiment) from this 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 electricity charges and the planned power consumption 602 by optimizing the objective function shown in equation (5) below using linear programming.

[0062]

number

[0063] In equation (5), "w1" and "w2" are weight coefficients of the objective function, and their values ​​are such as "1.0" and "1.1," respectively. The optimization unit 112 minimizes the objective function (equation (5)) which is obtained by weighting the sum of the weighted electricity charges (the first term of equation (5)) and the maximum energy value "z[j]" within the peak shift width (the second term of equation (5)). Note that the electricity charges are the sum of the products of the control plan energy amount 602 "x[i]" and the electricity charge unit price "c[i]" for each time "i". Thus, the objective function includes the sum of the products of the control plan energy amount 602 and the electricity charge unit price. Furthermore, the product in the objective function is the product of the control plan energy amount 602 for each time period (for each "i").

[0064] Here, by introducing the 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 equation (6) is a nonlinear optimization process, which can lead to long processing times and the possibility of getting stuck in local minima. Therefore, in this embodiment, equation (5), which introduces z[j] as an auxiliary variable, is set as the objective function. This allows the objective function to be linearized, making it possible to use linear optimization, i.e., linear programming.

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

[0068] However, by using equation (5) to make the objective function a linear function and applying linear programming, the processing time can be reduced and local optima can be avoided. In particular, by using linear programming, it is possible to avoid local optima and reduce processing time. The simplex method is preferred as the algorithm for solving linear programming problems, but other algorithms may also be applied.

[0069] Furthermore, if the term that minimizes the maximum amount of energy (the second term) is not included in equation (5), power may be concentrated during times when electricity rates are low, leading to undesirable results. The second term (the sum of z[i]) is introduced to mitigate this.

[0070] Figures 6A and 6B illustrate the effect of introducing the maximum energy value (the second term in equation (5) (the sum of z[j])).

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

[0072] In Figures 6A and 6B, the bar graphs show the forecast demand value 601 and the planned power consumption 602 within a certain peak shift range. Also, in Figures 6A and 6B, the actual curve 501 shows the change in the unit price of electricity over time.

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

[0074] Furthermore, by adjusting "w1" and "w2" in equation (5), it becomes possible to balance the reduction of electricity charges (the first term of equation (5)) and the maximum energy consumption (the second term of equation (5)). The user manually adjusts "w1" and "w2" based on the results of the optimization process. Alternatively, the optimization unit 112 may perform optimization processing for each of the multiple combinations of "w1" and "w2" using grid search and output the results. The user may then select a suitable combination of "w1" and "w2" based on the results of the optimization processing of equation (5) for each of the multiple 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. "Suitable" means the form of the control plan energy consumption 602 desired by the user.

[0075] In equation (5), if the value of "w1" is made larger than the value of "w2", the system will be optimized to minimize electricity costs. Conversely, if the value of "w2" is made larger than the value of "w1", the system will be optimized to eliminate the time variation of the planned control power amount of 602.

[0076] Figures 7A and 7B show examples of the results of the optimization process.

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

[0078] Furthermore, in Figures 7A and 7B, the actual curve 501 shows the change in electricity unit price over time.

[0079] In the bar graph shown in Figure 7A, the amount of electricity according to the forecast value 601 is highest at the time when the electricity price per unit is highest, as shown by the actual curve 501. In other words, the peak of the electricity price per unit shown by the actual curve 501 and the peak of the electricity quantity almost coincide.

[0080] In contrast, in the bar graph shown in Figure 7B, the peak of the actual curve 501 does not coincide with the peak of the power amount indicated by the control plan power amount 602. In other words, peak shifting has occurred in the bar graph shown in Figure 7B.

[0081] Through this peak shift, the optimization unit 112 shifts the days and times when electricity rates are high and electricity consumption is high to days and times when electricity rates are low. In this process, the shift is carried out in a manner that satisfies the constraints shown in equations (4) and (4a), thereby maintaining user comfort. In this way, the optimization unit 112 optimizes to reduce electricity rates and maximum electricity consumption. In this process, the constraints shown in equations (4) and (4a) allow for optimization while considering user comfort.

[0082] <Flowchart> Next, the procedure for the power consumption adjustment method according to this embodiment will be explained with reference to Figures 8 to 12. Figure 3 will be referred to as appropriate.

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

[0084] First, the preprocessing unit 111 acquires the input data 131 (S1). The input data 131 has already been explained in Figure 3, so its explanation will be omitted here.

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

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

[0087] Next, the control command generation unit 113 generates a demand for each of the outdoor units 2 on each floor based on the result of step S3 (S4). The demand is a control command in which information is stored in the form of what percentage the commanded output is of the maximum output of the outdoor unit 2. In step S4, the control command generation unit 113 distributes the planned control power 602 to each of the outdoor units 2 on each floor in a ratio in reverse order of the number of priorities, based on the current state and priority of the outdoor units 2. Details of step S4 and the "ratio in reverse order of the number of priorities" will be described later.

[0088] Subsequently, the control command generation 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, which stores air conditioning optimization plan information for each outdoor unit 2, based on the planning data. Then, the output processing unit 114 performs output processing to send the generated control plan information 132A to each of the target outdoor units 2 (S6). Details of step S6 will be described later.

[0090] Then, the control unit 11 shown in Figure 2 controls the outdoor unit 2 based on the control plan information 132A (S7). Step S7 is the "control step".

[0091] <Pre-treatment> Figure 9 is a flowchart showing the detailed procedure of the pretreatment (step S2 in Figure 8) performed in this embodiment.

[0092] First, the preprocessing unit 111 reads each of the input files that make up the input data 131 (S201). The input files have already been explained in Figure 3, so their explanation will be omitted here.

[0093] Next, the preprocessing unit 111 converts the format of the input file as needed (S202). For example, the preprocessing unit 111 converts a horizontal data format to a vertical data format.

[0094] Then, the preprocessing unit 111 performs file merging as necessary (S203).

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

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

[0097] In the control command generation process, the control command generation unit 113 determines the assignment of demands (control commands) to each of the outdoor units 2 that are to be controlled, based on the results of the optimization process performed by the optimization unit 112.

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

[0099] First, as a premise, the control plan power amount 602 optimized by the optimization unit 112 includes not only the power amount of the outdoor unit 2 on the floor being controlled, but also the power amount of the PC, lighting, etc. Hereafter, the indoor unit 3 and outdoor unit 2 on the floor being controlled will be referred to as the controlled indoor unit 3, outdoor unit 2, etc.

[0100] Thus, the control plan power consumption 602 calculated as a result of optimization includes the sum of the control plan power consumption 602 for all controlled devices (outdoor units 2).

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

[0102] In Figures 11A and 11B, we will consider the control command generation process related to the time indicated by the shaded bar graphs (reference numerals 541 and 542).

[0103] First, the control command generation unit 113 calculates the percentage increase or decrease (increase / decrease rate) of the control plan power amount 602, which is the result of the optimal processing, compared to the demand forecast value 601 at the same time (S401). In Figure 11B, the control plan power amount 602, indicated by reference numeral 542, is assumed to be 20% higher than the demand forecast value 601, indicated by reference numeral 541 in Figure 11A.

[0104] Next, the control command generation unit 113 obtains the sum of the current values ​​of the outdoor units 2 that are being controlled (S402). In the example shown in this flowchart, the sum of the current values ​​obtained is set to "5000A". The current value of the outdoor units 2 that are being controlled is obtained from the "inverter current value" of the current value information 131D, which will be described later in Figure 16. As mentioned above, the demand forecast value 601 includes not only the power consumption of the outdoor units 2 on the floor, but also the power consumption of PCs, lighting, etc. In contrast, the current value can be collected for each outdoor unit 2.

[0105] Next, the control command generation unit 113 multiplies the sum of the current values ​​obtained in step S402 by the increase / decrease rate calculated in step S401. This allows the control command generation unit 113 to calculate the increase / decrease in current values ​​for all the outdoor units 2 that are being controlled (S403). In the example shown in this flowchart, the calculation result in step S403 is 5000A × 0.2 = 1000A.

[0106] Next, the control command generation unit 113 calculates the allocation of the increase or decrease in current value calculated in step S403 to the outdoor units 2 to be controlled in order of priority (S404). The priority order is set in advance. The allocation of current value to the outdoor units 2 is performed for each outdoor unit 2 to be controlled, for example, in the reverse order of the priority number. For example, suppose that priority orders "1", "2", "3", and "4" are assigned to four outdoor units 2. In such a case, the control command generation unit 113 calculates the current value to be distributed in the reverse order of priority. That is, the control command generation unit 113 allocates 1000A to each of the outdoor units 2 with priority orders "1", "2", "3", and "4" in a ratio of 4:3:2:1 (reverse order of priority). Specifically, the control command generation unit 113 allocates "400A" to the outdoor unit 2 with priority "1" and "300A" to the outdoor unit 2 with priority "2". Similarly, the control command generation unit 113 allocates "200A" to the outdoor unit 2 with priority "3" and "100A" to the outdoor unit 2 with priority "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 ​​allocated to outdoor unit 2 is determined by the ratio in reverse order of priority, but the ratio of current values ​​allocated to each outdoor unit 2 may be predetermined regardless of priority.

[0108] Next, the control command generation unit 113 calculates the percentage increase or decrease in the current value allocated to each of the outdoor units 2 being controlled, relative to the current value. In other words, the control command generation unit 113 calculates the percentage increase or decrease in the current value (S405).

[0109] Then, the control command generation unit 113 generates a demand for each outdoor unit 2 based on the result of step S405 (S406). For example, suppose the current value of the outdoor unit 2 with priority "1" is "1200A". In step S406, an additional "400A" is allocated to this outdoor unit 2, 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 its maximum output. Note that the upper limit of the demand is 100, and if the demand exceeds 100% in step S406, the control command generation unit 113 lowers the demand to 100.

[0110] The control command generation unit 113 then generates planning data containing the demand for each outdoor unit 2, and outputs the planning data to the control unit 11 for each of the outdoor units 2 being controlled (S407). The planning data includes, for example, data that associates the ID of the outdoor unit 2 with its demand.

[0111] By performing this control command generation process, control commands for the outdoor unit 2 can be generated, and the outdoor unit 2 can be controlled. In particular, even if the control plan power amount 602 includes power amount information for equipment other than the outdoor unit 2, which is the controlled equipment, an appropriate control command for the outdoor unit 2 can be generated.

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

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

[0114] Next, the output processing unit 114 converts the results of the read control command generation process into plan data in a format that the control unit 11 can execute (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> Referring to Figures 13 to 16, examples of the demand forecast information 131A, electricity rate information 131B, priority information 131C, and current value information 131D that constitute the input data 131 will be explained.

[0117] (Demand forecast information 131A) Figure 13 shows an example of demand forecast information 131A.

[0118] As shown in Figure 13, the demand forecast information 131A has the following items: "Building," "Floor," "Date and Time," and "Demand Forecast Value." In other words, the demand forecast information 131A stores the demand forecast value 601 associated with the "Building," "Floor," and collection time of the demand forecast value 601 that are the targets of the demand forecast value 601 collection. Incidentally, the unit of the demand forecast value 601 is "kWh." The floor is identified by the "Building" and "Floor" fields.

[0119] (Electricity Rate Information 131B) Figure 14 shows an example of electricity rate information 131B. Note that input is not required in systems that do not have "electricity rate information 131B". In that case, electricity rate information 131B can be replaced with appropriate dummy data, such as "power reduction priority data by time of day".

[0120] As shown in Figure 14, in the electricity rate information 131B, the "electricity rate" is stored in association with the "date and time". For example, the electricity rate at "2023 / 10 / 31 10:30" is 10.55 (yen / kWh). The electricity rate information 131B includes the electricity rate for the date and time on which optimization will be performed. Note that input is not required in systems that do not have "electricity rate information 131B". In that case, the electricity rate information 131B will be replaced with appropriate dummy data, such as "power reduction priority data for each time period".

[0121] (Priority information 131C) Figure 15 shows an example of priority information 131C.

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

[0123] The "Outdoor Unit" field stores the ID of Outdoor Unit 2. In other words, priority information 131C stores the priority of Outdoor Unit 2, associated with its ID, the building, and the floor where it is installed.

[0124] (Current value information 131D) Figure 16 shows an example of current value information 131D.

[0125] Current Value Information 131D includes the following items: "Date and Time," "Building," "Floor," "Outdoor Unit," "Indoor Unit," "Power Consumption," and "Indoor Unit Status." In addition, Current Value Information 131D includes the following items: "Current Room Temperature," "Room Temperature Set Value," "Inverter Frequency," and "Inverter Current Value."

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

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

[0128] The "Inverter Frequency" field stores the current frequency (in Hz) of the inverter in outdoor unit 2. The "Inverter Current Value" field stores the current value (in amperes) of the inverter in outdoor unit 2.

[0129] <Control Planning Information 132A> Figure 17 shows an example of control plan information 132A.

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

[0131] The "Building" and "Floor" fields store information about the building and floor where the outdoor unit 2, which is the target of the demand control command, is installed. The "Outdoor Unit" field stores the ID of the outdoor unit 2 that is the target of the demand control command. The "Date and Time" field is the date and time when the demand control command is sent to the outdoor unit 2. The control command generation process is performed a little before the demand control command is sent. Then, when the date and time stored in the "Date and Time" field arrives, the control unit 11 sends the demand control command. The "Demand" field stores the content of the demand control command. If "100" is stored in the "Demand" field, it indicates that the outdoor unit 2 will operate at 100% output (maximum output).

[0132] In this embodiment, an optimization process is performed using the predicted power demand value 601 on the floor where the air conditioner 4 is installed as input, and a control plan power amount 602 that minimizes power costs while considering comfort is output. At this time, the power cost is optimized using equations (4) and (4a) as constraints, and the outdoor unit 2 is controlled based on the power plan (control plan information 132A) based on the optimization results. In other words, by optimizing the power cost using equations (4) and (4a) as constraints, power costs can be minimized without compromising comfort. As a result, the power cost of the outdoor unit 2, i.e., the air conditioner 4, can be reduced while considering comfort. Furthermore, cost savings through the reduction (minimization) of the power cost of the air conditioner 4 and contributions to GX (Green Transformation) can be expected.

[0133] In particular, according to this embodiment, by using linear programming as the optimization method, the electricity price can be made as low as possible within the balance range of "w1" and "w2" in equation (5).

[0134] The control unit 11 shown in Figure 3 may also output a log file related to the control of the outdoor unit 2.

[0135] Furthermore, in this embodiment, the control optimization unit 100 is integrated with the control device 1 as shown in Figure 2, but this is not limited to this configuration. For example, the control optimization unit 100 may be executed on a separate device (not shown) from the control device 1. In such a case, the control device 1 and the device on which the control optimization unit 100 is executed can communicate, and the results of the optimization process and the results of the control command generation process may be passed from the device on which the control optimization unit 100 is executed to the control device 1.

[0136] Furthermore, in this embodiment, the device controlled by the control device 1 (the controlled device) is assumed to be the outdoor unit 2, but it is not limited to this, as long as it is a device controlled by a single control device 1. For example, it could be a lighting device, etc.

[0137] The present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to illustrate the present invention clearly, and are not necessarily limited to those having all the configurations described.

[0138] Furthermore, each of the above-mentioned configurations, functions, pre-processing units 111 to output processing units 114, storage device 143, etc., may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, as shown in Figure 4, each of the above-mentioned configurations, functions, etc., may be implemented in software by having a processor such as a CPU interpret and execute a program that implements each function. Information such as programs, tables, and files that implement each function can be stored not only on an HD (Hard Disk), but also in memory 141, a recording device such as an SSD (Solid State Drive), or a recording medium such as an IC (Integrated Circuit) card, an SD (Secure Digital) card, or a DVD (Digital Versatile Disc).

[0139] Furthermore, in each embodiment, only those control lines and information lines deemed necessary for explanation are shown, and not all control lines and information lines are necessarily shown in the actual product. In practice, it can be assumed that almost all components are interconnected. [Explanation of Symbols]

[0140] 1. Control device (power consumption adjustment device) 2. Outdoor unit (controlled device) 11 Control Unit 100 Control Optimization Unit 110 Optimization Processing Unit 112 Optimization Unit 113 Control Command Generation Unit 501 Actual Curve (Electricity Rate Unit Price) 601 Demand forecast 602 Control Plan Power 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 that minimizes an objective function that includes the sum of the products of the control plan power amount and the power rate unit price, according to the constraints, Based on the control plan power amount calculated as a result of the optimization, a control unit controls the controlled device, which is the device to be controlled. It has, The aforementioned control plan power is the time-based distribution of power during the time period to be predicted. In the objective function, the product is the time-dependent product of the control planning energy. The aforementioned constraints are set such that the sum of the planned power consumption for control within a predetermined time range is proportional to the sum of the forecasted demand values ​​within that predetermined time range. Electric energy adjustment device.

2. The optimization unit, Perform the optimization using linear programming. The power consumption adjustment device according to feature 1.

3. The constraint condition is set such that, at a predetermined time, the planned power consumption for control does not exceed a predetermined value. The power consumption adjustment device according to feature 1.

4. There are multiple devices to be controlled, The control plan power amount calculated as a result of the optimization includes the sum of the control plan power amounts for all the controlled devices. The system includes a control command generation unit that generates control commands for each of the multiple controlled devices based on the control plan power amount calculated as a result of the optimization. The power consumption adjustment device according to feature 1.

5. The controlled device is an air conditioner. The power consumption adjustment device according to feature 1.

6. A control device that controls the equipment, An optimization step is to perform optimization to calculate the control plan power amount that minimizes an objective function that includes the sum of the products of the control plan power amount and the electricity rate per unit time, according to the constraints, A control step in which the controlled equipment is controlled based on the control plan power amount calculated as a result of the optimization, Execute, The aforementioned control plan power is the time-based distribution of power during the time period to be predicted. The aforementioned constraints are set such that the sum of the planned power consumption for control within a predetermined time range is proportional to the sum of the forecasted demand values ​​within that predetermined time range. How to adjust the amount of electricity.

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