KR20160136965A - System for performing optimal managing of complex equipments based thermal energy demanding forcasting and method thereof - Google Patents

System for performing optimal managing of complex equipments based thermal energy demanding forcasting and method thereof Download PDF

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KR20160136965A
KR20160136965A KR1020150071277A KR20150071277A KR20160136965A KR 20160136965 A KR20160136965 A KR 20160136965A KR 1020150071277 A KR1020150071277 A KR 1020150071277A KR 20150071277 A KR20150071277 A KR 20150071277A KR 20160136965 A KR20160136965 A KR 20160136965A
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daily
consumption
facility
day
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KR101705869B1 (en
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김선미
김미정
김현숙
한자경
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주식회사 케이티
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/10—Energy trading, including energy flowing from end-user application to grid

Abstract

An optimal operating system and method for a complex facility based on thermal energy demand forecasting is disclosed.
In this system, the service providing unit provides a user interface for providing an optimal operation of the complex facility based on the prediction of the demand for thermal energy, a real time processing unit performs operation control on the site facility according to the operation schedule, As shown in FIG. The optimum operation management unit performs a daily heat demand forecast based on the monthly heat demand forecast and calculates a daily production target amount for each on-site facility based on the daily heat demand forecast amount, And controls the consumption optimization process that calculates the applicable operation schedule.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an optimal operating system and a method for operating the same,

TECHNICAL FIELD The present invention relates to a complex facility optimal operating system and method based on thermal energy demand forecasting.

The most important goal of the MEG-EMS (Micro Energy Grid-Energy Management System) is to manage the M & V, measurement and verification of efficient operation of energy consumption facilities. And to maximize investment efficiency of heating, cooling and hot water supply facilities.

Basically, most of the thermal data is managed by the monthly gas bill, which makes it difficult to grasp the situation of real-time heat consumption, and it is almost impossible to control the facilities flexibly in accordance with the change of situation (temperature, visitor, to be.

In addition, when the facility managers in the building set the facility operation control value (set temperature, etc.) by season, most of the time, as a result, uniform facility operation is performed.

Accordingly, there is a demand for a system capable of effectively managing energy while flexibly responding to changes in various operating environments of customers, for example, seasons, days of the week, temperatures, holidays and the like.

SUMMARY OF THE INVENTION The present invention provides a multi-facility optimal operating system and a method thereof, which are based on prediction of a demand for heat energy, which maximizes investment efficiency of facilities.

According to an aspect of the present invention,

A service providing unit that provides a user interface for providing an optimal operation service of a complex facility based on the prediction of heat energy demand; A real-time processing unit for performing operation control on the on-site facility according to an operation schedule and collecting information on the operation of the on-site facility; And estimating the daily heat demand based on the monthly heat demand forecast, calculating a daily production target amount for each of the on-site facilities based on the daily heat demand forecast amount, and calculating a daily production target amount for each of the on-site facilities based on the daily production target amount And an optimum operation management unit for controlling a consumption optimization process for calculating a schedule.

Here, the optimal operation management unit estimates the monthly heat consumption amount based on the monthly heat consumption amount data, estimates the daily heat consumption amount by applying the temperature change weight and the weekly characteristic weight to the monthly heat consumption amount, and then uses the fixed energy A heat demand predicting unit for predicting the heat consumption amount by time by applying an energy consumption weight by area and time period; A production optimization process for calculating a daily production target amount for each of the on-site facilities by applying the daily heat demand consumption amount predicted by the heat demand forecasting unit, the efficiency for each on-site facility and the daily production allowable amount, And an operation scheduling unit for performing a consumption optimization process of generating the operation schedule for each site facility with respect to the daily production target amount by referring to the production unit price per unit time and the operation cost.

In addition, the heat demand predicting unit and the operation scheduling unit may prepare an initial operation schedule for each site facility, update the operation schedule according to information obtained in real time during operation of the site facility through the real-time processing unit, A verification and update unit for controlling the operation of the field facility according to an operation schedule; And a settlement unit for calculating a fee generated due to the operation of the site facility 500 according to an operation schedule created by the operation scheduling unit 420.

The heat demand predicting unit may include a monthly heat consumption predicting unit for predicting the monthly heat consumption based on the monthly heat consumption data to calculate the monthly heat consumption amount; A daily heat consumption predicting unit estimating a daily heat consumption by predicting a daily heat consumption by applying a temperature change weight and a weekly characteristic weight to the monthly heat consumption calculated by the monthly heat consumption predicting unit; Time consuming heat consumption by estimating the heat consumption by time by applying the energy consumption weight by time and the area using energy fixedly based on the daily heat consumption calculated by the daily heat consumption predicting unit A prediction unit; And a prediction controller for controlling the monthly heat consumption predicting unit, the daily heat consumption predicting unit and the time-series heat consumption predicting unit to control the prediction of the heat consumption of the object.

In addition, the operation scheduling unit may include a production optimizing unit that performs a production optimization process for calculating a daily production target amount for each site facility based on the daily heat demand calculated by the heat demand forecasting unit; And a consumption optimizing unit for performing a consumption optimization process of preparing an operation schedule for each site facility based on the daily production target amount calculated by the production optimizing unit.

In addition, the production optimizing unit may include an efficiency comparing unit that compares seasonal efficiencies of the field equipments, and then selects equipments in order of equipment having high efficiency; A production availability calculation unit for calculating a total quantity that can be produced per day by the site facility; A daily demand amount calculated by the heat demand predicting unit, a facility order selected by the efficiency comparison unit, and a total producible amount calculated by the production availability calculating unit, A target amount calculating unit; And a production amount control unit for controlling the efficiency comparison unit, the producible amount calculation unit, and the production target amount calculation unit to calculate a daily production target amount for each of the on-site facilities.

The consumption optimizing unit may further include a driving time calculating unit for calculating a daily driving time for each of the on-site facilities based on the daily production target amount calculated by the production optimizing unit, the preheating time for each of the on- A running cost calculating unit for calculating a running cost for each of the on-site facilities on the basis of a production cost per hour; An operation schedule creating unit for creating an operation schedule for each site facility based on a daily operation time calculated by the operation time calculating unit and an operation cost calculated by the operation cost calculating unit; And a scheduling control unit for controlling the operation time calculating unit, the operation cost calculating unit, and the operation schedule creating unit to create an operation schedule for each site facility.

In addition, the monthly heat consumption amount (Y)

Figure pat00001
here,
Figure pat00002
Is the monthly cooling coefficient,
Figure pat00003
Is the monthly heating coefficient,
Figure pat00004
Is a weekday coefficient,
Figure pat00005
CDD (Cooling Degree Days) is a degree of cooling, HDD (Heating Degree Days) is a degree of heating,
Figure pat00006
Is the number of weekdays of the month,
Figure pat00007
Is characterized by following the number of weekends of the month.

Also, the temperature change weight is set to a value obtained by dividing the sum of the day cooling degree day and the heating day by the total sum of the cooling degree day and the heating day of the month of the month.

In addition, the weekly characteristic weight is classified by characteristic of each day based on the monthly heat consumption data, and the amount of consumption per day is relatively indicated.

In addition, the weekly characteristic weight value has a plurality of values for each weekly characteristic classification, and the average of a plurality of values is used.

Also, the daily heat consumption (

Figure pat00008
) Is expressed by the following relational expression
Figure pat00009
here,
Figure pat00010
Lt; RTI ID = 0.0 >
Figure pat00011
Is the heating coefficient,
Figure pat00012
Is the Monday coefficient,
Figure pat00013
Is the Friday number,
Figure pat00014
Is a Saturday count,
Figure pat00015
Is a holiday (Sunday, holiday) coefficient,
Figure pat00016
Is the heat-day-before-the-day coefficient,
Figure pat00017
Is the heat consumption coefficient a week ago,
Figure pat00018
Has a value of 1 only on the day of the week as a month / gold / Saturday / day or a holiday, and has a value of 0 otherwise,
Figure pat00019
Is the day-to-day defensive estimate or day-to-day consumption (kcal)
Figure pat00020
Is the daily consumption estimate one week before or the daily consumption amount (kcal) a week before.

Further, the above-mentioned heat consumption by time (

Figure pat00021
) Is expressed by the following relational expression
Figure pat00022
here,
Figure pat00023
Is the daily heat consumption (kcal)
Figure pat00024
Is a fixed ratio,
Figure pat00025
Is the daytime flow coefficient per day.

Also, the fixed ratio represents a ratio of an area occupying fixed energy among the total area of the building, and the time-of-day flow coefficient by day represents a ratio of power consumption per day to day power consumption by day.

According to another aspect of the present invention,

Estimating the daily heat consumption based on the monthly heat consumption data, estimating the daily heat consumption by applying the temperature change weight and the weekly characteristic weight to the monthly heat consumption amount, and calculating the energy consumed weight by time and the area using the fixed energy And estimating a heat consumption amount by time; Performing a production optimization process for calculating a daily production target amount for each of the on-site facilities by applying the daily heat demand consumption amount, the efficiency for each site facility, and the daily production allowable amount; And performing a consumption optimization process of calculating the daily total operation time for each of the on-site facilities and creating an operation schedule for each on-site facility with respect to the daily production target amount referring to the production unit price and the operation cost for each time frame.

Here, after performing the consumption optimization process, the operation schedule for each site facility is updated according to the information collected in real time during the operation of each site facility according to the operation schedule for each site facility, .

Also, the temperature change weight is set to a value obtained by dividing the sum of the day cooling degree day and the heating day number by the total sum of the cooling degree day and the heating day of the month, and the weekly characteristic weight is classified into the weekly temperature characteristics based on the monthly heat consumption data And that the usage amount per day is relatively indicated.

Also, the pre-heating time for each on-site facility is included in calculating the total daily operating time for each on-site facility.

According to the present invention, it is possible to maximize the investment efficiency of the facilities for heating / heating / hot water supply to the heat and electricity energy consuming business sites by controlling the high efficient facilities to operate in a cost effective time.

1 is a graph illustrating a monthly gas consumption prediction graph according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating a concept of a weekday characteristic weight according to an embodiment of the present invention.
3 is a graph illustrating a daily heat consumption prediction graph according to an embodiment of the present invention.
FIG. 4 is a graph showing a predicted heat consumption amount for each time period according to an embodiment of the present invention.
5 is a diagram illustrating an example of a production optimization process according to an embodiment of the present invention.
6 is a diagram illustrating an example of a consumption optimization process according to an embodiment of the present invention.
FIG. 7 is a schematic view illustrating an environment in which an optimal operation mode of a complex facility according to an embodiment of the present invention is used.
8 is a diagram showing a specific configuration of the optimum operation management unit shown in FIG.
FIG. 9 is a diagram showing a specific configuration of the heat demand predicting unit shown in FIG. 8. FIG.
10 is a diagram showing a specific configuration of the operation scheduling unit shown in FIG.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when an element is referred to as "comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise. Also, the terms " part, "" module," and " module ", etc. in the specification mean a unit for processing at least one function or operation and may be implemented by hardware or software or a combination of hardware and software have.

Hereinafter, a multi-equipment optimum operating system 10 based on thermal energy demand forecasting according to an embodiment of the present invention will be described with reference to the drawings.

First, a multi-equipment optimal operation system 10 based on thermal energy demand forecasting according to an embodiment of the present invention can be divided into two parts, that is, a heat demand forecast portion and a demand forecast based facility operation scheduling portion.

First, the heat demand forecasting portion will be described.

Based on the monthly heat consumption (kcal, N㎥) data, the heat demand forecast estimates the daily consumption amount by reflecting the site weighting value and the weekly characteristic weighting value, and estimates the daily consumption data by the heat consumption model To perform daily heat demand forecasting for each site.

In addition, the daily heat demand forecast data is fixed by site, the flow energy characteristics, and the daytime flow coefficient by time to estimate the heat consumption by time.

In order to predict such a heat demand, a gas consumption base model Y is firstly established as shown in [Equation 1].

[Equation 1]

Figure pat00026

Here, Y is the monthly gas consumption prediction amount (Nm 3)

Figure pat00027
Is the monthly cooling coefficient,
Figure pat00028
Is the monthly heating coefficient,
Figure pat00029
Is a weekday coefficient,
Figure pat00030
CDD (Cooling Degree Days) is a degree of cooling, HDD (Heating Degree Days) is a degree of heating,
Figure pat00031
Is the number of weekdays of the month,
Figure pat00032
Is the number of weekends in a month. Here, the heating / cooling operation is the sum of the standard temperature (18.3 ° C) of the room and the average outdoor air temperature during the heating / cooling period. When the air temperature is higher than 18.3 ° C, the value obtained by subtracting 18.3 ° C from the temperature is called CDD In this case, the value obtained by subtracting the temperature at 18.3 ° C is referred to as HDD. This is well known, so a detailed explanation is omitted.

The baseline model Y is verified based on the 2-year monthly thermal energy consumption data of a general hospital. The result is shown in Fig. 1, and a basic model almost similar to the actual monthly gas consumption can be generated.

Next, the daily heat consumption is estimated by multiplying the temperature change weight and the weekday characteristic weight in Equation (1) above.

The temperature change weight is a value obtained by dividing the sum of the cooling index (CDD) and the heating index (HDD) of a specific day by the total sum of the cooling index and the heating index of the corresponding month,

Figure pat00033
) Can be obtained and can be expressed as [Equation 2].

&Quot; (2) "

Figure pat00034

Here, CDDa is the cooling index of the specific day a in one month, and HDDa is the heating index of the specific day a. Therefore, the denominator represents the sum of the heating and cooling index per month.

These daily temperature change weights and weekly characteristic weights (

Figure pat00035
) To estimate the daily consumption (
Figure pat00036
) Can be expressed by the following equation (3).

&Quot; (3) "

Figure pat00037

here,

Figure pat00038
Represents the monthly consumption amount calculated based on Equation (1) above.

Thus, the daily consumption estimate can be estimated by multiplying the monthly consumption by the daily temperature change weight and the weekly characteristic weight.

On the other hand, the weekly characteristic weight is a coefficient obtained by calculating the weighted day of the week based on the 2-year value of electricity consumption per day, assuming that the heat consumption pattern is similar to the electricity consumption pattern.

As shown in FIG. 2, the specific general hospitals to be simulated are classified into Monday / Tuesday to Friday / Saturday / day, and the usage amounts of other days are relatively indicated based on the usage amount of Monday, and each of the categories has 8 values (2 years, 8 weeks per month).

In the embodiment of the present invention, the average of eight values in one category is adopted, and the relative weight of the day is multiplied by the monthly consumption amount together with the daily temperature weight to estimate the daily heat consumption.

Next, based on the daily consumption estimates calculated as shown in Equation (3), the daily demand characteristics such as cooling day, heating day, month / holiday, As shown in Equation (4).

&Quot; (4) "

Figure pat00039

here,

Figure pat00040
Lt; RTI ID = 0.0 >
Figure pat00041
Is the heating coefficient,
Figure pat00042
Is the Monday coefficient,
Figure pat00043
Is the Friday number,
Figure pat00044
Is a Saturday count,
Figure pat00045
Is a holiday (Sunday, holiday) coefficient,
Figure pat00046
Is the heat-day-before-the-day coefficient,
Figure pat00047
Is the heat consumption coefficient a week ago,
Figure pat00048
Has a value of 1 only on the day of the week as a month / gold / Saturday / day or a holiday, and has a value of 0 otherwise,
Figure pat00049
Is the day-to-day defensive estimate or day-to-day consumption (kcal)
Figure pat00050
Is the daily consumption estimate one week ago or daily consumption (kcal) a week ago.

For Equation 4, for example, if the day of the week to be predicted is Monday

Figure pat00051
And the day of week variable has a value of 1.

Then, the coefficient value obtained through the regression analysis is substituted into the equation (4).

Based on the data of specific general hospitals, the coefficients of the regression analysis were 95% confidence level.

The results of regression analysis of the general hospitals are shown in Table 1 as follows.

[Table 1]

Figure pat00052

Figure pat00053

The daily consumption estimates obtained by applying the coefficients obtained from the above table to the equation (4) can be expressed by the following equation (5).

&Quot; (5) "

Figure pat00054
= 688631.8397054 * CDD + 230283.639946574 * HDD + 643426.722692934 *
Figure pat00055
+ 77832.4794508482 *
Figure pat00056
+ (-943365.972701576) *
Figure pat00057
+ (-597161.056890592) *
Figure pat00058
Figure pat00059
+ 0.632561424613761 *
Figure pat00060
+ 0.0242916407593964 *
Figure pat00061

Fig. 3 shows a result of visualizing Equation (5).

On the other hand, as the last stage of demand forecasting,

Figure pat00062
) Is multiplied by the area of fixed energy use and the energy consumption weight by time zone to estimate the heat consumption by time zone
Figure pat00063
) Is made as shown in Equation (6).

&Quot; (6) "

Figure pat00064

here,

Figure pat00065
Is the estimated heat consumption (kcal) by time period,
Figure pat00066
Is the daily consumption estimate (kcal)
Figure pat00067
Is a fixed ratio,
Figure pat00068
Represents the daytime flow coefficient by day.

In Equation (6), the fixed ratio refers to the ratio of the area occupied by the fixed energy among the total area of the building, and conversely, the flow ratio minus the fixed ratio from 1.

In the case of a specific general hospital, the sum of the outpatient area and the area of the hospital room was assumed to be the total area. The fixed ratio was obtained by the area of the hospital room relative to the total area, and the flow rate was obtained by the outpatient area.

The ratio of stationary to flow rate was 28.6%: 71.4%.

The daytime time-series flow coefficient is calculated by dividing the weekly characteristic weight (

Figure pat00069
, It is assumed that the heat consumption pattern per day is similar to the electricity consumption pattern, and the ratio of the power consumption per day to the power consumption per day (24 hours) per day (month / Respectively. And the average of the eight time-day flow coefficient values per hour is used in Equation 6

As a result of the prediction of the heat demand by time zone, the thermal energy consumption data of the 2-year general hospital can be obtained.

Next, the demand forecasting based facility operation scheduling part will be described.

In the production optimization stage, the daily production target for each facility is selected based on the seasonal efficiency of the facility, the unit price of the produced fuel, the rate of charge, and the daily production capacity. , And in the consumption optimization stage, an operation schedule for each day of production target amount is generated for each facility in consideration of the preheating time required for each equipment, production unit cost per hour, and operation cost.

Table 2 shows the factors that should be considered in terms of heat production by facility (dark letter in Table 2) and the order of policy to be given to each facility based on the above-mentioned predicted amount of heat demand by consumption in terms of consumption

[Table 2]

Figure pat00070

 For example, if a site has two heating requirements for heating and cooling and hot water, it predicts each daily heat demand first.

When there is a heat pump using electricity and an absorption type cold water heater using gas, the supply facilities according to the demand of the heating and cooling compares the seasonal efficiency of each facility and selects a facility with high efficiency.

Next, we compare the production fuel (night-time electricity / general electricity or LNG gas) of the corresponding facility by unit price, calculate the daily heat production capacity in consideration of the capacity and the number of facilities, Set the daily production target for each facility by rank.

In order to optimize the consumption of the equipment by time zone, the operation time (including the preheating time) for each facility is determined according to the daily production target amount, and the optimal operation schedule policy for each time zone is created after considering the production unit cost and the operation cost.

In the case of a general hospital, it is recommended that the operation is performed at 7 to 13 o'clock in the afternoon (14 to 17) because of the power peak in the afternoon (14 to 17). Considering this, 6 < / RTI >

Referring to FIG. 5, the demand for heating and cooling demand forecasting is estimated to be 12,558,251 kcal per day.

First, there are a heat pump and a cold / hot water system as facilities. The efficiency of the heat pump is 2.7, which is higher than 0.85 of the cold / hot water system, so the heat pump is selected as the first facility.

Next, for the annual production cost of each facility, the heat pump is at the rate of 30.7 Won / Mcal at nighttime electricity and the cold / hot water heater is at 1133 Won / Mcal for LNG gas.

Thereafter, the daily production capacity of each facility is calculated. It is assumed that there are eight heat pumps, a total capacity of 151 kWh, and a cold / hot water generator of 320 RT.

First, the cold water and water heater, which is the first facility, calculates the daily production capacity of 151kWh * 24h * 2.7 * 860 (kcal // kWh) = 8,414,920 kcal. = 19,740,672 kacl is produced per day.

Next, since the daily heating demand forecast amount is 12,558,251 kcal, if the first heat pump, which is the first equipment, is operated for 24 hours, the total production of 8,414,920 kcal is possible. Therefore, the daily production target of the first heat pump is set at 8,414,928 kcal, Since the amount of residual demand produced by the heat pump in the total demand forecast is 4,143,323 kcal, only this amount is the daily production target produced by the second-tier equipment.

Thus, it is possible to optimally set the daily production target amount for each facility based on the efficiency of each facility and the producible amount.

Referring to FIG. 6, when the daily production target amount is calculated for each facility as shown in FIG. 5, the operation time for each facility is first calculated in the consumption optimization step.

In the case of a heat pump, which is the first facility, the daily production capacity is set as the daily production target, so the daily operation time is 24 hours. However, in the case of the cold and hot water system, only a partial quantity is set as the production target. . In other words, since the production amount of the cold and hot water heater per hour is 320RT * 0.85 * 3024 = 822,528 kcal, the daily operation time for 4,143,323 / 822,528 = 5.04h hours is calculated in order to produce 4,143,323 kcal as the daily production target amount.

Next, since the preheating time is required for each facility, the total daily operating time for each facility is calculated considering the preheating time. In the case of a heat pump, which is a first-order facility, it does not need a preheating time by operating all day, but in the case of a cold water heater, it needs to operate only 5.04 hours. Accordingly, the daily total operation time of the heat pump as the first facility is set to 24 hours, and the 5.54 hours including the preheating time of 0.5 hours in the case of the second-order equipment, the cold water heater, is set as the total daily operation time.

Next, the unit price for each facility is calculated.

In the case of the first-order heat pump, it is 623 won / kWh from 23:00 am to 09:00 am, but it is 884 won / kWh from 10:00 pm to 22:00 pm.

In addition, it can be seen that the unit price of the cold / hot water heater, which is the second equipment, is 843 won / Nm 3 regardless of the time, based on LNG.

Next, it is necessary to calculate the operation cost by time for each facility.

In the case of a heat pump, which is a first-order facility, the operation cost is calculated by calculating the hourly operation cost because the operation cost of the heat pump is 24 hours and the previous production cost per hour is used as it is. Should be. Consideration should be given to the summer peak time of 14:00 to 17:00 and the unit price per hour. For example, the unit price per hour is 63.1 won from 23:00 to 09:00, and it is 109.2 won from 9:00 to 10:00, from 12:00 to 13:00 and from 17:00 to 23:00, from 10:00 to 12:00, City to 17 o'clock can be confirmed at 166.7 won.

Next, an operation schedule for each facility is prepared in consideration of the daily operation time, the production unit cost per unit time, and the operation cost calculated for the daily production target for each facility.

First, in the case of a heat pump which is the first equipment, the operation schedule is full 24 hours, so the operation schedule can be set from 0:00 to 23:49.

In the case of a cold / hot water heater, which is the next two-tier equipment, it is necessary to schedule the operation time to 5.54 hours, so that operation scheduling from 07:00 to 12:30 is possible based on the above consideration.

Next, an optimal operation system of a complex facility based on the prediction of the demand for thermal energy according to an embodiment of the present invention will be described in detail with reference to the drawings.

FIG. 7 is a view schematically showing an environment in which the optimal operation system 10 of the multiple facility according to the embodiment of the present invention is used.

7, the multi-equipment optimum operation system 10 according to the embodiment of the present invention includes a service providing unit 100, a database 200, a real-time processing unit 300, and an optimal operation management unit 400 do.

The service providing unit 100 includes a user interface for providing an optimal operation of a complex facility based on thermal energy demand prediction according to an embodiment of the present invention. The user can be provided with the optimum operation service of the complex facility to be managed through the service provider (100).

The database 200 stores operating information for each facility and various data collected from the external server 700. Such data may include electricity usage data, production unit price data per hour, weather information, and the like.

The real-time processing unit 300 controls equipment such as a heat pump, a cold water heater, and the like, which is controlled through the gateway 600 according to the optimal operation scheduling set by the optimal operation management unit 400.

In addition, the real-time processing unit 300 collects various types of information detected from the field facility 500 and stores the collected information in the database 200. Such information includes driving information, production information, and the like.

As described with reference to FIGS. 1 to 6, the optimal operation management unit 400 performs daily heat demand forecasting and performs management for production optimization and consumption optimization based on the predicted daily heat demand. In particular, the optimum operation management unit 400 creates a schedule for optimal operation for each facility, and controls the real-time processing unit 300 according to the created operation schedule to control the operation of the field facility 500.

FIG. 8 is a diagram showing a specific configuration of the optimum operation management unit 400 shown in FIG.

8, the optimal operation management unit 400 includes a heat demand predicting unit 410, an operation scheduling unit 420, a verification and updating unit 430, and a settlement unit 440.

The heat demand predicting unit 410 estimates the monthly heat consumption based on the monthly heat consumption data stored in the database 200 and estimates the daily heat consumption by applying the temperature change weight and the week characteristic weight to the monthly heat consumption After that, we estimate the heat consumption by time by applying energy consumption weights by area and time using stationary energy.

The operation scheduling unit 420 calculates a daily production target amount for each facility by applying the daily heat demand forecast amount predicted by the heat demand predicting unit 410, the efficiency for each site facility 500, and the daily production allowable amount, The total daily operation time for each facility is calculated, and a consumption optimization process is performed in which an operation schedule for each facility is generated by referring to the production cost per unit time and the operation cost.

The verification and updating unit 430 generates an initial operation schedule for each facility by the heat demand predicting unit 410 and the operation scheduling unit 420 and then transmits the real-time operation schedule to the real-time processing unit 300 in real- Updates the operation schedule according to the obtained information, and controls the operation of the field facility 500 according to the updated operation schedule.

The settlement unit 440 calculates a fee generated due to the operation of the field facility 500 according to the operation schedule created by the operation scheduling unit 420.

FIG. 9 is a diagram showing a specific configuration of the heat demand predicting unit 410 shown in FIG.

9, the heat demand predicting unit 410 includes a monthly heat consumption predicting unit 411, a daily heat consumption predicting unit 413, a time-based heat consumption predicting unit 415, and a prediction control unit 417 .

The monthly heat consumption predicting unit 411 estimates the monthly heat consumption based on the formula (1) based on the monthly heat consumption data stored in the database 200, and calculates the monthly heat consumption prediction amount. For a detailed description thereof, please refer to the above description.

The daily heat consumption predicting unit 413 estimates the monthly heat consumption predicted by the monthly heat consumption predicting unit 411 by applying a temperature change weight value and a weekly characteristic weight value calculated based on Equation (2) ], The daily heat consumption is predicted and the daily heat consumption forecast amount is calculated. A detailed description thereof is also given above.

The time-consuming heat consumption predicting unit 415 estimates the daily heat consumption predicted by the daily heat consumption predicting unit 413 based on the area using energy fixedly and the energy consumption weight per time unit, And calculates the heat consumption amount by time.

The prediction control unit 417 controls the monthly heat consumption predicting unit 411, the daily heat consumption predicting unit 413 and the time-series heat consumption predicting unit 415 to predict the heat consumption of the target object. At this time, the prediction control unit 417 predicts the heat consumption using various initial variables based on the monthly heat consumption data stored in the database 200 at the time of initial prediction. However, during the operation of the field facility 500 Real-time processing unit 300, so that the real-time heat consumption prediction can be performed by the operation of the field facility 500.

FIG. 10 is a diagram showing a specific configuration of the operation scheduling unit 420 shown in FIG.

As shown in FIG. 10, the operation scheduling unit 420 includes a production optimizing unit 421 and a consumption optimizing unit 423.

The production optimizing unit 421 calculates a daily production target amount for each of the field equipments 500 optimized for production based on the daily heat demand prediction amount calculated by the heat demand predicting unit 410.

The production optimization unit 421 includes an efficiency comparison unit 4211, a production availability calculation unit 4213, a production target amount calculation unit 4215, and a production amount control unit 4217.

The efficiency comparison unit 4211 compares the seasonal efficiencies of the field facility 500 and then selects the facility in the order of the facilities with high efficiency.

The production availability calculation unit 4213 calculates the total amount that can be produced per day by the field facilities 500.

The production target amount calculating unit 4215 calculates a target amount of heat demand based on the daily heat demand forecast amount calculated by the heat demand predicting unit 410, the equipment order selected by the efficiency comparing unit 4211, Based on the total amount, the daily production target for each on-site facility 500 is calculated.

The production amount control unit 4217 controls the efficiency comparing unit 4211, the production availability calculating unit 4213, and the production target amount calculating unit 4215 to calculate the daily production target amount for each of the field facilities 500.

Next, the consumption optimizing unit 423 creates an operation schedule for each on-site facility 500 that is optimized for consumption based on the daily production target amount calculated by the production optimizing unit 421. [

The consumption optimization unit 423 includes an operation time calculation unit 4231, an operation cost calculation unit 4233, an operation schedule creation unit 4235, and a scheduling control unit 4237. [

The operation time calculating unit 4231 calculates the daily operation time based on the daily production target amount calculated by the production optimizing unit 421, the preheating time per production facility 500, and the producible amount.

The operation cost calculation unit 4233 calculates the operation cost for each time period on the basis of the production cost per hour.

The operation schedule creating section 4235 creates an operation schedule for each site facility 500 on the basis of the daily operation time calculated by the operation time calculating section 4231 and the operation cost calculated by the operation cost calculating section 4233 .

The scheduling control unit 4237 controls the operation time calculating unit 4231, the operation cost calculating unit 4233 and the operation schedule creating unit 4235 to create an operation schedule for each site facility 500.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, It belongs to the scope of right.

Claims (18)

A service providing unit that provides a user interface for providing an optimal operation service of a complex facility based on the prediction of heat energy demand;
A real-time processing unit for performing operation control on the on-site facility according to an operation schedule and collecting information on the operation of the on-site facility; And
A production optimization process for estimating a daily heat demand based on a monthly heat demand forecast and calculating a daily production target quantity for each on-site facility based on a daily heat demand forecast quantity, The optimum operation management unit
And an optimal operating system of the complex facility. The method according to claim 1,
The optimum operation management unit,
Estimating the daily heat consumption based on the monthly heat consumption data, estimating the daily heat consumption by applying the temperature change weight and the weekly characteristic weight to the monthly heat consumption amount, and calculating the energy consumed weight by time and the area using the fixed energy A heat demand predicting unit for predicting a heat consumption amount by time frame; And
A production optimization process for calculating a daily production target amount for each of the on-site facilities by applying the daily heat demand consumption amount predicted by the heat demand predicting unit, the efficiency for each site facility and the daily production allowable amount, And an operation scheduling unit for performing a consumption optimization process of generating an operation schedule for each site facility with respect to the daily production target amount by referring to the production unit price per unit time and the operation cost,
And an optimal operating system of the complex facility. 3. The method of claim 2,
Wherein the heat demand predicting unit and the operation scheduling unit generate an initial operation schedule for each of the on-site facilities, update the operation schedule according to information obtained in real time during operation of the on-site facility through the real-time processing unit, A verifying and updating unit for controlling the operation of the field facility according to the verification result; And
A settlement unit 430 for calculating a fee generated due to the operation of the field facility 500 according to the operation schedule created by the operation scheduling unit 420,
Further comprising: an operating system for operating the complex facility; 3. The method of claim 2,
The heat demand predicting unit,
A monthly heat consumption predicting unit for predicting the monthly heat consumption based on the monthly heat consumption data to calculate the monthly heat consumption;
A daily heat consumption predicting unit estimating a daily heat consumption by predicting a daily heat consumption by applying a temperature change weight and a weekly characteristic weight to the monthly heat consumption calculated by the monthly heat consumption predicting unit;
Time consuming heat consumption by estimating the heat consumption by time by applying the energy consumption weight by time and the area using energy fixedly based on the daily heat consumption calculated by the daily heat consumption predicting unit A prediction unit; And
A predictive control unit for controlling the predicted heat consumption of the target object by controlling the monthly heat consumption predicting unit, the daily heat consumption predicting unit and the time-
And an optimal operating system of the complex facility. 3. The method of claim 2,
The operation scheduling unit,
A production optimizing unit for performing a production optimization process for calculating a daily production target amount for each of the on-site facilities based on a daily heat demand amount calculated by the heat demand predicting unit; And
And a consumption optimization unit for performing a consumption optimization process of creating an operation schedule for each on-site facility based on a daily production target amount calculated by the production optimization unit
And an optimal operating system of the complex facility. 6. The method of claim 5,
The production optimizing unit,
An efficiency comparison unit that compares seasonal efficiencies of the on-site facilities and then selects the facilities in the order of the facilities with high efficiency;
A production availability calculation unit for calculating a total quantity that can be produced per day by the site facility;
A daily demand amount calculated by the heat demand predicting unit, a facility order selected by the efficiency comparison unit, and a total producible amount calculated by the production availability calculating unit, A target amount calculating unit; And
A production amount control unit for controlling the efficiency comparing unit, the production availability calculating unit and the production target amount calculating unit to calculate a daily production target amount for each site facility,
And an optimal operating system of the complex facility. The method according to claim 6,
Wherein the consumption optimizing unit comprises:
A driving time calculating unit for calculating a daily driving time for each on-site facility based on a daily production target amount calculated by the production optimizing unit, a preheating time for each of the on-site facilities, and a total available production amount;
A running cost calculating unit for calculating a running cost for each of the on-site facilities on the basis of a production cost per hour;
An operation schedule creating unit for creating an operation schedule for each site facility based on a daily operation time calculated by the operation time calculating unit and an operation cost calculated by the operation cost calculating unit; And
A scheduling control section for controlling the operation time calculating section, the operation cost calculating section and the operation schedule creating section to create an operation schedule for each site facility,
And an optimal operating system of the complex facility. 3. The method of claim 2,
The monthly heat consumption amount (Y)
Figure pat00071

here,
Figure pat00072
Is the monthly cooling coefficient,
Figure pat00073
Is the monthly heating coefficient,
Figure pat00074
Is a weekday coefficient,
Figure pat00075
Is a holiday coefficient,
Cooling Degree Days (CDD) are days of cooling,
The HDD (Heating Degree Days) is a heating day,
Figure pat00076
Is the number of weekdays of the month,
Figure pat00077
Is the number of weekends of the month
Of the complex facility. 9. The method of claim 8,
Wherein the temperature change weight is set to a value obtained by dividing a sum of a day cooling degree day and a heating day by a total sum of a cooling degree day and a heating day of the month. 9. The method of claim 8,
Wherein the weekly characteristic weight is classified according to the daily heat consumption data and the usage amount for each day is expressed relatively. 11. The method of claim 10,
Wherein the day-of-week characteristic weight has a plurality of values for each characteristic group for each day of the week, and uses an average of a plurality of values. 11. The method of claim 10,
The daily heat consumption ( ) Is expressed by the following relational expression
Figure pat00079

here,
Figure pat00080
Lt; RTI ID = 0.0 >
Figure pat00081
Is the heating coefficient,
Figure pat00082
Is the Monday coefficient,
Figure pat00083
Is the Friday number,
Figure pat00084
Is a Saturday count,
Figure pat00085
Is a holiday (Sunday, holiday) coefficient,
Figure pat00086
Is the heat-day-before-the-day coefficient,
Figure pat00087
Is the heat consumption coefficient a week ago,
Figure pat00088
Has a value of 1 only on the day of the week as a month / gold / Saturday / day or a holiday, and has a value of 0 otherwise,
Figure pat00089
Is the day-to-day defensive estimate or day-to-day consumption (kcal)
Figure pat00090
Is the daily consumption estimate one week before or the daily consumption (kcal) a week ago
Of the complex facility. 13. The method of claim 12,
The above-mentioned time-consuming heat consumption (
Figure pat00091
) Is expressed by the following relational expression
Figure pat00092

here,
Figure pat00093
Is the daily heat consumption (kcal)
Figure pat00094
Is a fixed ratio,
Figure pat00095
Is the time zone flow coefficient per day
Optimal operating system for complex facilities. 14. The method of claim 13,
The fixed ratio represents the ratio of the area occupied by the fixed energy among the total area of the building,
The daytime flow coefficient for each day of the week represents the ratio of power consumption per day to the daily power consumption per day
And the optimum operating system of the complex facility. Estimating the daily heat consumption based on the monthly heat consumption data, estimating the daily heat consumption by applying the temperature change weight and the weekly characteristic weight to the monthly heat consumption amount, and calculating the energy consumed weight by time and the area using the fixed energy And estimating a heat consumption amount by time;
Performing a production optimization process for calculating a daily production target amount for each of the on-site facilities by applying the daily heat demand consumption amount, the efficiency for each site facility, and the daily production allowable amount; And
Performing a consumption optimization process of calculating the total daily operation time for each of the on-site facilities and creating an operation schedule for each of the on-site facilities with respect to the daily production target amount by referring to the production unit price and the operation cost by time
Wherein the method comprises: 16. The method of claim 15,
After performing the consumption optimization process,
Further comprising the step of updating the operation schedule for each field facility according to the information collected in real time during the operation of each field facility according to the operation schedule for each field facility to thereby control the operation for each field facility. 16. The method of claim 15,
The temperature change weight is set to a value obtained by dividing the sum of the day cooling degree day and the heating day number by the total sum of the cooling degree day and the heating day of the month,
The weekly characteristic weight is classified into characteristics per day based on monthly heat consumption data,
And the optimal operating method of the complex facility. 16. The method of claim 15,
Wherein the pre-heating time for each of the on-site facilities is included in calculating the total daily operating time for each on-site facility.
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