Energy system parameter optimization method and apparatus including air-cooled heat pump

Abstract

This invention discloses a method and apparatus for optimizing energy system parameters, including an air-cooled heat pump, relating to the field of integrated energy technology. The method includes: determining typical day and hourly temperature data, cooling and heating load data of the energy system on typical days based on historical temperature data of the energy system; determining hourly cooling capacity, hourly heating capacity, cooling energy consumption ratio, and heating energy consumption ratio of the energy system on typical days based on the hourly temperature data, hourly cooling and heating load data, and performance parameters of the energy system on typical days; determining the operating constraints, energy storage constraints, and load constraints of the energy system; and optimizing the equipment parameters in the energy system based on the operating constraints, energy storage constraints, load constraints, and cost constraints of the energy system. This invention can optimize the parameters of the energy system to improve the accuracy of energy system parameters.

Description

Technical Field

[0001] This invention relates to the field of integrated energy technology, and in particular to a method and apparatus for optimizing energy system parameters, including an air-cooled heat pump. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] In the context of fossil fuel depletion and continuous environmental degradation, new energy sources have developed rapidly. However, the problem of their absorption has led to low utilization efficiency. Heat pump systems are a currently actively promoted heating method. Air-cooled heat pumps, because they can both heat and cool, are one of the most common equipment types in heating and cooling systems. The heating / cooling capacity of air-cooled heat pumps varies with changes in ambient temperature and outlet water temperature, and their energy efficiency ratio also varies with changes in ambient temperature and outlet water temperature.

[0004] The aforementioned factors introduce complexity into the design of energy systems that include air-cooled heat pumps. Existing technologies use single typical daily hourly temperature and cooling load data, heat load data, and constant heating capacity, cooling capacity, or constant energy consumption ratio of the heat pump itself to plan and design the energy system of the air-cooled heat pump. However, this method cannot fully reflect the operating conditions of the air-cooled heat pump, and the parameters of the energy system are not accurate enough, resulting in a large deviation between the design and actual operation of the energy system. Summary of the Invention

[0005] This invention provides a method for optimizing energy system parameters, including an air-cooled heat pump, to improve the accuracy of energy system parameters. The method includes:

[0006] Based on the historical temperature data of the energy system, determine the typical day and the hourly temperature data, hourly cooling load data and hourly heating load data of the energy system on the typical day;

[0007] Based on the relationship between the cooling capacity of the energy system and the water supply temperature and ambient temperature, the relationship between the cooling energy consumption ratio of the energy system and the water supply temperature and ambient temperature, the relationship between the heating capacity of the energy system and the water supply temperature and ambient temperature, and the relationship between the heating energy consumption ratio of the energy system and the water supply temperature and ambient temperature, the performance parameters of the energy system are determined.

[0008] Based on the hourly temperature data, hourly cooling load data, hourly heating load data of the energy system on a typical day, and the performance parameters of the energy system, determine the hourly cooling capacity, hourly heating capacity, cooling energy consumption ratio, and heating energy consumption ratio of the energy system on the typical day.

[0009] Determine the operating constraints of the energy system;

[0010] Determine the energy storage constraints of the energy system;

[0011] The load constraints of the energy system are determined based on the hourly cooling load data and hourly heating load data of the typical day.

[0012] Based on the operational constraints, energy storage constraints, load constraints, and cost constraints of the energy system, the equipment parameters in the energy system are optimized.

[0013] This invention also provides an energy system parameter optimization device including an air-cooled heat pump, used to optimize energy system parameters to improve the accuracy of energy system parameters. The device includes:

[0014] The typical day determination module is used to determine the typical day and the hourly temperature data, hourly cooling load data and hourly heating load data of the energy system on the typical day based on the historical temperature data of the energy system.

[0015] The performance parameter determination module is used to determine the performance parameters of the energy system based on the relationship between the cooling capacity of the energy system and the supply water temperature and the ambient temperature, the relationship between the cooling energy consumption ratio of the energy system and the supply water temperature and the ambient temperature, the relationship between the heating capacity of the energy system and the supply water temperature and the ambient temperature, and the relationship between the heating energy consumption ratio of the energy system and the supply water temperature and the ambient temperature.

[0016] The energy consumption ratio determination module is used to determine the hourly cooling capacity, hourly heating capacity, cooling energy consumption ratio and heating energy consumption ratio of the energy system on the typical day based on the hourly temperature data, hourly cooling load data, hourly heating load data and the performance parameters of the energy system on the typical day.

[0017] The operation constraint module is used to determine the operation constraints of the energy system;

[0018] An energy storage constraint module is used to determine the energy storage constraint conditions of the energy system.

[0019] The load constraint module is used to determine the load constraint conditions of the energy system based on the hourly cooling load data and hourly heating load data of the typical day.

[0020] The optimization module is used to optimize the equipment parameters in the energy system based on the operating constraints, energy storage constraints, load constraints, and cost constraints of the energy system.

[0021] Compared with existing technologies that use a single typical day for energy system planning and design, this invention determines the typical day and its hourly temperature, cooling load, and heating load data based on historical temperature data. This makes the typical day more representative and covers all weather conditions. Furthermore, by analyzing the relationships between the energy system's cooling capacity, cooling energy consumption ratio, heating capacity, and heating energy consumption ratio with varying water and ambient temperatures, the performance parameters of the energy system are determined. This makes the energy system more closely reflect actual operating conditions. Based on the hourly temperature data, hourly cooling load data, hourly heating load data, and performance parameters of the energy system on a typical day, the hourly cooling capacity, hourly heating capacity, cooling energy consumption ratio, and heating energy consumption ratio of the energy system on the typical day are determined; the operating constraints of the energy system are determined; the energy storage constraints of the energy system are determined; based on the hourly cooling load data and hourly heating load data of the typical day, the load constraints of the energy system are determined; based on the operating constraints, energy storage constraints, load constraints, and cost constraints of the energy system, the equipment parameters of the energy system are optimized. This allows for parameter optimization of the energy system, making the designed energy system closer to the actual operating conditions, thereby improving the accuracy of the energy system parameters. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0023] Figure 1 This is a flowchart of an energy system parameter optimization method including an air-cooled heat pump provided in an embodiment of the present invention;

[0024] Figure 2 This is a specific example diagram of an energy system parameter optimization method including an air-cooled heat pump provided in an embodiment of the present invention;

[0025] Figure 3 This is a specific example diagram of an energy system parameter optimization method including an air-cooled heat pump provided in an embodiment of the present invention;

[0026] Figure 4This is a structural block diagram of an energy system parameter optimization device including an air-cooled heat pump provided in an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of an energy system parameter optimization device including an air-cooled heat pump, provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0029] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0030] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0031] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0032] Figure 1 This is a flowchart illustrating a method for optimizing energy system parameters including an air-cooled heat pump, as provided in an embodiment of the present invention. Figure 1 As shown, the energy system parameter optimization method including an air-cooled heat pump in this embodiment of the invention may include the following steps:

[0033] Step 101: Based on the historical temperature data of the energy system, determine the typical day and the hourly temperature data, hourly cooling load data and hourly heating load data of the energy system on the typical day;

[0034] Step 102: Based on the relationship between the cooling capacity of the energy system and the water supply temperature and ambient temperature, the relationship between the cooling energy consumption ratio of the energy system and the water supply temperature and ambient temperature, the relationship between the heating capacity of the energy system and the water supply temperature and ambient temperature, and the relationship between the heating energy consumption ratio of the energy system and the water supply temperature and ambient temperature, determine the performance parameters of the energy system.

[0035] Step 103: Based on the hourly temperature data, hourly cooling load data, hourly heating load data, and performance parameters of the energy system on the typical day, determine the hourly cooling capacity, hourly heating capacity, cooling energy consumption ratio, and heating energy consumption ratio of the energy system on the typical day.

[0036] Step 104: Determine the operating constraints of the energy system;

[0037] Step 105: Determine the energy storage constraints of the energy system;

[0038] Step 106: Determine the load constraints of the energy system based on the hourly cooling load data and hourly heating load data of the typical day;

[0039] Step 107: Optimize the equipment parameters in the energy system based on the operational constraints, energy storage constraints, load constraints, and cost constraints of the energy system.

[0040] Figure 2 This is a specific example diagram of an energy system parameter optimization method including an air-cooled heat pump provided in an embodiment of the present invention, as shown in the figure. Figure 2 As shown in the embodiments of the present invention, the specific process of determining a typical day may include:

[0041] Typical summer days are defined as those where the hourly temperature matches the hourly temperature of the summer outdoor calculation date for air conditioning; typical winter days are defined as those where the hourly temperature matches the hourly temperature of the winter outdoor calculation date for air conditioning. For example, based on relevant specifications for air conditioning systems, the hourly temperatures of the summer and winter outdoor calculation dates for air conditioning are determined separately. Dates in the summer with the same hourly temperature are defined as typical summer days; similarly, dates in the winter with the same hourly temperature are defined as typical winter days.

[0042] In this embodiment, the summer outdoor temperature calculation day is removed. For summer dates whose hourly temperatures differ from those of the summer outdoor temperature calculation day, the K-means algorithm is used to divide them into K1 clusters, and the cluster center of each cluster is determined as a typical summer day. Specifically, for typical summer days whose hourly temperatures are higher than those of the summer outdoor temperature calculation day, the hourly temperature of the summer outdoor temperature calculation day is determined as the hourly temperature of that typical summer day. For example, the number of summer days with the same hourly temperature as the summer outdoor temperature calculation day is... Based on relevant specifications for air conditioning systems, the summer outdoor calculation day for air conditioning is determined. The hourly temperature of a typical summer day is compared with the hourly temperature of the summer outdoor calculation day. For typical summer days where the hourly temperature is higher than the hourly temperature of the summer outdoor calculation day, the hourly temperature of the summer outdoor calculation day is determined as the hourly temperature of that typical summer day. For summer days where the hourly temperature differs from the hourly temperature of the summer outdoor calculation day, the K-means algorithm is used to divide them into K1 clusters. The cluster center of each cluster is determined as the typical summer day, and the number of data points in each cluster is the number of days represented by the typical summer day. The number of days represented by each typical summer day is as follows: In this example, a total of K1+1 typical summer days are identified, and the number of days corresponding to each typical day is as follows:

[0043] In this embodiment, the winter outdoor temperature calculation day is removed. For winter days whose hourly temperatures differ from the hourly temperatures of the winter outdoor temperature calculation day, the K-means algorithm is used to divide them into K2 clusters, and the cluster center of each cluster is determined as a typical winter day. Specifically, for typical winter days whose hourly temperatures are lower than the hourly temperatures of the winter outdoor temperature calculation day, the hourly temperatures of the winter outdoor temperature calculation day are determined as the hourly temperatures of that typical winter day. For example, the number of winter days with hourly temperatures the same as the hourly temperatures of the winter outdoor temperature calculation day is... Based on relevant specifications for air conditioning systems, the outdoor calculation day for winter air conditioning is determined. The hourly temperature of a typical winter day is compared with the hourly temperature of the outdoor calculation day. For typical winter days where the hourly temperature is lower than the hourly temperature of the outdoor calculation day, the hourly temperature of the outdoor calculation day is determined as the hourly temperature of that typical winter day. For winter days where the hourly temperature differs from the hourly temperature of the outdoor calculation day, the K-means algorithm is used to divide them into K2 clusters. The cluster center of each cluster is determined as the typical winter day, and the number of data points in each cluster is the number of days represented by the typical winter day. The number of days represented by each typical winter day is as follows: In this example, a total of K2+1 typical winter days were identified, and the number of days corresponding to each typical day was as follows:

[0044] Figure 3 A flowchart illustrating a specific example of an energy system parameter optimization method including an air-cooled heat pump provided in this embodiment of the invention is shown below. Figure 3 As shown, the energy system parameter optimization method including an air-cooled heat pump in this embodiment of the invention may further include:

[0045] This process involves determining hourly temperature, cooling load, and heating load data for multiple typical days and the energy system on those days. Based on this data, the load constraints of the energy system are determined. Furthermore, the hourly cooling capacity, heating capacity, cooling energy consumption ratio, and heating energy consumption ratio of the energy system on typical days are determined, along with the system's performance parameters, thus establishing operational constraints. Cost constraints are also determined, and if energy storage devices connected to the heat pump are present, energy storage constraints are identified. Finally, the equipment parameters within the energy system are optimized based on these operational, energy storage, load, and cost constraints. Multiple typical days are determined for different seasons, and the impact of meteorological factors on cooling and heating loads, as well as the impact of meteorological factors and performance parameters on the operation of the energy system, are fully considered to make the typical days more representative. This allows the energy system, including air-cooled heat pumps, to be close to the actual operating conditions during the design and planning stage, thereby increasing the accuracy of the energy system design and planning.

[0046] In one embodiment, based on the relationships between the cooling capacity of the energy system and the supply water temperature and ambient temperature, the cooling energy consumption ratio of the energy system and the supply water temperature and ambient temperature, the heating capacity of the energy system and the heating energy consumption ratio of the energy system and the supply water temperature and ambient temperature, the performance parameters of the energy system can be determined and expressed by the following formulas:

[0047]

[0048] Among them, OUT heat For heating the energy system; T water For water supply temperature; T atm The ambient temperature; The water supply temperature of the energy system under operating condition X, where X represents cool, xool, heat, and xheat, respectively, indicating cooling, cold storage, heating, and heat storage; COP heat The heating energy consumption ratio of the energy system; OUT cool Cooling capacity of the energy system; COP cool The energy consumption ratio of the energy system is refrigeration; k1~k8 and b1~b4 are constants.

[0049] In one embodiment, the energy system may include heat pump equipment and energy storage equipment.

[0050] In one embodiment, the energy system and heat pump equipment can operate in the following four operating conditions: cooling, cold storage, heating, and heat storage, and at any given time, the energy system and heat pump equipment can and can only operate in one operating condition.

[0051] In one embodiment, determining the operating constraints of the energy system may include: at any given time, determining the power command of the heat pump equipment based on the maximum power of the heat pump equipment and the number of heat pump equipment in the energy system, which can be expressed by the following formula:

[0052]

[0053] in, This is the power command for the heat pump equipment operating under condition X at time h in a typical day s, where X is taken as cool, xool, heat, and xheat, representing cooling, cold storage, heating, and heat storage, respectively; N(h) is the number of heat pump equipment put into operation at time h. The maximum power of the heat pump equipment operating under condition X at time h during a typical day s.

[0054] In this embodiment, determining the operating constraints of the energy system may further include: at any given time, determining the maximum power of the heat pump equipment based on its output power, which can be expressed by the following formula:

[0055]

[0056] in, The maximum power of the heat pump equipment operating under condition X at time h during a typical day; For heat pump equipment operating under typical day conditions X, the supply water temperature is... Ambient temperature The output power at that point.

[0057] In this embodiment, determining the operating constraints of the energy system may further include: at any given time, determining the energy consumption ratio of the heat pump equipment based on the energy consumption ratio of the heat pump equipment at ambient temperature and outlet water temperature, which can be expressed by the following formula:

[0058]

[0059] in, The energy consumption ratio of the heat pump equipment operating under condition X at time h during a typical day; For heat pump equipment operating under typical day conditions X, the supply water temperature is... Ambient temperature The energy consumption ratio is as follows.

[0060] In this embodiment, determining the operating constraints of the energy system may further include: at any given time, determining the power consumption of the heat pump equipment based on the power command and energy consumption ratio of the heat pump equipment, which can be expressed by the following formula:

[0061]

[0062] Among them, E rb (h) represents the power consumption of the heat pump equipment; The power command of the heat pump equipment operating under condition X at time h during a typical day s; Let h be the energy consumption ratio of the heat pump equipment operating under condition X at time h.

[0063] In this embodiment, the power command of the heat pump equipment is determined based on the operating conditions of the heat pump equipment.

[0064] In this embodiment, the number of heat pump devices put into operation at any given time does not exceed the total number of heat pump devices in the energy system.

[0065] In one embodiment, the cost constraint of the energy system can be:

[0066] min(C inv +C run )

[0067] Among them, C inv C represents the annual equivalent investment cost of the energy system. run This refers to the annual operating cost of the energy system.

[0068] In this embodiment, the annual equivalent investment cost C inv It can be the total investment cost of the energy system, equally allocated to each year of the operating cycle, and its calculation formula is as follows:

[0069]

[0070] Where N is the total number of devices in the energy system excluding heat pump equipment; α i It is the annual equivalent investment conversion factor for equipment in the energy system other than heat pump equipment; The rated capacity of equipment in the energy system other than heat pump equipment. The rated power of the heat pump equipment; c i c represents the unit investment cost of equipment in the energy system other than heat pump equipment. rb The unit investment cost of heat pump equipment; α rb This is the annual equivalent investment conversion factor for heat pump equipment.

[0071] Where, αi The calculation formula is as follows:

[0072]

[0073] Where m is the annual interest rate; Y is the lifespan of the equipment excluding the heat pump equipment.

[0074] In this embodiment, the annual operating cost C of the energy system run The calculation expression is:

[0075]

[0076] Among them, H o N represents the total annual operating hours of equipment in the energy system excluding heat pump equipment; N is the total number of equipment in the energy system excluding heat pump equipment. The cost of producing one unit of heat by the i-th device in the energy system during hour h; Let be the usage of the i-th device in the energy system during hour h; s is a typical day. The usage of heat pump equipment in a typical day in an energy system; This indicates the cost per unit of heat produced by the heat pump equipment in hour h.

[0077] In one embodiment, determining the energy storage constraints of an energy system may include:

[0078] At any given moment, the energy storage capacity of the energy storage device can be determined based on the power command of the heat pump equipment, the operating mode of the energy storage device in the energy system, and the maximum energy storage capacity. This can be expressed by the following formula:

[0079]

[0080] in, The energy storage capacity of the energy storage device at time h in a typical day; This is the power command for the heat pump device, where x can be x_heat or x_cool, representing whether the heat pump device is in heat storage or cold storage mode, respectively. This indicates that the energy storage device is operating in energy storage mode. This refers to the maximum energy storage capacity of the energy storage device.

[0081] In this embodiment, at any given time, the operating mode of the heat pump device is the same as that of the energy storage device. For example, at time h1, the energy storage device is in energy storage mode, and the heat pump device is in heat storage or cold storage mode; at time h2, the energy storage device is in energy supply mode, and the heat pump device is in heating or cooling mode.

[0082] In this embodiment, determining the energy storage constraints of the energy system may further include: at any given time, determining the energy supply power of the energy storage device based on its operating mode and maximum power supply, which can be expressed by the following formula:

[0083]

[0084] in, The power supply capacity of the energy storage device; This indicates that the energy storage device is operating in power supply mode. This refers to the maximum power output of the energy storage device.

[0085] In this embodiment, determining the energy storage constraints of the energy system may further include: at any given time, determining the power of the energy storage device based on its supply power and storage power, which can be expressed by the following formula:

[0086]

[0087] in, The power of the energy storage device; The energy storage capacity of the energy storage device; The power supply for energy storage devices.

[0088] In this embodiment, determining the energy storage constraints of the energy system may further include: at any given time, based on the heat storage capacity, time period, energy storage efficiency, power supply, energy storage power, and power of the energy storage device at the previous time, determining the heat storage capacity of the energy storage device can be expressed by the following formula:

[0089]

[0090] in, η represents the heat storage capacity of the energy storage device at time h and time h-1 in a typical day s; ΔT is the time period; η is the energy storage efficiency of the energy storage device. The energy storage capacity of the energy storage device; The power supply capacity of the energy storage device; The power of the energy storage device; This refers to the rated heat storage capacity of the energy storage device.

[0091] In this embodiment, the energy storage device includes a thermal storage device and a cold storage device.

[0092] In this embodiment, the energy storage device operates in two modes: a power supply mode and a power storage mode. The energy storage device cannot be in both power supply and power storage modes simultaneously.

[0093] In one embodiment, determining the load constraints of the energy system based on the hourly cooling load data and hourly heating load data of the typical day may include: at any given time, determining the heating load data of the energy system based on the power supply of the thermal storage device, the heating power command of the heat pump device, and the heating power of the devices in the energy system other than the thermal storage device; and determining the cooling load data of the energy system based on the power supply of the cold storage device, the cooling power command of the heat pump device, and the cooling power of the devices in the energy system other than the cold storage device.

[0094] In this embodiment, the load constraint condition of the energy system can be expressed by the following formula:

[0095]

[0096] in, For the heat load data of the energy system, This is the heating power command for the heat pump equipment. The power supply capacity for thermal storage equipment; The heating capacity of equipment in the energy system other than thermal storage devices; For energy system cooling load data, This is the cooling power command for the heat pump equipment. The power supply capacity for cold storage equipment; This refers to the cooling capacity of equipment in an energy system other than cold storage devices.

[0097] This invention also provides an energy system parameter optimization device including an air-cooled heat pump, as described in the following embodiments. Since the principle by which this device solves the problem is similar to the energy system parameter optimization method including an air-cooled heat pump, the implementation of this device can refer to the implementation of the energy system parameter optimization method including an air-cooled heat pump; repeated details will not be elaborated further.

[0098] Figure 4 This is a structural block diagram of an energy system parameter optimization device including an air-cooled heat pump provided in an embodiment of the present invention, such as... Figure 4 As shown, the energy system parameter optimization device 400 including an air-cooled heat pump in this embodiment of the invention may include:

[0099] Typical day determination module 401 is used to determine a typical day and hourly temperature data, hourly cooling load data and hourly heating load data of the energy system on the typical day based on the historical temperature data of the energy system;

[0100] The performance parameter determination module 402 is used to determine the performance parameters of the energy system based on the relationship between the cooling capacity of the energy system and the supply water temperature and the ambient temperature, the relationship between the cooling energy consumption ratio of the energy system and the supply water temperature and the ambient temperature, the relationship between the heating capacity of the energy system and the supply water temperature and the ambient temperature, and the relationship between the heating energy consumption ratio of the energy system and the supply water temperature and the ambient temperature.

[0101] The energy consumption ratio determination module 403 is used to determine the hourly cooling capacity, hourly heating capacity, cooling energy consumption ratio and heating energy consumption ratio of the energy system on the typical day based on the hourly temperature data, hourly cooling load data, hourly heating load data and the performance parameters of the energy system on the typical day.

[0102] The operation constraint module 404 is used to determine the operation constraint conditions of the energy system;

[0103] Energy storage constraint module 405 is used to determine the energy storage constraint conditions of the energy system;

[0104] The load constraint module 406 is used to determine the load constraint conditions of the energy system based on the hourly cooling load data and hourly heating load data of the typical day.

[0105] The optimization module 407 is used to optimize the equipment parameters in the energy system based on the operating constraints, energy storage constraints, load constraints, and cost constraints of the energy system.

[0106] In one embodiment, the typical day determination module 401 can be specifically used for:

[0107] Summer days with the same hourly temperature as the summer outdoor temperature calculation day for air conditioning are defined as typical summer days; winter days with the same hourly temperature as the winter outdoor temperature calculation day for air conditioning are defined as typical winter days.

[0108] Remove the summer outdoor calculation day for air conditioning. For summer dates whose hourly temperature is different from the hourly temperature of the summer outdoor calculation day for air conditioning, use the Kmeans algorithm to divide them into K1 clusters, and determine the cluster center of each cluster as the typical summer day. Among them, for typical summer days whose hourly temperature is higher than the hourly temperature of the summer outdoor calculation day for air conditioning, the hourly temperature of the summer outdoor calculation day for air conditioning is determined as the hourly temperature of that typical summer day.

[0109] Remove the winter outdoor calculation day for air conditioning. For winter dates whose hourly temperature is different from the hourly temperature of the winter outdoor calculation day for air conditioning, use the Kmeans algorithm to divide them into K2 clusters. The cluster center of each cluster is determined as the typical winter day. Among them, for the typical winter day whose hourly temperature is lower than the hourly temperature of the winter outdoor calculation day for air conditioning, the hourly temperature of the winter outdoor calculation day for air conditioning is determined as the hourly temperature of the typical winter day.

[0110] In one embodiment, the constraint execution module 404 can be specifically used for:

[0111] At any given time, the power command of the heat pump equipment is determined based on the maximum power and the number of heat pump equipment in the energy system; the maximum power of the heat pump equipment is determined based on the output power of the heat pump equipment; the energy consumption ratio of the heat pump equipment is determined based on the energy consumption ratio of the heat pump equipment at ambient temperature and outlet water temperature; and the power consumption of the heat pump equipment is determined based on the power command and energy consumption ratio of the heat pump equipment.

[0112] In one embodiment, the optimization module 407 can be specifically used for:

[0113] The cost constraints of the energy system are determined as follows:

[0114] min(C inv +C run )

[0115] Among them, C inv C is the annual equivalent investment cost of the energy system. run The annual operating cost of the energy system is [value].

[0116] In one embodiment, the energy storage constraint module 405 can be specifically used for:

[0117] At any given time, the energy storage power of the energy storage device is determined based on the power command of the heat pump device, the operating mode and maximum energy storage power of the energy storage device in the energy system; the energy supply power of the energy storage device is determined based on the operating mode and maximum energy supply power of the energy storage device; the power of the energy storage device is determined based on the energy supply power and energy storage power; the heat storage capacity of the energy storage device is determined based on the heat storage capacity, time period, energy storage efficiency of the energy storage device at the previous time, the energy supply power, energy storage power and power of the energy storage device at this time; wherein, the energy storage device includes heat storage device and cold storage device; the operating mode of the energy storage device includes energy supply mode and energy storage mode; the energy storage device cannot be in energy supply mode and energy storage mode simultaneously.

[0118] In one embodiment, the load constraint module 406 can be specifically used for:

[0119] At any given time, the heat load data of the energy system is determined based on the power supply of the heat storage device, the heating power command of the heat pump device, and the heating power of the devices in the energy system other than the heat storage device; the cold load data of the energy system is determined based on the power supply of the cold storage device, the cooling power command of the heat pump device, and the cooling power of the devices in the energy system other than the cold storage device.

[0120] Based on the aforementioned inventive concept, such as Figure 5 As shown, the present invention also proposes a computer device 500, including a memory 501, a processor 503, and a computer program 502 stored in the memory 501 and executable on the processor 503. When the processor 503 executes the computer program 502, it implements the aforementioned energy system parameter optimization method including an air-cooled heat pump.

[0121] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing energy system parameters including an air-cooled heat pump.

[0122] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described energy system parameter optimization method including an air-cooled heat pump.

[0123] In summary, compared with existing technologies that use a single typical day for energy system planning and design, the embodiments of the present invention determine the typical day and the hourly temperature, cooling load, and heating load data of the energy system on the typical day based on historical temperature data. This makes the typical day more representative and covers all weather conditions. Furthermore, by determining the relationships between the energy system's cooling capacity, cooling energy consumption ratio, heating capacity, and heating energy consumption ratio with varying water supply and ambient temperatures, the performance parameters of the energy system are determined. This makes the energy system more closely resemble actual operating conditions. Based on the hourly temperature data, hourly cooling load data, hourly heating load data, and performance parameters of the energy system on a typical day, the hourly cooling capacity, hourly heating capacity, cooling energy consumption ratio, and heating energy consumption ratio of the energy system on that typical day are determined; the operating constraints of the energy system are determined; the energy storage constraints of the energy system are determined; based on the hourly cooling load data and hourly heating load data on the typical day, the load constraints of the energy system are determined; based on the operating constraints, energy storage constraints, load constraints, and cost constraints of the energy system, the equipment parameters of the energy system are optimized. This allows for parameter optimization of the energy system, making the designed energy system closer to the actual operating conditions, thereby improving the accuracy of the energy system parameters.

[0124] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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, etc.) containing computer-usable program code.

[0125] This 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 block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing parameters of an energy system comprising an air-cooled heat pump, characterized in that, The method comprises the following steps: determining a typical day and hourly temperature data, hourly cooling load data and hourly heating load data of the energy system on the typical day according to historical temperature data of the energy system; determining performance parameters of the energy system according to the variation of refrigerating capacity of the energy system with water supply temperature and ambient temperature, the variation of refrigerating energy consumption ratio of the energy system with water supply temperature and ambient temperature, the variation of heating capacity of the energy system with water supply temperature and ambient temperature, and the variation of heating energy consumption ratio of the energy system with water supply temperature and ambient temperature; determining hourly refrigerating capacity, hourly heating capacity, refrigerating energy consumption ratio and heating energy consumption ratio of the energy system on the typical day according to hourly temperature data, hourly cooling load data, hourly heating load data of the energy system on the typical day and performance parameters of the energy system; determining operation constraints of the energy system; determining energy storage constraints of the energy system; determining load constraints of the energy system according to hourly cooling load data and hourly heating load data of the typical day; optimizing equipment parameters in the energy system according to the operation constraints, the energy storage constraints, the load constraints and cost constraints of the energy system; determining a typical day and hourly temperature data, hourly cooling load data and hourly heating load data of the energy system on the typical day according to historical temperature data of the energy system, comprising: determining a summer typical day by taking a summer date with the same hourly temperature as that of a summer air conditioning outdoor calculation day as the summer typical day, and determining a winter typical day by taking a winter date with the same hourly temperature as that of a winter air conditioning outdoor calculation day as the winter typical day; removing the summer air conditioning outdoor calculation day, and dividing summer dates with different hourly temperatures from that of the summer air conditioning outdoor calculation day into K1 clusters by using a Kmeans algorithm, and determining the cluster center of each cluster as a summer typical day; wherein, for a summer typical day with hourly temperature higher than that of the summer air conditioning outdoor calculation day, the hourly temperature of the summer air conditioning outdoor calculation day is determined as the hourly temperature of the summer typical day; removing the winter air conditioning outdoor calculation day, and dividing winter dates with different hourly temperatures from that of the winter air conditioning outdoor calculation day into K2 clusters by using a Kmeans algorithm, and determining the cluster center of each cluster as a winter typical day; wherein, for a winter typical day with hourly temperature lower than that of the winter air conditioning outdoor calculation day, the hourly temperature of the winter air conditioning outdoor calculation day is determined as the hourly temperature of the winter typical day; the energy system comprises a heat pump device and an energy storage device; the energy system and the heat pump device work in four working conditions: refrigeration condition, cold storage condition, heating condition and heat storage condition, and at any time, the energy system and the heat pump device can work in only one working condition; at any time, the working mode of the heat pump device is the same as that of the energy storage device, if the working mode of the energy storage device is energy storage mode, the heat pump device is in heat storage or cold storage mode; if the working mode of the energy storage device is energy supply mode, the heat pump device is in heat supply or cooling mode.

2. The method of claim 1, wherein, determining operation constraints of the energy system, comprising: at any time, determining a power instruction of the heat pump device according to a maximum power of the heat pump device and a number of the heat pump devices in the energy system; determining a maximum power of the heat pump device according to an output power of the heat pump device; determining an energy consumption ratio of the heat pump device according to an energy consumption ratio of the heat pump device at an ambient temperature and a water outlet temperature; and determining an electricity consumption power of the heat pump device according to the power instruction and the energy consumption ratio of the heat pump device.

3. The method of claim 1, wherein, the cost constraint of the energy system is: min(C inv +C run ) where C inv is the annual equivalent investment cost of the energy system, C run is the annual operating cost of the energy system.

4. The method of claim 1, wherein, determining energy storage constraints of the energy system, comprising: at any time, determining an energy storage power of the energy storage device according to the power instruction of the heat pump device, an operation mode and a maximum energy storage power of the energy storage device in the energy system; determining an energy supply power of the energy storage device according to the operation mode and the maximum energy supply power of the energy storage device; determining a power of the energy storage device according to the energy supply power and the energy storage power of the energy storage device; and determining an energy storage amount of the energy storage device according to the energy storage amount of the energy storage device at a previous time, a time period, an energy storage efficiency, the energy supply power, the energy storage power and the power of the energy storage device at the time; wherein the energy storage device comprises a heat storage device and a cold storage device; the operation mode of the energy storage device comprises an energy supply mode and an energy storage mode; and the energy storage device cannot be in the energy supply mode and the energy storage mode at the same time.

5. The method of claim 1, wherein, determining load constraints of the energy system according to the hourly cooling load data and the hourly heating load data of the typical day, comprising: at any time, determining a heating load data of the energy system according to an energy supply power of the heat storage device, a heating power instruction of the heat pump device and a heating power of devices other than the heat storage device in the energy system; and determining a cooling load data of the energy system according to an energy supply power of the cold storage device, a cooling power instruction of the heat pump device and a cooling power of devices other than the cold storage device in the energy system.

6. An energy system parameter optimization apparatus comprising an air-cooled heat pump, characterized by, comprising: a typical day determining module, configured to determine a typical day and hourly temperature data, hourly cooling load data and hourly heating load data of the energy system in the typical day according to historical temperature data of the energy system; a performance parameter determining module, configured to determine performance parameters of the energy system according to a change relationship of refrigerating capacity of the energy system with water supply temperature and ambient temperature, a change relationship of refrigerating energy consumption ratio of the energy system with water supply temperature and ambient temperature, a change relationship of heating capacity of the energy system with water supply temperature and ambient temperature, and a change relationship of heating energy consumption ratio of the energy system with water supply temperature and ambient temperature; an energy consumption ratio determining module, configured to determine hourly refrigerating capacity, hourly heating capacity, refrigerating energy consumption ratio and heating energy consumption ratio of the energy system in the typical day according to the hourly temperature data, the hourly cooling load data, the hourly heating load data of the energy system in the typical day and the performance parameters of the energy system; an operation constraint module, configured to determine operation constraints of the energy system; an energy storage constraint module, configured to determine energy storage constraints of the energy system; and a load constraint module configured to determine a load constraint condition of the energy system according to the hourly cooling load data and the hourly heating load data of the typical day; an optimization module configured to optimize a device parameter in the energy system according to the operation constraint condition, the energy storage constraint condition, the load constraint condition, and a cost constraint condition of the energy system; determining a typical day and the hourly temperature data, the hourly cooling load data and the hourly heating load data of the energy system in the typical day according to historical temperature data of the energy system, comprising: determining a summer typical day as a summer date with the same hourly temperature as an hourly temperature of a summer air conditioning outdoor calculation day, and determining a winter typical day as a winter date with the same hourly temperature as an hourly temperature of a winter air conditioning outdoor calculation day; removing the summer air conditioning outdoor calculation day, and using a Kmeans algorithm to divide summer dates with different hourly temperatures from the hourly temperature of the summer air conditioning outdoor calculation day into K1 clusters, and determining a cluster center of each cluster as a summer typical day; wherein, for a summer typical day with an hourly temperature higher than the hourly temperature of the summer air conditioning outdoor calculation day, the hourly temperature of the summer air conditioning outdoor calculation day is determined as the hourly temperature of the summer typical day; removing the winter air conditioning outdoor calculation day, and using a Kmeans algorithm to divide winter dates with different hourly temperatures from the hourly temperature of the winter air conditioning outdoor calculation day into K2 clusters, and determining a cluster center of each cluster as a winter typical day; wherein, for a winter typical day with an hourly temperature lower than the hourly temperature of the winter air conditioning outdoor calculation day, the hourly temperature of the winter air conditioning outdoor calculation day is determined as the hourly temperature of the winter typical day; the energy system comprises a heat pump device and an energy storage device; the energy system and the heat pump device work in four working conditions: a refrigeration working condition, a cold storage working condition, a heating working condition, and a heat storage working condition, and at any moment, the energy system and the heat pump device can work in only one working condition; at any moment, a working mode of the heat pump device is the same as a working mode of the energy storage device, if the working mode of the energy storage device is an energy storage mode, the heat pump device is in a heat storage or cold storage mode; if the working mode of the energy storage device is an energy supply mode, the heat pump device is in a heat supply or cold supply mode.

7. The apparatus of claim 6, wherein, the operation constraint module is specifically configured to: at any moment, determine a power instruction of the heat pump device according to a maximum power of the heat pump device and a number of the heat pump devices in the energy system; determine the maximum power of the heat pump device according to an output power of the heat pump device; determine an energy consumption ratio of the heat pump device according to an energy consumption ratio of the heat pump device under an ambient temperature and a water outlet temperature; and determine an electricity consumption power of the heat pump device according to the power instruction and the energy consumption ratio of the heat pump device.

8. The apparatus of claim 6, wherein, the optimization module is further configured to: determine the cost constraint condition of the energy system as: min(C inv +C run ) where C inv is the annual equivalent investment cost of the energy system, C run is the annual operating cost of the energy system.

9. The apparatus of claim 6, wherein, the energy storage constraint module is specifically configured to: At any moment, the energy storage power of the energy storage device is determined according to the power instruction of the heat pump device in the energy system, the working mode of the energy storage device in the energy system and the maximum energy storage power; the energy supply power of the energy storage device is determined according to the working mode of the energy storage device and the maximum energy supply power; 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; the heat storage amount of the energy storage device is determined according to the heat storage amount of the energy storage device at the last moment, the time period, the energy storage efficiency, the energy supply power, the energy storage power and the power of the energy storage device at the moment; wherein the energy storage device comprises a heat storage device and a cold storage device; the working mode of the energy storage device comprises an energy supply mode and an energy storage mode; the energy storage device cannot be in the energy supply mode and the energy storage mode at the same time.

10. The apparatus of claim 6, wherein, The load constraint module is specifically used for: At any moment, the heat load data of the energy system is determined according to the energy supply power of the heat storage device in the energy system, the heating power instruction of the heat pump device and the heating power of the devices other than the heat storage device in the energy system; the cold load data of the energy system is determined according to the energy supply power of the cold storage device in the energy system, the cooling power instruction of the heat pump device and the cooling power of the devices other than the cold storage device in the energy system.

11. 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 executes the computer program to realize the method in any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method in any one of claims 1 to 5.

13. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to realize the method in any one of claims 1 to 5.

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