CN116105314A - Energy system parameter optimization method and device comprising air-cooled heat pump - Google Patents

Energy system parameter optimization method and device comprising air-cooled heat pump Download PDF

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CN116105314A
CN116105314A CN202310072224.2A CN202310072224A CN116105314A CN 116105314 A CN116105314 A CN 116105314A CN 202310072224 A CN202310072224 A CN 202310072224A CN 116105314 A CN116105314 A CN 116105314A
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time
energy
energy system
determining
temperature
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熊显智
程晓绚
马万里
李嘉丰
李天泽
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China XD Electric Co Ltd
Xian XD Power Systems Co Ltd
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China XD Electric Co Ltd
Xian XD Power Systems Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F24F11/47—Responding to energy costs
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/65—Electronic processing for selecting an operating mode

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  • General Engineering & Computer Science (AREA)
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Abstract

The invention discloses an energy system parameter optimization method and device comprising an air-cooled heat pump, and relates to the technical field of comprehensive energy, wherein the method comprises the following steps: according to the historical temperature data of the energy system, determining the time-by-time temperature data, the time-by-time cold and heat load data of the typical day and the energy system on the typical day; according to the time-by-time temperature data, the time-by-time cold and heat load data and the performance parameters of the energy system in the typical day, determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day; determining an operation constraint condition, an energy storage constraint condition and a load constraint condition of an energy system; according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system, the equipment parameters in the energy system are optimized, and the parameter optimization of the energy system can be realized, so that the accuracy of the parameters of the energy system is improved.

Description

Energy system parameter optimization method and device comprising air-cooled heat pump
Technical Field
The invention relates to the technical field of comprehensive energy, in particular to an energy system parameter optimization method and device comprising an air-cooled heat pump.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
In a large environment where fossil energy is exhausted and the environment is continuously deteriorated, new energy is rapidly developed, however, the problem of the digestion thereof results in low utilization efficiency of the new energy. The heat pump system is a heating mode actively popularized at present, the air-cooled heat pump can supply heat and refrigerate, and is one of the most common equipment types in the heating system and the cooling system, the heating/refrigerating capacity of the air-cooled heat pump changes along with the change of the ambient temperature and the outlet water temperature, and the energy consumption ratio also changes along with the change of the ambient temperature and the outlet water temperature.
The above factors bring complexity to the design of the energy system comprising the air-cooled heat pump, in the prior art, the single typical day-by-day temperature and time-by-time temperature and cold load data and heat load data are adopted, and the energy system of the air-cooled heat pump is planned and designed by adopting constant heating capacity, refrigerating capacity or constant energy consumption ratio, but the method can not fully reflect the operation working condition of the air-cooled heat pump, the parameters of the energy system are inaccurate, and the design planning and actual operation deviation of the energy system are large.
Disclosure of Invention
The embodiment of the invention provides an energy system parameter optimization method comprising an air-cooled heat pump, which is used for optimizing parameters of an energy system to improve the accuracy of the parameters of the energy system, and comprises the following steps:
according to the historical temperature data of the energy system, determining the typical day and the time-by-time temperature data, the time-by-time cold load data and the time-by-time heat load data of the energy system on the typical day;
according to the change relation of the refrigerating capacity of the energy system along with the water supply temperature and the environmental temperature, the change relation of the refrigerating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the change relation of the heating capacity of the energy system along with the water supply temperature and the environmental temperature, and the change relation of the heating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the performance parameters of the energy system are determined;
determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day according to the time-by-time temperature data, the time-by-time cooling load data, the time-by-time heat load data and the performance parameters of the energy system in the typical day;
determining an operating constraint condition of the energy system;
Determining an energy storage constraint condition of the energy system;
determining load constraint conditions of the energy system according to the time-by-time cold load data and the time-by-time heat load data of the typical day;
and optimizing equipment parameters in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system.
The embodiment of the invention also provides an energy system parameter optimization device comprising the air-cooled heat pump, which is used for carrying out parameter optimization on an energy system so as to improve the accuracy of the energy system parameters, and comprises the following steps:
the typical day determining module is used for determining typical days and time-by-time temperature data, time-by-time cold load data and time-by-time heat load data of the energy system on the typical days according to the historical temperature data of the energy system;
the performance parameter determining module is used for determining the performance parameter of the energy system according to the change relation of the refrigerating capacity of the energy system along with the water supply temperature and the environmental temperature, the refrigerating energy consumption ratio of the energy system along with the change relation of the water supply temperature and the environmental temperature, the heating capacity of the energy system along with the change relation of the water supply temperature and the environmental temperature, and the heating energy consumption ratio of the energy system along with the change relation of the water supply temperature and the environmental temperature;
The energy consumption ratio determining module is used for determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day according to the time-by-time temperature data, the time-by-time cold load data, the time-by-time heat load data and the performance parameters of the energy system in the typical day;
an operation constraint module for determining an operation constraint condition of the energy system;
the energy storage constraint module is used for determining energy storage constraint conditions of the energy system;
the load constraint module is used for determining the load constraint condition of the energy system according to the time-by-time cold load data and the time-by-time hot load data of the typical day;
and the optimization module is used for optimizing the equipment parameters in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system.
Compared with the technical scheme that a single typical day is adopted to carry out planning design on an energy system in the prior art, the embodiment of the invention determines the typical day and the time-by-time temperature data, the time-by-time cold load data and the time-by-time heat load data of the energy system on the typical day according to the historical temperature data of the energy system, so that the typical day is more representative and can cover all weather conditions; according to the change relation of the refrigerating capacity of the energy system along with the water supply temperature and the environmental temperature, the change relation of the refrigerating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the change relation of the heating capacity of the energy system along with the water supply temperature and the environmental temperature, and the change relation of the heating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the performance parameters of the energy system are determined; thus, the energy system is more close to the actual operation condition; determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day according to the time-by-time temperature data, the time-by-time cooling load data, the time-by-time heat load data and the performance parameters of the energy system in the typical day; determining an operating constraint condition of the energy system; determining an energy storage constraint condition of the energy system; determining load constraint conditions of the energy system according to the time-by-time cold load data and the time-by-time heat load data of the typical day; according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system, the equipment parameters in the energy system are optimized, so that the parameter optimization of the energy system can be realized, and the energy system designed and planned is close to the actual operation condition, so that the accuracy of the parameters of the energy system is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a flowchart of a method for optimizing parameters of an energy system including an air-cooled heat pump according to an embodiment of the present invention;
fig. 2 is a diagram of a specific example of an energy system parameter optimization method including an air-cooled heat pump according to an embodiment of the present invention;
FIG. 3 is a diagram of a specific example of an energy system parameter optimization method including an air-cooled heat pump according to an embodiment of the present invention;
fig. 4 is a structural block diagram of an energy system parameter optimizing device including an air-cooled heat pump according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an apparatus for optimizing parameters of an energy system including an air-cooled heat pump according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.
The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are open-ended terms, meaning including, but not limited to. Reference to the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is used to schematically illustrate the practice of the present application, and is not limited thereto and may be appropriately adjusted as desired.
Fig. 1 is a flowchart of an energy system parameter optimization method including an air-cooled heat pump according to an embodiment of the present invention, as shown in fig. 1, where the energy system parameter optimization method including an air-cooled heat pump according to an embodiment of the present invention may include the following steps:
step 101, according to the historical temperature data of the energy system, determining the time-by-time temperature data, time-by-time cold load data and time-by-time heat load data of the energy system on the typical day and the typical day;
102, determining performance parameters of the energy system according to the change relation of the refrigerating capacity of the energy system along with the water supply temperature and the environmental temperature, the change relation of the refrigerating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, and the change relation of the heating capacity of the energy system along with the water supply temperature and the environmental temperature;
step 103, determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day according to the time-by-time temperature data, the time-by-time cooling load data, the time-by-time heat load data and the performance parameters of the energy system in the typical day;
104, determining operation constraint conditions of the energy system;
step 105, determining energy storage constraint conditions of the energy system;
step 106, determining the load constraint condition of the energy system according to the time-by-time cold load data and the time-by-time heat load data of the typical day;
and 107, optimizing equipment parameters in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system.
Fig. 2 is a diagram of a specific example of an energy system parameter optimization method including an air-cooled heat pump according to an embodiment of the present invention, as shown in fig. 2, in an embodiment of the present invention, a specific process for determining a typical day may include:
determining a summer date with the same time-by-time temperature as the summer air conditioner outdoor calculation day-by-time temperature as a summer typical day; the winter date, in which the time-by-time temperature is the same as the winter air conditioner outdoor calculation day time-by-time temperature, is determined as the winter typical day. For example, according to the related specifications for the air conditioning system, respectively determining the summer air conditioning outdoor calculation day time-by-time temperature and the winter air conditioning outdoor calculation day time-by-time temperature, and determining the date that the summer day time-by-time temperature is the same as the summer air conditioning outdoor calculation day time-by-time temperature as the summer typical day; the date on which the time-by-time temperature in the winter day is the same as the time-by-time temperature in the winter air conditioner outdoor calculation day is determined as the winter typical day.
In this embodiment, the summer air-conditioning outdoor calculation day is removed, and the summer days with different time-by-time temperatures from the summer air-conditioning outdoor calculation day are classified into K by Kmeans algorithm 1 The clustering centers of each cluster are determined to be typical days in summer; and for a typical summer day with a time-by-time temperature higher than the time-by-time temperature of the outdoor computing day of the air conditioner in summer, determining the time-by-time temperature of the outdoor computing day of the air conditioner in summer as the time-by-time temperature of the typical summer day. For example, the number of days in summer on which the time-by-time temperature is the same as the calculated time-by-time temperature outside the air conditioner in summer is
Figure BDA0004065118180000051
Determining a summer air-conditioning outdoor calculation day according to relevant specifications for an air-conditioning system, comparing the time-by-time temperature of a summer typical day with the time-by-time temperature of the summer air-conditioning outdoor calculation day, and determining the time-by-time temperature of the summer air-conditioning outdoor calculation day as the time-by-time temperature of the summer typical day for a summer typical day with the time-by-time temperature higher than the time-by-time temperature of the summer air-conditioning outdoor calculation day; aiming at summer dates with different time-by-time temperatures from the outdoor calculation day-by-time temperature of the summer air conditioner, the method adopts Kmeans algorithm to divide the summer dates into K 1 The clustering center of each cluster is determined as a summer typical day, the data quantity of each cluster is the number of days represented by the summer typical day, and the number of days represented by each summer typical day is +. >
Figure BDA0004065118180000052
I.e. in this case, K is co-determined 1 +1 summer typical days, each of which corresponds to a number of days of +.>
Figure BDA0004065118180000053
Figure BDA0004065118180000061
In the embodiment, the outdoor calculation day of the winter air conditioner is removed, and the winter days with different time-by-time temperatures from the outdoor calculation day of the winter air conditioner are divided into K by adopting Kmeans algorithm 2 The clustering centers of each cluster are determined to be typical days in winter; wherein, for a winter typical day when the time-by-time temperature is lower than the time-by-time temperature of the winter air-conditioning outdoor calculation day, the time-by-time temperature of the winter air-conditioning outdoor calculation day is determined as the time-by-time temperature of the winter typical day. For example, the number of days on the same winter day as the calculated day-by-day temperature outside the winter air conditioner is
Figure BDA0004065118180000062
Determining a winter air-conditioning outdoor calculation day according to relevant specifications for an air-conditioning system, comparing a time-by-time temperature of a winter typical day with a time-by-time temperature of a winter air-conditioning outdoor calculation day, and determining the time-by-time temperature of the winter air-conditioning outdoor calculation day as the time-by-time temperature of the winter typical day for a winter typical day with the time-by-time temperature lower than the time-by-time temperature of the winter air-conditioning outdoor calculation day; aiming at winter dates with different time-by-time temperatures from outdoor calculation day-by-time temperatures of an air conditioner in winter, the method is divided into K by adopting Kmeans algorithm 2 The clustering center of each cluster is determined as a winter typical day, the data quantity of each cluster is the number of days represented by the winter typical day, and the number of days represented by each winter typical day is +.>
Figure BDA0004065118180000063
I.e. in this case, K is co-determined 2 +1 winter typical days, each of which corresponds to a number of days of +.>
Figure BDA0004065118180000064
Figure BDA0004065118180000065
Fig. 3 is a flowchart of a specific example of an energy system parameter optimization method including an air-cooled heat pump according to an embodiment of the present invention, where as shown in fig. 3, the energy system parameter optimization method including an air-cooled heat pump according to an embodiment of the present invention may further include:
determining time-by-time temperature data, time-by-time cold load data and time-by-time heat load data of a plurality of typical day and energy systems on a typical day, and determining load constraint conditions of the energy systems according to the time-by-time temperature data, time-by-time cold load data and time-by-time heat load data of the plurality of typical day and energy systems on the typical day; according to the time-by-time temperature data, the time-by-time cooling load data and the time-by-time heat load data of the typical day and the energy system and the performance parameters of the energy system, determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day, and further determining the operation constraint condition of the energy system; determining a cost constraint condition of an energy system, and determining an energy storage constraint condition of the energy system if energy storage equipment connected with heat pump equipment exists; and optimizing equipment parameters in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system. A plurality of typical days are determined according to different seasons, the influence of gas image factors on cold load and heat load and the influence of meteorological factors and performance parameters on the operation of an energy system are fully considered, the typical days are more representative, and the energy system comprising the air-cooled heat pump is close to the actual operation condition in the design planning stage, so that the accuracy of the design planning of the energy system is improved.
In one embodiment, according to the relationship between the cooling capacity of the energy system and the water supply temperature and the ambient temperature, the relationship between the cooling energy consumption ratio of the energy system and the water supply temperature and the ambient temperature, the relationship between the heating capacity of the energy system and the water supply temperature and the ambient temperature, the relationship between the heating energy consumption ratio of the energy system and the water supply temperature and the ambient temperature, and the performance parameters of the energy system are determined, and can be represented by the following formula:
Figure BDA0004065118180000071
wherein, the liquid crystal display device comprises a liquid crystal display device,OUT heat the heating capacity of the energy system; t (T) water The water supply temperature; t (T) atm Is ambient temperature;
Figure BDA0004065118180000072
for the water supply temperature of the energy system under the working condition X, X is cool, xool, heat, xheat which respectively represents refrigeration, cold accumulation, heating and heat accumulation; COP of heat The heating energy consumption ratio of the energy system; OUT (OUT) cool The refrigerating capacity of the energy system; COP of cool The refrigeration energy consumption ratio of the energy system; k (k) 1 ～k 8 、b 1 ～b 4 Is constant.
In one embodiment, the energy system may include a heat pump device, an energy storage device.
In one embodiment, the energy system and the heat pump device may operate in four conditions: the energy system and the heat pump equipment can only work under one working condition at any moment.
In one embodiment, determining the operating constraints of the energy system may include: at any moment, according to the maximum power of the heat pump equipment and the number of the heat pump equipment in the energy system, the power instruction of the heat pump equipment is determined, and can be expressed by the following formula:
Figure BDA0004065118180000073
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004065118180000074
for a power instruction of the heat pump equipment operating under the working condition X at the moment h in a typical day s, X is cool, xool, heat, xheat which respectively represents refrigeration, cold accumulation, heating and heat accumulation; n (h) is the number of heat pump equipment put into the system at the moment h;
Figure BDA0004065118180000075
the maximum power of the heat pump equipment is operated in the X working condition at the moment h in the typical day s. />
In this embodiment, determining the operation constraint condition of the energy system may further include: at any time, the maximum power of the heat pump device is determined according to the output power of the heat pump device in the energy system, and can be expressed by the following formula:
Figure BDA0004065118180000076
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004065118180000077
the maximum power of the heat pump equipment operating under the X working condition at the moment h in the typical day s;
Figure BDA0004065118180000078
for the heat pump installation to be operated in X operating mode on a typical day s, the water supply temperature is +.>
Figure BDA0004065118180000079
Ambient temperature->
Figure BDA00040651181800000710
Lower output power.
In this embodiment, determining the operation constraint condition of the energy system may further include: at any moment, the energy consumption ratio of the heat pump equipment is determined according to the energy consumption ratio of the heat pump equipment at the ambient temperature and the outlet water temperature, and the energy consumption ratio can be expressed by the following formula:
Figure BDA0004065118180000081
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004065118180000082
the energy consumption ratio of the heat pump equipment in the X working condition at the moment h in the typical day s;
Figure BDA0004065118180000083
operating a heat pump installation on a typical day sUnder the working condition X, the water supply temperature is +.>
Figure BDA0004065118180000084
Ambient temperature->
Figure BDA0004065118180000085
The following energy consumption ratio.
In this embodiment, determining the operation constraint condition of the energy system may further include: at any moment, the power consumption of the heat pump equipment is determined according to the power command and the energy consumption ratio of the heat pump equipment, and the power consumption of the heat pump equipment can be expressed by the following formula:
Figure BDA0004065118180000086
wherein E is rb (h) The electric power is used for the heat pump equipment;
Figure BDA0004065118180000087
a power instruction of the heat pump equipment operating under the working condition X at the moment h in the typical day s; />
Figure BDA0004065118180000088
The energy consumption ratio of the heat pump equipment at the moment h under the working condition X.
In this embodiment, the power command of the heat pump apparatus is determined according to the operating conditions of the heat pump apparatus.
In this embodiment, the number of heat pump devices put into operation at any one time does not exceed the total number of heat pump devices of the energy system.
In one embodiment, the cost constraints of the energy system may be:
min(C inv +C run )
wherein C is inv Is the annual equal investment cost of the energy system, C run Is the annual running cost of the energy system.
In the present embodiment, the annual equal investment cost C inv It may be that the total investment costs of the energy system are equally distributed to the cost value of each year in the operating cycle, The calculation formula is as follows:
Figure BDA0004065118180000089
wherein N is the total number of devices in the energy system except for the heat pump device; alpha i The annual equity investment conversion coefficient of equipment except for heat pump equipment in the energy system is calculated;
Figure BDA00040651181800000810
for the rated capacity of the devices in the energy system, except for the heat pump device, < >>
Figure BDA00040651181800000811
Rated power for the heat pump equipment; c i C, unit investment cost of equipment except the heat pump equipment in the energy system rb The unit investment cost for the heat pump equipment; alpha rb The coefficient is converted for annual equal investment of heat pump equipment.
Wherein alpha is i The calculation formula of (2) is as follows:
Figure BDA00040651181800000812
wherein m is annual rate; y is the equipment life excluding the heat pump equipment.
In the present embodiment, annual operation cost C of the energy system run The computational expression is:
Figure BDA0004065118180000091
wherein H is o The total number of operating hours per year for other devices in the energy system other than the heat pump device; n is the total number of devices in the energy system except for heat pump devices;
Figure BDA0004065118180000092
the ith device in the energy system is smaller at the hThe cost of producing unit heat; />
Figure BDA0004065118180000093
The method is the usage amount of the ith equipment in the energy system in the h hour; s is the typical day; />
Figure BDA0004065118180000094
The heat pump equipment is used in the energy system on a typical day s; />
Figure BDA0004065118180000095
Representing the cost per unit of heat produced by the heat pump apparatus at hour h.
In one embodiment, determining the energy storage constraint of the energy system may include:
at any moment, according to the power instruction of the heat pump device, the working mode of the energy storage device in the energy system and the maximum energy storage power, the energy storage power of the energy storage device is determined, and can be expressed by the following formula:
Figure BDA0004065118180000096
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004065118180000097
the energy storage power of the energy storage device at the time h in the typical day s is used as energy storage power; />
Figure BDA0004065118180000098
The method is a power instruction of the heat pump equipment, wherein x can be x_heat or x_cool, and the x represents that the heat pump equipment is in a heat storage or cold storage mode respectively; />
Figure BDA0004065118180000099
Indicating that the working mode of the energy storage equipment is an energy storage mode; />
Figure BDA00040651181800000910
Is the maximum energy storage power of the energy storage device.
In this embodiment, at any time, the working mode of the heat pump device is the same as the working mode of the energy storage device, for example, at time h1, the working mode of the energy storage device is the energy storage mode, and the heat pump device is in the heat storage or cold storage mode; at time h2, the working mode of the energy storage device is the energy supply mode, and the heat pump device is in the heat supply or cold supply mode.
In this embodiment, determining the energy storage constraint condition of the energy source system may further include: at any moment, according to the working mode and the maximum energy supply power of the energy storage device, the energy supply power of the energy storage device is determined, and can be expressed by the following formula:
Figure BDA00040651181800000911
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA00040651181800000912
supplying energy to the energy storage device; />
Figure BDA00040651181800000913
Indicating that the working mode of the energy storage equipment is an energy supply mode; />
Figure BDA00040651181800000914
Maximum power supply of the energy storage device.
In this embodiment, determining the energy storage constraint condition of the energy source system may further include: at any moment, the power of the energy storage device is determined according to the energy supply power and the energy storage power of the energy storage device, and the power can be expressed by the following formula:
Figure BDA00040651181800000915
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA00040651181800000916
is the power of the energy storage device; />
Figure BDA00040651181800000917
The energy storage power of the energy storage equipment; />
Figure BDA00040651181800000918
The energy storage device is powered by energy.
In this embodiment, determining the energy storage constraint condition of the energy source system may further include: at any moment, according to the heat accumulation amount, the time period, the energy accumulation efficiency and the energy supply power, the energy accumulation power and the power of the energy accumulation device at the moment, the heat accumulation amount of the energy accumulation device is determined, and the heat accumulation amount can be expressed by the following formula:
Figure BDA0004065118180000101
/>
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004065118180000102
the heat is stored by the energy storage device at the time h and the time h-1 in the typical day s; deltaT is the time period; η is the energy storage efficiency of the energy storage device; />
Figure BDA0004065118180000103
The energy storage power of the energy storage equipment; />
Figure BDA0004065118180000104
Supplying energy to the energy storage device; />
Figure BDA0004065118180000105
Is the power of the energy storage device; />
Figure BDA0004065118180000106
Is the rated heat storage capacity of the energy storage equipment.
In this embodiment, the energy storage device includes a heat storage device and a cold storage device.
In this embodiment, the operation modes of the energy storage device include an energy supply mode and an energy storage mode; the energy storage device cannot be in both the energy supply mode and the energy storage mode.
In one embodiment, determining the load constraint of the energy system from the time-by-time cold load data and the time-by-time heat load data for the typical day may include: at any moment, determining heat load data of the energy system according to energy supply power of heat storage equipment in the energy system, a heating power instruction of heat pump equipment and heating power of equipment except the heat storage equipment in the energy system; and determining the cold load data of the energy system according to the energy supply power of the cold storage equipment in the energy system, the refrigerating power instruction of the heat pump equipment and the refrigerating power of equipment except the cold storage equipment in the energy system.
In this embodiment, determining the load constraints of the energy system may be represented by the following formula:
Figure BDA0004065118180000107
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004065118180000108
thermal load data for energy systems, +.>
Figure BDA0004065118180000109
A heating power instruction for the heat pump device; />
Figure BDA00040651181800001010
Supplying energy to the thermal storage device; / >
Figure BDA00040651181800001011
Heating power of equipment except heat storage equipment in the energy system; />
Figure BDA00040651181800001012
Cold load data for energy systems, +.>
Figure BDA00040651181800001013
A refrigeration power command for the heat pump device;
Figure BDA00040651181800001014
supplying energy to the cold storage device; />
Figure BDA00040651181800001015
Is the refrigeration power of the equipment except the cold storage equipment in the energy system.
The embodiment of the invention also provides an energy system parameter optimization device comprising the air-cooled heat pump, as described in the following embodiment. Because the principle of the device for solving the problems is similar to that of the energy system parameter optimization method comprising the air-cooled heat pump, the implementation of the device can be referred to the implementation of the energy system parameter optimization method comprising the air-cooled heat pump, and the repetition is omitted.
Fig. 4 is a block diagram of an energy system parameter optimization device including an air-cooled heat pump according to an embodiment of the present invention, as shown in fig. 4, an energy system parameter optimization device 400 including an air-cooled heat pump according to an embodiment of the present invention may include:
a typical day determining module 401, configured to determine, according to historical temperature data of the energy system, typical days and time-by-time temperature data, time-by-time cold load data, and time-by-time heat load data of the energy system on a typical day;
a performance parameter determining module 402, configured to determine, according to a relationship between a cooling capacity of the energy system and a water supply temperature and an environmental temperature, a relationship between a cooling energy consumption ratio of the energy system and a water supply temperature and an environmental temperature, a relationship between a heating capacity of the energy system and a water supply temperature and a environmental temperature, a relationship between a heating energy consumption ratio of the energy system and a water supply temperature and a environmental temperature, and a relationship between a heating energy consumption ratio of the energy system and a water supply temperature and a environmental temperature;
An energy consumption ratio determining module 403, configured to determine a time-by-time cooling capacity, a time-by-time heating capacity, a cooling energy consumption ratio, and a heating energy consumption ratio of the energy system in the typical day according to time-by-time temperature data, time-by-time cooling load data, time-by-time heat load data, and performance parameters of the energy system in the typical day;
an operation constraint module 404 for determining an operation constraint condition of the energy system;
an energy storage constraint module 405 for determining energy storage constraints of the energy system;
a load constraint module 406 for determining a load constraint condition of the energy system according to the time-by-time cold load data and the time-by-time hot load data of the typical day;
the optimizing module 407 is configured to optimize the device parameters in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition, and the cost constraint condition of the energy system.
In one embodiment, the typical day determination module 401 may be specifically configured to:
determining a summer date with the same time-by-time temperature as the summer air conditioner outdoor calculation day-by-time temperature as a summer typical day; determining a winter date with the same time-by-time temperature as the time-by-time temperature of the outdoor calculation day of the winter air conditioner as a winter typical day;
Removing the summer air conditioner outdoor calculation day, and dividing the summer days with different time-by-time temperatures with the summer air conditioner outdoor calculation day by adopting a Kmeans algorithm into K 1 The clustering centers of each cluster are determined to be typical days in summer; for a typical summer day with a time-by-time temperature higher than a time-by-time temperature of an outdoor calculation day of a summer air conditioner, determining the time-by-time temperature of the outdoor calculation day of the summer air conditioner as the time-by-time temperature of the typical summer day;
removing the outdoor calculation day of the winter air conditioner, and dividing the outdoor calculation day of the winter air conditioner into K according to the winter date with different time-by-time temperatures from the outdoor calculation day of the winter air conditioner by adopting a Kmeans algorithm 2 The clustering centers of each cluster are determined to be typical days in winter; wherein, for a winter typical day when the time-by-time temperature is lower than the time-by-time temperature of the winter air-conditioning outdoor calculation day, the time-by-time temperature of the winter air-conditioning outdoor calculation day is determined as the time-by-time temperature of the winter typical day.
In one embodiment, the operation constraint module 404 may be specifically configured to:
at any moment, determining a power instruction of the heat pump equipment according to the maximum power of the heat pump equipment and the quantity of the heat pump equipment in the energy system; determining the maximum power of the heat pump equipment according to the output power of the heat pump equipment; determining the energy consumption ratio of the heat pump equipment according to the energy consumption ratio of the heat pump equipment at the ambient temperature and the water outlet temperature; and determining the electric power of the heat pump equipment according to the power command and the energy consumption ratio of the heat pump equipment.
In one embodiment, the optimization module 407 may be specifically configured to:
the cost constraint conditions of the energy system are determined as follows:
min(C inv +C run )
wherein C is inv For the annual equity investment costs of the energy system, C run Is the annual operating cost of the energy system.
In one embodiment, the energy storage constraint module 405 may be specifically configured to:
at any moment, determining the energy storage power of the energy storage equipment according to the power instruction of the heat pump equipment, the working mode of the energy storage equipment in the energy system and the maximum energy storage power; determining the energy supply power of the energy storage equipment according to the working mode and the maximum energy supply power of the energy storage equipment; determining the power of the energy storage equipment according to the energy supply power and the energy storage power of the energy storage equipment; determining the heat accumulation amount of the energy storage equipment according to the heat accumulation amount, the time period, the energy storage efficiency of the energy storage equipment at the last moment, and the energy supply power, the energy storage power and the power of the energy storage equipment at the moment; wherein the energy storage device comprises a heat storage device and a cold storage device; the working modes of the energy storage equipment comprise an energy supply mode and an energy storage mode; the energy storage device cannot be in both the energy supply mode and the energy storage mode.
In one embodiment, the load constraint module 406 may be specifically configured to:
at any moment, determining heat load data of the energy system according to energy supply power of heat storage equipment in the energy system, a heating power instruction of heat pump equipment and heating power of equipment except the heat storage equipment in the energy system; and determining the cold load data of the energy system according to the energy supply power of the cold storage equipment in the energy system, the refrigerating power instruction of the heat pump equipment and the refrigerating power of equipment except the cold storage equipment in the energy system.
Based on the foregoing inventive concept, as shown in fig. 5, the present invention further proposes a computer device 500, including a memory 501, a processor 503, and a computer program 502 stored in the memory 501 and capable of running on the processor 503, where the processor 503 implements the foregoing method for optimizing parameters of an energy system including an air-cooled heat pump when executing the computer program 502.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the energy system parameter optimization method comprising the air-cooled heat pump when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, and the computer program realizes the energy system parameter optimization method comprising the air-cooled heat pump when being executed by a processor.
In summary, compared with the technical scheme that a single typical day is adopted to carry out planning design on an energy system in the prior art, the embodiment of the invention determines the typical day and the time-by-time temperature data, the time-by-time cold load data and the time-by-time heat load data of the energy system on the typical day according to the historical temperature data of the energy system, so that the typical day is more representative and can cover all weather conditions; according to the change relation of the refrigerating capacity of the energy system along with the water supply temperature and the environmental temperature, the change relation of the refrigerating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the change relation of the heating capacity of the energy system along with the water supply temperature and the environmental temperature, and the change relation of the heating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the performance parameters of the energy system are determined; thus, the energy system is more close to the actual operation condition; determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day according to the time-by-time temperature data, the time-by-time cooling load data, the time-by-time heat load data and the performance parameters of the energy system in the typical day; determining an operating constraint condition of the energy system; determining an energy storage constraint condition of the energy system; determining load constraint conditions of the energy system according to the time-by-time cold load data and the time-by-time heat load data of the typical day; according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system, the equipment parameters in the energy system are optimized, so that the parameter optimization of the energy system can be realized, and the energy system designed and planned is close to the actual operation condition, so that the accuracy of the parameters of the energy system is improved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (15)

1. The energy system parameter optimization method comprising the air-cooled heat pump is characterized by comprising the following steps of:
according to the historical temperature data of the energy system, determining the typical day and the time-by-time temperature data, the time-by-time cold load data and the time-by-time heat load data of the energy system on the typical day;
according to the change relation of the refrigerating capacity of the energy system along with the water supply temperature and the environmental temperature, the change relation of the refrigerating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the change relation of the heating capacity of the energy system along with the water supply temperature and the environmental temperature, and the change relation of the heating energy consumption ratio of the energy system along with the water supply temperature and the environmental temperature, the performance parameters of the energy system are determined;
determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day according to the time-by-time temperature data, the time-by-time cooling load data, the time-by-time heat load data and the performance parameters of the energy system in the typical day;
determining an operating constraint condition of the energy system;
determining an energy storage constraint condition of the energy system;
determining load constraint conditions of the energy system according to the time-by-time cold load data and the time-by-time heat load data of the typical day;
And optimizing equipment parameters in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system.
2. The method of claim 1, wherein determining a typical day and time-by-time temperature data, time-by-time cold load data, and time-by-time heat load data of the energy system on a typical day based on historical temperature data of the energy system comprises:
determining a summer date with the same time-by-time temperature as the summer air conditioner outdoor calculation day-by-time temperature as a summer typical day; determining a winter date with the same time-by-time temperature as the time-by-time temperature of the outdoor calculation day of the winter air conditioner as a winter typical day;
removing the summer air conditioner outdoor calculation day, and dividing the summer days with different time-by-time temperatures with the summer air conditioner outdoor calculation day by adopting a Kmeans algorithm into K 1 The clustering centers of each cluster are determined to be typical days in summer; for a typical summer day with a time-by-time temperature higher than a time-by-time temperature of an outdoor calculation day of a summer air conditioner, determining the time-by-time temperature of the outdoor calculation day of the summer air conditioner as the time-by-time temperature of the typical summer day;
removing the outdoor calculation day of the winter air conditioner, and dividing the outdoor calculation day of the winter air conditioner into K according to the winter date with different time-by-time temperatures from the outdoor calculation day of the winter air conditioner by adopting a Kmeans algorithm 2 Clustering, clustering center of each clusterDetermining as a winter typical day; wherein, for a winter typical day when the time-by-time temperature is lower than the time-by-time temperature of the winter air-conditioning outdoor calculation day, the time-by-time temperature of the winter air-conditioning outdoor calculation day is determined as the time-by-time temperature of the winter typical day.
3. The method of claim 1, wherein determining the operating constraints of the energy system comprises:
at any moment, determining a power instruction of the heat pump equipment according to the maximum power of the heat pump equipment and the quantity of the heat pump equipment in the energy system; determining the maximum power of the heat pump equipment according to the output power of the heat pump equipment; determining the energy consumption ratio of the heat pump equipment according to the energy consumption ratio of the heat pump equipment at the ambient temperature and the water outlet temperature; and determining the electric power of the heat pump equipment according to the power command and the energy consumption ratio of the heat pump equipment.
4. The method of claim 1, wherein the cost constraints of the energy system are:
min(C inv +C run )
wherein C is inv For the annual equity investment costs of the energy system, C run Is the annual operating cost of the energy system.
5. The method of claim 1, wherein determining the energy storage constraint of the energy system comprises:
At any moment, determining the energy storage power of the energy storage equipment according to the power instruction of the heat pump equipment in the energy system, the working mode of the energy storage equipment in the energy system and the maximum energy storage power; determining the energy supply power of the energy storage equipment according to the working mode and the maximum energy supply power of the energy storage equipment; determining the power of the energy storage equipment according to the energy supply power and the energy storage power of the energy storage equipment; determining the heat accumulation amount of the energy storage equipment according to the heat accumulation amount, the time period, the energy storage efficiency of the energy storage equipment at the last moment, and the energy supply power, the energy storage power and the power of the energy storage equipment at the moment; wherein the energy storage device comprises a heat storage device and a cold storage device; the working modes of the energy storage equipment comprise an energy supply mode and an energy storage mode; the energy storage device cannot be in both the energy supply mode and the energy storage mode.
6. The method of claim 1, wherein determining the load constraints of the energy system based on the time-by-time cold load data and the time-by-time heat load data for the typical day comprises:
at any moment, determining heat load data of the energy system according to energy supply power of heat storage equipment in the energy system, a heating power instruction of heat pump equipment and heating power of equipment except the heat storage equipment in the energy system; and determining the cold load data of the energy system according to the energy supply power of the cold storage equipment in the energy system, the refrigerating power instruction of the heat pump equipment and the refrigerating power of equipment except the cold storage equipment in the energy system.
7. An energy system parameter optimizing device comprising an air-cooled heat pump, comprising:
the typical day determining module is used for determining typical days and time-by-time temperature data, time-by-time cold load data and time-by-time heat load data of the energy system on the typical days according to the historical temperature data of the energy system;
the performance parameter determining module is used for determining the performance parameter of the energy system according to the change relation of the refrigerating capacity of the energy system along with the water supply temperature and the environmental temperature, the refrigerating energy consumption ratio of the energy system along with the change relation of the water supply temperature and the environmental temperature, the heating capacity of the energy system along with the change relation of the water supply temperature and the environmental temperature, and the heating energy consumption ratio of the energy system along with the change relation of the water supply temperature and the environmental temperature;
the energy consumption ratio determining module is used for determining the time-by-time refrigerating capacity, the time-by-time heating capacity, the refrigerating energy consumption ratio and the heating energy consumption ratio of the energy system in the typical day according to the time-by-time temperature data, the time-by-time cold load data, the time-by-time heat load data and the performance parameters of the energy system in the typical day;
an operation constraint module for determining an operation constraint condition of the energy system;
The energy storage constraint module is used for determining energy storage constraint conditions of the energy system;
the load constraint module is used for determining the load constraint condition of the energy system according to the time-by-time cold load data and the time-by-time hot load data of the typical day;
and the optimization module is used for optimizing the equipment parameters in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition and the cost constraint condition of the energy system.
8. The apparatus of claim 7, wherein the typical day determination module is specifically configured to:
determining a summer date with the same time-by-time temperature as the summer air conditioner outdoor calculation day-by-time temperature as a summer typical day; determining a winter date with the same time-by-time temperature as the time-by-time temperature of the outdoor calculation day of the winter air conditioner as a winter typical day;
removing the summer air conditioner outdoor calculation day, and dividing the summer days with different time-by-time temperatures with the summer air conditioner outdoor calculation day by adopting a Kmeans algorithm into K 1 The clustering centers of each cluster are determined to be typical days in summer; for a typical summer day with a time-by-time temperature higher than a time-by-time temperature of an outdoor calculation day of a summer air conditioner, determining the time-by-time temperature of the outdoor calculation day of the summer air conditioner as the time-by-time temperature of the typical summer day;
Removing the outdoor calculation day of the winter air conditioner, and dividing the outdoor calculation day of the winter air conditioner into K according to the winter date with different time-by-time temperatures from the outdoor calculation day of the winter air conditioner by adopting a Kmeans algorithm 2 The clustering centers of each cluster are determined to be typical days in winter; wherein, for a winter typical day when the time-by-time temperature is lower than the time-by-time temperature of the winter air-conditioning outdoor calculation day, the time-by-time temperature of the winter air-conditioning outdoor calculation day is determined as the time-by-time temperature of the winter typical day.
9. The apparatus of claim 7, wherein the operation constraint module is specifically configured to:
at any moment, determining a power instruction of the heat pump equipment according to the maximum power of the heat pump equipment and the quantity of the heat pump equipment in the energy system; determining the maximum power of the heat pump equipment according to the output power of the heat pump equipment; determining the energy consumption ratio of the heat pump equipment according to the energy consumption ratio of the heat pump equipment at the ambient temperature and the water outlet temperature; and determining the electric power of the heat pump equipment according to the power command and the energy consumption ratio of the heat pump equipment.
10. The apparatus of claim 7, wherein the optimization module is further to:
the cost constraint conditions of the energy system are determined as follows:
min(C inv +C run )
wherein C is inv For the annual equity investment costs of the energy system, C run Is the annual operating cost of the energy system.
11. The apparatus of claim 7, wherein the energy storage constraint module is specifically configured to:
at any moment, determining the energy storage power of the energy storage equipment according to the power instruction of the heat pump equipment in the energy system, the working mode of the energy storage equipment in the energy system and the maximum energy storage power; determining the energy supply power of the energy storage equipment according to the working mode and the maximum energy supply power of the energy storage equipment; determining the power of the energy storage equipment according to the energy supply power and the energy storage power of the energy storage equipment; determining the heat accumulation amount of the energy storage equipment according to the heat accumulation amount, the time period, the energy storage efficiency of the energy storage equipment at the last moment, and the energy supply power, the energy storage power and the power of the energy storage equipment at the moment; wherein the energy storage device comprises a heat storage device and a cold storage device; the working modes of the energy storage equipment comprise an energy supply mode and an energy storage mode; the energy storage device cannot be in both the energy supply mode and the energy storage mode.
12. The apparatus of claim 7, wherein the load constraint module is specifically configured to:
at any moment, determining heat load data of the energy system according to energy supply power of heat storage equipment in the energy system, a heating power instruction of heat pump equipment and heating power of equipment except the heat storage equipment in the energy system; and determining the cold load data of the energy system according to the energy supply power of the cold storage equipment in the energy system, the refrigerating power instruction of the heat pump equipment and the refrigerating power of equipment except the cold storage equipment in the energy system.
13. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 6 when executing the computer program.
14. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 6.
15. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the method of any of claims 1 to 6.
CN202310072224.2A 2023-01-19 2023-01-19 Energy system parameter optimization method and device comprising air-cooled heat pump Pending CN116105314A (en)

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