Distributed Coordinated Regulation Method and System for Power Direct Control Variable Frequency Air Conditioner

Abstract

This application relates to a distributed collaborative regulation system for power-direct-controlled variable frequency air conditioners. The system includes an individual layer, a gateway layer, a coordination layer, and a scheduling layer. The individual layer consists of multiple power-direct-controlled variable frequency air conditioners and corresponding user digital twin intelligent agents. The gateway layer consists of multiple gateways, each connecting to several variable frequency air conditioners via a local communication protocol and communicating with the coordination layer via the MQTT protocol. The coordination layer includes at least one coordinator, which receives load control targets sent by the scheduling layer and calculates regulation signals based on the load control targets and aggregated power and sensitivity information reported by the gateway layer. The scheduling layer is used to formulate load control targets for the variable frequency air conditioner cluster. The design of the individual layer, gateway layer, coordination layer, and scheduling layer avoids the problems of high computational pressure and high single-point failure risk in centralized systems, while also solving the problems of inconsistent control targets and poor global coordination in fully distributed systems.

Description

Technical Field

[0001] This application relates to the field of intelligent control, and in particular to a method, system, electronic device and storage medium for decentralized coordinated regulation of power direct control type variable frequency air conditioners. Background Technology

[0002] Inverter air conditioning (IAC) is a flexible load with considerable potential, which can be directly dispatched by the power grid or participate in market operation through entities such as virtual power plants and load aggregators. Existing air conditioning control strategies have a relatively small impact on user experience when setting temperature, but air conditioning power is a passive response quantity. Furthermore, the specific power / frequency control strategies and parameters of different brands and models of air conditioners vary and are difficult to obtain, resulting in significant response uncertainty and making it difficult to accurately smooth out fluctuations in renewable energy power at the minute or even second level.

[0003] Furthermore, classic air conditioning cluster coordinated control schemes employ a centralized architecture, requiring all air conditioners to send their own parameters and status information to the central controller. However, this can lead to privacy leaks, communication congestion, and computational difficulties. Distributed architectures, on the other hand, require individuals to communicate with each other to form a strongly connected graph, such as multi-agent networking schemes. However, this requires multiple iterations, has poor real-time performance, and faces the possibility of network connectivity being disrupted during network attacks. Summary of the Invention

[0004] This application provides a method, system, electronic device, and storage medium for decentralized coordinated regulation of power direct-control variable frequency air conditioners, so as to at least solve the problem that it is impossible to achieve fast and reliable regulation of variable frequency air conditioner clusters in related technologies.

[0005] In a first aspect, embodiments of this application provide a distributed coordinated regulation system for a power-direct-control variable frequency air conditioner, the system comprising: an individual layer, a gateway layer, a coordination layer, and a scheduling layer. The individual layer consists of multiple power direct-control variable frequency air conditioners and corresponding user digital twin intelligent agents. Each user digital twin intelligent agent has self-sensing, decision-making and power adjustment functions. The gateway layer consists of multiple gateways. Each gateway connects to several variable frequency air conditioners via a local communication protocol. It is used to aggregate the power and sensitivity information of the variable frequency air conditioners connected to it and communicate with the coordination layer via the MQTT protocol. The coordination layer includes at least one coordinator, which is used to receive the load control target sent by the scheduling layer, and calculate the adjustment signal based on the load control target and the aggregated power and sensitivity information reported by the gateway layer. The scheduling layer is used to formulate load control targets for the variable frequency air conditioning cluster based on external regulation instructions obtained from the power system or the power market, and then send them to the coordination layer.

[0006] In some embodiments, the adjustment signal is a temperature offset rate, and the coordination layer includes: The adjustment calculation module is used to determine the global temperature offset rate based on the offset rate calculation model, according to the indoor air temperature and the upper and lower limits of the user's preferred temperature. The offset calculation model includes:

[0007] in, l This represents the global temperature offset rate. T a Indoor air temperature T a max and T a min These represent the upper and lower limits of the user's preferred indoor temperature. T a best for T a max and T a min The midpoint.

[0008] In some embodiments, the coordination layer further includes: The adjustment update module is used to obtain the adjustment signal for the next iteration period based on the adjustment signal iteration model, according to the adaptive step size and the adjustment signal of the current iteration period. The update direction of the adjustment signal is determined by the sign of the difference between the total power of the cluster and the target power, and the magnitude is proportional to the absolute value of the difference and inversely proportional to the sum of the absolute values ​​of the aggregated adjustment sensitivity reported by all gateways. The iterative model for the adjustment signal includes:

[0009] in, l C,j For the first j The adjustment signal for the next iteration. l C,j+1 For the first j The adjustment signal of +1 iteration, G The number of gateways in the cluster. P sub,g,j Gateway g exist l C,j Total power belowP target For load control targets, r For feedback depth, δ j max To adjust the upper limit of the step size, W sub,g,j Gateway g exist l C,j The polymerization sensitivity below.

[0010] In some embodiments, the coordination layer further includes: The sensitivity calculation module is used to determine the sensitivity of a single inverter air conditioner by measuring the rate of change of its electrical power with respect to the regulating signal. The sensitivity calculation formula includes:

[0011] in, w i For the first i The sensitivity of Taiwan inverter air conditioners P AC,i For the first i The power consumption of a variable frequency air conditioner. l i For the first i The adjustment signal corresponding to the inverter air conditioner.

[0012] In some embodiments, the individual layer calculates its own temperature offset rate adjustable range at the beginning of the sampling period and reports it to the gateway layer, which then summarizes the data to determine the selectable range of the global adjustment signal.

[0013] In some embodiments, the gateway layer adopts the publish / subscribe mode of the MQTT protocol. When the coordinator loses connection with the message server abnormally, the message server automatically publishes a preset fault notification message to a predetermined topic to trigger the hosting mechanism, automatically elect a new coordinator, and resume the iteration process.

[0014] In some embodiments, the coordination layer further includes: The instruction generation module is used to determine whether the total power corresponding to the current adjustment signal is closer to the load control target than the measured total power at the previous moment. If so, it immediately generates a power adjustment instruction and instructs each of the variable frequency air conditioners to respond to the power adjustment instruction.

[0015] Secondly, embodiments of this application provide a method for decentralized coordinated regulation of a power-direct-control inverter air conditioner, the method being applied to a decentralized coordinated regulation system for a power-direct-control inverter air conditioner as described in any one of claims 1 to 7, the method comprising: The dispatching layer formulates load control targets for the variable frequency air conditioning cluster based on external adjustment instructions obtained from the power system or the power market, and sends them to the coordination layer. The coordination layer sends an initial adjustment signal to the gateway layer; The individual layer receives the initial adjustment signal through the gateway layer, calculates the required power and sensitivity based on the initial adjustment signal, and reports the power and sensitivity to its respective gateway. The gateway layer aggregates the power and sensitivity data reported by the individual layers and sends it to the coordination layer; The coordination layer receives the load control target sent by the scheduling layer, and calculates the adjustment signal based on the load control target and the aggregated power and sensitivity information reported by the gateway layer.

[0016] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the power direct control type variable frequency air conditioner decentralized coordinated adjustment method as described in the second aspect above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power direct control type variable frequency air conditioner decentralized coordinated adjustment method as described in the second aspect above.

[0018] Compared to related technologies, the power direct-control variable frequency air conditioning distributed collaborative regulation system provided in this application integrates a power adaptive allocation strategy and a distributed, loosely coupled architecture. It adopts a distributed collaborative mode of "centralized scheduling + hierarchical coordination + local autonomy": the scheduling layer is responsible for the top-level design of the global objectives, the coordination layer is responsible for load allocation calculation within the region, and the user digital twin intelligent agents at the individual layer possess autonomous decision-making capabilities. This avoids the problems of excessive computational pressure and high risk of single-point failure in centralized systems, and solves the problems of inconsistent control objectives and poor global coordination in fully distributed systems, achieving rapid and reliable regulation of variable frequency air conditioning clusters. The four-layer architecture of the individual layer, gateway layer, coordination layer, and scheduling layer achieves complete decoupling of functional modules; equipment expansion or technology upgrades at any layer will not affect other layers. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a structural block diagram of a power direct-control type inverter air conditioner distributed collaborative adjustment system according to an embodiment of this application; Figure 2 This is a schematic diagram of a PDC-IAC cluster distributed collaborative architecture according to an embodiment of this application; Figure 3 This is a flowchart illustrating the updating of an individual parameter according to an embodiment of this application; Figure 4 This is a flowchart of parameter calculation for a coordinator according to an embodiment of this application; Figure 5 This is an overall workflow diagram of an IAC cluster coordination system according to an embodiment of this application; Figure 6 This is a flowchart of a distributed coordinated adjustment method for a power direct-control type inverter air conditioner according to an embodiment of this application; Figure 7 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0021] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0022] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0023] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0024] This embodiment provides a distributed coordinated control system for a power-direct-control variable frequency air conditioner. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0025] Figure 1 This is a structural block diagram of a power direct-control type inverter air conditioner distributed coordinated regulation system according to an embodiment of this application, such as... Figure 1 As shown, the system includes: an individual layer 11, a gateway layer 12, a coordination layer 13, and a scheduling layer 14.

[0026] The individual layer 11 consists of multiple power direct-control variable frequency air conditioners and corresponding user digital twin intelligent agents. Each user digital twin intelligent agent has self-sensing, decision-making and power adjustment functions.

[0027] Gateway layer 12 consists of multiple gateways. Each gateway connects to several variable frequency air conditioners through a local communication protocol. It is used to aggregate the power and sensitivity information of the variable frequency air conditioners connected to it and communicate with the coordination layer through the MQTT protocol.

[0028] Coordination layer 13 includes at least one coordinator, which receives load control targets sent by the scheduling layer and calculates control signals based on the load control targets and aggregated power and sensitivity information reported by the gateway layer.

[0029] The dispatch layer 14 is used to formulate load control targets for the variable frequency air conditioning cluster based on external regulation instructions obtained from the power system or the power market, and then send them to the coordination layer.

[0030] The distributed collaborative architecture of the power direct-control inverter air conditioner (PDC-IAC) cluster in this embodiment is based on a self-regulating collaborative distributed system, which can alleviate communication and computing burdens and avoid privacy exposure between individuals, thereby achieving fast and reliable control of the IAC cluster. It includes a four-layer structure: scheduling layer, coordination layer, gateway layer, and individual layer. Figure 2 This is a schematic diagram of a PDC-IAC cluster distributed collaborative architecture according to an embodiment of this application, such as... Figure 2 As shown, from a bottom-up perspective: Individual Layer: The IAC (Integrated Air Conditioning Unit) is a digital twin intelligent agent combining "air conditioning equipment and user." It possesses self-sensing and decision-making capabilities, and can respond to adjustment signals based on room, air conditioning, and user preference parameters, interacting and adjusting locally with the user and gateway. A single gateway can connect dozens of IACs to form a subsystem through on-site communication (such as WiFi, Zigbee, and RS-485).

[0031] Gateway Layer: The intermediate gateway layer employs the Message Queuing Telemetry Transport (MQTT) protocol. This protocol is an application layer protocol based on a publish / subscribe model, consuming minimal bandwidth and computing resources, exhibiting good compatibility with low-power, low-processing-capability embedded devices, and facilitating cross-platform deployment. The message queue (MQ) provides a common logical space (data domain) for information transmission between subsystems and the coordination layer, ensuring loosely coupled system integration. The gateway can be viewed as a local aggregator decoupling subsystems; the failure or exit of some subsystems will not affect the overall system availability. Each gateway subscribes to the required data resources from the message queue management module (deployed on the MQ server) and listens for and receives relevant data published by other gateways or servers. Newly added gateways, after registering and obtaining a unique device ID, only need to establish a physical connection with the MQ server without interfering with other parts. This layered, distributed, data-driven architecture easily achieves online horizontal scaling and vertical recursive integration, providing a foundation for plug-and-play and lightweight deployment. The database can permanently store important data temporarily stored in the message queue for backup, facilitating fault recovery or later analysis. The gateway forwards the adjustment signals to the IAC agent, collects and aggregates its feedback information (individual power and sensitivity), and then publishes the results to the data domain. These intermediate results do not contain specific information about individual users, effectively shielding privacy and reducing the data dimensionality of subsequent calculations and transmissions.

[0032] Coordination Layer: After receiving the total cluster load target set by the scheduling layer, the coordination layer initiates the adjustment process, coordinating the state adjustments of individual components. The coordinator does not need to know the number of components or detailed models; it mainly updates the adjustment signals based on collected feedback information from subsystems to ensure the total load continuously approaches the adjustment target. If the coordinator fails, a management mechanism can be triggered, allowing a new coordinator to download the necessary information from the message queue or database and continue organizing the iteration.

[0033] Dispatch layer: The dispatch layer comprehensively derives the load regulation target of the IAC cluster based on information from external sources (power grid and market) or uncontrollable sources (new energy and rigid loads) to meet the normal state requirements of the power grid or local resources, such as new energy consumption, power fluctuation smoothing, and economic operation. It can also carry out emergency load increases / decreases in the form of demand response or direct load control.

[0034] Physically, the gateways and MQ servers form a star topology; logically, this architecture avoids high concentration of information. On one hand, interactive data is stored in message queues, resulting in weak coupling between gateways and servers, supporting asynchronous transmission and real-time expansion, and preventing modules from affecting each other. On the other hand, the collaborative control method of this invention only requires a small amount of external characteristic data from individual IACs, allowing IACs to process their respective tasks with high cohesion, avoiding the transmission of large amounts of raw data. Due to the publish / subscribe model, the coordinator, gateways, and servers do not need to worry about the destination of published messages, and subscribers only listen to the messages they need, avoiding inefficient server polling in point-to-point communication and not significantly increasing system costs. To improve reliability, the MQ server can adopt a master-slave mode to avoid communication crashes, for example, mirroring the MQ online to other servers, automatically switching to the mirror queue or restarting when the MQ server fails. Because the collaborative adjustment requires very few iterations, the system can quickly recover to its original state and achieve convergence after queue switching or restarting.

[0035] In some embodiments, the adjustment signal is a temperature offset rate, and the coordination layer includes: The adjustment calculation module is used to determine the global temperature offset rate based on the offset rate calculation model, according to the indoor air temperature and the upper and lower limits of the user's preferred temperature. The offset calculation model includes:

[0036] in, l This represents the global temperature offset rate. T a Indoor air temperature T a max and T a min These represent the upper and lower limits of the user's preferred indoor temperature. T a best for T a max and T a min The midpoint.

[0037] To describe the air conditioner user experience, the temperature offset ratio (TOR) is used to measure user comfort. When running in cooling mode, l The smaller the value, the greater the potential for room temperature to rise, the greater the potential for air conditioning load reduction (positive reserve), and the smaller the potential for load increase (negative reserve); the opposite is true for heating.

[0038] In the specific IAC cluster distributed collaborative algorithm, the individual components are first finely modeled. The thermodynamic model of the room to which the IAC belongs adopts the second-order equivalent thermal parameter (ETP) model, and its differential equation is:

[0039] in, T a,t and T m,t They are respectively t The temperature of indoor air and solids at all times; T o,t for t The outside temperature at that moment; R a The thermal resistance between indoor and outdoor air. R m The thermal resistance between the indoor solid and the indoor air; C a and C m These are the heat capacities of indoor air and solids, respectively. Q AC,t For air conditioning t Cooling / heating power at any given moment, a positive number; s t For air conditioning t The working status at all times, during cooling s t =-1, during heating s t =1, when closed s t =0; Q i,t and Q s,t They are respectively t Real-time indoor heat source thermal power and indoor solar radiation thermal power; The average human body radiant heat power; v t for t The number of people in the room at any given time. Of the above parameters, R m , C m and C a It is mainly related to the layout of objects inside the room. R a It is mainly related to the material and structure of the room's outer shell. Q AC,t This mainly depends on the inverter's operating frequency. Qi,t Related to direct indoor heat sources, such as cooking utensils and electric lights. Q s,t This is related to the amount of sunlight that enters the room through the window; q i,t and q s,t These are the distribution coefficients of indoor heat source heat power and solar radiation heat power acting on indoor air, respectively.

[0040] The load control time granularity is small (generally on the order of minutes or seconds), therefore, the room thermodynamic process can be discretized and approximated as linear. Its time-period recursive form is as follows:

[0041] in, k For time period numbering, Δ t This represents the sampling time interval. In practice, the IAC agent can obtain the optimal estimates of the aforementioned model parameters by continuously collecting room temperature data and combining this with online parameter identification methods. The model parameter identification program can be deployed either at the gateway layer or within the IAC agent, depending on the processing performance of the actual device.

[0042] An IAC (Insulated Air Conditioner) converts electrical energy into cooling / heating energy by exchanging heat between the refrigerant and indoor / outdoor air through the work of its compressor. Since the majority of an air conditioner's power consumption comes from the compressor, this embodiment will only consider the regulation of this part. The IAC can control the compressor's frequency or speed via an inverter, thereby adjusting the cooling / heating power and electrical power. Q AC The relationship between the compressor frequency and the compressor frequency is as follows:

[0043] In the formula, a AC (<0) b AC (>0) c AC The correlation coefficient; f AC This refers to the compressor frequency, i.e., the control command. and These represent the lower and upper limits of the compressor frequency, respectively. The relationship between air conditioner power and compressor frequency is as follows:

[0044] In the formula, n AC (>0) and m AC For the corresponding coefficients, P ACThis refers to the IAC electrical power. In actual modeling, if detailed coefficient values ​​cannot be obtained from the manufacturer, the above coefficients can be determined using the maximum, minimum, and rated operating condition performance indicators indicated on the nameplate. Q - P The relationship between the IAC and the control interface is fitted. Based on the IAC's control interface open type: if the open interface is a power control interface, the set power is used as the control signal output by the coordination system. To achieve precise power control, the IAC itself has a power closed-loop control function, with the control quantity being the compressor speed, which can be implemented based on the PID control algorithm built into the air conditioner manufacturer. If the open interface is a compressor speed control interface, the compressor speed is used as the control signal output by the coordination system. This method of power control is open-loop and relies on precise modeling of the IAC. In engineering practice, it is recommended to use the power interface open method, then calculate the power control signal based on the IAC's fitting model, and finally, the IAC itself completes the precise closed-loop power control.

[0045] In implementation, it can be expanded or adjusted according to grid dispatch and user requirements, requiring only changes to the calculation methods of cluster load targets and regulation signals. IAC clusters can leverage their regulatory value in various scenarios. For example, when there are significant fluctuations in renewable energy or rigid loads in local or external power systems, power compensation can be achieved by collectively increasing or decreasing the speed of IAC compressors, thus providing flexibility to the power system. This embodiment presents a day-ahead and real-time dual-stage IAC cluster dispatch strategy. In the day-ahead stage, the dispatch layer needs to solve the following problem:

[0046] st Constraint 1 Constraint 2 Constraint 3 Constraint 1 indicates that the day-ahead scheduling objective is to minimize the net load fluctuation of the system, with a time scale ranging from hours to tens of minutes. In the formula, and They are respectively t Pre-scheduled values ​​for system net load and IAC cluster load at any given time; and They are respectively t Forecast values ​​of renewable energy output and rigid load at any given time. T represents the total number of time periods. Constraints 2 and 3 indicate that IAC cluster scheduling must satisfy power and energy boundaries, respectively. In the formula, and These are the lower and upper limits of the IAC cluster scheduling power, respectively. and These represent the lower and upper limits of the cumulative dispatched power of the IAC cluster, respectively. During the real-time phase, both renewable energy and rigid loads in the system will experience fluctuations, which can be used to correct the real-time adjustment targets of the IAC cluster.

[0047] In the formula, and They are respectively t Time period k The actual power of renewable energy and rigid loads at each sampling point; and These are the lower and upper limits of the real-time power of the IAC cluster, respectively; This serves as the real-time adjustment target for the IAC cluster. The sampling timescale ranges from minutes to seconds. At the beginning of each sampling period, each individual first determines its own power boundary, which is then aggregated by the coordinator. If the cluster adjustment target exceeds or is exactly at the cluster's total power boundary, all individuals are informed to operate at the corresponding boundary; otherwise, a feedback-coordination procedure is initiated.

[0048] To fairly regulate each air conditioner and effectively tap into its flexibility potential, a globally unified TOR (Total Parameter) is adopted as the regulation signal (consistency metric). In each interaction, each IAC uses this as its state adjustment target. Therefore, without loss of generality, the collaborative regulation principle of the IAC cluster is as follows:

[0049] In the formula, i Number the air conditioner. N This represents the total number of air conditioners in the cluster. Physical constraints of IACs or rooms can affect regulation characteristics, which can be handled automatically by individuals during interaction.

[0050] Using TOR as a regulating signal to coordinate the cluster: First, the response characteristics of IAC to the regulating signal are derived, and a feedback-based regulation approach is proposed; then, parameter update rules for each layer are designed based on convergence conditions; finally, combined with the aforementioned distributed architecture, an interaction process considering managed fault tolerance mechanism is designed.

[0051] For an individual, given the air conditioning operating mode, user preferences, and external temperature conditions at the beginning of the sampling period, the TOR and air conditioning cooling / heating power at the end of the sampling period are... Q AC Electric power P AC and compressor frequency f AC The relationships between them are all monotonic. The regulatory characteristics of individual IACs, i.e., the regulatory signal, can be derived. l The relationship between electrical power and power is as follows:

[0052] This formula represents a nonlinear quadratic function relationship between TOR and electric power. In the formula, α i , β i and γ i The coefficients are calculated using the following formulas:

[0053]

[0054]

[0055]

[0056] In some embodiments, the coordination layer further includes: The sensitivity calculation module is used to determine the sensitivity of a single inverter air conditioner by measuring the rate of change of its electrical power with respect to the regulating signal. Within the adjustable range of IAC operation, its adjustment sensitivity is defined as:

[0057] in, w i For the first i The sensitivity of Taiwan inverter air conditioners P AC,i For the first i The power consumption of a variable frequency air conditioner. l i For the first i The adjustment signal corresponding to the inverter air conditioner.

[0058] Greater sensitivity means a greater response of the IAC power to minute changes in TOR. When the IAC load reaches its limit or communication is interrupted, individuals will lose their ability to continue responding or cease data exchange; at this point, the sensitivity should naturally become 0. Combining this with the principle of coordinated regulation in IAC clusters, we can conclude that:

[0059] The accelerometer sensitivity is related to room and IAC model parameters as well as power, reflecting the individual accelerometer elasticity under the influence of internal and external factors.

[0060] This embodiment also derives and designs data update rules for each level based on the ADS architecture. The coordination process can be summarized as follows: In each iteration, the coordinator publishes a unified adjustment signal, and all IACs use it as the adjustment target to calculate power and sensitivity, reporting both to the gateway. The gateway then aggregates the intermediate results and publishes them to the coordinator. The coordinator updates the adjustment signal based on the collected total power and sensitivity, and then starts the next iteration. The specific parameter update processes at the individual layer, gateway layer, and coordination layer are described below.

[0061] In some embodiments, the coordination layer further includes an instruction generation module, which determines whether the total power corresponding to the current adjustment signal is closer to the load control target than the measured total power at the previous moment. If so, it immediately generates a power adjustment instruction and instructs each inverter air conditioner to respond to the power adjustment instruction.

[0062] Within a sampling period, when the set target power for the cluster is within the adjustable range of all IACs, an iterative adjustment process is initiated. If individual units are allowed to operate at their maximum limits, the user-set indoor temperature boundary can be exceeded during this sampling period, considering only the IAC's speed / power boundary. j In this iteration, the IAC agent needs to calculate the regulation signal. l C,j The required electrical power. First, assume that at the end of the sampling period, the room's TOR can reach... l C,j The required cooling / heating power can then be calculated using the thermodynamic model of the room where the IAC is located. Q * AC,i,j for:

[0063] When the required cooling / heating power exceeds the IAC performance range, it is fixed at the boundary. That is, the actual available cooling / heating power. Q AC,i,j for:

[0064] In the formula, and The first i The maximum and minimum cooling / heating power of each IAC are calculated as follows:

[0065] After obtaining the actual cooling / heating power requirements, and combining the relationship between the air conditioner's electrical power and the compressor frequency, the compressor frequency under this operating condition can be calculated. f AC,i,j for:

[0066] Furthermore, the electrical power of the IAC can be calculated using the offset rate calculation model. P AC,i,j for:

[0067] To accelerate cluster load adjustment, this embodiment employs an "iteration-based adjustment" mode. After each collection of aggregated information, the coordination layer first compares the current calculated total power value with the real-time measured value. If the adjustment deviation of the calculated value is less than the adjustment deviation of the real-time measurement, the coordination layer issues an adjustment notification, and each IAC adjusts its power based on the latest calculated value without waiting for convergence to the required accuracy; otherwise, it maintains the power level from the previous moment. Figure 3 This is a flowchart illustrating the updating of an individual parameter according to an embodiment of this application.

[0068] In each iteration, the gateway needs to collect the power and sensitivity reports from all IACs under it and summarize them separately. Therefore, the gateway's computational task is:

[0069] In the formula, P sub,g,j Gateway g exist l C,j Total power below; g Number the gateway; n g Gateway g The number of IACs connected; here i Then it is the gateway. g The IAC number below. W sub,g,j Gateway g exist l C,j The overall sensitivity is crucial for updating the modulation signal, therefore it must be considered in conjunction with... P sub,g,j The data is sent to the coordinator along with the data. In addition to connecting heterogeneous IAC individuals, the gateway plays two other roles in the aggregation calculation of the intermediate layer. First, it can reduce the amount of communication and alleviate the computational burden on the coordination layer. Second, because the data domain is shared, other gateways besides the coordinator may also be at risk of eavesdropping on each other, but they still cannot infer data such as individual model parameters and regulation characteristics of other subsystems from these intermediate results, thus protecting privacy.

[0070] In some embodiments, the coordination layer further includes: an adjustment update module, used to obtain the adjustment signal for the next iteration period based on the adjustment signal iteration model, according to the adaptive step size and the adjustment signal of the current iteration period, wherein the update direction of the adjustment signal is determined by the sign of the difference between the total power of the cluster and the target power, the magnitude of which is proportional to the absolute value of the difference and inversely proportional to the sum of the absolute values ​​of the aggregated adjustment sensitivity reported by all gateways; In each iteration, the coordinator issues a unified adjustment signal and then determines whether the cluster adjustment accuracy meets the requirements based on feedback data from the gateway. If it does, a convergence notification or state adjustment notification is issued; otherwise, the adjustment signal needs to be updated further. The adjustment signal iteration model includes:

[0071] in, l C , j For the first j The adjustment signal for the next iteration. l C , j+1 For the first j The adjustment signal of +1 iteration, δ j For adaptive step size, G The number of gateways in the cluster. P sub,g,j Gateway g exist l C , j Total power below The target for load regulation. δ j To adjust the signal update step size, it is automatically adjusted in each iteration based on changes in the overall cluster response capability. The total cluster load can converge to the target value. The conditions are:

[0072] in, δ j max To adjust the upper limit of the signal update step size, the calculation method is as follows:

[0073] Therefore, the calculation method for the adjustment signal should be described as follows:

[0074] It can be seen that the change in the adjustment signal is negatively correlated with the total sensitivity (absolute value). When factors such as increasing the number of IACs, widening the user's preferred temperature range, increasing the ambient temperature, or increasing the response power lead to a larger global sensitivity (absolute value), the step size... δ j This will promptly reduce the rate to avoid overshoot; conversely, the rate of increase will decrease it. In other words, the update of the regulation signal is actually determined from the bottom up by a comprehensive combination of factors, including the individual IAC, the environment, and the regulation target. This principle ensures the adaptability of the cluster regulation process, enabling it to converge quickly and reliably. r For feedback depth, the preferred option is... r =0.5, convergence is fastest around this value, no further adjustments are needed. 0< r The range of values ​​<1 is mathematically convergent, meaning the system can resist, to some extent, false data injection attacks (FDIA) or delays targeting the communication link. FDIA misleads the coordination layer's decisions by tampering with transmitted data, posing a high risk, especially in public network environments. When an attacker tampers with the sensitivity data uploaded by the IAC individual or gateway, it is equivalent to altering the data used by the coordinator for calculation. r Value. For example, if the total sensitivity received by the coordinator is larger than the true value, it is equivalent to r becoming smaller; conversely, it is equivalent to r becoming smaller. r It gets bigger. Therefore, in abnormal situations, the coordinator can adjust the settings in a timely manner based on the direction of data distortion or packet loss. r The value can be determined. However, as long as it can still be guaranteed after FDIA. If the conditions are met, power tracking can eventually be completed.

[0075] In some embodiments, the individual layer calculates its own temperature offset rate adjustable range at the beginning of the sampling period and reports it to the gateway layer, which then summarizes the data to determine the selectable range of the global adjustment signal.

[0076] For IAC clusters, due to the random and dispersed initial temperature states of individuals and the potentially large differences in model parameters, the overall aggregated "regulation signal-cluster power" curve may be discontinuous. If the regulation signal update encounters a "discontinuity," individuals on either side of the TOR adjustable range will be located at the upper and lower boundaries, i.e., "dead zones." When encountering a dead zone, the total sensitivity collected by the coordinator becomes 0. Since it is in the denominator in the calculation of the step size upper limit, if not handled, it will cause the step size and regulation signal to become infinite, making the calculation impossible. Avoiding dead zones is also necessary during regulation signal initialization. Theoretically, within a sampling period, the regulation signal should take values ​​within the union of the TOR adjustable ranges of all IACs, and the smallest element of this set should be... and the largest element The calculation method is as follows:

[0077] in, and The firsti The lower and upper limits of the electrical power of each IAC are calculated as follows:

[0078] The above calculations are easily performed under the distributed architecture of this invention. At the beginning of the sampling period, each IAC agent calculates the TOR boundary corresponding to its power boundary. and The system proactively reports the value to the gateway; the gateway only needs to determine the minimum value among them. and maximum value Then simply publish it to the coordinator; the calculation method is as follows:

[0079] Then the coordinator determines the range of adjustment signal values ​​based on the TOR boundaries of all subsystems. The calculation method is as follows:

[0080] Finally, the coordinator Random values ​​are selected as l C The initial value for iteration. Similarly, when a dead zone is encountered in the iteration, the adjustment signal for the next iteration is still randomly selected again in this interval until the total sensitivity of the feedback is no longer 0, at which point the dead zone is exited.

[0081] Figure 4 This is a flowchart illustrating the parameter calculation process for a coordinator according to an embodiment of this application, such as... Figure 4 As shown, this parameter calculation does not require changes to the adjustment target and the underlying model. Because the individual information is reduced in dimensionality, the coordinator receives less data; in each iteration, it only needs to obtain two data points: the aggregate power and aggregate sensitivity of the subsystem. Performance requirements are low, so in engineering, a gateway in the intermediate layer can also be considered to perform the coordinator function, thereby reducing system construction costs.

[0082] In addition, under this architecture, individuals can choose to shut down to meet low-load requirements. At the beginning of the sampling period, individuals are calculating... l When considering power boundaries, the room temperature under extreme operating and shutdown conditions can be easily calculated. When considering extreme operating conditions, if the TOR interval corresponding to the power boundary overlaps with the [-1,1] interval, the adjustable range is taken from the overlapping portion; otherwise, the operating point closest to the [-1,1] boundaries is taken. If the IAC operates at minimum power, and its temperature is still below the preferred lower room temperature limit when cooling, or above the preferred upper room temperature limit when heating, then shutdown is allowed during this period. If individual shutdown is permitted, then when... In this special case, all individuals are prioritized for shutdown based on their TOR values ​​when powered off (from smallest to largest during cooling, and from largest to smallest during heating). This can be reported by the gateway, sorted by the coordinator, and then the closest priority is obtained. The shutdown combinations (similar to market quotations and clearing, which will not be elaborated further) are at the lower limit of the total power of all IACs at startup. Based on this, the load is reduced by shutting down the system, thereby meeting the cluster regulation target as much as possible while minimizing the impact on user comfort.

[0083] In some embodiments, the gateway layer adopts the publish / subscribe pattern of the MQTT protocol. When the coordinator loses connection with the message server abnormally, the message server automatically publishes a preset fault notification message to the predetermined topic to trigger the hosting mechanism, automatically elect a new coordinator and resume the iteration process.

[0084] With randomized scale, parameters, and initial state, this collaborative process requires only a few iterations. Furthermore, the convergence speed is almost unaffected when some individuals or gateways fail, thus the coordination system already possesses high adaptability and fault tolerance for the underlying and intermediate layers. To further enhance the system's fault tolerance for the coordination layer, this embodiment designs a coordinator hosting mechanism. Based on the "last will and testament" (LWT) function unique to MQTT technology, when a device unexpectedly loses connection due to power or network failure, or detects a network attack, it can immediately publish a pre-edited will message to the data domain, informing other devices of its offline status. In this way, the failed coordinator can trigger the hosting mechanism through this preset message, thereby notifying the system to select a backup computing device (gateway or server) as the new coordinator (through random election or round-robin election, etc.). The new coordinator also needs to subscribe to the aggregated power and aggregated sensitivity information of other gateways, and download the adjustment target and the power, sensitivity, adjustment signal, and boundary data calculated in the previous iteration from the message queue or database, so as to continue organizing iterations or initialization. In this way, the coordinator function is completely delegated to the new device, and the subsystems and individuals are "unaware" of this switchover process, ensuring a smooth transition from abnormal to normal states, which meets the requirements of a self-regulating decentralized system. This delegation mechanism is essentially a moving target defense (MTD) technology. Besides triggering delegation via LWT when the coordinator fails, the same delegation mechanism can also be used to dynamically change the coordinator during normal operation, thereby increasing the uncertainty of the coordination system's communication network, increasing the cost of attack, and reducing the success rate of attacks. Figure 5 This is an overall workflow diagram of an IAC cluster coordination system according to an embodiment of this application.

[0085] The aforementioned system integrates adaptive power allocation strategies with a decentralized, loosely coupled architecture, making it largely unaffected by changes in scale or regulation objectives. It requires no parameter tuning and exhibits strong adaptability and scalability. It is unaffected by the departure or addition of numerous individuals or coordinators. The four-layer architecture—individual layer, gateway layer, coordination layer, and scheduling layer—achieves complete decoupling of functional modules. Expansion or upgrades of equipment in any layer will not affect other layers: the individual layer can add air conditioners and corresponding digital twin agents; the gateway layer can expand the number of gateways to cover more air conditioners; the coordination layer can add coordinators to improve computing power; and the scheduling layer can update regulation strategies to adapt to new grid demands. This solves the fault tolerance and scalability problems of traditional centralized systems, where a change in one part affects the whole system.

[0086] The system adopts a distributed collaborative model of "centralized scheduling + hierarchical coordination + local autonomy": the scheduling layer is responsible for the top-level design of global objectives, the coordination layer is responsible for load allocation calculation within the region, and the individual layer of user digital twin intelligent agents has autonomous decision-making capabilities. This model avoids the problems of excessive computational pressure and high risk of single point of failure in centralized systems, and solves the problems of inconsistent control objectives and poor global coordination in fully distributed systems.

[0087] Each inverter air conditioner is equipped with a dedicated user digital twin intelligent agent. Its self-sensing function can collect the air conditioner's operating status in real time, and its autonomous decision-making function can perform fine-tuned power adjustments based on received adjustment signals and local conditions. Each gateway can aggregate the power and sensitivity information of several inverter air conditioners connected to it and report the aggregated information to the coordination layer, instead of directly reporting the raw data from each air conditioner. This design significantly reduces the amount of data uploaded to the coordination layer, lowers the computational burden on the coordination layer and the load on the communication links, and improves the overall operating efficiency of the system.

[0088] After receiving the load control target from the scheduling layer, the coordination layer calculates and sends out adjustment signals based on the aggregated power and sensitivity information reported by the gateway layer. This design achieves precise matching between the global control target and the actual situation in the region: the coordinator can formulate differentiated adjustment strategies based on the power levels and sensitivity differences of air conditioning clusters in different regions, avoiding the problem of poor control effect caused by regional differences and improving the accuracy and efficiency of global load control.

[0089] In real-time adjustment, power oscillations caused by collaborative interaction are basically not generated, and no new unstable factors are introduced. It is more reliable and practical than distributed methods, and can still show high load tracking accuracy and robustness under the conditions of data distortion and communication delay. It can better cope with the risks of "physical fluctuations + human attacks" in the real world.

[0090] The system exhibits agility and robustness under abnormal conditions. With numerous individual communication failures and additions, power oscillations are minimally observed, and the adjustment speed and accuracy are largely unaffected. When subjected to spoofed data injection attacks or data distortion, the system demonstrates a wide range of resistance to attacks and maintains high accuracy even with communication delays, exhibiting enhanced fault tolerance and security overall.

[0091] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0092] This embodiment also provides a decentralized collaborative adjustment method for a power direct control type inverter air conditioner. Figure 6 This is a flowchart of a distributed coordinated regulation method for a power direct-control inverter air conditioner according to an embodiment of this application. The method is applied to the aforementioned distributed coordinated regulation system for a power direct-control inverter air conditioner. Figure 6 As shown, the process includes the following steps: In step S601, the dispatch layer formulates the load control target of the variable frequency air conditioning cluster based on the external adjustment instructions obtained from the power system or the power market, and sends it to the coordination layer.

[0093] Step S602: The coordination layer sends an initial adjustment signal to the gateway layer.

[0094] In step S603, the individual layer receives the initial adjustment signal through the gateway layer, calculates the required power and sensitivity based on the initial adjustment signal, and reports the power and sensitivity to its respective gateway.

[0095] In step S604, the gateway layer aggregates the power and sensitivity data reported by the individual layers and sends them to the coordination layer.

[0096] In step S605, the coordination layer receives the load control target sent by the scheduling layer, and calculates the control signal based on the load control target and the aggregated power and sensitivity information reported by the gateway layer.

[0097] It should be noted that the specific examples in this embodiment can refer to the examples described in the above system embodiments and optional implementations, and will not be repeated here.

[0098] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0099] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0100] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0101] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0102] In one embodiment, Figure 7 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 7 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 7 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a distributed coordinated control method for a power direct-control type variable frequency air conditioner.

[0103] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0105] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A power direct-control type variable frequency air conditioner decentralized coordinated regulation system, characterized in that, The system comprises: an individual layer, a gateway layer, a coordination layer, and a scheduling layer. The individual layer consists of multiple power direct-control variable frequency air conditioners and corresponding user digital twin intelligent agents. Each user digital twin intelligent agent has self-sensing, decision-making and power adjustment functions. The gateway layer consists of multiple gateways. Each gateway connects to several variable frequency air conditioners via a local communication protocol. It is used to aggregate the power and sensitivity information of the variable frequency air conditioners connected to it and communicate with the coordination layer via the MQTT protocol. The coordination layer includes at least one coordinator, which is used to receive the load control target sent by the scheduling layer, and calculate the adjustment signal based on the load control target and the aggregated power and sensitivity information reported by the gateway layer. The scheduling layer is used to formulate load control targets for the variable frequency air conditioning cluster based on external regulation instructions obtained from the power system or the power market, and then send them to the coordination layer.

2. The system according to claim 1, characterized in that, The adjustment signal is a temperature offset rate, and the coordination layer includes: The adjustment calculation module is used to determine the global temperature offset rate based on the offset rate calculation model, according to the indoor air temperature and the upper and lower limits of the user's preferred temperature. The offset calculation model includes: in, l This represents the global temperature offset rate. T a Indoor air temperature T a max and T a min These represent the upper and lower limits of the user's preferred indoor temperature. T a best for T a max and T a min The midpoint.

3. The system according to claim 2, characterized in that, The coordination layer also includes: The adjustment update module is used to obtain the adjustment signal for the next iteration period based on the adjustment signal iteration model, according to the adaptive step size and the adjustment signal of the current iteration period. The update direction of the adjustment signal is determined by the sign of the difference between the total power of the cluster and the target power, and the magnitude is proportional to the absolute value of the difference and inversely proportional to the sum of the absolute values ​​of the aggregated adjustment sensitivity reported by all gateways. The iterative model for the adjustment signal includes: in, l C,j For the first j The adjustment signal for the next iteration. l C,j+1 For the first j The adjustment signal of +1 iteration, G The number of gateways in the cluster. P sub,g,j Gateway g exist l C,j Total power below P target For load control targets, r For feedback depth, δ j max To adjust the upper limit of the step size, W sub,g,j Gateway g exist l C,j The polymerization sensitivity below.

4. The system according to claim 1, characterized in that, The individual layer also includes: The sensitivity calculation module is used to determine the sensitivity of a single inverter air conditioner by measuring the rate of change of its electrical power with respect to the regulating signal. The sensitivity calculation formula includes: in, w i For the first i The sensitivity of Taiwan inverter air conditioners P AC,i For the first i The power consumption of a variable frequency air conditioner. l i For the first i The adjustment signal corresponding to the inverter air conditioner.

5. The system according to claim 1, characterized in that, At the beginning of the sampling period, each individual layer calculates its own adjustable temperature offset range and reports it to the gateway layer. The coordination layer then summarizes the data and determines the selectable range of the global adjustment signal.

6. The system according to claim 1, characterized in that, The gateway layer adopts the publish / subscribe mode of the MQTT protocol. When the coordinator loses connection with the message server abnormally, the message server automatically publishes a preset fault notification message to the predetermined topic to trigger the hosting mechanism, automatically elect a new coordinator and resume the iteration process.

7. The system according to claim 3, characterized in that, The coordination layer also includes: The instruction generation module is used to determine whether the total power corresponding to the current adjustment signal is closer to the load control target than the measured total power at the previous moment. If so, it immediately generates a power adjustment instruction and instructs each of the variable frequency air conditioners to respond to the power adjustment instruction.

8. A method for decentralized coordinated regulation of a power direct-control type inverter air conditioner, characterized in that, The method is applied to a power direct-control type variable frequency air conditioner distributed coordinated regulation system as described in any one of claims 1 to 7, and the method includes: The dispatching layer formulates load control targets for the variable frequency air conditioning cluster based on external adjustment instructions obtained from the power system or the power market, and sends them to the coordination layer. The coordination layer sends an initial adjustment signal to the gateway layer; The individual layer receives the initial adjustment signal through the gateway layer, calculates the required power and sensitivity based on the initial adjustment signal, and reports the power and sensitivity to its respective gateway. The gateway layer aggregates the power and sensitivity data reported by the individual layers and sends it to the coordination layer; The coordination layer receives the load control target sent by the scheduling layer, and calculates the adjustment signal based on the load control target and the aggregated power and sensitivity information reported by the gateway layer.

9. An electronic device comprising 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 computer program, it implements the decentralized coordinated adjustment method for power direct control type variable frequency air conditioner as described in claim 8.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the decentralized coordinated adjustment method for power direct control type variable frequency air conditioners as described in claim 8.

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