JP3340288B2 - Thermal storage plant control device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a control device of a heat accumulation plant which is capable of reducing operation cost of heat source equipment and contriving stable operation definitely. SOLUTION: A thermal load prediction means 23 predicts a thermal load value on the day from a thermal load record stored in a thermal load memory means 22. In a heat accumulation plant operation project means 24, a total sum of a target function about operation cost reduction of each heat supply equipment and safety operation is looked for in the operation plan of the heat accumulation plant from hereditary algorithm so as to minimize the total sum. From the operation plan of this heat supply, the operation of each of heat supply equipment 12 and 13 is controlled by a heat supply equipment control means 25.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a control device for a heat storage plant having a heat source device and a heat storage tank, which can efficiently operate the heat source device.

[0002]

2. Description of the Related Art In general, a facility such as a building is provided with a heat storage plant. In this heat storage plant, hot water and cold water are generated by a heat source device such as a heat pump, stored in a heat storage tank, and supplied to a heat load device such as a cooling and heating device as needed. In this case, a heat storage plant using an electric method generates and stores heat at night and generates hot and cold water during the day and radiates the stored heat, thereby utilizing nighttime power or performing peak cutting. And efficient operation.

[0003] However, in such an operation, it is difficult to secure the number of specialized operators, and due to recent labor costs, it has become necessary to operate the vehicle unattended, for example, only at night. In this case, it is necessary to perform the automated operation comprehensively based on the expert knowledge of the operator, but it has not been realized at present.

[0004]

As described above, conventionally, unless a specialized operator is secured, the heat source equipment and the heat storage tank cannot be operated efficiently, and the reliability of the entire apparatus is low. There was a problem.

[0005] The present invention has been made in view of such a point, without securing a specialized operator,
An object of the present invention is to provide a control device for a heat storage plant that can automatically and efficiently operate a heat storage plant having heat source devices.

[0006]

SUMMARY OF THE INVENTION The present invention relates to a heat storage plant control device for controlling the operation of a heat storage plant having a cold water heat source device and a hot water heat source device, wherein a past heat load result from each heat source device is stored. Heat load result storage means, heat load prediction means for predicting the heat load value of the day based on the heat load results stored in the heat load result storage means, and heat load of the day predicted by the heat load prediction means. Heat storage plant operation planning means for obtaining an operation plan of a heat storage plant so as to minimize the sum of objective functions relating to operation cost reduction and stable operation of each heat source device based on the load prediction value, and heat storage obtained by the heat storage plant operation planning means Heat source device control means for controlling the operation of each heat source device based on the operation plan of the plant, wherein the heat storage plant operation planning means Using a genetic algorithm that represents the ON / OFF of the water heat source equipment and the hot water heat source equipment with a bit sequence of 0/1, the heat storage plant is designed to minimize the sum of the objective functions for reducing the operation cost and stable operation of each heat source equipment. A heat storage plant control device for obtaining an operation plan.

According to the present invention, the heat load predicting means predicts the heat load value of the day based on the actual heat load stored in the actual heat load storage means. Next, based on the heat load predicted value, the heat storage plant operation planning means uses a genetic algorithm to perform a heat storage plant operation plan using a genetic algorithm so as to minimize the sum of objective functions related to the operation cost reduction and stable operation of each heat source device. Ask. Based on the operation plan of the heat storage plant, the operation of each heat source device is controlled by the heat source device control means.

[0008]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is an overall configuration diagram showing one embodiment of a control device for a heat storage plant according to the present invention. In FIG. 1, a heat storage plant 10 has a heat storage tank 11 provided in a basement or the like of a building. The heat storage tank 11 stores a cold water tank 11a provided with cold water used for cooling and hot water used for heating. It comprises a hot water tank 11b. A plurality of various heat pumps (HP) 12 are installed in the cold water tank 11a. That is, the heat pump 12
For example, in the summer, water at about 10 ° C. is converted to water at about 5 ° C. In addition, cold water tank 11a and hot water tank 11b
Is provided with a heat recovery type heat pump (DB) 13. The heat recovery type heat pump 13 uses cold water of about 5 ° C. and 4
Hot water of about 5 ° C. can be produced at the same time, and the heat output at this time can be used for the cold water tank 11a and the hot water tank 11b. The cold water and the hot water stored in the cold water tank 11a and the hot water tank 11b are supplied to the cold water header (CHD) 16 and the hot water header (HH) by pumps 14 and 15, respectively.
D) 17 to the heat load (HL) 18 side.

A heat storage plant control device 20 according to the present invention.
Is a heat source heat pump 12 of the heat storage plant 10 described above.
And a heat source equipment such as a heat recovery type heat pump 13. The heat storage plant control device 20 includes a process input / output unit 21, a heat load result storage unit 22 to which past heat load results are input, A heat load prediction unit 23 for predicting a heat load, a heat storage plant operation planning unit 24 for obtaining an operation plan of the heat storage plant, a heat source device control unit 25 for controlling operation of each heat source device, a heat storage plant parameter storage unit 26, Human interface input / output unit 27
And

In FIG. 1, a process input / output unit 21
While inputting the measurement signal from the heat storage plant 10,
A control signal is output to the heat storage plant 10. Further, the heat load result storage means 22 stores the past heat load results (temperature, flow rate, etc.) of the heat load 10 together with the time and the day of the week. Furthermore, heat load prediction means 2
Numeral 3 predicts the heat load statistically based on the actual heat load stored in the actual heat load storage means 22.

On the other hand, the heat storage plant operation planning means 24
Based on the parameters stored in the heat storage plant parameter storage means 26 and the heat load prediction value predicted by the heat load prediction means 23, the main cooling water guidance and the hot water guidance are determined, and the operation plan of the heat storage plant 10 is obtained. . Further, the heat source device control unit 25 controls the heat source device based on the target plan value obtained by the heat storage plant operation planning unit 24 and the measurement signal of the heat storage plant 10 input from the process input / output unit 21. Is output to the process input / output 21. The heat storage plant parameter values are input from the human interface input / output unit 27.

Next, the operation of this embodiment having such a configuration will be described. In FIG. 1, the actual heat load of the heat load 18 is measured from the temperature and the flow rate of the incoming and outgoing water, and is arranged as the actual heat load through the process input / output unit 21 based on internal signals such as time and day of the week. The result is stored in the result storage unit 22.

Next, based on the actual heat load stored in the actual heat load storage means 22, a predicted value for the next N hours of the heat load 10 is obtained by the heat load prediction means 23, and the heat storage plant operation is performed. Using the capacity of the heat storage tank 11 and the rated capacities of the heat source devices 12 and 13 by the planning means 24, a planned target value for N hours in the future is obtained.

Hereinafter, this point will be specifically described.
For the sake of simplicity, a prediction value and a plan value for N = 24 hours in the future are calculated every hour for prediction and planning. The heat load prediction by the heat load prediction means 22 is performed as follows. 1 every hour in the heat load storage means 22
The daily heat load data is filed for each day of the week, for example, for holidays, weekdays, and special days. Now, when the thermal load prediction unit 23 from the human interface input unit 27 is day of the day is input, the average value pattern ^y w day W thermal load prediction unit 23 Niso (i) (i = 1~24) Is obtained from the thermal load result storage means 22. Next, since the actual heat load up to the previous day has been obtained, the following autoregressive model is used to predict the heat load on the k-th day.

[0015]

(Equation 1) Here, a ₁ , a ₂ ,... Are auto-regression parameters,
It can be given in advance, and it is also possible to perform the least squares estimation (Kalman filter) sequentially in real time.

[0016]

(Equation 2) Is obtained.

Next, the heat storage plant operation plan by the heat storage plant operation planning means 24 is established as follows.

Since the heat storage plant 10 shown in FIG. 1 has the heat recovery type heat pump 13, the demand for cooling
a is divided by the heat storage tank capacity of each of the hot water tanks 11b, the sizes are compared, and the larger one is considered to be manual. Hereinafter, a planning method for a heat load (cooling / heating) to be manually operated will be described. * Formulation First, consider minimizing the sum of the following three objective functions. Electricity rate Peak cut Safe operation of heat source equipment Now, the operation state of heat source equipment at discrete time n

[0019]

(Equation 3) Expressed by Here, k = k ₁ + k ₂ , where k _{1 is} the number of HPs and k _{2 is} the number of DBs.

Each constant is defined as follows. _{y (n) = [y 1} (n) y 2 (n) ... y k (n)] T: absolute value condition _{a = [a 1 a 2 ...} a k] input and output coefficient of the HP / DB W = [W ₁ W ₂ ... W ₁ ]: HP / DB ON / OFF penalty vector η = diag [η ₁ η ₂ ... Η _m ]: Efficiency of cold / hot water tank [1 / h] diag [] is a diagonal matrix element Represents Q (n) = [Q ₁ (n) Q ₂ (n)... Q _m (n)] ^T : cold / hot water demand vector Q = diag [Q ₁ Q ₂ ... Q _k ]: refrigeration capacity [Mcal / h] H = diag [H ₁ H ₂ ... H _m ]: Heat capacity of cooling / hot water tank [Mcal] Q _pj j = 1, 2,... K ₂ : Heating / cooling capacity ratio CD: General electricity rate unit price [yen / KWh] , CP: peak cut penalty unit price [yen / KWh] Then, the objective function J becomes min J = J _E + J _P + J _O (2). However, And STT and STP indicate the start of the penalty cut and the yield time, respectively.

Here, J _E represents energy cost, ie, electricity bill, J _P represents peak cut penalty, ie, peak cut, and J _O represents ON / OFF switching penalty, ie, safe operation of the heat source equipment.

Further, the constraint condition is as follows.

[0023]

(Equation 4) And At this time, this optimization problem becomes an integer programming problem.

In the present embodiment, a genetic algorithm (GA: Genetic Argorithum
The method of solving in s) is described. <Step 1> ON / OFF of HP and DB at time i
Is represented by the following bit sequence of 0/1. 1 2 3 4... K X (i): [1110... 0] K: total number of HPs and DBs 1234 ... k | 1234 ... k | ... | 1234 ... k Bit string 1110 ... 0 | 0001 ... 0 | ... | 0100 ... 1 1 | 2 | ... | n This bit string is called an individual. The number of individuals can be arbitrarily selected, but here, the number is r. <Step 2> For each individual, an evaluation function (objective function) based on the constraints (6) to (12) described above.
Equation (2) is calculated. This calculated value is called fitness. The fitness of an individual who does not satisfy the constraint condition is given a very large value. <Step 3> The ratio of the fitness of each individual to the sum of all individuals is determined. From the individuals with a small ratio, only r pieces are preferentially left in the next generation. Others are eliminated. <Step 4> A pair of individuals (parents) is selected at random, and a certain position of the bit string is randomly determined, and the individuals are divided into subsequences there. The subsequences are exchanged for each set. This operation is called crossover. Crossover, new 2
Two individuals (children) are generated. Individuals that failed to participate in the cross are eliminated. However, it is assumed that the operation of dividing into partial columns at the crossover is performed for each bit sequence corresponding to each time (see FIGS. 2A and 2B)). <Step 5> For each individual, one bit is randomly selected from the partial sequence at each time, and 0/1 is inverted with a certain probability. This is called a mutation (see FIG. 2B) -C). <Step 6> The average of the fitness of this generation is obtained, and the difference from the average of the fitness of the previous generation is calculated. If the difference is equal to or less than a certain value, the individual having the minimum fitness is adopted as a solution, and the algorithm ends.

As described above, based on the heat load predicted value obtained by the equation (1) by the genetic algorithm, every hour,
The operation status x (n) of the heat source device for 24 hours is optimally planned.

Next, the control of the heat source equipment is performed as follows. (A) When the accumulated error of the heat load prediction is within the allowable range, the heat source device control means 25 executes the unit control as planned based on the heat storage target value every hour. That is, since the predicted heat load value is obtained every hour, the error between the past predicted heat load value and the actual heat load value at that time is also calculated every hour. And the present of the day is J
Then, the cumulative error of the thermal load prediction up to J-1 is obtained. If the cumulative error of the thermal load prediction is within an allowable range, the number of heat source devices 2 and 3 is controlled as planned. (B) When the accumulated error of the heat load prediction exceeds the allowable value, if the time required to achieve the heat storage target value can be changed only by changing the time within the next m hours, the heat source device control means 25 controls the number of units. The timing is shifted, and the unit control is executed when the heat storage target value is reached. If a new heat source device needs to be started within the next m hours, or if the start is planned but the heat source devices 12 and 13 do not need to be started, the plan is corrected by starting or continuing.

In order to execute these controls, in addition to the above-mentioned regular load prediction, the thermal load prediction means 23 calculates the following cumulative error of the thermal load prediction m hours later. That is, assuming that the day of the week of the day is w, the average heat load y ^w (i) (i = 1 to 24) for each hour of the day of the week is stored in the actual heat load storage unit 22. Based on this, integration is performed every m hours, and the cumulative error Z ^w (j) (j =
1 to l) are obtained. (However, m × l = 24) On the other hand, if the current day is j, the results up to j-1 have been obtained.

[0028]

(Equation 5) It is.

Further, b _1, b _2, ... it is parameters of the autoregressive, a _1, a _2, it is also possible to provide in advance the same manner ... and it is also possible to estimate recursive least squares.

By using the accumulated error thus obtained, the ON / OFF of the heat source device will be performed within m hours in the future.
Can be easily determined by the heat source device control means 25 as to whether or not it is necessary other than the plan. (C) When the cumulative error in the thermal load prediction has returned to an acceptable level. Return to planned unit control. At this time, the above-described operation plan of the heat storage plant is executed again, and a re-plan is made.

As described above, according to the present embodiment, it is possible to perform planned heat storage using nighttime electric power in anticipation of a daytime heat load, and systematically cut a daytime load peak. Will be able to In addition, even if the heat load prediction that is the premise of planned operation has an error,
Since the number of heat source devices for stable supply can be controlled, efficient automatic control can be achieved.
In the above embodiment, for simplicity, the case of using an autoregressive model for heat load prediction has been described. However, a multiple regression model using the weather forecast of the day input from the human interface input / output unit 27 as an input is described. It is also possible to use it.

That is, the algorithm for predicting the heat load and the algorithm for controlling the heat source equipment are not limited to the equations (1) and (13), respectively. ), The following multiple regression models of equations (14) and (15) are used.

[0033]

(Equation 6) By using such a multiple regression model, it is possible to absorb daily errors and increase control accuracy.

In the equations (14) and (15), the heat load of the day may be predicted based only on the weather forecast value of the day.

[0035]

As described above, according to the present invention, an operation plan is created using a genetic algorithm so as to minimize the sum of objective functions related to reduction of operation cost and stable operation of each heat source device. Since the operation of each heat source device is controlled based on the operation plan, the operation cost of each heat source device can be reduced and the operation can be reliably performed.

[Brief description of the drawings]

FIG. 1 is a schematic diagram showing one embodiment of a control device for a heat storage plant according to the present invention.

FIG. 2 is a diagram showing an operation of the thermal storage plant operation planning means using a genetic algorithm.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 11 Heat storage tank 12 Heat source heat pump 13 Heat recovery type heat pump 21 Process input / output part 22 Thermal load result storage means 23 Thermal load prediction means 24 Thermal storage plant operation planning means 25 Heat source equipment control means 26 Thermal storage plant parameter storage means 27 Human interface input / output part

────────────────────────────────────────────────── ─── Continuation of the front page (56) References JP-A-7-225038 (JP, A) JP-A 7-160663 (JP, A) JP-A 5-256495 (JP, A) JP-A 7-250 269926 (JP, A) JP-A-5-88715 (JP, A) JP-A-63-38855 (JP, A) JP-A-8-5126 (JP, A) JP-A-6-147598 (JP, A) (58) Field surveyed (Int. Cl. ⁷ , DB name) F24F 11/02 102

Claims (4)

(57) [Claims] 1. A heat storage plant control device for controlling the operation of a heat storage plant having a cold water heat source device and a hot water heat source device, comprising: a heat load result storage means for storing past heat load results from each heat source device; Based on the actual heat load stored in the actual heat load storage means, a heat load prediction means for predicting the heat load value of the day, and based on the heat load prediction value of the day predicted by the heat load prediction means, Based on the thermal storage plant operation planning means for determining the thermal storage plant operation plan so as to minimize the sum of the objective functions related to the operation cost reduction and stable operation of the heat source equipment, and based on the thermal storage plant operation plan determined by the thermal storage plant operation planning means, A heat source equipment control means for controlling the operation of each heat source equipment, wherein the heat storage plant operation planning means comprises a cold water heat source equipment and a hot water ON / OFF of the source equipment 0
A control system for a heat storage plant, characterized in that an operation plan of the heat storage plant is determined using a genetic algorithm represented by a bit string of / 1 so as to minimize the sum of objective functions related to a reduction in operation cost and stable operation of each heat source device. 2. The heat load result storage means stores past heat load results together with day of the week information, and heat load prediction means inputs the day of the week information to predict the heat load of the day. The control device for a heat storage plant according to claim 1. 3. The heat load record storage means stores past heat load records together with weather information, and the heat load prediction means inputs a weather forecast value of the day and predicts the heat load of the day. The control device for a high heat plant according to claim 1. 4. The heat load predicting means further has a function of calculating a cumulative error of a heat load prediction after a predetermined time in the future, and the heat source device control means has a function of calculating a cumulative error of the heat load prediction after a predetermined time. The control device for a heat storage plant according to claim 1, further comprising a function of controlling operation of the heat source device.