JPH0926804A - Heat source operation management device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a heat source operation management device which can lead out a multipurpose optimum solution of a heat source operation plan and also can perform a cost effectiveness optimum operation, an energy saving property optimum operation and an environment protection optimum operation respectively. SOLUTION: A heat source device includes the boilers BO1 and BO2 which generate the steam to supply the heat, the absorption refrigerating machines AR1 and AR2 which uses the steam as a drive source to supply the cold, and the turbo refrigerating machines TR1 and TR2 which uses the electric power as a drive source to supply the cold. Then a multipurpose optimization means is added to optimize the evaluation standards of the cost effectiveness, the energy saving properties and the environmental properties via the fuzzy multipurpose mixture 0-1 planning method, together with an extraction means of an evaluation item selection structure that, applies the fuzzy structure modeling for operation of the boilers BO1 and BO2, the machines AR1 and AR2, and the machines TR1 and TR2 respectively.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a heat source operation management device for realizing rational operation control of heat source equipment such as a boiler and a refrigerator.

[0002]

2. Description of the Related Art In order to realize rational operation control of a heat source device in a large-scale heat source or a heat storage system, the energy demand on the secondary side is accurately predicted and then an optimum operation plan of the device is prepared. Will be required. In general, the optimum operation means a cost minimum in most cases, but the range of the optimum meaning is gradually expanding due to the growing global interest in resource depletion problems and global environmental problems. That is, in determining the operation strategy of the heat source system, decision making from the viewpoint of multi-objective optimization including evaluation criteria other than economic efficiency is required.

[0003]

SUMMARY OF THE INVENTION The present invention has been made in view of the above circumstances, and regards the optimal operation planning problem of a heat source system as a multi-objective decision making problem consisting of a plurality of evaluation criteria, and has a dialogue with a decision maker. The multi-objective optimal solution of the heat source operation plan can be derived by using the preference information about the operation strategy obtained by, and the economical optimal operation aiming at the minimization of energy cost in the operation stage, the minimum of primary energy consumption Energy-saving optimal operation with the goal of
Another object of the present invention is to provide a heat source operation management device capable of performing optimum operation of environmental conservation with the goal of minimizing carbon dioxide emissions.

[0004]

In order to achieve the above object, the present invention uses a fuzzy multi-purpose mixed 0-1 planning method in a heat source operation management device for managing the operation of a heat source device that supplies hot heat or cold heat. It is characterized by comprising multi-objective optimization means for optimizing a quantifiable evaluation criterion and means for extracting a preference structure of evaluation items using fuzzy structure modeling.

[0005]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described below in detail with reference to the drawings. FIG. 1 is a configuration explanatory view showing an embodiment of the present invention. In FIG. 1, AR1 is an absorption chiller (for example,
Refrigerating capacity 600 USRt, electric motor output 9.2 kW, steam consumption 2700 kg / h), AR2 is an absorption type refrigerating machine which supplies cold heat by using steam as a driving source (for example, refrigerating capacity 600
USRt, electric motor output 9.3kW, steam consumption 2700
kg / h), TR1 and TR2 are turbo refrigerators (for example, a refrigerating capacity of 600 USR) that use electric power as a driving source to supply cold heat.
t, electric motor output 400 kW), BO1 is a heavy oil-fired steam boiler in which heavy oil generates steam at a drive source to supply heat (for example, evaporation amount 4800 kg / h, electric motor output 12 kW, fuel consumption amount 302 kg / h), BO2 is city A gas-fired steam boiler that supplies steam by generating heat from a gas (for example, evaporation amount 4800 kg / h, electric motor output 12 kW,
Fuel consumption 310 Nm ³ / h), CTAR1, CTAR
2 is an open type cooling tower (cooling capacity 3120 Mcal / h, electric motor output 22 kW), CTTR1 and CTTR2 are open type cooling tower (cooling capacity 2340 Mcal / h, electric motor output 1
6.5 kW), CPAR1, CPAR2, CPTR1,
CPTR2 is a cold water pump, CDPAR1 and CD respectively
PAR2, CDPTR1 and CDPTR2 are cooling water pumps, 10 is a load having a heat demand and a cold demand, and
Reference numeral 11 is a steam header for pressure adjustment, 12 and 13 are cold water headers for pressure adjustment, 14 is a hot well tank for condensing the steam from the load 10 and supplying it to the steam boilers BO1, BO2, and 15 is a monitoring control panel 17. A monitoring table for performing fuzzy multi-purpose optimal operation planning of the heat source system by receiving operation information, 16 is a display provided on the monitoring table 15,
Reference numeral 17 denotes the absorption refrigerating machines AR1 and AR2 and the turbo refrigerating machines TR1 and TR in response to the instruction of the optimum operation from the monitoring table 15.
2, steam boiler BO1, BO2, cooling tower CTAR1, C
TAR2, CTTR1, CTTR2, cold water pump CPA
R1, CPAR2, CPTR1, CPTR2, cooling water pump CDPAR1, CDPAR2, CDPTR1, CD
It is a supervisory control panel that controls the PTR2.

That is, when the load 10 is a cold heat demand such as cooling, the absorption refrigerators AR1 and AR2 and the turbo refrigerator T are used.
Cold water from R1 and TR2 is supplied to the load 10 via the cold water header 12. On the other hand, when the load 10 is in the heat demand such as heating and hot water supply, the steam from the steam boilers BO1 and BO2 is supplied to the load 10 via the steam header 11.
A part of the steam from the steam boilers BO1 and BO2 is supplied to the absorption refrigerators AR1 and AR2 via the steam header 11.

FIG. 2 shows a calculation flowchart up to the formulation of a multi-purpose optimum operation plan in the monitoring table 15 of the heat source device of FIG. Step [1]: (a) The input / output characteristics and energy balance of various heat source devices constituting the heat source device, and the connection relationship between them are defined. (Model Setting of Heat Source System) Step [1]: (b) Predict the demand for cold heat (cooling) and hot heat (heating, hot water supply) inside the building that is the energy supply destination. (Setting of Heat Demand Curve) Step [2]: (a) Define a function that quantifies the evaluation standard for evaluating the optimality of the operation plan as a specific numerical value. (Setting of objective function) For example, only one of [evaluation criteria] [quantification target] 1, economic energy cost 2, energy saving primary energy consumption 3, environmental conservation CO ₂ emission, etc. Set.

Step [3]: Using mathematical programming,
Solve the problem set in step [2]. Here, there is only one type of objective function. (Solution of Single Objective Planning Problem) Step [4]: For all prepared evaluation criteria,
Check whether the optimization calculation is performed individually. If not completed, the process returns to step [2], and if completed, the process proceeds to step [5].

Step [5]: Since the optimization calculation results obtained for each evaluation standard have different units, an evaluation standard of satisfaction of the decision maker is provided as a means for comprehensively judging the optimality. Here, when the evaluation value of each evaluation criterion can be scale-converted into the degree of satisfaction, the process proceeds to step [8],
If the scale cannot be converted, the process proceeds to step [6].
(The scale of satisfaction is [0, 1] interval value) Step [6]: The evaluation value of each evaluation criterion changes depending on which evaluation criterion is optimized. (For example, the energy cost when optimizing for economic efficiency and the energy cost when optimizing for environmental conservation have different normal values.) Of these possible values, the best value is the satisfaction level 1 or the worst value. The provisional membership function (reference membership function) is defined by making the value of 0 correspond to the degree of satisfaction 0. For example, FIG. 3A shows a membership function that associates economic evaluation with satisfaction, and FIG. 3B shows a membership function that associates energy saving evaluation with satisfaction.

Step [7]: The fuzzy structure modeling (FSM) is used to quantify the weighting (preference structure) potentially possessed by the decision maker. Step [8]: The relationship between the evaluation value of the evaluation standard and the degree of satisfaction of the decision maker is defined by the membership function. (If it is via step [5], it is directly expressed as a function, and if it is via step [7], the function is determined using the standard membership function and the weighting value.) Step [9]: Satisfaction criterion Solves a single-objective multi-objective programming problem using mathematical programming. (Solution to maximize satisfaction.) Figure 4 shows an example of the heat demand curve on the summer representative day. In FIG. 4, the cooling load is for cooling and the heating load is for hot water supply.

Next, a multi-objective decision-making model in the decision-making method for multi-objective optimization will be described. That is, there is a multi-objective linear programming method as a means for solving an optimization problem that considers a plurality of evaluation criteria. In multi-objective programming problems, evaluation criteria are often in a trade-off relationship, so the decision of the optimum point when there is no objective decision material is left to the subjective judgment of the decision maker. Need information on the importance of the evaluation criteria. Therefore, the multi-objective decision-making model of the heat source device requires a framework that can solve both the multi-objective planning problem and the weighted evaluation criterion problem.

In this model, as a multi-objective linear programming method,
Fuzzy multi-objective 0-1 programming (Fuzzy Multi) that can perform comprehensive evaluation by converting scales of non-commutative objective functions into a measure of satisfaction of decision makers
iojective Mixed 0-1 Prog
(ramming: FMOP) was applied. The FMOP problem when only fuzzy goals exist is expressed by the following equation.

[0013]

[Equation 1] Where z (x) is the objective function, μ (z (x)) is the membership function representing the satisfaction of the decision maker for the objective function value, x
_k represents a 0-1 variable.

As a means for identifying the importance of the evaluation criteria, fuzzy structural modeling (Fuzzy Structural) capable of extracting the preference structure of the evaluation items, which is inherent in the decision maker, in the form of a directed graph through dialogue.
Modeling: FSM) was applied. Since the structure graph created by FSM is a hierarchical diagram in which the evaluation criteria with higher importance are arranged higher, the importance of each evaluation criterion can be obtained by scoring the entire graph for each hierarchy. Become.

Next, the determination of the membership function by dialogue will be described. That is, as shown in FIG. 5, in the above equation (1), the membership function μ _i (z _i (x))
Can be easily defined by directly determining the point z _i ¹ with satisfaction level ¹ and the point z _i ⁰ with satisfaction level 0 through dialogue with the decision maker. In this process, since it is considered that the importance is woven at the same time, the work of identifying the importance by FSM is unnecessary. However, when the decision maker cannot quantify his or her own satisfaction, these points are not fixed and it is impossible to determine the function shape. Therefore, the following function is defined so that such a decision maker can set the membership function in consideration of the importance of the evaluation standard.

[0016]

[Equation 2] Here, ω _i represents the importance of the evaluation criterion i, and z _i ¹ and z _i ⁰ represent evaluation values of the evaluation criterion i that define points of satisfaction 1 and satisfaction 0 of the decision maker, respectively. σ is a positive parameter.

Further, since the relationship between the evaluation value and the importance cannot be uniquely determined only by the above formula, the importance of the evaluation criterion i is ω when there are n items of indiscriminate evaluation criteria regarding the importance.
_It is assumed that _i = 1 / n, z _i ⁰ is the worst (maximum) value in the set of values f _{i, j} of the evaluation standard i taken when the evaluation standard j is optimized.

[0018]

(Equation 3) Therefore, the following equation is obtained from the equations (2) and (4).

[0019]

(Equation 4)

Next, the weighting of the evaluation criteria and the setting of the membership function in the numerical simulation will be described. That is, for the heat source device of FIG. 1, a simulation of the optimum operation plan was performed according to the calculation flow of FIG. Table 1 shows the evaluation criteria selected as the optimality evaluation criteria and the optimization goals.

[0021]

[Table 1]

Next, the question "how important is the evaluation criterion i to be more important than the evaluation criterion j from the viewpoint of optimum heat source operation" is presented to the virtual decision maker, and the evaluation based on the subjective evaluation is presented. Structuring between standards was carried out. FIG. 6 shows a dependent matrix obtained by a pairwise comparison between evaluation criteria and a structural model generated based on it (threshold value: p = 0.55,
Fuzzy structure parameter: λ = −0.32). In this case, the structural model has three levels (Level1 to Level).
13), the economic efficiency (eco) and the environmental conservation (env) are at level 1, the energy saving (egy) is at level 2, and the functional integrity (fun) and controllability (ctl) are at level 3. ing. From these results, the weight of economy is 0.40, energy saving is 0.22, environmental conservation is 0.38, and functional conservation and controllability are 0, and functional conservation and controllability are evaluated. Excluded from the target. Therefore, the value that defines the membership function that represents the satisfaction of the decision maker regarding the economic efficiency, the energy saving property, and the environmental conservation property from the expression (5) is as follows.

[0023]

(Equation 5) Where eco, egy, and env are economics,
This means optimizing energy conservation and environmental conservation.

Next, the calculation conditions will be described. That is,
As the objective function of the multi-objective programming problem, the unit price, primary energy amount and C of various utilities used for quantitatively calculating economic efficiency, energy saving property, and environmental conservation property, respectively.
Table 2 shows the basic unit for O ₂ emissions.

[0025]

[Table 2] Regarding the constraint conditions, the heat demand fluctuation curve by time,
In addition to the partial load characteristics of the equipment itself, the connection relationship between the equipment, and the constraint expression expressing the heat balance, in order to avoid operating the heat source equipment in the low load region where energy efficiency is not good,
The load factor (ratio of the actual output to the rated output) was set so as not to operate such that the load ratio was 20% or less.

Next, the calculation result will be described. That is,
The results of the simulation are shown in FIGS. The graph of FIG. 7 shows changes with time of the optimum operation pattern of main heat source devices for each operation strategy. The input rate on the vertical axis means the ratio of the actual input to the rated input. In addition, FIG. 8 shows operating costs, energy consumption, CO
₂ Shows the total amount of emissions by operation strategy.

From the figure, it can be seen that there is a difference in the operation pattern of each device between the economical optimal operation, the energy saving optimal operation, the environmental conservation optimal operation and the fuzzy multi-purpose optimal operation in the heat source device. In the economically optimal operation, since the energy cost of electric power energy is higher than that of fossil fuel when converted to primary energy, the operation priority of the turbo chillers TR1 and TR2 driven by electric power is low. . Therefore, the absorption chillers AR1 and AR2 are preferentially operated with respect to the cooling load, but the means for generating steam as the drive source thereof is the cheapest city gas in terms of primary energy as the drive source. The gas-fired steam boiler BO2 that operates is preferentially operated. On the contrary, in the energy-saving optimal operation, the turbo refrigerators TR1, TR2 are
And the heavy oil-fired steam boiler BO1 is operated with priority. The environmental conservation optimal operation shows an operational characteristic close to the operational pattern shown by the economical optimal operation. Also, in the case of fuzzy multi-purpose optimum operation, it is understood that it shows almost the intermediate operating characteristics of the three, reflecting the fact that economic efficiency, energy saving and environmental conservation are taken into consideration at the same time.

From the above results, a significant difference appears in the optimum operation pattern of the heat source device depending on how the evaluation criteria are given. Therefore, the method of selecting a driving strategy by a decision maker in a multipurpose decision making environment is extremely important. Is.

As the heat source device, a device for supplying cold water or hot water using fossil fuel, such as a cold / hot water generator, or a device for supplying cold water or hot water by using electric power, such as a heat pump, is similarly used. Can handle. In addition, as evaluation criteria, quantifiable ones other than economic efficiency, energy saving and environmental conservation can be treated in the same manner.

[0030]

As described above, according to the present invention, the optimal operation planning problem of the heat source system is regarded as a multi-objective decision-making problem consisting of a plurality of evaluation criteria, and the preference regarding the operation strategy acquired through the dialogue with the decision maker. Information can be used to derive multi-objective optimal solutions for heat source operation plans. Also, economically optimal operation that aims at minimizing energy costs in the operation stage, energy-saving optimal operation that aims at minimizing primary energy consumption. It is possible to perform operation and optimum operation for environmental conservation with the goal of minimizing carbon dioxide emissions.

[Brief description of drawings]

FIG. 1 is a configuration explanatory view showing an embodiment of the present invention.

FIG. 2 shows a calculation flowchart up to the formulation of a multi-purpose optimum operation plan in the monitoring table of the heat source operation management device of FIG.

FIG. 3 is a characteristic diagram showing an example of setting reference membership functions of FIG.

FIG. 4 is a characteristic diagram showing an example of cool / heat demand characteristics of a summer representative day used in the present invention.

FIG. 5 is a characteristic diagram showing an example of the shape of a membership function used in the present invention.

FIG. 6 is an explanatory diagram showing an example of a paired comparison matrix and a fuzzy structure model used in the present invention.

FIG. 7 is a characteristic diagram showing an example of an optimal operation pattern of a heat source device for each operation strategy in a simulation using the present invention.

FIG. 8 is a characteristic diagram showing an example of the relationship between the optimum driving strategy and the evaluation value in the simulation using the present invention.

[Explanation of symbols]

AR1, AR2 ... Absorption refrigerator, TR1, TR2 ... Turbo refrigerator, BO1 ... Heavy oil-fired steam boiler, BO2 ... Gas-fired steam boiler, CTAR1, CTAR2, CTTR
1, CTTR2 ... Open type cooling tower, CPAR1, CPAR
2, CPTR1, CPTR2 ... Cold water pump, CDPAR
1, CDPAR2, CDPTR1, CDPTR2 ... Cooling water pump, 10 ... Load having hot heat demand and cold heat demand,
11 ... Steam header, 12, 13 ... Cold water header, 14 ... Hot well tank, 15 ... Monitoring console, 16 ... Display, 17 ... Monitoring control panel.

Claims (1)

[Claims] 1. A heat source operation management device for managing the operation of a heat source device for supplying hot or cold heat, comprising: a multi-objective optimizing means for optimizing a quantifiable evaluation criterion using a fuzzy multi-objective mixed 0-1 programming method. And a heat source operation management device, comprising: means for extracting a preference structure of evaluation items using fuzzy structure modeling.