JP2002206785A - Air-conditioning heat source equipment optimum operation controller - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an air-conditioning heat source equipment optimum operation controller capable of realizing an efficient operation of a heat source equipment. SOLUTION: A heat load inferring unit 11 obtains a heat load inferring value based on weather data and a heat load performance value of air conditioning/heat source plant 20. A heat source operation primary planning unit 13 obtains a primary operation plan of the heat source equipment by an optimizing method based on the heat load inferring value and a plant simple model held in a plant simple model unit 12. A heat source operation secondary planning unit 15 retrieves the vicinity of the primary operation plan of the equipment as an initial value by the optimizing method based on air-conditioning/heat source plant model held in an air-conditioning/heat source plant model unit 14, and obtains an optimum operation plan of the equipment. A plant controller 16 controls heat source equipments 22, 23 and 24 of the plant 20 via a process I/O unit 17 based on the optimum operation plan of the equipment.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an air conditioning / heat source plant for a building or the like, and more particularly to an optimal operation control of an air conditioning / heat source device for efficiently operating a heat storage tank and a heat source device in an air conditioning / heat source plant for a building or the like. Related to the device.

[0002]

2. Description of the Related Art Conventionally, as an air conditioning / heating plant control system for a building or the like, an air conditioning system equipped with a heat load device such as an air conditioner has been used.
A heat source plant is used. In such an air conditioning / heat source plant, cold water and hot water are produced by a heat source device such as a refrigerator or a heat pump, are stored in a heat storage tank, and are supplied to a heat load device such as an air conditioner as needed. . Here, in an air-conditioning / heat-source plant in which an electric-type heat source device is used, heat is generated at night and stored in the heat storage tank, and heat stored in the heat storage tank is radiated for air conditioning during the day. As a result, efficient operation is performed by using inexpensive nighttime electric power or performing peak cutting to shift daytime peak consumption of energy to another time zone.

[0003] By the way, in order to perform the above-mentioned efficient operation in an air conditioning / heat source plant, a specialized operator is required. However, such a specialized operator is difficult to secure and requires an operator. Since a large labor cost is required, it is desired to perform an automatic operation using a computer or the like.

Conventionally, as a method for performing the automatic operation, an optimization method for minimizing the sum of objective functions related to a power rate, a gas rate, and the like while covering the required heat amount has been used. In general, a method of obtaining an operation plan of a plurality of heat source devices using a simple plant model in which an objective function is formulated by the following formula is generally used.

[0005]

As described above, conventionally, an automatic operation of an air-conditioning / heat source plant is determined by obtaining an operation plan of a plurality of heat source devices using a simple plant model using a simple approximation formula. It is carried out. However, in the above-described conventional method, since a simple approximate expression such as a linear expression or a primary delay expression is used in a simple plant model, it is difficult to secure the accuracy of the operation plan obtained by the optimization method. There is a problem.

The present invention has been made in view of the above points, and is an appropriate and highly accurate operation plan for an air conditioning / heat source plant such as a building from the viewpoint of energy saving, low cost operation, peak cut, and the like. Can be automatically created to achieve efficient operation of heat source equipment.
It is an object of the present invention to provide an air-conditioning heat source device optimal operation control device.

[0007]

SUMMARY OF THE INVENTION The present invention relates to an air conditioner / heat source equipment optimal operation controller for controlling the operation of an air conditioner / heat source plant such as a building, based on weather data and actual heat load values of the air conditioner / heat source plant. A heat load prediction means for obtaining a heat load prediction value, and a simple plant model holding a simple plant model representing a relationship between a heat load input and an energy consumption output for each heat source device constituting the air conditioning / heat source plant by a simple approximation formula. Means for determining a primary operation plan of a heat source device by an optimization method based on a means and a heat load prediction value obtained by the heat load prediction means and a plant simple model held by the plant simple model means. 1
Air conditioner / heat source plant model means for holding an air conditioner / heat source plant model obtained by combining various physical models relating to the air conditioner / heat source plant; heat load prediction values obtained by the heat load estimating means; Based on the air conditioning / heat source plant model held in the heat source plant model means, the primary operation plan of the heat source equipment obtained by the heat source operation primary planning means is searched as an initial value by an optimization method, and the vicinity thereof is searched. And a heat source operation secondary planning means for obtaining an optimum operation plan of the heat source device.

According to the present invention, in an air conditioning / heat source plant such as a building, when an operation plan of a heat source device is created by an optimization method, an objective function and a constraint condition accurately represent an actual process. Automatically create an appropriate and accurate operation plan from the viewpoint of energy saving, low cost operation, peak cut, etc., because the model is used for checking, to realize efficient operation of heat source equipment. Can be.

[0009]

Embodiments of the present invention will be described below with reference to the drawings. 1 to 12 are diagrams for explaining an embodiment of an air conditioner / heat source equipment optimal operation control device according to the present invention.

First, an air conditioner / heat source plant such as a building controlled by the air conditioner / heat source equipment optimal operation control device according to the present invention will be described with reference to FIGS.

FIG. 11 is a diagram showing an example of an air conditioning / heat source plant. As shown in FIG. 11, the air conditioning / heat source plant 2
0 is a heat source device 22, 23, 24 such as a refrigerator and a heat pump (HP), and these heat source devices 22, 23, 2
4 is provided with a heat storage tank 21 for storing cold water and hot water produced by the method 4 and a cooling tower 25, so that cold water, hot water and steam can be supplied to the air conditioner 30.

FIG. 12 is a diagram showing details of the air conditioner 30 of the air conditioner / heat source plant 20 shown in FIG. As shown in FIG. 12, the air conditioner 30 includes an air conditioner 31 as a heat load device, and the outside air sent into the air conditioner 31 is adjusted in temperature and humidity by the air conditioner 31. ,
The air is sent into the room 33 through the blower 32. Further, the air discharged from the room 33 is exhausted through the return air fan 38. The air conditioner 31 is located between the outside and the air conditioner 31.
A pipe 37 is connected between the air conditioner and the room 33 and between the room 33 and the outside. Further, a damper 39 is provided in the vicinity of the inlet and the outlet of the pipe 37 with the outside.

The air conditioner 31 has a cooling coil (C
C), a heating coil (HC), a humidifier (HF), and the like are provided, and cold water, hot water, steam, and the like are supplied through a heat storage tank 21 and heat source devices 22, 23, 24 as shown in FIG. It is supposed to be. Air conditioning equipment 3
0 is a controller (DDC: Direct Digital Contr
oller) 36 is provided so that the supply amount of cold water, hot water, steam, and the like to the air conditioner 31 can be adjusted based on the measurement values of the thermometer 34 and the hygrometer 35 installed in the room 33. It has become.

Next, referring to FIG. 1, a description will be given of an air conditioner / heat source equipment optimal operation control device for controlling the operation of the air conditioner / heat source plant 20 shown in FIGS.

As shown in FIG. 1, the air conditioner / heat source equipment optimum operation control device controls the operation of the air conditioner / heat source plant 20 via the process input / output unit 17. The process input / output unit 17 takes in a measurement signal from the air conditioning / heat source plant 20 and outputs a control signal to the air conditioning / heat source plant 20.

The air-conditioning heat source equipment optimal operation control device includes a heat load prediction unit 11, a simple plant model unit 12, a primary heat source operation planning unit 13, an air conditioning / heat source plant model unit 14, a secondary heat source operation planning unit 15, and a plant control unit. A section 16 is provided.

The heat load predicting unit 11 calculates a heat load predicted value (cooling / heating load) based on weather data such as a weather forecast value and a maximum / minimum outside air temperature forecast value and an actual heat load value of the air conditioning / heat source plant 20. (Predicted value). The weather data used by the heat load prediction unit 11 can be manually input at a predetermined time on the previous day, but it is possible to use an online system such as the Japan Meteorological Association provided on the Internet or the like. It is also possible to automatically obtain the information via a communication means.

The plant simple model section 12 includes a refrigerator and a heat pump (H) constituting the air conditioning / heat source plant 20.
For each heat source device 22, 23, 24 such as P), a simple approximation such as a linear expression or a polygonal line is used to express the relationship between the heat input (cold water heat and hot water heat) load input and the energy consumption output (for example, power consumption and fuel consumption). This holds the plant simplified model represented by the equation.

The primary heat source operation planning unit 13 generates a genetic algorithm (GA: Genetic Algorithm) based on the heat load prediction value obtained by the heat load prediction unit 11 and the simple plant model held in the simple plant model unit 12. Algorithms)
The primary operation plan of the heat source device is obtained by an optimization method using the above.

The air conditioner / heat source plant model unit 14 holds an air conditioner / heat source plant model obtained by combining various physical models related to the air conditioner / heat source plant 20. The air-conditioning / heat source plant model includes (1) an indoor heat transfer model (a heat transfer model for rooms and walls), (2) a heat exchange model for a cooling coil, a heating coil, a humidifier, etc. of an air conditioner,
Combination of various physical models such as (3) calculation formula of psychrometric chart, (4) temperature distribution model of heat storage tank, and (5) model of heat source equipment (absorption refrigerator, turbo refrigerator, heat pump, etc.) Things.

The heat source operation secondary planning unit 15 generates a genetic algorithm based on the heat load prediction value obtained by the heat load prediction unit 11 and the air conditioning / heat source plant model stored in the air conditioning / heat source plant model unit 14. The primary operation plan of the heat source device obtained by the heat source operation primary planning unit 13 is searched as an initial value and an area near the primary operation plan is searched by an optimization method using the above-described method to obtain an optimal operation plan of the heat source device.

Note that the primary operation plan of the heat source equipment obtained by the primary heat source operation planning unit 13 is
Before passing as an initial value to 5, a simulation is performed using an air conditioning / heat source plant model, and it is checked whether the constraint conditions are satisfied and the operation is actually possible. If the operation plan does not satisfy the constraint condition, a simulation is performed until there is an operation plan that satisfies the constraint condition, such as the previous operation plan and the operation plan two before, and the constraint condition is satisfied. Only the operation plan to be performed may be passed to the secondary heat source operation planning unit 15 as an initial value.

The plant control unit 16 controls the air conditioning / heat source plant 20 shown in FIG. 11 through the process input / output unit 17 based on the optimal operation plan of the heat source equipment obtained by the heat source operation secondary planning unit 15. It controls the heat source devices 22, 23, 24 such as a refrigerator and a heat pump (HP).

Next, the operation of the present embodiment having the above configuration will be described.

First, the heat load prediction unit 11 predicts a heat load (cooling / heating load prediction) based on weather data such as a weather forecast value and a maximum / minimum outside air temperature forecast value and an actual heat load value of the air conditioning / heat source plant 20. Value).

[Preparation of primary operation plan] Next, heat source operation 1
Based on the heat load prediction value obtained by the heat load prediction unit 11 and the simple plant model held in the simple plant model unit 12, the next planning unit 13 uses a heat source by an optimization method using a genetic algorithm or the like. Find the primary operation plan for the equipment.

Plant simple model Here, heat source devices 22, 23, 24 such as a refrigerator and a heat pump (HP) constituting the air conditioning / heat source plant 20
Is the operating state (run / stop) at discrete time k of X
(k), assuming that the heat load prediction value obtained by the heat load prediction unit 11 is Q _d (k), the problem of obtaining the primary operation plan of the heat source device is as follows. This is formulated as an optimization problem (integer programming problem) for obtaining the operating state X (k) of each heat source device to be minimized.

(Objective function) min J = J _e + J _p + J _s (1) In the above equation (1), J _e , J _p , and J _s are expressed by the following equations (2), (3), and (4), respectively. It is represented as

[0029]

(Equation 1) Here, when obtaining the operating state X (k) of each heat source device by the above equations (1) to (4), if the following constraint conditions are not satisfied with respect to the heat supply condition, the plant capacity, and the like. No.

(Constraints) V (k) −η · V (k−1) −F × (k) + Q _d (k) = 0 (5) X (k) = X (k−1) + Y (k) ... (6) | Y (k) | <ΔY ... (7) P l <P · X (k) <P u ... (8) V l <V (k) <V u ... (9) here And J _e : a penalty function related to power consumption (here, it is assumed that a heat source device (a turbo refrigerator, a heat pump, or the like) that does not use fuel such as gas as energy is used. If there is a heat source device to be used, a penalty function relating to fuel consumption is similarly added.) _Jp : Penalty function relating to power consumption during peak cut time (peak cut time; (ex) summer; k = 13 to
15) _{J s:} penalty function for the heat source device start-stop number W _e: Power related penalty weight W _p: peak cut hours power penalty weight for W _s: penalty weight for the heat source equipment start-stop number k: discrete time L: 14 (or 24) X (k) = (x ₁ (k), x ₂ (k),..., X _n (k)); x _i (k)
= 0,1: heat source equipment shutdowns variable vector _{P = (p 1, p 2} , ..., p n): heat source device power vector Y of _{(k) = (y 1 (} k), y 2 (k) _{, ..., y n (k)} ); y i (k)
= 0,1: where shutdown state change vector of the heat source _{device, y i (k) = x} i (k) -x i (k-1) is a ... (10).

V (k) = (v _h (k), v _c (k)) T: heat storage vector of the heat storage tank, v _h (k) is the hot water heat storage, and v _c (k) is the cold water heat storage V ₁ = (v _hl , v _cl ): the minimum heat storage amount vector of the heat storage tank V _u = (v _hu , v _cu ): the maximum heat storage amount vector of the heat storage tank P ₁ = (p ₁₁ , p ₂₁ ,..., _Pnl) ): minimum power vector of the heat source equipment _{P u = (p 1u, p} 2u, ..., p nu): maximum power vector of the heat source equipment eta: heat storage coefficient (η = 1-ε; ε is heat loss coefficient) F = F _1h , f _2h , ..., f _nh f _1c , f _2c , ..., f _nc : Heat production capacity matrix of the heat source equipment, f _1h , f _2h , ..., f _nh are hot water production capacities, f _1c , f _2c, ..., _{f nc} is the cold water-producing capacity _{Q d (k) = (q} dh, q dc) T: hot water demand _{q dh,} the cold water demand _{q dc} (1) Formulation of optimization problem Here, the optimization problem for obtaining the primary operation plan of the heat source equipment is defined by each heat source that minimizes the objective function represented by the above equation (1). Equipment operating state X (k)
(K = 1, 2,..., L). For example, when five heat source devices (n = 5) and L = 14, 70
(= 5 × 14) 0-1 variable sequence.

In the objective function represented by the above equation (1), the first item _Je is an item relating to power consumption. When considering L = 14 hours (o'clock 8 o'clock to 21) problem is to minimize J _e maximizes cheap nighttime power consumption of power cost, i.e. nocturnal production heat that Means

The second item _Jp is an item relating to power consumption in a peak cut time zone (a regular contract time zone for adjusting a power rate). In the case of a peak cut, a large monthly discount according to the power receiving contract will be applied, while a penalty will be imposed if the peak cut is not made. Therefore, it is an item that should be especially minimized.

The third item _Js is an item relating to the number of times of starting and stopping of the heat source equipment. Minimizing the J _s the air-conditioning /
This means that stable operation of the heat source plant 20 is to be achieved.

On the other hand, among the constraint conditions of the above equations (5) to (9), the first equation (5) represents a state based on the heat flow of the air conditioning / heat source plant 20. The second equation (6) represents a time change of the operating state X (k) of each heat source device, which is a controlled variable. The third equation (7) expresses X in one discrete time.
This is for restricting the change of (k). The last two equations (8) and (9) represent restrictions on power consumption and heat storage in a cross section.

Note that P · X in the objective function (the above equations (1) to (4)) and the constraint conditions (the above equations (5) to (9))
The element of (k) can be expressed as in the following equation (11).

[0037]

(Equation 2) The above equation (11) can be expressed as the following equation (12).

P _i (k) = a _i · S _i (k) + b _i (12) where S _i (k) is a heat load of the i-th heat source device.

As described above, the relationship between the heat input (the amount of heat of cold water and the amount of heat of hot water) and the energy consumption output (for example, power consumption and fuel consumption) in the heat source device is expressed by a simple approximate expression such as a linear expression or a broken line. be able to. Note that here, the calorie (cold water calorie and hot water calorie) load input and energy consumption output (for example, power consumption and fuel consumption)
Is approximated by one straight line, but it is also possible to approximate by two or more broken lines. In order to improve the accuracy of the simplified approximation equation, the coefficient of the above equation (12) is calculated based on the output characteristics of the heat source device obtained by performing an offline simulation using an air conditioning / heat source plant model in advance. A _i , b approximating the output characteristics well
_i (each coefficient in the case of a polygonal line) may be calculated.

The optimization problem (integer programming problem) formulated as described above is converted by the heat source operation primary planning unit 13 into
It is solved using an optimization method such as a genetic algorithm, and thereby a primary operation plan of the heat source device is obtained.

In the integer programming problem, discrete values (0,
The problem includes the discrete variable taking 1), and the problem to be solved becomes extremely large depending on the number of discrete variables. The size of the problem is
An example is given in which the operation plan for L = 14 hours (8 o'clock to 21:00 o'clock) is optimized by using, for example, all enumeration methods with five heat source devices (n = 5). Since there are 2 5 5 operation states (operation / stop) every hour, it is 32 to obtain an operation plan for 14 hours.
The calculation and evaluation of (= 2 ⁵ ) to the 14th power are required. Some of the actual air conditioning / heat source plants 20 have 10 heat source devices, and even if there are several heat source devices, each unit has a complicated mode (step operation for cold water and hot water, etc.). There are also things. In addition, 30 minutes or 1
In some cases, it is desirable to obtain an operation plan such as 5 minutes. For this reason, the problem to be solved is inevitably increased.

In order to determine the operation plan of the heat source equipment online and in real time, it is necessary to solve such an integer programming problem at high speed. A genetic algorithm is suitable for this.

FIG. 2 is a flowchart for explaining the processing procedure of the genetic algorithm. The genetic algorithm is an algorithm that simulates the evolutionary process of an organism from the perspective of an information medium called a gene, and has been widely studied in the field of optimization problems. In the genetic algorithm, crossover, mutation, and propagation are performed on individuals having various gene sequences while performing selection processes using constraints and evaluation functions multiple times (multiple generations). It seeks a solution (individual) with good fitness. Note that the fitness can be defined as the reciprocal of the objective function represented as the above equation (1).

Here, a genetic algorithm is applied to an individual having a gene sequence for each heat source device, which represents the operating state (run / stop) for each hour unit by 1/0.
FIG. 3 is a diagram showing an example of an individual having such a gene sequence, and shows a case where five heat pumps (HP) are used as heat source devices. In this case, the gene of one individual is represented by 70 bits (= 5 × 14).

As shown in FIG. 2, first, n individuals having a random gene sequence are generated, and these are set as an initial population (step 101).

Next, among the n individuals generated in step 101, those which do not satisfy the constraints of the above formulas (5) to (9) are represented by a gene sequence which does not satisfy the above formulas (5) to (9). And the state of the gene that does not satisfy the constraints contained in the selected gene sequence (1/0)
To change. Then, when n individuals satisfying the constraint conditions can be generated, the fitness of each individual and the average value of the fitness in the generation are calculated (step 102).

Next, individuals having a fitness (a value of the objective function is large) smaller than a predetermined ratio with respect to the maximum fitness in the population are eliminated, and the constraint condition is not satisfied at this time. If there is an individual, select it (Step 1)
03).

Next, the individual having the maximum fitness is multiplied by the number of individuals selected in step 103 (step 10).
4).

Next, as shown in FIGS. 4 (a) and 4 (b), individuals are randomly paired with each other by a predetermined crossover probability (ratio to the total number of individuals). Loci are randomly selected, and the same loci of each individual are crossed at one point (step 10).
5). One-point crossover refers to a continuous series of genes (Fig. 4
In the case of (b), two consecutive genes) are crossed with each other.

Next, as shown in FIG. 4 (c), individuals are randomly selected by a predetermined mutation rate (ratio to the total number of individuals), and the gene at an arbitrary locus of each selected individual is bit-selected. Invert (step 106).

In this way, steps 101 to 106
At the end of the process, whether the average value of the fitness in that generation is less than or equal to a predetermined value compared with the average value of the fitness values of the previous and previous times, or whether it exceeds a predetermined number of repetitions. Judgment (Step 10
7), Step 101 until these end conditions are satisfied.
To 106 are repeated. If the above-mentioned termination condition is satisfied, the entire process is terminated, and an operation plan is obtained based on the individual having the highest fitness of the generation.

[Preparation of Secondary Operation Plan] Next, the heat source operation secondary planning unit 15 calculates the heat load prediction value obtained by the heat load prediction unit 11 and the air conditioner / heat source / heat source plant model unit 14 held by the air conditioner / heat source plant model unit 14. Based on the heat source plant model and an optimization method using a genetic algorithm or the like, the primary operation plan of the heat source device obtained by the heat source operation primary planning unit 13 is searched as an initial value to search the vicinity thereof, and the heat source device is searched. To find the optimal operation plan.

Air-conditioning / heat source plant model Here, the air-conditioning / heat source plant model held in the air-conditioning / heat source plant model unit 14 is an air-conditioning / heat source plant model.
An air-conditioning / heat source plant model obtained by combining various physical models with respect to each other is stored. In addition, air conditioning /
Heat source plant models include (1) indoor heat transfer models (heat transfer models for rooms and walls, etc.), (2) heat exchange models for cooling coils, heating coils and humidifiers for air conditioners, and (3) air line diagrams. It combines various physical models such as a calculation formula, (4) a temperature distribution model of a heat storage tank, and (5) a model of a heat source device (such as an absorption refrigerator, a turbo refrigerator, and a heat pump).

(Air Conditioning Process) First, referring to FIG. 5, a procedure of a simulation of an air conditioning process using an air conditioning / heat source plant model will be described.

First, building data such as the size of a room to be air-conditioned, the heat capacity, the wall area, and the heat transmission coefficient of the wall are set (step 201). Further, an outside air temperature, an outside air humidity, a solar radiation amount, and the like are set as weather data relating to a heat load (cooling / heating load) of the air conditioner (step 202). further,
The calorific value of a person, the calorific value of an electric device (such as an OA device), and lighting are calculated in advance for the simulation time in accordance with the change in the occupancy rate of the person and set as indoor heat load data ( Step 203). In addition, the amount of heat obtained through the window glass surface based on the amount of solar radiation is calculated and set in advance.

Here, in this air conditioning process, a LOC (local object controller) used for control is used.
Is also simulated, the L in step 205 is set at intervals of the sampling control cycle (an integer multiple of the simulator calculation cycle).
An OC control algorithm is performed (step 204).
In step 205, the PID algorithm (speed type) is calculated mainly based on the deviation between the room temperature set value and the room temperature and the deviation between the humidity set value and the room humidity.

After that, the state points (temperature, humidity, enthalpy, etc.) of the air in the air conditioner are calculated in the order of the cooling coil, the heating coil and the humidifier in the order of the theoretical calculation formula (heat exchange model) of the heat exchanger and the air line. It is obtained by using a calculation formula in the figure (step 206). Further, a state point of indoor air is obtained using an indoor heat transfer model described later (step 20).
7). Further, a state point of the air at the inlet of the air conditioner is obtained by calculating the mixture of the recirculated air and the introduced outside air (step 2).
08).

The processes of steps 204 to 208 described above are repeated until the simulation end time (steps 209 and 210).

(Indoor Heat Transfer Model) FIG. 6 is a diagram for explaining an indoor heat transfer model in an air conditioning / heat source plant model.

As shown in FIG. 6, the wall of the room is divided into an outer wall (with a window) 51, an inner wall 52, a floor 53 and a ceiling 54, and the structure of the wall is approximated by two layers of concrete and heat insulating material. The state equations in this case are represented by the following equations (13) to (1).
It is shown in 5).

[0061]

(Equation 3) Here, i = 1, 2, 3, and 4.

[0062] _{Also, C RM = C a · ρ} · VOL + CPF C a: air specific heat (J / kg · K) ρ : density of the air ^(kg / m 3) VOL: chamber volume ^(m 3) CPF: Furniture Heat capacity (J / K) dt: Calculation cycle (s) K _RMi = A _i · α _Ri A _i : Wall area (m ² ) α _Ri : Indoor surface heat transfer coefficient (W / m ² · K) C _Ai , C _Bi: wall heat capacity of site _{i (J / K) K Ai} , K Bi: heat transmission coefficient × wall area of the portion _{i (W / K) T Ai} , T Bi: wall temperature at the site i (℃) _T R: room temperature (° C.) _T 1 :( equivalent) outside temperature (° C.) _{T 2:} underfloor temperature (° C.) _{T 3:} the temperature of the adjacent room (° C.) _{T 4:} ceiling temperature (° C.) _{G O:} draft amount (kg / s) _{Q H:} calorific value of human (W) _{Q E:} amount of heat generated by the electrical equipment (W) _{Q L:} calorific value of the illumination (W) _{Q C:} or window glass surface Et acquisition calorimetry _{(W) H ES:} removal from the chamber heat _{(= C a · G D ·} (T R -T
_RD )) (W) G _D : Supply air amount (kg / s) T _RD : Supply air temperature (° C.)

Note that the indoor humidity is similarly calculated as follows.
It can be obtained by solving the equilibrium equation of water.

(Temperature distribution model of heat storage tank)
A temperature distribution model of the heat storage tank in the heat source plant model will be described.

FIG. 7 is a diagram schematically showing a heat source plant simulated using an air conditioning / heat source plant model.

As shown in FIG. 7, as a heat source plant,
A cold (hot) water heat storage system using a horizontal heat storage tank 63 having two heat source devices 61 and 62 is assumed. Note that reference numeral 6
Reference numeral 4 denotes a heat load device. Here, the heat storage tank 63
It is divided into n small sections, each of which is numbered 1 to n. These small sections may be physically divided by a moat or the like, or may be virtually divided. However, each of the divided small sections becomes a completely mixed heat storage tank, and the water temperature in the tank becomes constant.

FIG. 8 is a diagram for explaining a temperature distribution model of the heat storage tank in the air conditioning / heat source plant model, and shows the heat storage tank near the small section i.

I: small section number (i = 1,..., N) v: heat storage tank (small section) volume [m ³ ] θ: temperature of the heat storage tank (in the small section) [° C.] ul: (small section) Inner) flow rate [m ³ / s] t: time [s] Here, θin, ulin, θout, and ulout may be plural depending on the small section.

The temperature distribution in the heat storage tank as shown in FIG. 8 is obtained as follows.

(A) Flow rate The flow ul (t, i) of water in the heat storage tank at time t is obtained from the following equation (16) based on the inflow amount and outflow amount in the small section i.

[0071]

(Equation 4) (B) Water temperature Based on the flow rate ul (t, i) and volume v (i) in the small section i, the water temperature θ (t + Δt, i) of the small section i after the time Δt [sec] is calculated by the following equation (17). ).

[0072]

(Equation 5) Here, η is the heat storage efficiency.

(Physical Model of Heat Source Equipment) Next, a physical model of the heat source equipment in the air conditioning / heat source plant will be described. Here, an absorption refrigerator will be described as an example of the heat source device.

FIG. 9 is a diagram schematically showing heat source equipment (absorption refrigerator) simulated using an air conditioning / heat source plant model. Note that the absorption refrigerator is a stable refrigerator having few moving parts, and usually uses an aqueous solution of LiBr (lithium bromide) as an absorbent and water as a refrigerant. Since the absorption refrigerator is governed by the dynamic characteristics of the heat system, it has excellent self-stability.

As shown in FIG. 9, the absorption refrigerator has four containers of an evaporator 71, an absorber 72, a generator 73, and a condenser 74, in which water as a refrigerant circulates. This constitutes a refrigeration cycle.

More specifically, first, the water contained in the evaporator 71 removes heat of vaporization from the cold water in the tube to become steam, and is dissolved in the LiBr aqueous solution of the absorber 72,
Dilute the aqueous solution. The dilute LiBr aqueous solution diluted in the absorber 72 is sent to the generator 73, heated by the generator 73, and separated again into water vapor and the LiBr aqueous solution.
Among them, the steam is cooled by the condenser 74 and condensed into water, and returns to the evaporator 71 at the starting point. On the other hand, the condensed concentrated LiBr aqueous solution is sent again to the absorber 72 and used as an absorbent.

The refrigeration cycle is represented by a Duhring diagram (“h
FIG. 10 shows the above.
FIG. 10 shows a cycle of water rotating counterclockwise with respect to four vessels (evaporator, absorber, generator and condenser), and a cycle of aqueous LiBr solution reciprocating between the absorber and the generator. Exists. The water in the evaporator has a low temperature (about 6 ° C.) and a low vapor pressure (6 mmHg = 800 Pa). The water in this evaporator and the aqueous LiBr solution (4
(0 ° C.) has the same water vapor pressure (in fact, the vapor pressure in the absorber is only slightly lower than the vapor pressure in the evaporator). Similarly, LiBr aqueous solution in the generator (150 ° C)
And the water in the condenser has the same steam pressure (70 mmHg = 930
0 Pa) (again, the vapor pressure in the condenser is only slightly lower than the vapor pressure in the generator).

Here, in order to realize such a refrigeration cycle, each container (evaporator, absorber, generator and condenser) is modeled as follows.

Here, the symbols are as follows: T: temperature (° C.) C: specific heat (kJ / kg ° C.) Conc: concentration (weight ratio) W: weight (kg) r: latent heat of vaporization (kJ / kg) q _w : Latent heat of evaporation (water) (kJ / kmol) G: flow rate (kg / sec) α: heat transfer coefficient (kJ / h ° C.) h: enthalpy (kJ)

The subscripts attached to the respective symbols are as follows: 'a': absorber 'r': generator 'c': condenser 'v': evaporator 'LB': LiBr 'w': water 'vpr': steam.

Further, regarding other variables, T _clt : average temperature of cold water (° C.) G _r : amount of evaporating water of the generator (kg / sec) G _a : amount of absorbed steam (kg / sec) T _vpr : heated steam Temperature (° C).

Hereinafter, each container (evaporator, absorber,
The model will be described for each of the generator and the condenser.

(1) Absorber A concentrated LiBr aqueous solution as an absorbent for absorbing water vapor generated in the evaporator 71 is always supplied from the generator 73 to the absorber 72. The concentration of the LiBr aqueous solution in the ordinary absorber 72 is about 60%, and the temperature is about 40 ° C. The steam pressure equilibrates with steam at about 6 ° C. Due to this property,
Li in the absorber 72 until the water in the evaporator 71 drops to 6 ° C.
The Br aqueous solution continues to absorb water vapor. On the other hand, by absorbing water vapor, heat of condensation of water vapor is generated in the LiBr aqueous solution, and the temperature rises. As the temperature rises, the steam pressure rises and the cooling capacity falls. In order to prevent this, the LiBr aqueous solution is always cooled by cooling water. When water vapor is absorbed, the concentration of the LiBr aqueous solution decreases. This also results in an increase in steam pressure and a decrease in cooling capacity. Then, in order to concentrate the LiBr aqueous solution again, the generator 73 is used.
The dilute LiBr aqueous solution is continuously sent to the apparatus.

(Heat Balance)

(Equation 6) (Water balance)

(Equation 7) (LiBr balance)

(Equation 8) (Condensation heat)

(Equation 9) Here, mu _w molecular weight of water, [Delta] T _a represents the temperature rise of the water corresponding to the increased vapor pressure of LiBr aqueous solution when the temperature of the absorber 72 rises once and obtained from Duhring diagram.

(2) Generator The dilute LiBr aqueous solution is sent from the absorber 72 to the generator 73. In the generator 73, the diluted LiBr aqueous solution is heated to evaporate water and concentrate. As the heating source,
Use hot steam. The concentrated LiBr aqueous solution is supplied to the absorber 7
Sent to 2. The generated steam is sent to the condenser 74.

(Heat balance)

(Equation 10) (Water balance)

[Equation 11] (LiBr balance)

(Equation 12) (Heat of evaporation)

(Equation 13) Here, mu _w molecular weight of water, [Delta] T _r represents the temperature rise of the water corresponding to the increased vapor pressure of LiBr aqueous solution when the temperature of the generator 73 rises once and obtained from Duhring diagram.

(3) Condenser The condenser 74 is kept at a lower temperature than the generator 73, and the steam evaporated in the generator 73 is condensed. It is constantly cooled by cooling water to keep it at a low temperature. The condensed water is sent to the evaporator 71 again.

(Heat balance)

[Equation 14] (Water balance)

(Equation 15) (4) Evaporator In the evaporating air 71, cold water flows in the tube. Water is sprayed from an upper nozzle, and the water evaporates from the surface of the tube, thereby removing heat of vaporization from the tube. Thereby, the cold water is cooled.

(Heat balance)

(Equation 16) (Water balance)

[Equation 17] Note that the controller performs a PI calculation so that the cold water supply temperature becomes a set value, and controls the flow rate of the heated steam.

[0090]

(Equation 18) Here, K _p : proportional gain, T _i : integration time.

Here, the heat source operation secondary planning unit 15 includes the air conditioning / heat source plant model and the heat load prediction unit 1 as described above.
1 based on the heat load prediction value obtained by the first heat source operation planning unit 13 by an optimization method using the same genetic algorithm as used in the first heat source operation planning unit 13. With the primary operation plan of the heat source device set as an initial value, the vicinity is searched to find an optimal operation plan of the heat source device. In addition, the heat source operation secondary planning unit 15 performs the air conditioning / air conditioning / holding stored in the heat source plant model unit based on the operation plan near the primary operation plan of the heat source device obtained by the heat source operation primary planning unit 13. A simulation is performed using a heat source plant model, and the amount of power and gas used,
Process values such as the calorific value of the cold water and hot water, the calorie consumption, and the temperature change are accurately calculated and output. Thereby, the calculation of the objective function and the checking of the constraint condition are accurately performed based on the output value.

At this time, before passing the primary operation plan of the heat source equipment obtained by the primary heat source operation planning unit 13 to the secondary heat source operation planning unit 15 as an initial value, the air conditioning / heat source plant model is used. The simulation is performed to check whether the constraint conditions are satisfied and the operation is actually possible. If the operation plan does not satisfy the constraint condition, a simulation is performed until there is an operation plan that satisfies the constraint condition, such as the previous operation plan and the operation plan two before, and the constraint condition is satisfied. Only the operation plan to be performed is passed to the heat source operation secondary planning unit 15 as an initial value.

Finally, the plant control section 16 sends the optimal operation plan of the heat source equipment determined by the secondary heat source operation planning section 15 to the air conditioning / heat source plant 20 via the process input / output section 17, and the air conditioning / heat source plant 20 In the plant 20, FIG.
The heat source equipment 22, 23, 24 such as a refrigerator and a heat pump (HP) of the air conditioning / heat source plant 20 shown in FIG.

As described above, according to the present embodiment, in the air conditioning / heat source plant 20 such as a building, the heat source devices 22 and 2
When creating 3, 24 operation plans by optimization method,
Since the objective function and constraints are checked using an air-conditioning / heat source plant model that accurately represents the actual process, an appropriate and high-precision operation plan can be automatically created from the viewpoints of energy saving, low-cost operation, and peak cut. In this way, efficient operation of the heat source devices 22, 23, 24 can be realized.

In the above-described embodiment, the genetic algorithm is used as the optimization method in the primary heat source operation planning unit 13 and the secondary heat source operation planning unit 15. However, the present invention is not limited to this. In at least one of the primary planning unit 13 and the heat source operation secondary planning unit 15, a mixed integer programming method may be used as an optimization technique. Here, the 0-1 integer programming problem formulated as described above can be generally solved by a branch-and-bound method (literature (translated by Iri, Konno, Tone: "Optimization Handbook", Chapter VI, Asakura Shoten (1995)). For this reason, in the mixed integer programming, the whole problem can be solved by solving a small problem defined by a branch and bound method or the like by a linear programming and repeating this for all the small problems.

[0096]

As described above, according to the present invention, an appropriate and high-precision operation plan is automatically created from the viewpoint of energy saving, low cost operation, peak cut, etc. Driving can be realized.

[Brief description of the drawings]

FIG. 1 is a diagram showing an embodiment of an air-conditioning heat source device optimal operation control device according to the present invention.

FIG. 2 is a flowchart illustrating a processing procedure of a genetic algorithm.

FIG. 3 is a diagram showing an example of an individual having a gene sequence.

FIG. 4 is a diagram schematically showing an operation on a gene sequence by a genetic algorithm.

FIG. 5 is a flowchart for explaining a procedure of a simulation of an air conditioning process using an air conditioning / heat source plant model.

FIG. 6 is a diagram for explaining an indoor heat transfer model in an air conditioning / heat source plant model.

FIG. 7 is a diagram schematically showing a heat source plant simulated using an air conditioning / heat source plant model.

FIG. 8 is a diagram for explaining a temperature distribution model of a heat storage tank in an air conditioning / heat source plant model.

FIG. 9 is a diagram schematically showing a heat source device (absorption refrigerator) simulated using an air conditioning / heat source plant model.

FIG. 10 is a diagram showing the operation of the heat source device (absorption refrigerator) shown in FIG. 9 on a Duhring diagram.

FIG. 11 is a diagram showing an example of an air conditioning / heat source plant to which the air conditioning heat source device optimal operation control device according to the present invention is applied.

12 is a diagram showing details of the air conditioning equipment of the air conditioning / heat source plant shown in FIG.

[Explanation of symbols]

 Reference Signs List 11 Heat load prediction unit 12 Simple plant model unit 13 Primary heat source operation planning unit 14 Air conditioning / heat source plant model unit 15 Secondary heat source operation planning unit 16 Plant control unit 17 Process input / output unit 20 Air conditioning / heat source plant

 ────────────────────────────────────────────────── ─── Continuing from the front page (72) Inventor Yuichi Hanada 1-1-1 Shibaura, Minato-ku, Tokyo F-term in Toshiba Corporation head office (reference) 3L060 AA08 CC01 CC19 DD02 DD08 EE22

Claims (6)

[Claims] 1. An air conditioner / heat source equipment optimal operation control device for controlling the operation of an air conditioner / heat source plant such as a building, wherein a heat load for obtaining a predicted heat load value based on weather data and an actual heat load value of the air conditioner / heat source plant. Prediction means; plant simple model means for holding a plant simple model representing a relationship between a heat load input and energy consumption output by a simple approximation formula for each heat source device constituting an air conditioning / heat source plant; Heat source operation primary planning means for obtaining a primary operation plan of a heat source device by an optimization method based on the heat load predicted value obtained by the above and the plant simple model held by the plant simple model means; Air conditioning that combines various physical models related to heat source plants / Air conditioning that holds heat source plant models /
A heat source plant model means; and an air conditioning / heat source plant model stored in the air conditioning / heat source plant model means, based on the heat load predicted value obtained by the heat load prediction means, and the heat source operation 1 by an optimization method. 1 of heat source equipment determined by the next planning means
An air-conditioning heat source equipment optimal operation control device, comprising: a heat source operation secondary planning means for searching for the neighborhood of the next operation plan as an initial value and finding an optimal operation plan of the heat source equipment. 2. The air-conditioning heat source equipment optimal operation according to claim 1, wherein at least one of said heat source operation primary planning means and said heat source operation secondary planning means uses a genetic algorithm as said optimization method. Control device. 3. The air conditioning heat source equipment optimization according to claim 1, wherein at least one of said heat source operation primary planning means and said heat source operation secondary planning means uses a mixed integer programming method as said optimization method. Operation control device. 4. The air-conditioning heat source equipment optimal operation control device according to claim 1, wherein said heat load prediction means acquires said weather data from an external system via communication means. 5. A coefficient of a simple approximation formula of a plant simple model stored in said simple plant model means is calculated based on output characteristics of heat source equipment obtained by performing a simulation using an air conditioning / heat source plant model. The air-conditioning heat source equipment optimal operation control device according to claim 1, wherein: 6. A simulation using an air conditioning / heat source plant model before passing the primary operation plan of the heat source equipment obtained by the primary heat source operation planning means to the secondary heat source operation planning means as an initial value. 2. The air-conditioning heat source equipment optimum operation control device according to claim 1, wherein only the operation plan satisfying the constraint condition is passed to the heat source operation secondary planning means as an initial value.