CN117649062B - Engineering configuration method and device for multi-heat source combined operation energy station system capacity - Google Patents
Engineering configuration method and device for multi-heat source combined operation energy station system capacity Download PDFInfo
- Publication number
- CN117649062B CN117649062B CN202410122835.8A CN202410122835A CN117649062B CN 117649062 B CN117649062 B CN 117649062B CN 202410122835 A CN202410122835 A CN 202410122835A CN 117649062 B CN117649062 B CN 117649062B
- Authority
- CN
- China
- Prior art keywords
- load
- energy
- building
- energy forms
- park
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000010586 diagram Methods 0.000 claims abstract description 29
- 238000009826 distribution Methods 0.000 claims abstract description 29
- 238000005265 energy consumption Methods 0.000 claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 238000000354 decomposition reaction Methods 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000010438 heat treatment Methods 0.000 abstract description 14
- 230000006870 function Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000011835 investigation Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000000178 monomer Substances 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000004378 air conditioning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 229920001296 polysiloxane Polymers 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses an engineering configuration method and device for the capacity of a multi-heat source combined operation energy station system, wherein the method comprises the following steps: predicting based on park historical cold and hot load data to obtain a cold and hot load distribution diagram; decomposing the cold-hot electric load distribution diagram to obtain load data; determining a middle load range section according to the load data, and distinguishing a base load and a peak shaving load by taking the middle load as a limit; respectively determining energy forms of base load and peak regulation load, and adjusting equipment capacity of the energy forms to determine investment operation cost; and (3) configuring the energy source of the park according to the equipment capacity corresponding to the minimum value of the investment operation cost. The problems that an existing multi-heat-source heating system is single in configuration scheme, poor in applicability and high in cost are effectively solved, and therefore an economic and feasible energy configuration scheme can be formulated according to energy consumption requirements of a park, and the operation cost of the park can be optimized.
Description
Technical Field
The application relates to the technical field of energy configuration, in particular to an engineering configuration method and device for multi-heat source combined operation energy station system capacity.
Background
The multi-heat source heat supply system is characterized in that a plurality of heat sources exist at different positions in the same heat supply pipe network. The largest heat source is called a main heat source, and the rest small heat sources are called peak regulating heat sources, which are also called peak load heat sources. The multi-heat source heating system is a large, medium and small district heating boiler room. The heat sources may be distributed at any location in the network.
The energy consumption requirements of different industries (such as manufacturing industry, food processing industry, chemical industry, logistics storage and big data) respectively have the characteristics of the industries, and the existing multi-heat source heat supply system has single configuration scheme and poor applicability and cannot meet the use requirements of different industries. And the energy sources in various forms are matched for operation, so that the cost is high.
Disclosure of Invention
The embodiment of the application solves the problems of single configuration scheme, poor applicability and high cost of the traditional multi-heat source heating system by providing the engineering configuration method for the system capacity of the multi-heat source combined operation energy station, and realizes the engineering configuration method for the system capacity of the multi-heat source combined operation energy station.
In a first aspect, an embodiment of the present application provides an engineering configuration method for operating a system capacity of an energy station by multiple heat sources, including: predicting based on park historical cold and hot load data to obtain a cold and hot load distribution diagram; decomposing the cold-hot electric load distribution diagram to obtain load data; determining a middle load range interval according to the load data, and distinguishing a base load and a peak shaving load by taking the middle load as a limit; respectively determining energy forms of the base load and the peak shaving load, and adjusting equipment capacity of the energy forms to determine investment operation cost; and configuring park energy according to the equipment capacity corresponding to the minimum value of the investment operation cost.
With reference to the first aspect, in one possible implementation manner, the predicting, based on the campus historical cold-hot load data, a cold-hot load profile includes: classifying buildings in the park and determining energy consumption scenes; according to the park historical cold and hot load data and the energy utilization scene, carrying out energy utilization analysis on each building after classification to obtain a corresponding building load rate; and carrying out load prediction on each building according to the building load rate to obtain the cold-hot electric load distribution diagram of the park.
With reference to the first aspect, in one possible implementation manner, the predicting the load of each building according to the building load factor to obtain the cold-hot electric load distribution diagram of the park includes: and respectively predicting a predicted building load of a certain type in the park according to the building load rate, wherein the formula is as follows:
; in the/> Representing the predicted building load of a type of building in a campus at time i,/>Representing the building load of a certain type of building in a park at time i on weekdays,/>Representing said building load rate at time i for a building of this type on a campus on weekdays,/>Representing the building load of a certain type of building in a park at the moment i under holidays,/>Representing the building load rate of the type of building in the campus at the moment i on holidays; determining the predicted building loads for all types of buildings to construct the thermoelectric load profile for a campus.
With reference to the first aspect, in one possible implementation manner, the load data includes a load interval, a load hour number and a load time frequency number.
With reference to the first aspect, in a possible implementation manner, the adjusting the device capacity of the energy source form determines investment operation cost, including: determining the capacity of the apparatus for each of the energy forms; determining energy costs for each of the energy forms separately; wherein the energy costs include initial investment and operating costs; and determining the investment operation cost of the energy form according to the energy cost and the equipment capacity.
With reference to the first aspect, in one possible implementation manner, the formula for determining the capacity of the device of each energy source form is as follows:
; in the/> Representing the base load,/>Representing the peak shaver load,/>Represents the maximum value in the load data, m represents the total number of kinds of the energy forms of the base load, n represents the total number of kinds of the energy forms of the peak shaver load,/>Said device capacity representing said energy form of said base load,/>Said device capacity in the form of said energy representing said peak shaver load,/>The number of types of the energy forms representing the base load,/>The number of types of the energy forms representing the peak shaving load.
With reference to the first aspect, in a possible implementation manner, the formula for determining the investment operation cost of the energy form according to the energy cost and the equipment capacity is as follows:
; wherein P represents the investment operating cost,/> Annual investment operating costs representing said base load,/>Annual investment operating costs representing the peak shaver load, m representing the total number of categories of the energy forms of the base load, n representing the total number of categories of the energy forms of the peak shaver load,/>The number of types of the energy forms representing the base load,/>The number of types of the energy forms representing the peak shaving load,/>Representing said initial investment in said energy form of said base load per unit load,/>Representing the operating costs of the energy form of the base load per unit time,/>Representing said initial investment in said energy form of said peak shaver load per unit load,/>Said operating costs in the form of said energy representing said peak shaver load per unit time,/>Said device capacity representing said energy form of said base load,/>Said device capacity in the form of said energy representing said peak shaver load,/>Time hours representing the base load,/>Representing the number of hours of peak shaver load.
In a second aspect, an embodiment of the present application provides an engineering configuration apparatus for operating a capacity of an energy station system in combination with multiple heat sources, including: the prediction module is used for predicting based on the park historical cold-hot load data to obtain a cold-hot load distribution diagram; the decomposition module is used for decomposing the cold-hot electric load distribution diagram to obtain load data; the distinguishing module is used for determining a middle load range interval according to the load data and distinguishing a base load and a peak shaving load by taking the middle load as a limit; the adjusting module is used for respectively determining the energy forms of the base load and the peak shaving load, adjusting the equipment capacity of the energy forms and determining the investment operation cost; and the configuration module is used for configuring the energy sources of the park according to the equipment capacity corresponding to the minimum value of the investment operation cost.
In a third aspect, an embodiment of the present application provides an apparatus, including: a processor; a memory for storing processor-executable instructions; the processor, when executing the executable instructions, implements a method as described in the first aspect or any one of the possible implementations of the first aspect.
In a fourth aspect, embodiments of the present application provide a non-transitory computer readable storage medium comprising instructions for storing a computer program or instructions which, when executed, cause a method as described in the first aspect or any one of the possible implementations of the first aspect to be implemented.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
According to the embodiment of the application, loads of different types of energy sources throughout the year and energy source/load characteristics of different industrial parks can be obtained through predicting the cold-hot electric load distribution diagram, the energy source forms of the base load and the peak shaving load can be respectively determined by distinguishing the base load and the peak shaving load, the stable operation of the system can be maintained, and the energy sources of the parks can be configured according to the equipment capacity corresponding to the minimum value of investment operation cost, so that the cost can be saved. The problems that an existing multi-heat-source heating system is single in configuration scheme, poor in applicability and high in cost are effectively solved, and therefore an economic and feasible energy configuration scheme can be formulated according to energy consumption requirements of a park, and the operation cost of the park can be optimized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the embodiments of the present application or the drawings used in the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an engineering configuration method for multi-heat source combined operation energy station system capacity provided by an embodiment of the application;
FIG. 2 is a flowchart of a method for predicting a cold-hot electrical load distribution map based on park historical cold-hot load data according to an embodiment of the present application;
FIG. 3 is a flow chart of the method for determining investment and operating costs for adjusting the capacity of a device in the form of energy provided by an embodiment of the present application;
FIG. 4 is a schematic structural diagram of an engineering configuration device for multi-heat source combined operation energy station system capacity according to an embodiment of the present application;
FIGS. 5a and 5b are graphs showing examples of load factor curves of various types of buildings on holidays and workdays according to embodiments of the present application;
FIG. 6 is an exemplary diagram of a cold-hot electrical load distribution diagram provided by an embodiment of the present application;
fig. 7 is a graph showing the distinction between the base load and the peak shaver load according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Some of the techniques involved in the embodiments of the present application are described below to aid understanding, and they should be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, for the sake of clarity and conciseness, descriptions of well-known functions and constructions are omitted in the following description.
Base load refers to a load that remains stable and persists in the system, such as industrial electricity. Since the characteristics of the base load are continuously stable, the requirements on the energy supply quality and the energy supply reliability are high. Peak load refers to a transient, high peak load in the system that typically occurs during a specific period of time or event, such as an increase in summer air conditioning load, an increase in winter heating load, an increase in holiday electrical load, etc. The peak shaving load has the characteristics of large fluctuation and relatively low requirements on energy supply quality and energy supply reliability.
Fig. 1 is a flowchart of an engineering configuration method for multi-heat source combined operation energy station system capacity, which is provided by an embodiment of the present application, and includes steps 101 to 105. Wherein fig. 1 is only one execution sequence shown in the embodiment of the present application, and does not represent the only execution sequence of the engineering configuration method of the system capacity of the multi-heat source combined operation energy station, and the steps shown in fig. 1 may be executed in parallel or in reverse under the condition that the final result can be achieved.
Step 101: and predicting based on the park historical cold-hot load data to obtain a cold-hot load distribution diagram. In the embodiment of the application, because the energy demand and the energy consumption time of different types of buildings in different parks are different, the load of the park building group cannot be determined through the accumulation of the loads of each building monomer, so that the building loads of each type of building are classified and analyzed in an energy consumption scene, and then the load of the park building is obtained, and further, the cold-heat-electricity load distribution diagram of the park is predicted and obtained, and particularly as shown in fig. 2, the method comprises the following steps 201 to 203.
Step 201: the buildings in the campus are classified and energy usage scenarios are determined. In the embodiment of the present application, building groups in a campus are exemplarily classified into three types of office buildings, commercial buildings and residential buildings, and energy use scenarios are exemplarily classified into workdays and holidays.
Step 202: and carrying out energy consumption analysis on each classified building according to the park historical cold and hot load data and the energy consumption scene to obtain a corresponding building load rate. In the embodiment of the application, the historical cold and hot load data of the park is analyzed according to the energy use situation, and different types of building load rates are obtained by combining actual investigation. As shown in fig. 5 a-5 b, fig. 5a is an exemplary graph of load factor curves of various kinds of buildings on holidays provided by the present application, and fig. 5b is an exemplary graph of load factor curves of various kinds of buildings on weekdays provided by the present application.
Step 203: and carrying out load prediction on each building according to the building load rate to obtain a cold-hot electric load distribution diagram of the park. In the embodiment of the application, a scene analysis method is adopted to predict the loads of various buildings under different energy consumption scenes to obtain the predicted building loads in a certain time period, and the specific formula is as follows:
. In the/> Representing the predicted building load at time i for a type of building in the campus,Representing the building load of a certain type of building in a park at time i on weekdays,/>Representing the building load rate of the type of building in the park at time i on weekdays,/>Representing the building load of a certain type of building in a park at the moment i under holidays,/>And (3) representing the building load rate of the type of building in the park at the moment i on holidays.
In one embodiment of the application, the building load factor is the ratio of building load to peak building load. The building load is calculated from the indoor and outdoor temperatures, and can also be obtained through investigation. Building load and building load rate vary with the energy usage characteristics of different seasons. Illustratively, the building load calculation method is as follows:
. In the/> Represents the building load of a certain type of building in a park, k represents the heat transfer coefficient of the building enclosure, f represents the area of the building enclosure, and/>Representing the outdoor temperature of the corresponding building,/>Indicating the indoor temperature of the corresponding building.
The predicted building load of all types of buildings is determined according to the calculation method so as to construct a cold-hot electric load distribution diagram of the park. An exemplary plot of a cold-hot electrical load profile provided by the present application is shown in fig. 6, which shows predicted building loads for 3624 hours.
Step 102: and decomposing the cold-hot electric load distribution diagram to obtain load data. The load data comprises a load interval, a load hour number and a load time frequency. In the embodiment of the application, a cold-hot electrical load distribution diagram is decomposed into ten load sections, which are respectively load data of ten load sections of 0-10%, 10-20%, 20-30%, 30-40%, 40-50%, 50-60%, 60-70%, 70-80%, 80-90% and 90-100% of peak load. These ten load sections are decomposed stepwise, specifically as follows.
，(/>)；，(/>)；......；，(/>). Wherein T represents the number of load hours in heating season,/>、/>、/>、/>Respectively represent that the heating season load section accounts for 0-10%, 10-20% and/orLoad hours of 90% -100%、/>、/>、/>Respectively represent the load interval ratio of 0-10%, 10-20%, and/orLoad interval of 90% -100%Representing the maximum value of load data,/>The nth load data is represented, and table 1 below is an example of decomposed load data.
TABLE 1 load data instance TABLE
Step 103: and determining an intermediate load range section according to the load data, and distinguishing the base load and the peak shaving load by taking the intermediate load as a boundary. Specifically, the multi-heat source combined heat supply requires that each heat source has the following characteristics: the energy form that carries the base load must have a high load factor and low operating costs. The peak load energy form requires a low initial investment, but the operating costs may be relatively high. In the embodiment of the application, an intermediate load range is determined according to the load data, the intermediate load is taken as a boundary to distinguish the base load from the peak shaving load, the base load is used for bearing a longer time frequency (the time frequency represents the running time of equipment and is usually more than 70 percent), and the rest is the peak shaving load. Illustratively, as shown in FIG. 7, 1000kw (kilowatts) is taken as the intermediate load, the thermal load above the intermediate load is the peak shaver load, and the thermal load below the intermediate load is the base load.
Step 104: and respectively determining energy forms of the base load and the peak shaving load, and adjusting the equipment capacity of the energy forms to determine the investment operation cost. In the embodiment of the application, the energy form of the base load is exemplified as a ground source heat pump, and the energy form of the peak shaving load is exemplified as an electric boiler. The implementation steps of determining investment operating costs by adjusting the capacity of the equipment in the form of energy are shown in fig. 3, and include steps 301 to 303, which are specifically as follows.
Step 301: the capacity of the device for each energy source is determined. Specifically, the equipment capacity (initial investment is high, the energy form with lower operation cost) of the base heat source for bearing the base load and the equipment capacity (initial investment is low, the energy form with higher operation cost) of the peak shaving heat source for bearing the peak shaving load are established according to the above-determined intermediate load range section, base load and peak shaving load, and the formula is as follows:
. In the/> Representing the base load,/>Representing peak load,/>Represents the maximum value in the load data, m represents the total number of types of energy forms of the base load, n represents the total number of types of energy forms of the peak shaving load,/>Equipment capacity in the form of energy representing the base load,/>Equipment capacity in energy form representing peak shaving load,/>The number of types of energy forms representing the base load,/>The number of types of energy forms representing peak shaving load. The above formula can be known/>, based on the cold-hot electrical load distribution diagram of the parkM and n are the total number of categories of energy forms of base load and peak shaving load, respectively, which can be set according to the energy demand of the campus. Therefore, the above is simplified as/>And/>The sum isFrom this, the capacity of the plant in the form of energy of a plurality of groups of base load and peak shaving load can be obtained.
Illustratively herein, there isThe equipment capacity (ground source heat pump) of the energy form of the base load was set to 1.6kw (heating season full season operation, 3624 h), and the equipment capacity (electric boiler) of the energy form of the peak shaving load was set to 3.7kw (30 to 100%, running time 522h, load time frequency 14.4%). And the energy form corresponding to the operation peak regulation load is operated when the load interval accounts for 30-100% so as to stabilize the supply and demand relationship of the park. The number of load hours, which is the operating time, is 522 hours, which is the load time in which the load interval is 30 to 100%. The load time frequency is the ratio of the load hours (522 h) to the heating season full season run time (3624 h).
The device capacities in the form of energy for the base load can also be set to 2.1kw, 2.6kw, 3.2kw, 3.7kw and 4.2kw, respectively the device capacities in the form of energy for the peak shaver load can be set to 3.2kw, 2.7kw, 2.1kw, 1.6kw and 1.1kw.
Step 302: the energy costs for each energy form are determined separately. Wherein, the energy cost comprises initial investment and operation cost. Illustratively, the initial investment and operating costs of the ground source heat pump and the electric boiler are shown in Table 2 below.
TABLE 2 initial investment and operating cost of ground source heat pump and electric boiler
Wherein cop represents the energy efficiency ratio of the ground source heat pump.
Step 303: and determining the investment operation cost of the energy form according to the energy cost and the equipment capacity. In the embodiment of the application, the calculation formula of the investment operation cost is as follows:
. Wherein P represents investment operation cost,/> Annual investment operating costs representing base load,/>Annual investment operating costs representing peak load, m representing the total number of categories of energy forms of the base load, n representing the total number of categories of energy forms of the peak load,/>The number of types of energy forms representing the base load,/>The number of types of energy forms representing peak load,/>Representing the initial investment in energy form for base load per unit load,/>Running cost in the form of energy representing the base load per unit time,/>Initial investment in energy form representing peak load per unit load,/>Running cost in energy form representing peak load per unit time,/>Equipment capacity in the form of energy representing the base load,/>Equipment capacity in energy form representing peak shaving load,/>Time hours representing the base load,/>The number of hours of peak shaver load is indicated.
The investment operating costs are calculated from the above-described values of the plant capacities in the form of energy of the base load and peak shaving load, respectively, as shown in table 3 below, for example.
Table 3 capital operating cost table for base load to peak shaving load
Step 105: and (3) configuring the energy source of the park according to the equipment capacity corresponding to the minimum value of the investment operation cost. Specifically, the calculated investment operation costs are compared, the minimum value of the investment operation costs, namely 2.1MW of the ground source heat pump and 3.2MW of the electric boiler, is determined, and the energy sources of the park are configured according to the equipment capacity corresponding to the minimum value of the investment operation costs.
It should be noted that the above list of energy forms, the data in tables 1,2 and 3 are only examples of the present application, and not limiting the scope of the present application, and those skilled in the art should appreciate that the energy forms of different types of parks may be modified according to the energy requirements of the parks, and the initial investment and running cost of the energy forms may be modified according to the actual situation.
Although the application provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive labor. The order of steps recited in the present embodiment is only one way of performing the steps in a plurality of steps, and does not represent a unique order of execution. When implemented by an actual device or client product, the method of the present embodiment or the accompanying drawings may be performed sequentially or in parallel (e.g., in a parallel processor or a multithreaded environment).
As shown in fig. 4, the embodiment of the application further provides an engineering configuration device 400 for operating the capacity of the energy station system by combining multiple heat sources. The device comprises: the prediction module 401, the decomposition module 402, the differentiating module 403, the adjustment module 404, and the configuration module 405 are specifically as follows.
The prediction module 401 is configured to predict a cold-hot electrical load distribution map based on the park historical cold-hot load data. The prediction module 401 is specifically configured to classify and analyze energy usage scenario for each type of building because the energy demand and energy usage time of different types of buildings in different parks are different, and the load of the park building group cannot be determined by accumulating the loads of each building monomer, so as to obtain the load of the park building, and further predict and obtain the distribution diagram of the cold, heat and electricity loads of the park, which is specifically as follows.
The buildings in the campus are classified and energy usage scenarios are determined. In the embodiment of the present application, building groups in a campus are exemplarily classified into three types of office buildings, commercial buildings and residential buildings, and energy use scenarios are exemplarily classified into workdays and holidays.
And carrying out energy consumption analysis on each classified building according to the park historical cold and hot load data and the energy consumption scene to obtain a corresponding building load rate. In the embodiment of the application, the historical cold and hot load data of the park is analyzed according to the energy use situation, and different types of building load rates are obtained by combining actual investigation. As shown in fig. 5 a-5 b, fig. 5a is an exemplary graph of load factor curves of various kinds of buildings on holidays provided by the present application, and fig. 5b is an exemplary graph of load factor curves of various kinds of buildings on weekdays provided by the present application.
And carrying out load prediction on each building according to the building load rate to obtain a cold-hot electric load distribution diagram of the park. In the embodiment of the application, a scene analysis method is adopted to predict the loads of various buildings under different energy consumption scenes to obtain the predicted building loads in a certain time period, and the specific formula is as follows:
. In the/> Representing the predicted building load at time i for a type of building in the campus,Representing the building load of a certain type of building in a park at time i on weekdays,/>Representing the building load rate of the type of building in the park at time i on weekdays,/>Representing the building load of a certain type of building in a park at the moment i under holidays,/>And (3) representing the building load rate of the type of building in the park at the moment i on holidays.
In one embodiment of the application, the building load factor is the ratio of building load to peak building load. The building load is calculated from the indoor and outdoor temperatures, and can also be obtained through investigation. Building load and building load rate vary with the energy usage characteristics of different seasons. Illustratively, the building load calculation method is as follows:
. In the/> Represents the building load of a certain type of building in a park, k represents the heat transfer coefficient of the building enclosure, f represents the area of the building enclosure, and/>Representing the outdoor temperature of the corresponding building,/>Indicating the indoor temperature of the corresponding building.
The decomposition module 402 is configured to decompose the cold-hot electrical load profile to obtain load data. The load data comprises a load interval, a load hour number and a load time frequency. The decomposition module 402 is specifically configured to decompose the cold-hot electrical load distribution diagram into ten load sections, which are load data of ten load sections of 0-10%, 10-20%, 20-30%, 30-40%, 40-50%, 50-60%, 60-70%, 70-80%, 80-90%, and 90-100% of the peak load. These ten load sections are decomposed stepwise, specifically as follows.
，(/>)；，(/>)；......；，(/>). Wherein T represents the number of load hours in heating season,/>、/>、/>、/>Respectively represent that the heating season load section accounts for 0-10%, 10-20% and/orLoad hours of 90% -100%、/>、/>、/>Respectively represent the load interval ratio of 0-10%, 10-20%, and/orLoad interval of 90% -100%Representing the maximum value of load data,/>The nth load data is represented, and table 1 below is an example of decomposed load data.
TABLE 1 load data instance TABLE
The differentiating module 403 is configured to determine a middle load range interval according to the load data, and differentiate the base load from the peak shaving load with the middle load as a boundary. The distinguishing module 403 is specifically configured to combine multiple heat sources to supply heat to each heat source: the energy form that carries the base load must have a high load factor and low operating costs. The peak load energy form requires a low initial investment, but the operating costs may be relatively high. In the embodiment of the application, an intermediate load range is determined according to the load data, the intermediate load is taken as a boundary to distinguish the base load from the peak shaving load, the base load is used for bearing a longer time frequency (the time frequency represents the running time of equipment and is usually more than 70 percent), and the rest is the peak shaving load. Illustratively, as shown in FIG. 7, 1000kw (kilowatts) is taken as the intermediate load, the thermal load above the intermediate load is the peak shaver load, and the thermal load below the intermediate load is the base load.
The adjustment module 404 is configured to determine energy forms of the base load and peak shaving load, respectively, and adjust equipment capacity of the energy forms to determine investment operating costs. The adjustment module 404 is specifically configured to set the energy form of the base load as an example ground source heat pump and the energy form of the peak shaving load as an example electric boiler. The implementation steps for determining the investment operation cost by adjusting the equipment capacity of the energy form are specifically as follows.
The capacity of the device for each energy source is determined. Specifically, the equipment capacity (initial investment is high, the energy form with lower operation cost) of the base heat source for bearing the base load and the equipment capacity (initial investment is low, the energy form with higher operation cost) of the peak shaving heat source for bearing the peak shaving load are established according to the above-determined intermediate load range section, base load and peak shaving load, and the formula is as follows:
. In the/> Representing the base load,/>Representing peak load,/>Represents the maximum value in the load data, m represents the total number of types of energy forms of the base load, n represents the total number of types of energy forms of the peak shaving load,/>Equipment capacity in the form of energy representing the base load,/>Equipment capacity in energy form representing peak shaving load,/>The number of types of energy forms representing the base load,/>The number of types of energy forms representing peak shaving load. The above formula can be known/>, based on the cold-hot electrical load distribution diagram of the parkM and n are the total number of categories of energy forms of base load and peak shaving load, respectively, which can be set according to the energy demand of the campus. Therefore, the above is simplified as/>And/>The sum isFrom this, the capacity of the plant in the form of energy of a plurality of groups of base load and peak shaving load can be obtained.
Illustratively herein, there isThe equipment capacity (ground source heat pump) of the energy form of the base load was set to 1.6kw (heating season full season operation, 3624 h), and the equipment capacity (electric boiler) of the energy form of the peak shaving load was set to 3.7kw (30 to 100%, running time 522h, load time frequency 14.4%). And the energy form corresponding to the operation peak regulation load is operated when the load interval accounts for 30-100% so as to stabilize the supply and demand relationship of the park. The number of load hours, which is the operating time, is 522 hours, which is the load time in which the load interval is 30 to 100%. The load time frequency is the ratio of the load hours (522 h) to the heating season full season run time (3624 h).
The device capacities in the form of energy for the base load can also be set to 2.1kw, 2.6kw, 3.2kw, 3.7kw and 4.2kw, respectively the device capacities in the form of energy for the peak shaver load can be set to 3.2kw, 2.7kw, 2.1kw, 1.6kw and 1.1kw.
The energy costs for each energy form are determined separately. Wherein, the energy cost comprises initial investment and operation cost. Illustratively, the initial investment and operating costs of the ground source heat pump and the electric boiler are shown in Table 2 below.
TABLE 2 initial investment and operating cost of ground source heat pump and electric boiler
Wherein cop represents the energy efficiency ratio of the ground source heat pump.
And determining the investment operation cost of the energy form according to the energy cost and the equipment capacity. In the embodiment of the application, the calculation formula of the investment operation cost is as follows:
. Wherein P represents investment operation cost,/> Annual investment operating costs representing base load,/>Annual investment operating costs representing peak load, m representing the total number of categories of energy forms of the base load, n representing the total number of categories of energy forms of the peak load,/>The number of types of energy forms representing the base load,/>The number of types of energy forms representing peak load,/>Representing the initial investment in energy form for base load per unit load,/>Running cost in the form of energy representing the base load per unit time,/>Initial investment in energy form representing peak load per unit load,/>Running cost in energy form representing peak load per unit time,/>Equipment capacity in the form of energy representing the base load,/>Equipment capacity in energy form representing peak shaving load,/>Time hours representing the base load,/>The number of hours of peak shaver load is indicated.
The investment operating costs are calculated from the above-described values of the plant capacities in the form of energy of the base load and peak shaving load, respectively, as shown in table 3 below, for example.
Table 3 capital operating cost table for base load to peak shaving load
The configuration module 405 is configured to configure the campus energy source with the equipment capacity corresponding to the minimum of investment operating costs. The configuration module 405 is specifically configured to compare the calculated investment operation costs, determine a minimum value of the investment operation costs, that is, 2.1MW of the ground source heat pump and 3.2MW of the electric boiler, and configure the energy of the campus with the equipment capacity corresponding to the minimum value of the investment operation costs.
It should be noted that the above list of energy forms, the data in tables 1,2 and 3 are only examples of the present application, and not limiting the scope of the present application, and those skilled in the art should appreciate that the energy forms of different types of parks may be modified according to the energy requirements of the parks, and the initial investment and running cost of the energy forms may be modified according to the actual situation.
Some of the modules of the apparatus of the present application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The apparatus or module set forth in the embodiments of the application may be implemented in particular by a computer chip or entity, or by a product having a certain function. For convenience of description, the above devices are described as being functionally divided into various modules, respectively. The functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present application. Of course, a module that implements a certain function may be implemented by a plurality of sub-modules or a combination of sub-units.
The methods, apparatus or modules described in this application may be implemented in computer readable program code means and in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (english: application SPECIFIC INTEGRATED Circuit; ASIC), programmable logic controller and embedded microcontroller, examples of the controller including but not limited to the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The embodiment of the application also provides equipment, which comprises: a processor; a memory for storing processor-executable instructions; the processor, when executing the executable instructions, implements the method according to the embodiments of the present application.
Embodiments of the present application also provide a non-transitory computer readable storage medium having stored thereon a computer program or instructions which, when executed, cause a method as described in embodiments of the present application to be implemented.
In addition, each functional module in the embodiments of the present invention may be integrated into one processing module, each module may exist alone, or two or more modules may be integrated into one module.
The storage medium includes, but is not limited to, a random access Memory (English: random Access Memory; RAM), a Read-Only Memory (ROM), a Cache (English: cache), a hard disk (English: HARD DISK DRIVE; HDD), or a Memory Card (English: memory Card). The memory may be used to store computer program instructions.
From the above description of embodiments, it will be apparent to those skilled in the art that the present application may be implemented in software plus necessary hardware. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product or may be embodied in the implementation of data migration. The computer software product may be stored on a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., comprising instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in the various embodiments or portions of the embodiments of the application.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment is mainly described as a difference from other embodiments. All or portions of the present application are operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the present application; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (5)
1. The engineering configuration method for the system capacity of the multi-heat source combined operation energy station is characterized by comprising the following steps of:
The cold-hot electric load distribution diagram is obtained by prediction based on park historical cold-hot load data, and is specifically as follows:
classifying buildings in the park and determining energy consumption scenes;
According to the park historical cold and hot load data and the energy utilization scene, carrying out energy utilization analysis on each building after classification to obtain a corresponding building load rate;
Carrying out load prediction on each building according to the building load rate to obtain the cold-hot electric load distribution diagram of the park, wherein the method specifically comprises the following steps: and respectively predicting a predicted building load of a certain type in the park according to the building load rate, wherein the formula is as follows: q i＝Qwiαwi+Qhiαhi; wherein Q i represents the predicted building load of a type of building in the campus at time i, Q wi represents the building load of a type of building in the campus at time i on a weekday, α wi represents the building load rate of a type of building in the campus at time i on a weekday, Q hi represents the building load of a type of building in the campus at time i on a holiday, and α hi represents the building load rate of a type of building in the campus at time i on a holiday; determining the predicted building loads for all types of buildings to construct the thermoelectric load profile for a campus;
Decomposing the cold-hot electric load distribution diagram to obtain load data;
Determining a middle load range interval according to the load data, and distinguishing a base load and a peak shaving load by taking the middle load as a limit;
Respectively determining energy forms of the base load and the peak shaving load, and adjusting equipment capacity of the energy forms to determine investment operation cost; the method comprises the following steps: determining the capacity of the apparatus for each of the energy forms; wherein the formula for determining the capacity of the device for each of the energy forms is as follows:
Wherein Q JC represents the base load, Q TF represents the peak shaver load, maxQ i represents the maximum value in the load data, m represents the total number of kinds of the energy forms of the base load, n represents the total number of kinds of the energy forms of the peak shaver load, Q α represents the equipment capacity of the energy forms of the base load, Q β represents the equipment capacity of the energy forms of the peak shaver load, α represents the number of kinds of the energy forms of the base load, β represents the number of kinds of the energy forms of the peak shaver load;
determining energy costs for each of the energy forms separately; wherein the energy costs include initial investment and operating costs;
Determining an investment operating cost of the energy form based on the energy cost and the equipment capacity; wherein the formula for determining the investment operation cost of the energy form according to the energy cost and the equipment capacity is as follows:
Wherein P represents the investment operation cost, P α represents the annual investment operation cost of the base load, P β represents the annual investment operation cost of the peak shaver load, m represents the total number of kinds of the energy forms of the base load, n represents the total number of kinds of the energy forms of the peak shaver load, α represents the number of kinds of the energy forms of the base load, β represents the number of kinds of the energy forms of the peak shaver load, ε α represents the initial investment of the energy forms of the base load per unit load, ε α represents the operation cost of the energy forms of the base load per unit time, ε β represents the initial investment of the energy forms of the peak shaver load per unit time, η β represents the operation cost of the energy forms of the peak shaver load per unit time, Q3445 represents the equipment capacity of the energy forms of the base load, Q β represents the equipment capacity of the peak shaver load per unit time, and T α represents the equipment capacity of the peak shaver load per unit time, T β hours;
and configuring park energy according to the equipment capacity corresponding to the minimum value of the investment operation cost.
2. The method of claim 1, wherein the load data comprises a load interval, a load hours, and a load time frequency.
3. An engineering configuration device for multi-heat source combined operation energy station system capacity, which is characterized by comprising:
the prediction module is used for predicting based on park historical cold and hot load data to obtain a cold and hot load distribution diagram, and specifically comprises the following steps:
classifying buildings in the park and determining energy consumption scenes;
According to the park historical cold and hot load data and the energy utilization scene, carrying out energy utilization analysis on each building after classification to obtain a corresponding building load rate;
Carrying out load prediction on each building according to the building load rate to obtain the cold-hot electric load distribution diagram of the park, wherein the method specifically comprises the following steps: and respectively predicting a predicted building load of a certain type in the park according to the building load rate, wherein the formula is as follows: q i＝Qwiαwi+Qhiαhi; wherein Q i represents the predicted building load of a type of building in the campus at time i, Q wi represents the building load of a type of building in the campus at time i on a weekday, α wi represents the building load rate of a type of building in the campus at time i on a weekday, Q hi represents the building load of a type of building in the campus at time i on a holiday, and α hi represents the building load rate of a type of building in the campus at time i on a holiday; determining the predicted building loads for all types of buildings to construct the thermoelectric load profile for a campus;
the decomposition module is used for decomposing the cold-hot electric load distribution diagram to obtain load data;
The distinguishing module is used for determining a middle load range interval according to the load data and distinguishing a base load and a peak shaving load by taking the middle load as a limit;
The adjusting module is used for respectively determining the energy forms of the base load and the peak shaving load, adjusting the equipment capacity of the energy forms and determining the investment operation cost; the method comprises the following steps: determining the capacity of the apparatus for each of the energy forms; wherein the formula for determining the capacity of the device for each of the energy forms is as follows:
Wherein Q JC represents the base load, Q TF represents the peak shaver load, maxQ i represents the maximum value in the load data, m represents the total number of kinds of the energy forms of the base load, n represents the total number of kinds of the energy forms of the peak shaver load, Q α represents the equipment capacity of the energy forms of the base load, Q β represents the equipment capacity of the energy forms of the peak shaver load, α represents the number of kinds of the energy forms of the base load, β represents the number of kinds of the energy forms of the peak shaver load;
determining energy costs for each of the energy forms separately; wherein the energy costs include initial investment and operating costs;
Determining an investment operating cost of the energy form based on the energy cost and the equipment capacity; wherein the formula for determining the investment operation cost of the energy form according to the energy cost and the equipment capacity is as follows:
Wherein P represents the investment operation cost, P α represents the annual investment operation cost of the base load, P β represents the annual investment operation cost of the peak shaver load, m represents the total number of kinds of the energy forms of the base load, n represents the total number of kinds of the energy forms of the peak shaver load, α represents the number of kinds of the energy forms of the base load, β represents the number of kinds of the energy forms of the peak shaver load, ε α represents the initial investment of the energy forms of the base load per unit load, ε α represents the operation cost of the energy forms of the base load per unit time, ε β represents the initial investment of the energy forms of the peak shaver load per unit time, η β represents the operation cost of the energy forms of the peak shaver load per unit time, Q3445 represents the equipment capacity of the energy forms of the base load, Q β represents the equipment capacity of the peak shaver load per unit time, and T α represents the equipment capacity of the peak shaver load per unit time, T β hours;
and the configuration module is used for configuring the energy sources of the park according to the equipment capacity corresponding to the minimum value of the investment operation cost.
4. An apparatus for performing an engineering configuration method for multi-heat source co-operating energy station system capacity, comprising:
A processor;
a memory for storing processor-executable instructions;
the processor, when executing the executable instructions, implements the method of any one of claims 1 to 2.
5. A non-transitory computer readable storage medium comprising instructions for storing a computer program or instructions which, when executed, cause the method of any one of claims 1 to 2 to be implemented.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410122835.8A CN117649062B (en) | 2024-01-30 | 2024-01-30 | Engineering configuration method and device for multi-heat source combined operation energy station system capacity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410122835.8A CN117649062B (en) | 2024-01-30 | 2024-01-30 | Engineering configuration method and device for multi-heat source combined operation energy station system capacity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117649062A CN117649062A (en) | 2024-03-05 |
| CN117649062B true CN117649062B (en) | 2024-05-17 |
Family
ID=90048141
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410122835.8A Active CN117649062B (en) | 2024-01-30 | 2024-01-30 | Engineering configuration method and device for multi-heat source combined operation energy station system capacity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117649062B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104864560A (en) * | 2015-05-06 | 2015-08-26 | 上海申瑞继保电气有限公司 | Air conditioner electricity consumption pre-estimating method in office building |
| CN112418479A (en) * | 2020-09-02 | 2021-02-26 | 国网江苏省电力有限公司无锡供电分公司 | Optimal configuration method for park comprehensive energy system |
| CN113971530A (en) * | 2021-10-29 | 2022-01-25 | 国网福建省电力有限公司福州供电公司 | Novel power system source network and storage cooperation oriented power balancing method |
| CN114118535A (en) * | 2021-11-08 | 2022-03-01 | 国电南瑞科技股份有限公司 | Optimal configuration method of park comprehensive energy system considering engineering practicability |
| CN115392709A (en) * | 2022-08-24 | 2022-11-25 | 广东电网有限责任公司 | Multi-energy park planning method, system, computer equipment and storage medium |
| CN117035325A (en) * | 2023-08-15 | 2023-11-10 | 武汉科技大学 | Regional energy planning partitioning method based on dynamic planning method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8626643B2 (en) * | 2007-05-03 | 2014-01-07 | Orion Energy Systems, Inc. | System and method for a utility financial model |
| US10101730B2 (en) * | 2014-05-01 | 2018-10-16 | Johnson Controls Technology Company | Incorporating a load change penalty in central plant optimization |
-
2024
- 2024-01-30 CN CN202410122835.8A patent/CN117649062B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104864560A (en) * | 2015-05-06 | 2015-08-26 | 上海申瑞继保电气有限公司 | Air conditioner electricity consumption pre-estimating method in office building |
| CN112418479A (en) * | 2020-09-02 | 2021-02-26 | 国网江苏省电力有限公司无锡供电分公司 | Optimal configuration method for park comprehensive energy system |
| CN113971530A (en) * | 2021-10-29 | 2022-01-25 | 国网福建省电力有限公司福州供电公司 | Novel power system source network and storage cooperation oriented power balancing method |
| CN114118535A (en) * | 2021-11-08 | 2022-03-01 | 国电南瑞科技股份有限公司 | Optimal configuration method of park comprehensive energy system considering engineering practicability |
| CN115392709A (en) * | 2022-08-24 | 2022-11-25 | 广东电网有限责任公司 | Multi-energy park planning method, system, computer equipment and storage medium |
| CN117035325A (en) * | 2023-08-15 | 2023-11-10 | 武汉科技大学 | Regional energy planning partitioning method based on dynamic planning method |
Non-Patent Citations (1)
| Title |
|---|
| 基于负荷预测的园区供热系统运行优化技术;于波;卢欣;李浩;郑鑫;赵军;苏鹏伟;;电力建设;20171201(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117649062A (en) | 2024-03-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Neukomm et al. | Grid-interactive efficient buildings technical report series: Overview of research challenges and gaps | |
| Vossos et al. | Energy savings from direct-DC in US residential buildings | |
| Aryandoust et al. | The potential and usefulness of demand response to provide electricity system services | |
| Sanandaji et al. | Improved battery models of an aggregation of thermostatically controlled loads for frequency regulation | |
| Ali et al. | Effective and comfortable power control model using Kalman filter for building energy management | |
| Ramdaspalli et al. | Transactive control for efficient operation of commercial buildings | |
| CN101645602B (en) | Power load energy-saving management and control method and system | |
| Steen et al. | A review and categorization of grid-interactive efficient building technologies for building performance simulation | |
| Ku et al. | Building electric energy prediction modeling for BEMS using easily obtainable weather factors with Kriging model and data mining | |
| CN117649062B (en) | Engineering configuration method and device for multi-heat source combined operation energy station system capacity | |
| Ali et al. | Enhanced power control model based on hybrid prediction and preprocessing/post-processing | |
| Guo et al. | Robust optimization for home-load scheduling under price uncertainty in smart grids | |
| Carr et al. | Managing smart grids using price responsive smart buildings | |
| Öchsner et al. | Research platform: decentralized energy system for sector coupling | |
| Pranee et al. | Experimental study and modeling of automatic home energy management system using AI | |
| Arikiez et al. | Minimizing the electricity cost of coordinating houses on microgrids | |
| Hu et al. | Model predictive control of inverter air conditioners responding to real-time electricity prices in smart grids | |
| Kuo et al. | Random feasible directions algorithm with a generalized Lagrangian relaxation algorithm for solving unit commitment problem | |
| Li et al. | On market-based coordination of thermostatically controlled loads with user preference | |
| CN117592763B (en) | Method and device for configuring energy system capacity | |
| US20230236560A1 (en) | Net zero energy facilities | |
| Ajithaa et al. | Experimental Verification of Peak Load Reduction through DR Inspired IoT-based HEMS | |
| Kaur et al. | Design of demand response based load management solutions for a university campus | |
| US20230268734A1 (en) | Controlling the electrical load of a load facility using demand response | |
| Mahmoudi et al. | The effect of cooling loads management on electric power supply system of K erman province by the year 2031 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |