Air Conditioning Resource Management and Control Method Based on Cloud Platform for New Energy Consumption

Abstract

This invention claims protection for a method for air conditioning resource management and control based on a cloud platform for renewable energy consumption, comprising: step 1) constructing a cloud platform for renewable energy consumption to realize information interaction of air conditioning systems; step 2) establishing an energy consumption assessment model for air conditioning groups, constructing a consumption task allocation problem for aggregated groups under the conditions of ensuring completion and user comfort, and achieving the optimal solution; step 3) based on this structure, dividing the air conditioners into several aggregated groups according to the similarity of parameters, with each group collaboratively completing the consumption task, and controlling the air conditioners within the group through a predictive control model. This invention proposes a method for air conditioning resource management and control based on a cloud platform for renewable energy consumption, considering the establishment of a cloud platform to manage distributed air conditioning systems, fully leveraging the air conditioning system to improve the utilization rate of clean energy in the power grid, thereby effectively improving the economic efficiency and flexibility of system operation, and enhancing its ability to absorb renewable energy.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning energy utilization planning, specifically involving a method for air conditioning resource management and control based on a cloud platform for new energy consumption. Background Technology

[0002] Against the backdrop of the "dual carbon" goal of large-scale penetration of new and clean energy, the intermittent and unpredictable nature of new energy sources can impact the stability and operational safety of the power system. Therefore, an effective method for integrating new energy sources is needed to ensure the stable operation of the power system. The air conditioning resource management and control method based on a cloud platform for new energy integration is a complex system involving multiple technologies and disciplines, including new energy technologies, cloud computing, the Internet of Things, big data analytics, and control theory. The main objective of this method is to improve energy efficiency by refining the management and control of air conditioning resources, thereby ensuring the more effective utilization of new energy sources and guaranteeing the normal operation of air conditioning systems.

[0003] The core of this method is a cloud-based resource management platform. This platform can collect and analyze various data, including the generation and consumption of new energy sources, the operating status of air conditioners, and energy consumption. Through cloud computing and big data analysis, the platform can predict the supply and demand of new energy sources and the energy consumption of air conditioners in real time, and then formulate optimal control strategies. These strategies may include controlling the on / off status of air conditioners, adjusting operating modes, and even adjusting indoor temperature. In this way, the normal operation of air conditioners can be ensured, while new energy sources can be effectively utilized, achieving the goals of energy conservation and environmental protection.

[0004] Furthermore, this method requires an effective control system to enable remote monitoring and control of the air conditioning system. The control system is typically based on Internet of Things (IoT) technology, which can collect and transmit data in real time via wireless networks, allowing control strategies to be executed in real time. This air conditioning resource management and control method based on a cloud platform for renewable energy consumption may face many challenges in practical applications, such as data accuracy and real-time performance, the effectiveness and execution of control strategies, and system stability and security. Therefore, continuous technological innovation and system optimization are still necessary.

[0005] CN111190355B discloses a cloud platform-based method and system for joint control of air conditioners and water heaters. The method includes: acquiring air conditioner parameters for each air conditioner in the cloud platform; dividing the air conditioners into M groups based on the air conditioner parameters; determining a first estimated aggregate power using a Monte Carlo model with parameter probability distribution; acquiring water heater parameters for each water heater in the cloud platform; dividing the water heaters into L groups based on the water heater parameters; determining a second estimated aggregate power using a Monte Carlo model with parameter probability distribution; determining the absorption task for each group of air conditioners and the absorption task for each group of water heaters based on the estimated aggregate power; determining a first control signal using sliding mode control law based on the absorption task for each group of air conditioners; determining a second control signal based on the absorption task for each group of water heaters; and controlling the air conditioners and water heaters based on the first and second control signals to complete clean energy absorption, improve clean energy utilization, and ensure the stable operation of the power grid.

[0006] The control method used in this patent is sliding mode control. Sliding mode control often triggers high-frequency switching behavior during the control process, which may lead to increased energy loss, noise generation, and accelerated wear of mechanical components or electronic equipment. Furthermore, for power systems or electronic systems, high-frequency switching may cause electromagnetic interference, thereby affecting the normal operation of other electronic equipment.

[0007] This patent employs predictive control, whose core feature is its ability to optimize a finite future time period in each control step. This means that predictive control methods can predict the future behavior of the system and optimize the control strategy based on these predictions, making them highly suitable for handling input, output, and state constraints in practical applications. By adjusting the optimization objective, constraints, and prediction time domain, it can more intuitively regulate the controller's performance to a certain extent. Summary of the Invention

[0008] This invention aims to solve the problems of the prior art mentioned above. It proposes a method for air conditioning resource management and control based on a cloud platform for renewable energy consumption. The technical solution of this invention is as follows:

[0009] A method for managing and controlling air conditioning resources based on a cloud platform for renewable energy consumption, comprising the following steps:

[0010] Step 1) Construct a cloud platform for new energy consumption to realize information interaction of air conditioning systems;

[0011] Step 2) Establish an energy consumption assessment model for the air conditioning group. Under the conditions of ensuring completion and user comfort, construct a consumption task allocation problem for the aggregated group and achieve the optimal solution.

[0012] Step 3) Divide the air conditioners into several aggregation groups based on the similarity of parameters. Each group works together to complete the consumption task, and the air conditioners in the group are controlled by a predictive control model.

[0013] Furthermore, the cloud platform for new energy consumption in step 1) is divided into a power grid control and monitoring layer, a cloud platform management layer, a control optimization layer, and an information acquisition layer. The power grid control and monitoring layer uploads its active and reactive power target values ​​to the cloud platform management layer, while the cloud platform management layer uploads the load availability of the air conditioning system. The communication protocol adopts a common central air conditioning protocol and uses a standard OPC communication interface. The control optimization layer adjusts the control of each aggregation group according to the optimal algorithm and the collected relevant information to achieve the consumption target. Then, a predictive control model is used to control the air conditioning within the group. Sensors in the information acquisition layer collect information such as outlet water temperature, return water temperature, outdoor temperature, indoor temperature, humidity, and wind speed from each air conditioning system and upload it to the local controller. The local controller periodically executes water temperature control logic to determine the start-up and shutdown status of each unit in the aggregation group, achieving precise indoor temperature control based on an accurate building thermodynamic model.

[0014] Furthermore, step 2) establishes an energy consumption assessment model for the air conditioning group, constructs a consumption task allocation problem for the aggregated group while ensuring economy and user comfort, and achieves the optimal solution, specifically including:

[0015] The dynamic evolution of the internal temperature of the air conditioning system is determined by the indoor temperature θ. i (t) and the air conditioning on / off state variable s i The first-order differential equation of (t) describes:

[0016]

[0017] Where T a,i It is the ambient temperature, C i For the heat capacity of the i-th air conditioning unit, R i Let P be the thermal resistance of the i-th air conditioning unit. i The average power consumption of the air conditioning unit is given, assuming the ambient temperature remains constant over a period of time, and the state variable is s. i (t) Its value is switched according to the switching criteria, as follows:

[0018]

[0019] Among them, when the indoor temperature θ i (t) is greater than the set maximum upper limit temperature maxθ i When (t), s i (t) = 1 indicates that the air conditioner is on; when the indoor temperature θ i (t) is less than the set maximum lower limit temperature minθ i When (t), s i (t) = 0 indicates that the air conditioner is off;

[0020] In addition, the temperature constraints for buildings are as follows:

[0021]

[0022] Among them, T air,h and T air,c These are the room temperatures during heating and cooling, respectively.

[0023] The minimum start-stop time constraint is:

[0024]

[0025]

[0026] In the formula, This is the minimum startup hold time, meaning the system must remain operational for a period of time after startup and cannot be shut down. The minimum downtime hold time is defined as the period during which the system must remain in a non-working state and cannot be started after a shutdown.

[0027] The number of air conditioners in each group of aggregated air conditioning systems is limited to:

[0028] n min ≤n≤n max

[0029] Where n max n represents the maximum number of air conditioners. min This represents the minimum number of air conditioners required.

[0030] Then, for time t, the aggregated power consumption of the air conditioning system A is:

[0031]

[0032] Where P A (t) represents the electricity consumption of aggregate A at time t, N represents the number of air conditioners in the aggregate, and P represents the total electricity consumption. i This represents the electricity consumption of a single air conditioner within aggregate class A.

[0033] Furthermore, step 2) addresses the consumption task allocation problem for the aggregated group and aims to achieve the optimal solution, specifically including:

[0034] The problem for determining the target power is defined as follows:

[0035] f = min||P des -P i (t+Δt)||

[0036] Among them, P i (t+Δt) represents the estimated power consumption of the aggregated air conditioning system participating in load following service, P des To achieve the target of waste disposal;

[0037] To solve the power allocation problem, air conditioners are divided into aggregation groups, and an artificial immune algorithm is used for the solution. The affinity calculation formula in the algorithm is as follows:

[0038]

[0039]

[0040] f is the objective function value, A v For the affinity of antibody v, S v,s For the affinity between antibody v and s, k v,s represents the similarity between antibodies v and s, and n is the antibody length;

[0041]

[0042]

[0043] C v The concentration of antibody v is N; the number of iterations is P. r α represents the reproduction rate, and α is a parameter for evaluating the diversity of antibody v.

[0044] Furthermore, step 3) involves dividing the air conditioners into several aggregation groups based on parameter similarity. Each group collaboratively completes the consumption task, and the predictive control model is used to control the air conditioners within the group. Specifically, this includes:

[0045] In each control cycle, the system predicts and updates the control command for the next cycle based on the current real-time state, performs optimal solution based on system parameters and dynamic model constraints, updates its output sequence, and adopts the first control in the control sequence as the actual control command for each aggregated air conditioning system in the next cycle; in the next cycle, the system state update and predictive control will be repeated again.

[0046] Since the question is...

[0047] min J=||y Tin [k+1]-y Tin.ref ||2

[0048]

[0049] Where: y Tin For the model output value; y Tin.ref For output reference power; u max u min These are the upper and lower limits of the control quantity, which are the maximum and minimum controllable air conditioning power in the integrated air conditioning system;

[0050] The optimal control rate is

[0051]

[0052] Here, A and B are both N×N coefficient matrices in the aggregation class, x[k] is its state variable, and V is its perturbation variable.

[0053] The optimization strategy is solved according to the following settings:

[0054] 1) If Then let the control result

[0055] 2) If Then let the control result

[0056] 3) If Then let the control result

[0057] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the air conditioning resource management and control method based on a cloud platform for renewable energy consumption as described in any one of the claims.

[0058] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the air conditioning resource management and control method based on a cloud platform for renewable energy consumption as described in any one of the claims.

[0059] A computer program product includes a computer program that, when executed by a processor, implements the air conditioning resource management and control method based on a cloud platform for renewable energy consumption as described in any one of the claims.

[0060] The advantages and beneficial effects of this invention are as follows:

[0061] The present invention provides a cloud platform-based method for managing and controlling air conditioning resources. By refining the management and control of air conditioning resources, this method can effectively utilize new energy sources, avoid waste, and improve energy efficiency.

[0062] The main energy consumed by air conditioning systems built on cloud platforms that integrate renewable energy sources is renewable energy, such as solar and wind power, which are inexhaustible resources in nature. Compared to traditional electricity sources, using this type of energy can significantly reduce electricity costs. Furthermore, the cloud platform can monitor and analyze the operating status and energy consumption of the air conditioning system in real time, adjusting the operating mode according to actual needs to avoid unnecessary energy waste and further reduce operating costs.

[0063] Cloud-based air conditioning resource management and control methods enable more efficient utilization of renewable energy. Traditional air conditioning systems typically operate at maximum load during specific times of the day, while remaining at low load or idle for the rest of the time, leading to energy waste. Cloud platforms, however, can adjust the operating status of air conditioners in real time based on renewable energy supply and actual demand, ensuring optimal operation most of the time and thus improving energy efficiency.

[0064] Furthermore, by establishing a cloud platform for real-time monitoring and control of air conditioning operation, the normal operation of the air conditioning system can be ensured, service quality improved, air conditioning resources effectively managed and controlled, and operating costs saved. The cloud platform can accurately predict and flexibly allocate renewable energy supply, effectively solving the problems of intermittency and instability in renewable energy supply and ensuring the stable operation of the power system.

[0065] A cloud-based approach to air conditioning resource management and control, utilizing renewable energy, can achieve efficient energy utilization. Furthermore, this method offers a potential solution for the large-scale application of renewable energy and the construction of smart energy systems.

[0066] Traditional resource management typically doesn't consider building a cloud platform. Utilizing a cloud platform for centralized management and information exchange aligns with today's big data and IoT technologies, ensuring all devices can communicate with the central platform in real time, thus providing a real-time view and control of the entire system. This centralized management approach makes optimizing and controlling the entire system more convenient and efficient.

[0067] An energy consumption assessment model for the air conditioning group was established and tasks were assigned. Through optimal allocation, the system was ensured to operate in an economical, efficient, and user-satisfying state. This also provides a clear objective and guidance for the next step of aggregated control.

[0068] Predictive control models enable fine-grained control of air conditioners within each aggregation group, allowing them to work collaboratively to achieve overall optimization goals. Aggregation reduces control complexity, while predictive control ensures future considerations, resulting in better system adaptability and robustness.

[0069] This method combines cloud technology, optimization strategies, and predictive control to provide a comprehensive, efficient, and user-friendly solution for air conditioning resource management and control. Attached Figure Description

[0070] Figure 1 This is a flowchart of a preferred embodiment of the present invention, which describes a method for managing and controlling air conditioning resources based on a cloud platform for renewable energy consumption.

[0071] Figure 2This is a network topology diagram of a cloud platform based on new energy consumption, provided by a preferred embodiment of the present invention.

[0072] Figure 3 The present invention provides a flowchart of an artificial immune algorithm for solving the optimal solution of the consumption task allocation problem based on a group of aggregated air conditioning systems, according to a preferred embodiment.

[0073] Figure 4 The present invention provides a preferred embodiment of a group predictive control architecture based on a converged air conditioning system. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0075] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0076] Please see Figure 1 As shown in the figure, this application provides a control method for a central air conditioning system based on solar photovoltaic power generation in commercial buildings, including the following specific steps:

[0077] Step 1) Construct a cloud platform for new energy consumption to realize information interaction of air conditioning systems;

[0078] Step 2) Establish an energy consumption assessment model for the air conditioning group. Under the conditions of ensuring completion and user comfort, construct a consumption task allocation problem for the aggregated group and achieve the optimal solution.

[0079] Step 3) Based on this structure, the air conditioners are divided into several aggregation groups according to the similarity of parameters. Each group works together to form a consumption task, and the air conditioners in the group are controlled by a predictive control model.

[0080] This application first considers a solar photovoltaic central air conditioning system composed of 600 central air conditioning units. The construction of a cloud platform for renewable energy consumption in step S1, enabling information exchange within the air conditioning system, comprises the following steps:

[0081] The cloud platform for renewable energy consumption is structured with hierarchical management and network communication, dividing the entire platform into a power grid control and monitoring layer, a cloud platform management layer, a control optimization layer, and an information acquisition layer. Figure 2 The network topology diagram of this cloud platform based on renewable energy consumption is shown. The power grid control and supervision layer is responsible for uploading its active and reactive power target values ​​to the cloud platform management layer, while the cloud platform management layer uploads the load availability information of the air conditioning system. The communication protocol for the air conditioning system adopts a common central air conditioning protocol and uses a standard OPC communication interface.

[0082] In step S2, an energy consumption assessment model for the air conditioning group was established. Under the conditions of ensuring completion and user comfort, a consumption task allocation problem for the aggregated group was constructed, and the optimal solution was achieved.

[0083] First, the dynamic evolution of the internal temperature of the air conditioning system is influenced by factors including the indoor temperature θ. i (t) and the air conditioning on / off state variable s i The first-order differential equation of (t) is described as follows:

[0084]

[0085] Where T a,i It is the ambient temperature, C i For the heat capacity of the i-th air conditioning unit, R i Let P be the thermal resistance of the i-th air conditioning unit. i This represents the average power consumption of the air conditioning unit. It is assumed that the ambient temperature remains constant over a period of time.

[0086] State variable s i (t) Switch its value according to the switching criteria, for

[0087]

[0088] When the indoor temperature θ i (t) is greater than the set maximum upper limit temperature maxθ i When (t), s i (t) = 1 indicates that the air conditioner is on; when the indoor temperature θ i (t) is less than the set maximum lower limit temperature minθ i When (t), s i (t) = 0 indicates that the air conditioner is off.

[0089] In addition, the temperature constraints for buildings are as follows:

[0090]

[0091] Among them, T air,h and T air c represents the room temperature during heating and cooling, respectively.

[0092] The minimum start-stop time constraint is:

[0093]

[0094]

[0095] In the formula, This is the minimum startup hold time, meaning the system must remain operational for a period of time after startup and cannot be shut down. This is the minimum downtime hold time, meaning the system must remain in a non-working state and cannot be started for a period of time after shutdown.

[0096] The number of air conditioners in each group of aggregated air conditioning systems is limited to:

[0097] n min ≤n≤n max

[0098] Where n max n represents the maximum number of air conditioners. min This represents the minimum number of air conditioners required.

[0099] Then, for time t, the aggregated power consumption of the air conditioning system A is:

[0100]

[0101] The problem for determining the target power is as follows:

[0102] f = min||P des -P i (t+Δt)||

[0103] Among them, P i (t+Δt) represents the estimated power consumption of the aggregated air conditioning system participating in load following service, P des To meet the target of waste disposal.

[0104] To solve the power allocation problem, the air conditioners are divided into aggregate groups, and an artificial immune algorithm is used for the solution. The computational process of the artificial immune algorithm is as follows: Figure 3 The flowchart of the artificial immune algorithm is shown below. The affinity calculation formula in the algorithm is as follows:

[0105]

[0106]

[0107] f is the value of the objective function. A v For the affinity of antibody v, S v,s For the affinity between antibody v and s, k v,s denoted as the similarity between antibodies v and s, and n as the antibody length.

[0108]

[0109]

[0110] C v The concentration of antibody v is N; the number of iterations is P. r α represents the reproduction rate, and α is a parameter for evaluating the diversity of antibody v.

[0111] For step S3, based on this structure, the air conditioners are divided into several aggregation groups according to the similarity of parameters. Each group works together to form a consumption task, and the air conditioners within the group are controlled through a predictive control model. Among these, Figure 4 This is a diagram of the predictive control architecture for the aggregated air conditioning system.

[0112] Since the question is...

[0113] min J=||y Tin [k+1]-y Tin.ref ||2

[0114]

[0115] Where: y Tin For the model output value; y Tin.ref For output reference power; u max u min These are the upper and lower limits of the controllable quantity, which are the maximum and minimum controllable air conditioning power in the integrated air conditioning system.

[0116] Its optimal control rate is

[0117]

[0118] The optimization strategy is solved according to the following settings:

[0119] 1) If Then let the control result

[0120] 2) If Then let the control result

[0121] 3) If Then let the control result

[0122] In each control cycle, the system predicts and updates the control commands for the next cycle based on the current real-time state. It then performs optimal solutions based on system parameters and dynamic model constraints, updates its output sequence, and adopts the first control command from the sequence as the actual control command for each aggregated air conditioning system in the next cycle. In the next cycle, the system state update and predictive control will repeat.

[0123] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0124] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0125] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0126] The above embodiments should be understood as illustrative only and not as limiting the scope of protection of the present invention. After reading the description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for managing and controlling air conditioning resources based on a cloud platform for renewable energy consumption, characterized in that, Includes the following steps: Step 1) Construct a cloud platform for new energy consumption to realize information interaction of air conditioning systems; Step 2) Establish an energy consumption assessment model for the air conditioning group. Under the conditions of ensuring completion and user comfort, construct a consumption task allocation problem for the aggregated group and achieve the optimal solution. Step 3) Divide the air conditioners into several aggregation groups based on the similarity of parameters. Each group works together to complete the consumption task, and the air conditioners in the group are controlled by a predictive control model. Step 2) establishes an energy consumption assessment model for the air conditioning group. Under the conditions of ensuring economy and user comfort, it constructs a consumption task allocation problem for the aggregated group and achieves the optimal solution, specifically including: The dynamic evolution of the internal temperature of the air conditioning system is influenced by the indoor temperature. Air conditioning on / off state variables The first-order differential equation describes: in It is the ambient temperature. Let the heat capacity of the i-th air conditioning unit be . Let be the thermal resistance of the i-th air conditioning unit. The average power consumption of the air conditioning unit is given, assuming the ambient temperature remains constant over a period of time. The state variable is... Its value is switched according to the switching criteria, as follows: Among them, when the indoor temperature Temperature greater than the set maximum upper limit hour, This indicates that the air conditioner is on; when the indoor temperature... Temperature less than the set maximum lower limit hour, This indicates that the air conditioner is off; In addition, the temperature constraints for buildings are as follows: in, and These are the room temperatures during heating and cooling, respectively. The minimum start-stop time constraint is: In the formula, This is the minimum startup hold time, meaning the system must remain operational for a period of time after startup and cannot be shut down. The minimum downtime hold time is defined as the period during which the system must remain in a non-working state and cannot be started after a shutdown. The number of air conditioners in each group of aggregated air conditioning systems is limited to: in This represents the maximum number of air conditioners. This represents the minimum number of air conditioners required. Then, for time t, the aggregated power consumption of the air conditioning system A is: in Let A be the electricity consumption of aggregate class A at time t. To aggregate the number of air conditioners inside, This refers to the electricity consumption of a single air conditioner within category A. Step 2) addresses the consumption task allocation problem for the aggregated group and aims to achieve the optimal solution, specifically including: The problem for determining the target power is defined as follows: in, This represents the estimated power consumption of the aggregated air conditioning system participating in load following service. To achieve the target of waste disposal; To solve the power allocation problem, air conditioners are divided into aggregation groups, and an artificial immune algorithm is used for the solution. The affinity calculation formula in the algorithm is as follows: The objective function value, For the affinity of antibody v, For the affinity between antibody v and antibody S, represents the similarity between antibodies v and s, and n is the antibody length; The concentration of antibody v is denoted as ; N is the number of iterations. For reproductive rate, Parameters for evaluating the diversity of antibody v; Step 3) involves dividing the air conditioners into several aggregation groups based on parameter similarity. Each group collaboratively completes the consumption task, and the predictive control model is used to control the air conditioners within the group. Specifically, this includes: In each control cycle, the system predicts and updates the control command for the next cycle based on the current real-time state, performs optimal solution based on system parameters and dynamic model constraints, updates its output sequence, and adopts the first control in the control sequence as the actual control command for each aggregated air conditioning system in the next cycle; in the next cycle, the system state update and predictive control will be repeated again. Since the question is... Where: Output values ​​for the model; For output reference power; , These are the upper and lower limits of the control quantity, which are the maximum and minimum controllable air conditioning power in the integrated air conditioning system; The optimal control rate is in, and All are aggregate classes The coefficient matrix, Let it be its state variable. It is its perturbation variable; The optimization strategy is solved according to the following settings: 1) If Then let the control result ; 2) If Then let the control result ; 3) If Then let the control result .

2. The air conditioning resource management and control method based on a cloud platform for renewable energy consumption as described in claim 1, characterized in that, Step 1) The cloud platform for renewable energy consumption is divided into a power grid control and monitoring layer, a cloud platform management layer, a control optimization layer, and an information acquisition layer. The power grid control and monitoring layer uploads its active and reactive power target values ​​to the cloud platform management layer, while the cloud platform management layer uploads the load availability of the air conditioning system. The communication protocol adopts a common central air conditioning protocol and uses a standard OPC communication interface. The control optimization layer adjusts and controls each aggregation group according to the optimal algorithm and the collected relevant information to achieve the consumption target. Then, the predictive control model is used to control the air conditioning in the group. The information acquisition layer uses sensors to collect information such as outlet water temperature, return water temperature, outdoor temperature, indoor temperature, humidity, and wind speed in each air conditioning system and uploads it to the local controller. The local controller periodically executes the water temperature control logic to determine the start-up and shutdown status of each unit in the aggregation group, and achieves precise indoor temperature control based on an accurate building thermodynamic model.

3. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the air conditioning resource management and control method based on a cloud platform for renewable energy consumption as described in any one of claims 1 to 2.

4. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the air conditioning resource management and control method based on a cloud platform for new energy consumption as described in any one of claims 1 to 2.

5. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the air conditioning resource management and control method based on a cloud platform for new energy consumption as described in any one of claims 1 to 2.

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