Energy collaborative optimization control method and device for smart park
By establishing load prediction, power generation prediction and energy storage prediction models, and combining genetic algorithms to optimize energy distribution strategies, the problem of insufficient effectiveness of the energy scheduling scheme in smart parks is solved, which improves energy utilization and reduces operating costs.
Patent Information
- Application Number
- CN202510719387.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-30
- Publication Date
- 2025-08-12
AI Technical Summary
The existing smart park energy scheduling optimization solution has reduced energy utilization and increased operating costs due to inconsistent data formats and communication protocols between various energy systems.
By obtaining the historical operation data, historical meteorological data, real-time operation data and real-time meteorological data of the target smart park, a load prediction model, a power generation prediction model and an energy storage prediction model are established, and combined with the genetic algorithm to optimize the cost of electricity, the energy allocation strategy is determined.
Accurately determine the energy operation status within the target period, improve energy utilization, reduce operating costs, and achieve the effectiveness of energy scheduling optimization.
Smart Images
Figure CN120471230A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of energy management technology, and in particular to a method and device for collaborative energy optimization control of a smart park. Background Art
[0002] The threat posed by global climate change to ecosystems and human life is becoming increasingly serious. Against the backdrop of the global economy's transition to a low-carbon economy, more and more industrial parks are responding to climate change by reducing greenhouse gas emissions.
[0003] As a major source of carbon emissions, optimizing energy scheduling in industrial smart parks has become a crucial step in the low-carbon transition. However, due to factors such as inconsistent data formats and communication protocols across energy systems, current smart park energy scheduling optimization solutions struggle to accurately predict the overall operation of smart parks. This, in turn, results in insufficient effectiveness of scheduling optimization solutions, reduced energy utilization, and increased operating costs for smart parks.
[0004] Therefore, how to improve the effectiveness of energy scheduling optimization solutions for smart parks, improve energy utilization rates for smart parks, and reduce operating costs has become a technical problem that needs to be solved urgently. Summary of the Invention
[0005] In view of this, it is necessary to provide an energy collaborative optimization control method and device for a smart park to solve the problem that the current smart park energy scheduling optimization solution is insufficient in effectiveness, resulting in reduced energy utilization and increased operating costs in the smart park.
[0006] In order to solve the above problems, in a first aspect, the present invention provides an energy collaborative optimization control method for a smart park, comprising: Obtain historical operation data, historical meteorological data, real-time operation data, and real-time meteorological data of the target smart park; Using historical operating data and real-time meteorological data as inputs to the load forecasting model, the load of the target smart park within the target period is obtained. Using historical operating data and historical meteorological data as inputs to the power generation forecasting model, the power generation of the target smart park within the target period is obtained. Using historical operating data and real-time operating data as inputs to the energy storage forecasting model, the energy storage capacity of the target smart park within the target period is obtained. With the lowest electricity cost as the optimization goal, the energy allocation strategy of the target smart park during the target period is determined based on the load, power generation and storage capacity of the target smart park during the target period, as well as the electricity price during the target period; Among them, historical operation data and real-time operation data include at least one of the following: cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cold storage capacity, chiller COP, water pump power consumption, and cooling unit power consumption. The load forecasting model and the power generation forecasting model are trained based on historical operation data and historical meteorological data. The energy storage forecasting model is trained based on historical operation data. The target period is a preset period in the future.
[0007] In one possible implementation, the optimization objective is to minimize electricity cost, and based on the load, power generation, and energy storage capacity of the target smart park during the target period, and the electricity price during the target period, an energy allocation strategy for the target smart park during the target period is determined, including: Determine the power consumption of the target smart park during the target period based on the load, power generation, and energy storage capacity of the target smart park during the target period; Construct an electricity cost function based on the electricity price during the target period and the electricity consumption of the smart park during the target period; Based on the constructed electricity cost function, with the lowest electricity cost as the optimization goal, the genetic algorithm is combined to determine the load, power generation and energy storage capacity of the target smart park at each moment in the target period.
[0008] In one possible implementation, the load of the target smart park during the target period includes the cooling and heating load and the electricity load of the target smart park during the target period.
[0009] In a possible implementation, the method further includes: With the goal of minimizing the energy consumption of the water system of the target smart park, the water system control parameters of the target smart park are determined based on the cooling and heating loads of the target smart park at each moment during the target period. The water system of the target smart park includes a chiller system, a water pump system and a cooling tower system. The water system control parameters of the target smart park include the number of operating chillers, the outlet water temperature of the chiller, the number of operating water pumps, the supply and return water temperature difference of the water pump, the number of operating cooling towers and the cooling tower water temperature.
[0010] In one possible implementation, determining the water system control parameters of the target smart park includes: Using the chiller's outlet water temperature and cooling water temperature constraints, the water pump's flow constraint, and the cooling tower's air volume constraint as constraint functions, the water system control parameters of the target smart park are determined. The chiller's outlet water temperature and cooling water temperature constraints, the pump's flow rate constraints, and the cooling tower's air volume constraints are determined based on the following formulas:
[0011]
[0012]
[0013] in, Indicates the outlet water temperature of the chiller. represents the outdoor air wet-bulb temperature, Indicates the cooling water temperature of the chiller. Indicates the rated flow of the pump. Indicates the flow rate of the pump, Indicates the rated air volume of the cooling tower. Indicates the air volume of the cooling tower.
[0014] In one possible implementation, the cooling and heating loads of the target smart park during the target period are obtained by taking the historical cooling and heating loads, real-time outdoor temperature, real-time relative humidity, and real-time outdoor dew point temperature as inputs of the load forecasting model and outputting the load forecasting model.
[0015] In one possible implementation, the electric load of the target smart park during the target period is obtained by taking the historical electric load, real-time outdoor temperature, and real-time outdoor dew point temperature as inputs of the load forecasting model and outputting the load forecasting model.
[0016] On the other hand, the present invention also provides an energy collaborative optimization control device for a smart park, comprising: An acquisition module is used to obtain historical operation data, historical meteorological data, real-time operation data, and real-time meteorological data of the target smart park; A prediction module is used to use historical operating data and real-time meteorological data as inputs to a load prediction model to obtain the load of a target smart park during a target period, and to use historical operating data and historical meteorological data as inputs to a power generation prediction model to obtain the power generation of the target smart park during a target period. Historical operating data and real-time operating data are used as inputs to an energy storage prediction model to obtain the energy storage capacity of the target smart park during a target period. A determination module is used to determine the energy allocation strategy of the target smart park within the target period based on the load, power generation and storage capacity of the target smart park within the target period and the electricity price within the target period, with the lowest electricity cost as the optimization goal; Among them, historical operation data and real-time operation data include at least one of the following: cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cold storage capacity, chiller COP, water pump power consumption, and cooling unit power consumption. The load forecasting model and the power generation forecasting model are trained based on historical operation data and historical meteorological data. The energy storage forecasting model is trained based on historical operation data. The target period is a preset period in the future.
[0017] In a second aspect, the present invention further provides a control device, comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the energy collaborative optimization control method of the smart park described in any of the above implementation methods.
[0018] In a third aspect, the present invention also provides a computer-readable storage medium for storing computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the energy collaborative optimization control method for a smart park described in any of the above-mentioned implementation methods.
[0019] The beneficial effects of the present invention are: the energy collaborative optimization control method and device for the smart park provided by the present invention predict the energy operation status of the target smart park through the load prediction model, the power generation prediction model and the energy storage prediction model, and can accurately determine the energy operation status of the target smart park within the target period, providing a basis for subsequent energy scheduling optimization, and then, based on the energy operation status of the target smart park within the target period, considering the dynamic electricity price, and taking the lowest electricity cost as the optimization goal, determining the energy allocation strategy of the target smart park within the target period, can ensure the effectiveness of energy scheduling optimization, thereby improving the energy utilization rate of the smart park and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 A schematic flow chart of an embodiment of the energy collaborative optimization control method for a smart park provided by the present invention; Figure 2 A schematic flow chart of an embodiment of the energy collaborative optimization control process for a smart park provided by the present invention; Figure 3 A schematic structural diagram of an embodiment of the energy collaborative optimization control device for a smart park provided by the present invention; Figure 4 This is a schematic structural diagram of an embodiment of the control device provided by the present invention. DETAILED DESCRIPTION
[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.
[0022] In the description of the embodiments of the present invention, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.
[0023] The terms "first," "second," and so on, used in the embodiments of the present invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, technical features designated as "first" or "second" may explicitly or implicitly include at least one such feature.
[0024] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.
[0025] The present invention provides a method and device for collaborative energy optimization control of a smart park, which are described below.
[0026] Figure 1 A flow chart of an embodiment of the energy collaborative optimization control method for a smart park provided by the present invention is shown as follows: Figure 1 As shown in Figure 2, the energy collaborative optimization control method for smart parks includes: S101. Obtain historical operation data, historical meteorological data, real-time operation data, and real-time meteorological data of a target smart park.
[0027] It should be noted that the operating data of the target smart park may include cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cooling capacity, chiller COP, water pump power consumption, cooling unit power consumption, etc. Meteorological data may include outdoor temperature, relative humidity, outdoor dew point temperature, etc.
[0028] S102. Use historical operating data and real-time meteorological data as inputs to the load forecasting model to obtain the load of the target smart park during the target period. Use historical operating data and historical meteorological data as inputs to the power generation forecasting model to obtain the power generation of the target smart park during the target period. Use historical operating data and real-time operating data as inputs to the energy storage forecasting model to obtain the energy storage capacity of the target smart park during the target period.
[0029] It should be noted that by predicting the energy operation status of the target smart park through the load forecasting model, power generation forecasting model and energy storage forecasting model, the energy operation status of the target smart park in the future can be accurately determined, providing a basis for subsequent energy scheduling optimization.
[0030] S103. Taking the lowest electricity cost as the optimization goal, based on the load, power generation and storage capacity of the target smart park during the target period, and the electricity price during the target period, determine the energy allocation strategy of the target smart park during the target period.
[0031] It should be noted that by taking the energy operation status of the target smart park during the target period as the basis, considering the dynamic electricity price, and taking the lowest electricity cost as the optimization goal, the energy allocation strategy of the target smart park during the target period is determined, which can ensure the effectiveness of energy scheduling optimization, thereby improving the energy utilization rate of the smart park and reducing operating costs.
[0032] Among them, historical operation data and real-time operation data include at least one of the following: cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cold storage capacity, chiller COP, water pump power consumption, and cooling unit power consumption. The load forecasting model and the power generation forecasting model are trained based on historical operation data and historical meteorological data. The energy storage forecasting model is trained based on historical operation data. The target period is a preset period in the future.
[0033] It should be noted that the load forecasting model and power generation forecasting model are obtained by training with historical operating data and historical meteorological data, and the energy storage forecasting model is obtained by training with historical operating data. This can ensure the accuracy of the forecasting data and thus ensure the effectiveness of energy scheduling optimization.
[0034] To sum up, the energy collaborative optimization control method for a smart park provided by an embodiment of the present invention predicts the energy operation status of a target smart park through a load prediction model, a power generation prediction model, and an energy storage prediction model, and can accurately determine the energy operation status of the target smart park within the target period, providing a basis for subsequent energy scheduling optimization. Then, based on the energy operation status of the target smart park within the target period, considering dynamic electricity prices, and taking the lowest electricity cost as the optimization goal, the energy allocation strategy of the target smart park within the target period is determined, which can ensure the effectiveness of energy scheduling optimization, thereby improving the energy utilization rate of the smart park and reducing operating costs.
[0035] In some embodiments of the present invention, the optimization objective of minimizing electricity cost is to determine the energy allocation strategy of the target smart park within the target period based on the load, power generation, and energy storage capacity of the target smart park within the target period, and the electricity price within the target period, including: Determine the power consumption of the target smart park during the target period based on the load, power generation, and energy storage capacity of the target smart park during the target period; Construct an electricity cost function based on the electricity price during the target period and the electricity consumption of the smart park during the target period; Based on the constructed electricity cost function, with the lowest electricity cost as the optimization goal, the genetic algorithm is combined to determine the load, power generation and energy storage capacity of the target smart park at each moment in the target period.
[0036] It should be noted that: when determining the energy allocation strategy of the target smart park within the target period based on the load, power generation and energy storage of the target smart park within the target period, and the electricity price within the target period, the electricity consumption of the target smart park within the target period can be determined based on the load, power generation and energy storage of the target smart park within the target period, and then an electricity cost function is constructed based on the electricity price within the target period and the electricity consumption of the smart park within the target period. Then, through the constructed electricity cost function, with the lowest electricity cost as the optimization goal, the load, power generation and energy storage of the target smart park at each moment within the target period are determined in combination with the genetic algorithm, thereby improving the overall efficiency of the system, reducing operating costs, and improving the utilization rate of renewable energy.
[0037] For example, the power generation side can reduce fuel costs and backup needs by dynamically adjusting the start and shutdown and output plans of units; the load side can respond to load reduction during high-price periods and load transfer during low-price periods through peak shaving and valley filling and peak shifting measures, thereby reducing users' electricity costs; the energy storage side can optimize charging and discharging strategies according to electricity price fluctuations, charging when prices are low and discharging when prices are high, thereby maximizing economic benefits.
[0038] In some embodiments of the present invention, the load of the target smart park during the target period includes the cooling and heating load and the electricity load of the target smart park during the target period.
[0039] It should be noted that by dividing the load into cooling and heating load and electricity load, a basis can be provided for the subsequent optimization control of the central air-conditioning water system.
[0040] In some embodiments of the present invention, the method further comprises: With the goal of minimizing the energy consumption of the water system of the target smart park, the water system control parameters of the target smart park are determined based on the cooling and heating loads of the target smart park at each moment during the target period. The water system of the target smart park includes a chiller system, a water pump system and a cooling tower system. The water system control parameters of the target smart park include the number of operating chillers, the outlet water temperature of the chiller, the number of operating water pumps, the supply and return water temperature difference of the water pump, the number of operating cooling towers and the cooling tower water temperature.
[0041] It should be noted that the control parameters of the chiller, water pump and cooling tower affect each other. For example, an increase in the outlet water temperature of the chiller will improve the efficiency of the chiller and reduce the energy consumption of the chiller, but it will also lead to more chilled water flow requirements and increase the energy consumption of the chilled water pump. Reducing the number of cooling towers in operation and reducing the operating frequency of the cooling tower fans can effectively reduce the energy consumption of the cooling tower, but it will lead to an increase in the cooling water return temperature, which in turn will increase the condensing pressure of the chiller, resulting in a decrease in the efficiency of the refrigeration unit and an increase in the energy consumption of the chiller at the same cooling capacity. The present invention determines the control parameters of the water system of the target smart park based on the cold and hot loads of the target smart park at each moment within the target period, and can achieve optimized energy saving of the water system and further reduce operating costs.
[0042] In some embodiments of the present invention, determining the water system control parameters of the target smart park includes: Using the chiller's outlet water temperature and cooling water temperature constraints, the water pump's flow constraint, and the cooling tower's air volume constraint as constraint functions, the water system control parameters of the target smart park are determined. The chiller's outlet water temperature and cooling water temperature constraints, the pump's flow rate constraints, and the cooling tower's air volume constraints are determined based on the following formulas:
[0043]
[0044]
[0045] in, Indicates the outlet water temperature of the chiller. represents the outdoor air wet-bulb temperature, Indicates the cooling water temperature of the chiller. Indicates the rated flow of the pump. Indicates the flow rate of the pump, Indicates the rated air volume of the cooling tower. Indicates the air volume of the cooling tower.
[0046] It should be noted that when determining the water system control parameters of the target smart park, the above constraint functions can be used to ensure the normal operation of the system.
[0047] In some embodiments of the present invention, historical heating and cooling loads, real-time outdoor temperature, real-time relative humidity, and real-time outdoor dew point temperature can be used as inputs to a load forecasting model, and the load forecasting model outputs the heating and cooling loads of a target smart park within a target period.
[0048] In some embodiments of the present invention, historical electricity load, real-time outdoor temperature, and real-time outdoor dew point temperature can be used as inputs to a load forecasting model, and the load forecasting model outputs the electricity load of a target smart park within a target period.
[0049] Combine Figure 2 From the above, the energy collaborative optimization control process of the present invention specifically includes the following steps: 1. The hourly power generation, cooling and heating loads, and electric loads are obtained through the power generation model, cooling and heating load forecasting model, and electric load forecasting model.
[0050] 2. Combined with electricity price data and energy storage data, with the goal of optimal economic performance, determine the hourly electrochemical storage / discharge capacity, hourly cooling / discharge capacity, hourly cooling and heating loads, hourly total electricity consumption, and hourly total electricity costs.
[0051] 3. By analyzing the hourly cooling and heating loads, combined with the water pump performance model, cooling tower performance model and water pump performance model, the water system control parameters including the chilled water return temperature difference, cooling water return temperature difference, chiller outlet water temperature and cooling tower return water temperature are determined.
[0052] The present invention can achieve multiple goals: optimizing power output at the power generation end, reducing transmission and distribution losses in the power grid, flexible user response, and efficient operation of the energy storage system, thereby improving the overall efficiency of the system, reducing operating costs, and increasing the utilization rate of renewable energy.
[0053] In order to better implement the energy collaborative optimization control method of the smart park in the embodiment of the present invention, based on the energy collaborative optimization control method of the smart park, correspondingly, Figure 3 As shown, an embodiment of the present invention further provides an energy collaborative optimization control device for a smart park. The energy collaborative optimization control device 300 for a smart park includes: An acquisition module 301 is used to acquire historical operation data, historical meteorological data, real-time operation data, and real-time meteorological data of a target smart park; Prediction module 302 is configured to use historical operating data and real-time meteorological data as inputs to a load prediction model to determine the load of a target smart park during a target period, use historical operating data and historical meteorological data as inputs to a power generation prediction model to determine the power generation of the target smart park during the target period, and use historical operating data and real-time operating data as inputs to an energy storage prediction model to determine the energy storage capacity of the target smart park during the target period. The determination module 303 is configured to determine an energy allocation strategy for the target smart park within the target period based on the load, power generation, and energy storage of the target smart park within the target period, and the electricity price within the target period, with the lowest electricity cost as the optimization goal; Among them, historical operation data and real-time operation data include at least one of the following: cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cold storage capacity, chiller COP, water pump power consumption, and cooling unit power consumption. The load forecasting model and the power generation forecasting model are trained based on historical operation data and historical meteorological data. The energy storage forecasting model is trained based on historical operation data. The target period is a preset period in the future.
[0054] The energy collaborative optimization control device 300 for a smart park provided in the above embodiment can implement the technical solution described in the above embodiment of the energy collaborative optimization control method for a smart park. The specific implementation principles of the above modules or units can be found in the corresponding contents in the above embodiment of the energy collaborative optimization control method for a smart park, and will not be repeated here.
[0055] like Figure 4 As shown, the present invention also provides a control device 400. The control device 400 includes a processor 401, a memory 402 and a display 403. Figure 4 Only some of the components of the control device 400 are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.
[0056] In some embodiments, the processor 401 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 402 , such as the magnetic resonance image optimization method of the present invention.
[0057] In some embodiments, processor 401 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, multiple clouds, or any combination thereof.
[0058] In some embodiments, the memory 402 may be an internal storage unit of the control device 400, such as a hard disk or memory of the control device 400. In other embodiments, the memory 402 may also be an external storage device of the control device 400, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the control device 400.
[0059] Furthermore, the memory 402 may include both an internal storage unit of the control device 400 and an external storage device. The memory 402 is used to store application software installed in the control device 400 and various data.
[0060] In some embodiments, display 403 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen. Display 403 is used to display information on control device 400 and to display a visual user interface. Components 401-403 of control device 400 communicate with each other via a system bus.
[0061] In one embodiment, when the processor 401 executes the smart park energy collaborative optimization control program in the memory 402, the following steps may be implemented: Obtain historical operation data, historical meteorological data, real-time operation data, and real-time meteorological data of the target smart park; Using historical operating data and real-time meteorological data as inputs to the load forecasting model, the load of the target smart park within the target period is obtained. Using historical operating data and historical meteorological data as inputs to the power generation forecasting model, the power generation of the target smart park within the target period is obtained. Using historical operating data and real-time operating data as inputs to the energy storage forecasting model, the energy storage capacity of the target smart park within the target period is obtained. With the lowest electricity cost as the optimization goal, the energy allocation strategy of the target smart park during the target period is determined based on the load, power generation and storage capacity of the target smart park during the target period, as well as the electricity price during the target period; Among them, historical operation data and real-time operation data include at least one of the following: cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cold storage capacity, chiller COP, water pump power consumption, and cooling unit power consumption. The load forecasting model and the power generation forecasting model are trained based on historical operation data and historical meteorological data. The energy storage forecasting model is trained based on historical operation data. The target period is a preset period in the future.
[0062] It should be understood that when the processor 401 executes the energy collaborative optimization control program of the smart park in the memory 402, in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.
[0063] Furthermore, the embodiments of the present invention do not specifically limit the type of control device 400. The control device 400 may be a portable electronic device such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in other embodiments of the present invention, the control device 400 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).
[0064] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions of the energy collaborative optimization control method of the smart park provided by the above-mentioned method embodiments.
[0065] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.
[0066] The above is a detailed introduction to the energy collaborative optimization control method and device for the smart park provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. A method for energy collaborative optimization control in a smart park, characterized in that: include: Obtain historical operation data, historical meteorological data, real-time operation data, and real-time meteorological data of the target smart park; Using historical operating data and real-time meteorological data as inputs to the load forecasting model, the load of the target smart park within the target period is obtained. Using historical operating data and historical meteorological data as inputs to the power generation forecasting model, the power generation of the target smart park within the target period is obtained. Using historical operating data and real-time operating data as inputs to the energy storage forecasting model, the energy storage capacity of the target smart park within the target period is obtained. With the lowest electricity cost as the optimization goal, the energy allocation strategy of the target smart park during the target period is determined based on the load, power generation and storage capacity of the target smart park during the target period, as well as the electricity price during the target period; Among them, historical operation data and real-time operation data include at least one of the following: cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cold storage capacity, chiller COP, water pump power consumption, and cooling unit power consumption. The load forecasting model and the power generation forecasting model are trained based on historical operation data and historical meteorological data. The energy storage forecasting model is trained based on historical operation data. The target period is a preset period in the future.
2. The energy collaborative optimization control method for a smart park according to claim 1 is characterized in that: The optimization goal is to minimize electricity cost, determine the energy allocation strategy of the target smart park within the target period based on the load, power generation and storage capacity of the target smart park within the target period, and the electricity price within the target period, including: Determine the power consumption of the target smart park during the target period based on the load, power generation, and energy storage capacity of the target smart park during the target period; Construct an electricity cost function based on the electricity price during the target period and the electricity consumption of the smart park during the target period; Based on the constructed electricity cost function, with the lowest electricity cost as the optimization goal, the genetic algorithm is combined to determine the load, power generation and energy storage capacity of the target smart park at each moment in the target period.
3. The energy collaborative optimization control method for a smart park according to claim 2 is characterized in that: The load of the target smart park during the target period includes the cooling and heating load and the electricity load of the target smart park during the target period.
4. The energy collaborative optimization control method for a smart park according to claim 3 is characterized in that: The method further comprises: With the goal of minimizing the energy consumption of the water system of the target smart park, the water system control parameters of the target smart park are determined based on the cooling and heating loads of the target smart park at each moment during the target period. The water system of the target smart park includes a chiller system, a water pump system and a cooling tower system. The water system control parameters of the target smart park include the number of operating chillers, the outlet water temperature of the chiller, the number of operating water pumps, the supply and return water temperature difference of the water pump, the number of operating cooling towers and the cooling tower water temperature.
5. The energy collaborative optimization control method for a smart park according to claim 4 is characterized in that: Determining the water system control parameters of the target smart park includes: Using the chiller's outlet water temperature and cooling water temperature constraints, the water pump's flow constraint, and the cooling tower's air volume constraint as constraint functions, the water system control parameters of the target smart park are determined. The chiller's outlet water temperature and cooling water temperature constraints, the pump's flow rate constraints, and the cooling tower's air volume constraints are determined based on the following formulas: in, Indicates the outlet water temperature of the chiller. represents the outdoor air wet-bulb temperature, Indicates the cooling water temperature of the chiller. Indicates the rated flow of the pump. Indicates the flow rate of the pump, Indicates the rated air volume of the cooling tower. Indicates the air volume of the cooling tower.
6. The energy collaborative optimization control method for a smart park according to claim 3 is characterized in that: The cooling and heating loads of the target smart park during the target period are obtained by taking the historical cooling and heating loads, real-time outdoor temperature, real-time relative humidity, and real-time outdoor dew point temperature as inputs of the load forecasting model and outputting them from the load forecasting model.
7. The energy collaborative optimization control method for a smart park according to claim 3 is characterized in that: The electric load of the target smart park during the target period is obtained by taking the historical electric load, real-time outdoor temperature and real-time outdoor dew point temperature as inputs of the load forecasting model and outputting it.
8. An energy collaborative optimization control device for a smart park, characterized in that: include: An acquisition module is used to obtain historical operation data, historical meteorological data, real-time operation data, and real-time meteorological data of the target smart park; A prediction module is used to use historical operating data and real-time meteorological data as inputs to a load prediction model to obtain the load of a target smart park during a target period, and to use historical operating data and historical meteorological data as inputs to a power generation prediction model to obtain the power generation of the target smart park during a target period. Historical operating data and real-time operating data are used as inputs to an energy storage prediction model to obtain the energy storage capacity of the target smart park during a target period. A determination module is used to determine the energy allocation strategy of the target smart park within the target period based on the load, power generation and storage capacity of the target smart park within the target period and the electricity price within the target period, with the lowest electricity cost as the optimization goal; Among them, historical operation data and real-time operation data include at least one of the following: cooling and heating loads, electrical loads, photovoltaic power generation, wind power generation, battery power, water tank cold storage capacity, chiller COP, water pump power consumption, and cooling unit power consumption. The load forecasting model and the power generation forecasting model are trained based on historical operation data and historical meteorological data. The energy storage forecasting model is trained based on historical operation data. The target period is a preset period in the future.
9. A control device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the energy collaborative optimization control method of the smart park as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the energy collaborative optimization control method for a smart park as described in any one of claims 1 to 7.