Multi-type air conditioner cooperative control method based on user-equipment dual regulation cost

Abstract

This invention discloses a collaborative control method for multiple types of air conditioners based on user-equipment dual regulation costs. The method includes acquiring the operating status information of each type of air conditioner within the controlled area and the user's comfort preference settings; constructing a user-equipment dual regulation cost model for each air conditioner, the model including user satisfaction cost and equipment regulation loss cost; establishing a control optimization model with the objective of minimizing the dual regulation costs of all participating air conditioners, constrained by grid power balance and the operating characteristics of each type of air conditioner; and using a distributed optimization algorithm to solve the control optimization model, obtaining optimized control commands for each type of air conditioner and issuing them for execution. This invention achieves collaborative optimization control of multiple types of air conditioners, effectively balancing user experience and equipment health status while meeting grid regulation requirements, and improving the overall benefits of air conditioner load participation in grid interaction.

Description

Technical Field

[0001] This invention belongs to the field of power demand-side management and air conditioning load regulation technology, specifically involving a multi-type air conditioning collaborative control method and system based on user-equipment dual regulation costs. Background Technology

[0002] As an important flexible resource on the demand side, the large-scale regulation of air conditioning load is of great value for peak shaving and valley filling of the power grid and promoting the consumption of new energy. With the popularization and diversification of air conditioning equipment, real-world building scenarios often contain multiple types of air conditioning equipment, such as split-type fixed-frequency air conditioners, split-type variable-frequency air conditioners, and central air conditioning systems. Different types of air conditioners have significant differences in operating characteristics, regulation methods, and loss mechanisms, which poses challenges to the coordinated control of air conditioning load.

[0003] Currently, research on air conditioning load control strategies largely focuses on single-type air conditioning equipment, making it difficult to apply to complex scenarios where multiple types of air conditioners coexist. Some studies focus on the start-stop control of fixed-frequency air conditioners, utilizing their thermal inertia for rotational control; others focus on the continuous power regulation capabilities of variable-frequency air conditioners; still others focus on the chilled water temperature setting or the control of the number of main units in central air conditioning systems. However, existing methods lack a framework for unified modeling and collaborative optimization of multiple types of air conditioners, making it difficult to achieve aggregated control of heterogeneous air conditioning resources.

[0004] On the other hand, existing air conditioning control strategies have limitations in constructing objective functions. Most strategies aim to minimize power deviation or maximize economic benefits, rarely considering the dual impact of control behavior on both the user and equipment sides. Frequent control may reduce indoor thermal comfort and affect user experience; at the same time, frequent start-ups and shutdowns, rapid power adjustments, or long-term operation under suboptimal conditions can accelerate equipment aging and increase the risk of failure. Existing research often treats user comfort and equipment wear and tear as independent constraints, lacking a cost model that can uniformly quantify and weigh the combined effects of both. For example, in grid demand response events, frequent adjustments to air conditioning to quickly respond to dispatch instructions may meet grid demand, but may come at the cost of sacrificing user comfort and shortening equipment lifespan. Summary of the Invention

[0005] To overcome the shortcomings of the existing technologies, this invention provides a multi-type air conditioning collaborative control method and system based on user-equipment dual regulation costs. By constructing a unified and quantitative dual regulation cost model that quantifies the impact of user comfort and equipment regulation losses, and establishing a control optimization model that includes power grid power balance constraints and multi-type air conditioning operation constraints, a distributed optimization algorithm is used to solve the model, generating optimized control commands for each type of air conditioner, thereby realizing the collaborative control of multiple types of air conditioners. This effectively balances user experience and equipment health status while meeting the power grid regulation requirements, and improves the overall benefits of air conditioning load participation in power grid interaction.

[0006] Specifically, the present invention discloses a multi-type air conditioning collaborative control method based on user-equipment dual adjustment costs, comprising the following steps:

[0007] Step S1: Obtain the operating status information of various air conditioning devices in the area to be controlled, as well as the user's comfort preference settings;

[0008] The operating status information includes at least: the current indoor temperature of the space served by the air conditioning equipment, the current cooling capacity of the air conditioning equipment, and the equipment operating parameters determined according to the type of air conditioning equipment; the user comfort preference setting information includes at least: the user-set desired temperature range, which is used to quantify the comfort loss caused by the indoor temperature deviating from the desired temperature range in the user satisfaction cost.

[0009] Considering the different operating parameters of different types of air conditioning equipment, at least one of the following should be selected based on the actual conditions of the controlled space:

[0010] When the air conditioning equipment is a split-type inverter air conditioner, the equipment operating parameters include the current compressor frequency, compressor frequency adjustment rate, optimal operating frequency range, and the deviation value between the current compressor frequency and the optimal operating frequency range.

[0011] When the air conditioning equipment is a split-type fixed-frequency air conditioner, the equipment operating parameters include the current start / stop status, historical start / stop event records, current continuous running time, and current continuous downtime.

[0012] When the air conditioning equipment is a central air conditioning system, the equipment operating parameters include the current start / stop status of the refrigeration unit, the cooling capacity adjustment rate, the current load rate, and the preset surge risk load range and high component stress load range.

[0013] Step S2: Construct a user-equipment dual adjustment cost model for various types of air conditioning equipment. The user-equipment dual adjustment cost model includes user satisfaction cost and equipment adjustment loss cost. The user satisfaction cost quantifies the impact of air conditioning control on user comfort, and the equipment adjustment loss cost quantifies the adjustment loss caused by air conditioning control to the equipment itself.

[0014] Step S3: Establish a control optimization model. The control optimization model aims to minimize the user-equipment dual regulation cost of all air conditioning equipment involved in regulation. The constraints of the control optimization model include power grid power balance constraints and operating constraints of various types of air conditioning.

[0015] Step S4: Use a distributed optimization algorithm to solve the control optimization model, obtain the optimized control commands for each type of air conditioning equipment, and issue them to achieve coordinated control of multiple types of air conditioning equipment.

[0016] Furthermore, the preferred process for constructing the user satisfaction cost is as follows: First, establish a continuous indoor temperature model based on the equivalent thermal parameter method, and discretize the model to obtain the dynamic temperature change within a time step determined by the current room temperature, the current cooling capacity, and the cooling capacity adjustment amount; then, based on the current room temperature and the dynamic temperature change, predict the indoor temperature at the next moment, and calculate the deviation between the predicted temperature and the user-set temperature; finally, based on the deviation, introduce an asymmetric penalty coefficient and a setpoint sensitivity coefficient to calculate the user satisfaction cost.

[0017] The continuous indoor temperature model is expressed as follows: , For the air conditioning equipment number, It is an air conditioning unit. The current indoor temperature of the refrigerated space; This is the equivalent heat capacity of the space; The thermal resistance between this space and the outside; Outdoor temperature; For a certain type of air conditioning equipment cooling capacity, Indicates the type of air conditioner.

[0018] In the aforementioned cost of building user satisfaction, the controller is able to predict the impact of the control behavior on the room temperature in advance, rather than relying solely on the static temperature measurement at the current moment, thus improving the predictability and accuracy of the control; the user's subjective feelings are transformed into objective numerical indicators, enabling the optimization model to directly aim at minimizing the deviation, ensuring that the control results meet the user's expectations.

[0019] Furthermore, the preferred user satisfaction cost is expressed as: ,in This is the temperature deviation penalty adjustment coefficient. real numbers, Let the penalty function be expressed as: ,in, The asymmetric penalty coefficient is used. Set point sensitivity adjustment factor, , , is the setpoint sensitivity coefficient. The general comfort reference temperature; The desired temperature set by the user;

[0020] This refers to the temperature deviation index. , To predict indoor temperature; This refers to the dynamic change in temperature. This is the reference temperature.

[0021] By setting an asymmetric penalty coefficient in the cost function, the different sensitivities of users to "overcooling" and "overheating" are reflected. Users' tolerance for deviations from the set temperature is asymmetric; for example, in summer cooling mode, higher temperatures are more likely to cause discomfort than lower temperatures. The introduction of the asymmetric penalty coefficient makes the cost model more consistent with the human body's thermal comfort perception. Different users have different sensitivities to temperature fluctuations; setting a setpoint sensitivity coefficient allows the model to adaptively adjust according to individual user characteristics, achieving differentiated comfort assurance.

[0022] Furthermore, regarding the construction of equipment regulation loss costs, the preferred approach is to determine the equipment regulation loss costs for different types of air conditioning equipment, specifically as follows:

[0023] For split-type inverter air conditioners, the equipment adjustment loss cost includes the losses generated during the dynamic adjustment of the compressor frequency and the losses generated when the equipment operates in a non-optimal frequency range, expressed as: ,

[0024] in, This is the serial number for a split-type inverter air conditioner. For the first Adjustment loss cost of variable frequency air conditioning equipment For the first Taiwan inverter air conditioner power adjustment range To dynamically adjust the stress index, the corresponding weighting coefficient ; The deviation from the stress index within the operating range, and the corresponding weighting coefficient. ; and The value is a positive real number, and is set according to the operating characteristics of the air conditioning equipment, the degree of impact on the equipment lifespan, and the actual control requirements, or determined through historical operating data.

[0025] For split-type fixed-frequency air conditioners, the equipment regulation loss cost is triggered by the compressor's start-stop switching event, and the generation of this loss cost is limited by the minimum start-stop time constraint. This equipment regulation loss cost is expressed as: ,in, This is the serial number for a split-type fixed-frequency air conditioner. No. Adjustment loss cost of split-type fixed-frequency air conditioning equipment. For the first Power adjustment of a fixed-frequency air conditioner For cost function, This is the time since the last start / stop. For the first The on / off changes of a fixed-frequency air conditioner;

[0026] For a central air conditioning system, the equipment regulation loss cost includes the start-up and shutdown losses of the refrigeration unit, the dynamic losses during the rapid adjustment of cooling capacity, and the losses during operation within a specific load range, expressed as: ,in, For the first Taiwan's central air conditioning system equipment adjustment loss cost, For the first Taiwan central air conditioning power adjustment capacity The weighting coefficient for start / stop events. For start-stop event stress indicators, To quickly adjust the weighting coefficient for cooling capacity, To quickly adjust the stress index for cooling capacity, The deviation weighting factor for the load range operation. The deviation from stress index during operation within the load range; , , The value is a positive real number, and is set according to the operating characteristics of the air conditioning equipment, the degree of impact on the equipment lifespan, and the actual control requirements, or determined through historical operating data.

[0027] Furthermore, the preferred objective function, which aims to minimize the user-device dual adjustment cost, is: ,

[0028] in, User satisfaction cost weighting coefficient positive real numbers, This represents the total number of air conditioning units involved in the regulation. For air conditioning equipment The amount of power regulation; The weighting coefficient for equipment adjustment loss costs. positive real numbers, For air conditioning equipment Equipment adjustment and loss costs.

[0029] Furthermore, the preferred constraints include:

[0030] Power balance constraints of the power grid: ;

[0031] In the formula, For the number of inverter air conditioners, For the first Taiwan inverter air conditioner power adjustment range For the number of fixed-frequency air conditioners, For the first Power adjustment of a fixed-frequency air conditioner For the number of central air conditioning units, For the first Taiwan central air conditioning power adjustment capacity Adjust power to the system target;

[0032] The power adjustment range constraints of the three types of air conditioners:

[0033] ,in, For the first The minimum operating power of a variable frequency air conditioner. For the first The maximum operating power of the inverter air conditioner For the current moment The actual power of the inverter air conditioner For the first The minimum power of a fixed-frequency air conditioner. For the first The maximum power of a fixed-frequency air conditioner For the current moment The actual power of a fixed-frequency air conditioner For the first The minimum operating power of a central air conditioning system in Taiwan. For the first The maximum operating power of the central air conditioning unit. For the current moment, the first The actual power of the central air conditioning unit.

[0034] Furthermore, the preferred distributed optimization algorithm employs the adaptive alternating direction multiplier method to solve the control optimization model. In this distributed optimization framework, a central coordinator is configured, which is communicatively connected to various types of air conditioning equipment to perform the aggregation and updating of global variables, while each air conditioning unit is responsible for its local optimization calculations.

[0035] Specifically, the adaptive alternating direction multiplier method solves the control optimization model through the following iterative process:

[0036] At the beginning of each iteration, each air conditioning unit independently performs local optimization calculations and updates its control variables based on its own user-equipment dual adjustment cost model and the global information received from the coordinator.

[0037] Each air conditioning unit uploads the updated local optimization results to the central coordinator. The central coordinator collects the local optimization results of all air conditioning units and updates the global variables to meet system-level constraints such as power grid balance.

[0038] The central coordinator further updates the dual variables and feeds back the updated global information and dual variables to each air conditioning device as input for the next round of local optimization.

[0039] In the above iterative process, an adaptive penalty parameter adjustment mechanism is introduced to dynamically update the penalty parameter, thereby improving the convergence performance and robustness of the algorithm. Specifically, in each iteration, the original residual and dual residual of the current iteration step are calculated, and the penalty parameter for the next iteration is determined by comparing their relative magnitudes.

[0040] Based on the updated penalty parameters, the next round of local variable updates, global variable updates, and dual variable updates is performed. This process is repeated until the preset convergence criterion is met, and finally the optimal power adjustment command for each type of air conditioning equipment is output, realizing the coordinated control of multiple types of air conditioners.

[0041] Based on the same inventive concept, this invention also discloses a multi-type air conditioning collaborative control system based on user-equipment dual adjustment costs. This system executes the above method, specifically including:

[0042] The status acquisition and preference setting module is used to acquire the operating status information of various types of air conditioners in the building and the user's comfort preference settings;

[0043] The cost model construction module is used to construct a user-equipment dual adjustment cost model for each air conditioning device based on the acquired operating status information and comfort preference settings. The model includes user satisfaction cost and equipment adjustment loss cost.

[0044] An optimization model construction module is used to establish a control optimization model. The control optimization model aims to minimize the user-equipment dual regulation cost of all air conditioning equipment involved in regulation, and the constraints of the control optimization model include power grid power balance constraints and operating constraints of various types of air conditioning.

[0045] The collaborative control module is used to solve the control optimization model through a distributed optimization algorithm, generate and issue optimized control commands for various types of air conditioning equipment, and realize collaborative control of multiple types of air conditioning equipment.

[0046] Compared with the prior art, the beneficial technical effects of the present invention using the above technical solution are as follows:

[0047] 1) This invention breaks through the limitation of existing strategies that are mostly targeted at a single type of air conditioning equipment, and constructs a collaborative control framework that can be compatible with split-type variable frequency air conditioners, split-type fixed frequency air conditioners and central air conditioning systems at the same time. It realizes unified modeling and aggregated control of heterogeneous air conditioning loads, and significantly improves the applicability and versatility of the control strategy in scenarios where multiple types of air conditioners coexist.

[0048] 2) By constructing a refined user-device dual adjustment cost model, this invention systematically quantifies the impact of air conditioning control on user comfort and the wear and tear on the equipment itself, solving the problem of the separation between user-side and equipment-side objectives, and achieving an optimized balance between user experience and equipment health.

[0049] 3) This invention adopts a distributed optimization framework, which decomposes large-scale centralized optimization into local sub-problems of each air conditioner and global coordination problem of the coordinator, thereby reducing computational complexity, improving system scalability, and effectively protecting user privacy. Attached Figure Description

[0050] Figure 1 This is a diagram of the architecture of a multi-type air conditioning collaborative control system based on user-equipment dual adjustment costs, as presented in this invention.

[0051] Figure 2 This is a flowchart of the multi-type air conditioning collaborative control method based on user-equipment dual adjustment costs of the present invention. Detailed Implementation

[0052] To further explain the technical content of the present invention, specific embodiments are provided below in conjunction with the accompanying drawings.

[0053] Example 1: A Multi-Type Air Conditioning Cooperative Control Method Based on User-Equipment Dual Adjustment Costs

[0054] Taking a certain building as the area to be controlled, based on such Figure 1The system architecture shown demonstrates how a building load controller interacts with different types of air conditioning equipment, including split-type inverter air conditioners, split-type fixed-frequency air conditioners, and central air conditioning systems, to achieve coordinated control.

[0055] It should be noted that the air conditioning equipment mentioned in this invention refers to the smallest control unit capable of independently participating in power regulation and executing the distributed optimization algorithm.

[0056] Specifically: For split-type air conditioners (including inverter and fixed-frequency models), each independent indoor and outdoor unit combination constitutes an air conditioning unit; for central air conditioning systems, for accurate modeling, each control unit with independent adjustment capabilities is considered an air conditioning unit. The control unit can be a physical terminal device or a logically defined independent temperature control zone. For example... Figure 2 As shown, the method specifically includes: First, the building load controller collects real-time operating status information from each air conditioning unit, including at least the current indoor temperature of the space served by the air conditioning unit, the current cooling capacity of the air conditioning unit, and equipment operating parameters determined according to the type of air conditioning unit. The equipment operating parameters include at least one of the following:

[0057] When the air conditioning equipment is a split-type inverter air conditioner, the equipment operating parameters include the current compressor frequency, compressor frequency adjustment rate, optimal operating frequency range, and the deviation value between the current compressor frequency and the optimal operating frequency range.

[0058] When the air conditioning equipment is a split-type fixed-frequency air conditioner, the equipment operating parameters include the current start / stop status, historical start / stop event records, current continuous running time, and current continuous downtime.

[0059] When the air conditioning equipment is a central air conditioning system, the equipment operating parameters include the current start / stop status of the refrigeration unit, the cooling capacity adjustment rate, the current load rate, as well as the preset surge risk load range and high component stress load range.

[0060] User comfort preference setting information includes at least: the user-defined desired temperature range, used to quantify the comfort loss caused by indoor temperature deviating from the desired temperature range in the user satisfaction cost;

[0061] Secondly, based on the acquired air conditioning status information and user preference settings, a refined user-equipment dual regulation cost (UDC) model is constructed for each air conditioning device involved in regulation. This model has two core components: one is the user satisfaction cost, which quantifies the loss of comfort perceived by the user when the indoor temperature deviates from the user's expected comfort range due to air conditioning regulation, and converts it into economic costs; the other is the equipment regulation loss cost, which quantifies the additional physical wear, efficiency reduction, or potential lifespan reduction caused to the air conditioning device when executing regulation commands and changing its operating state, and also converts it into economic costs. Thirdly, based on the UDC model constructed for each air conditioner, the system further establishes a control optimization model with minimizing the overall UDC of all air conditioners involved in regulation in the building as the core optimization objective, while strictly considering the power balance constraints from the power grid and the inherent operating characteristic constraints of each type of air conditioning device.

[0062] Finally, to efficiently solve this optimization problem involving a large number of heterogeneous devices and complex constraints, a distributed optimization algorithm is adopted. In this embodiment, the adaptive alternating direction multiplier method is used to coordinate the local decisions of each air conditioning device through iterative calculation. Finally, a set of optimized control instructions for each type of air conditioner that minimizes the overall UDC and satisfies all constraints is obtained. These instructions are then sent to the corresponding air conditioning devices for execution, thereby realizing intelligent, economical, and comfortable collaborative control of multi-type air conditioning clusters in the building.

[0063] The process of constructing the user satisfaction cost in this embodiment specifically includes: First, for buildings equipped with a certain type of air conditioner... For refrigerated spaces, the indoor temperature is described using the equivalent thermal parameter method:

[0064]

[0065] In the formula, It is an air conditioning unit. The current indoor temperature of the refrigerated space ( ); The equivalent heat capacity of this space ( ); The thermal resistance between this space and the outside ( ); Outdoor temperature ( ). For a certain type of air conditioning equipment cooling capacity ( ), Indicates the type of air conditioner.

[0066] Discretizing the above equation using the forward Euler method yields the result from the initial room temperature. Initial cooling capacity and cooling capacity adjustment Get a time step The subsequent dynamic change in temperature :

[0067]

[0068] Then, based on the regulated predicted indoor temperature With the user's desired temperature To account for the discrepancy between temperature and user satisfaction costs, when calculating user satisfaction costs, it's important to consider that users' perception of temperature changes is not asymmetrical. Generally, discomfort caused by rising temperature (getting hotter) is greater than discomfort caused by falling temperature (getting colder). Therefore, an asymmetric penalty coefficient is used to reflect this difference. This is reflected in the user's sensitivity to deviations from different set temperature points, and is addressed through a set point sensitivity adjustment factor. The embodiment, its calculation method is as follows:

[0069]

[0070] in Setpoint sensitivity coefficient The temperature is a general comfort reference temperature. A penalty function is used. To calculate the cost of user satisfaction, the penalty function is defined as follows:

[0071]

[0072] in, This refers to the temperature deviation index. , The reference temperature is used to normalize temperature deviations; the final user satisfaction cost. for:

[0073]

[0074] in, For air conditioning equipment User satisfaction cost For air conditioning equipment The amount of power regulation, This represents the temperature deviation penalty adjustment coefficient. In this embodiment, the construction of equipment adjustment loss costs is modeled in detail for different types of air conditioning equipment: for split-type inverter air conditioners... Its equipment adjustment loss cost Two main aspects are considered. The first aspect is the additional electromagnetic and mechanical stress generated on the power module and motor by the compressor's dynamic frequency adjustment process, which is quantified as a dynamic adjustment stress index. :

[0075]

[0076] in, It is the change in frequency. It is the rate of change of the reference frequency. Let i be the power adjustment amount of the i-th inverter air conditioner; To control the cycle, For the first The ratio coefficient between the frequency and power of a split-type inverter air conditioner compressor;

[0077] The second aspect is the efficiency decline and accelerated aging caused by air conditioning equipment operating in a non-optimal operating frequency range for extended periods, which is quantified as the deviation stress index of the operating range. :

[0078]

[0079] in It is the initial frequency. It is the rated frequency. It is the optimal efficiency point frequency. It is the first The current operating power of the split-type inverter air conditioner. The total adjustment loss cost of the inverter air conditioning equipment is:

[0080]

[0081] in, For the first Adjustment loss cost of split-type inverter air conditioning equipment. To dynamically adjust the stress index, the corresponding weighting coefficient ; The deviation from the stress index within the operating range, and the corresponding weighting coefficient. ; and The value is a positive real number, and is set according to the operating characteristics of the air conditioning equipment, the degree of impact on the equipment lifespan, and the actual control requirements, or determined through historical operating data.

[0082] For split-type fixed-frequency air conditioners Its equipment adjustment loss cost It is mainly related to start-stop switching events, through the first Switching variables of a fixed-frequency air conditioner , Indicates startup. Indicates shutdown. This indicates no change. At the same time, to avoid damage to the equipment from excessively frequent start-ups and shutdowns, the minimum start-up time must also be considered. and minimum shutdown time The constraints, its cost function for:

[0083]

[0084] in It is the time since the last switch. It is a relatively large penalty value. and It's the basic cost of switching. This is an indicator variable for changes in on / off state. The total regulation loss cost of the fixed-frequency air conditioning equipment is:

[0085]

[0086] in, For cost function, Let j be the time since the j-th fixed-frequency air conditioner last started or stopped. For the first The on / off changes of a fixed-frequency air conditioner.

[0087] For central air conditioning systems The operating power of its refrigeration unit With its total cooling capacity There is a certain relationship between them, which is usually described by the Coefficient of Performance (COP).

[0088]

[0089] in For partial load factor, This refers to the rated cooling capacity. The COP value is usually the partial load factor. A function of can generally be expressed as a quadratic polynomial:

[0090]

[0091] in The coefficient of performance is the performance coefficient under rated operating conditions. The fitting coefficients are real numbers, and their specific values ​​are determined based on the actual performance characteristics of the central air conditioning equipment. Define the function. , representing the cooling capacity function corresponding to power, for a given power , The unique physically feasible solution to the above equation ,in , Let K be the minimum and maximum total cooling capacity that the central air conditioning system k can provide under equipment operating constraints.

[0092]

[0093] Total cooling capacity generated by the central air conditioning system It needs to be based on the various spaces it cools. ,in, , This refers to the collection of spaces refrigerated by a central air conditioning system. It involves allocating the comprehensive cooling load demand of the areas covered by the central air conditioning system to obtain the actual cooling capacity of each space. Generally, the allocation can be based on the weighting of the deviations between the temperature of each space and the set temperature:

[0094]

[0095] in, It is a central air conditioning system The first service Priority weighting of each cooling space It is a magnification factor, and its value is a positive real number. For the index variable of the cooling space during the summation process, For central air conditioning system The first service Priority weighting of each cooling space For a small positive number, Values In one embodiment, it is preferable to This is to prevent calculation instability caused by excessively small or negative temperature differences. For cooling space The current indoor temperature, For cooling space The set temperature.

[0096] The equipment adjustment loss cost of central air conditioning mainly includes three parts:

[0097] Part 1: Start-up and shutdown losses of the refrigeration unit, assessed through stress indicators of start-up and shutdown events. To quantify, Indicates a start / stop operation has occurred. This indicates that it did not occur;

[0098] Part Two: Thermal or mechanical stresses generated during rapid dynamic adjustment of cooling capacity, and stress indices related to dynamic adjustment of cooling capacity. To measure:

[0099]

[0100] in It's a change in cooling capacity. It is the reference refrigeration rate of change. For the first The initial operating power of the central air conditioning system before the current adjustment. For the first Power regulation of Taiwan's central air conditioning system. Let be a function of the cooling capacity of the chiller at a given operating power. For the first The rated cooling capacity of the central air conditioning system in Taiwan For the first The initial cooling capacity of the central air conditioning system before the current adjustment;

[0101] Part Three: The inherent losses or risks incurred by the unit operating within a specific load range, as a result of deviations from stress parameters during the operating range. To measure:

[0102]

[0103] in For partial load factor, , Designed to operate within the unit's optimal efficiency range, the value is typically lower at medium to high loads, but increases significantly at extremely low and extremely high loads.

[0104]

[0105] in and These are the low-load and high-load risk warning lines, respectively. It is the theoretically optimal efficiency point. These are all parameters characterizing loss properties, among which, Weighted for low load loss. Weighting for high load losses, To penalize weights for deviations from optimal efficiency, , , The value is a positive real number, which can be determined based on the operating characteristics of the central air conditioning equipment, historical operating data, or performance curve data provided by the manufacturer. For low load growth rate, For high load growth rates, the total equipment regulation loss cost of central air conditioning is the weighted sum of the costs corresponding to these three stress indicators:

[0106]

[0107] in, For the first Taiwan's central air conditioning system equipment adjustment loss cost, Let k be the power adjustment amount of the central air conditioning unit. The weighting coefficient for start / stop events. For start-stop event stress indicators, To quickly adjust the weighting coefficient for cooling capacity, To quickly adjust the stress index for cooling capacity, The deviation weighting factor for the load range operation. This refers to the deviation from the stress index during operation within the load range.

[0108] It should be noted here that when constructing the user-equipment dual adjustment cost model, the air conditioning equipment is uniformly used as the basic unit. For split air conditioners, the model is directly modeled as a single air conditioner unit; for central air conditioning systems, the model is modeled as each control unit considered as an air conditioning equipment. Therefore, regardless of the physical form of the system, the method of this invention can perform distributed optimization calculations using a unified data structure.

[0109] In this embodiment, the specific process of solving the control optimization model using the adaptive alternating direction multiplier method is as follows: First, the power regulation of all air conditioning equipment involved in the control within the building, i.e., the load change caused by the control actions of the air conditioning equipment, is calculated. As decision variables, we construct an optimization objective function, which is to minimize the weighted sum of the user satisfaction cost and the equipment adjustment loss cost of all air conditioners:

[0110] ,

[0111] in, User satisfaction cost weighting coefficient This represents the total number of air conditioning units involved in the regulation. For air conditioning equipment User satisfaction cost For air conditioning equipment The amount of power regulation; The weighting coefficient for equipment adjustment loss costs. For air conditioning equipment Equipment adjustment and loss costs.

[0112] This optimization problem must satisfy the power balance constraint of the power grid:

[0113]

[0114] In the formula, For the number of inverter air conditioners, For the first Taiwan inverter air conditioner power adjustment range For the number of fixed-frequency air conditioners, For the first Power adjustment of a fixed-frequency air conditioner For the number of central air conditioning units, For the first Taiwan central air conditioning power adjustment capacity Adjust power to the system target;

[0115] And the power adjustment range constraints of the three types of air conditioners themselves:

[0116]

[0117] in, For the first The minimum operating power of a variable frequency air conditioner. For the first The maximum operating power of the inverter air conditioner For the current moment, the first The actual power of the inverter air conditioner For the first The minimum power of a fixed-frequency air conditioner. For the first The maximum power of a fixed-frequency air conditioner For the current moment, the first The actual power of a fixed-frequency air conditioner For the first The minimum operating power of a central air conditioning system in Taiwan. For the first The maximum operating power of the central air conditioning unit. For the current moment, the first The actual power of the central air conditioning unit.

[0118] To efficiently solve this large-scale optimization problem, the Adaptive Alternating Direction Multiplier Method (ADMM) is adopted. First, a global auxiliary variable is introduced. and the corresponding dual variables Constructing the augmented Lagrange function :

[0119]

[0120] in, , The penalty parameter is used. In the ADMM iterative process, let the current iteration step be... It mainly includes three core steps: the first step is to update local variables, for each air conditioning unit. Solve independently:

[0121]

[0122] The second step is global variable updating, where the central coordinator gathers local decision information from all air conditioners. and dual variables Through the Perform operations such as projection to update global auxiliary variables To meet the total power balance constraint , Adjust power to the system target; For air conditioning equipment Permissible power adjustment range.

[0123] The third step is to update the dual variables:

[0124]

[0125] To improve the convergence speed and robustness of the algorithm, an adaptive penalty parameter was also used during the iteration process. The adjustment mechanism compares the original residuals. and dual residual The relative size is used to dynamically adjust the penalty parameters for the next round. By iterating through the above process until the preset convergence criterion is met, the optimal power adjustment command for each air conditioning unit can be obtained.

[0126] To implement the control method proposed in this invention within existing building air conditioning control systems, corresponding modifications are required. These modifications primarily involve enhancing communication capabilities and adding or upgrading computing units at the hardware level, as well as implementing algorithms and establishing data interaction protocols at the software level. Specifically, this includes the following aspects:

[0127] First, a central coordinator should be deployed or designated at the building or park level. This coordinator is responsible for executing the global variable update and dual variable update steps in the Adaptive Alternating Direction Multiplier Algorithm (ADMM). The central coordinator needs to have sufficient computing power to process all local decision information uploaded by the air conditioning systems involved in regulating them. It is equipped with a two-way communication interface to transmit updated global information. and and penalty parameters Distributed to each air conditioner unit.

[0128] Secondly, for each air conditioning unit participating in the regulation, its local controller or agent controller needs to be configured or upgraded. These local controllers are responsible for executing the local variable update steps in the ADMM algorithm, specifically including: receiving global information from the central coordinator. and as well as It collects indoor temperature data in real time and obtains the user-set temperature and operating characteristic parameters of the air conditioning equipment; solves local optimization subproblems to obtain local optimal decisions and uploads them to the central coordinator; and receives and executes the final control commands issued by the central coordinator.

[0129] Currently, low-cost, high-performance embedded microcontrollers, single-board computers, and programmable logic controllers are very mature and widely used, and are fully capable of undertaking the aforementioned local optimization computing and communication tasks.

[0130] At the communication level, a standardized communication protocol and data format should be established for information exchange between the central coordinator and each local controller. This protocol needs to support the reliable and timely transmission of various variables during the iteration process and effectively handle potential network latency and data loss issues.

[0131] At the software level, corresponding algorithm modules need to be deployed on the central coordinator and each local controller. The central coordinator software needs to implement global variable updates, dual variable updates, and adaptive penalty parameter adjustment mechanisms; the local controller software needs to implement the construction of the user-device dual regulation cost model and the algorithm for solving local subproblems.

[0132] Through the aforementioned hardware deployment, communication design, and software configuration, traditional, independently operating air conditioning systems can be transformed into a distributed control system capable of collaborative optimization and intelligent response, thereby realizing the multi-type air conditioning collaborative control method based on user-equipment dual regulation costs proposed in this invention.

[0133] This embodiment, through the aforementioned method, enables refined collaborative control of building systems containing multiple types of air conditioners. Its core advantages are: First, it innovatively constructs a user-equipment dual regulation cost model. This model not only comprehensively quantifies the specific impact of air conditioning regulation on user comfort but also meticulously assesses the potential operational losses and lifespan impacts of regulation behavior on different types of air conditioning equipment, providing a solid quantitative foundation for achieving an optimal balance between user needs and equipment economics. Second, by introducing an adaptive alternating direction multiplier method for distributed optimization, it effectively solves the problems of high computational complexity, heavy communication burden, and user privacy leakage faced in centralized control of large-scale air conditioning clusters, giving the control strategy good scalability and practical applicability. Finally, this embodiment, while meeting the power dispatch requirements of the power grid, can intelligently guide each air conditioning device to participate in demand response in the most economical and comfortable way by dynamically optimizing the overall UDC of the building's air conditioning cluster, thereby improving the overall efficiency and sustainability of building energy management.

[0134] Example 2

[0135] This invention also discloses a multi-type air conditioning collaborative control system based on user-equipment dual adjustment costs, the system comprising:

[0136] The status acquisition and preference setting module is responsible for collecting the real-time operating status of various types of air conditioners and the user's comfort preference settings from various sensors and user interfaces in the building;

[0137] The cost modeling module, based on the acquired operating status information and comfort preference settings, constructs a user-equipment dual adjustment cost model for each air conditioning unit. This model specifically includes:

[0138] User satisfaction cost is calculated based on the deviation between the predicted indoor temperature and the user's set temperature. It takes into account the user's dissatisfaction cost caused by the temperature deviating from the set point, and considers asymmetry and set point sensitivity.

[0139] Equipment adjustment loss cost: For different types of air conditioning equipment, calculate the equipment loss cost caused by the adjustment operation.

[0140] An optimization model construction module is used to establish a control optimization model; the control optimization model aims to minimize the dual regulation cost of users and equipment, and the constraints of the control optimization model include power grid power balance constraints and operating constraints of various types of air conditioners;

[0141] The collaborative control module uses a distributed optimization algorithm to solve the optimization problem established by the optimization decision module. This module coordinates the various air conditioning devices to perform iterative calculations until the optimal control strategy is converged. Finally, it generates control commands for each air conditioning device and sends them to the corresponding air conditioning execution units, thereby achieving collaborative, economical, and comfortable operation of multiple types of air conditioners.

[0142] Through the cooperation between modules, the system achieves intelligent and precise collaborative control of various types of air conditioners, maximizing the balance between user comfort and equipment operating economy while meeting the power grid demand response.

[0143] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for cooperative control of multi-type air conditioners based on user-equipment dual adjustment cost, characterized in that, Includes the following steps: Step S1: Obtain the operating status information of various air conditioning devices in the area to be controlled, as well as the user's comfort preference settings; Step S2: Based on the information obtained in Step S1, construct a user-equipment dual regulation cost model for various types of air conditioning equipment. The user-equipment dual regulation cost model includes user satisfaction cost and equipment regulation loss cost. The user satisfaction cost quantifies the impact of air conditioning regulation on user comfort, and the equipment regulation loss cost quantifies the regulation loss caused by air conditioning regulation to the equipment itself. Step S3: Establish a control optimization model. The control optimization model aims to minimize the user-equipment dual regulation cost of all air conditioning equipment involved in regulation. The constraints of the control optimization model include power grid power balance constraints and operating constraints of various types of air conditioning. Step S4: Use a distributed optimization algorithm to solve the control optimization model, obtain the optimized control commands for each type of air conditioning equipment, and issue them to achieve coordinated control of multiple types of air conditioning equipment.

2. The method of claim 1, wherein the method further comprises: The operating status information in step S1 includes at least: the current indoor temperature of the space served by the air conditioning equipment, the current cooling capacity of the air conditioning equipment, and the equipment operating parameters determined according to the type of air conditioning equipment; the user's comfort preference setting information includes at least: the user's set desired temperature range, which is used to quantify the comfort loss caused by the indoor temperature deviating from the desired temperature range in the user satisfaction cost.

3. The multi-type air conditioning collaborative control method based on user-equipment dual adjustment costs according to claim 2, characterized in that, The equipment operating parameters include at least one of the following: When the air conditioning equipment is a split-type inverter air conditioner, the equipment operating parameters include the current compressor frequency, compressor frequency adjustment rate, optimal operating frequency range, and the deviation value between the current compressor frequency and the optimal operating frequency range. When the air conditioning equipment is a split-type fixed-frequency air conditioner, the equipment operating parameters include the current start / stop status, historical start / stop event records, current continuous running time, and current continuous downtime. When the air conditioning equipment is a central air conditioning system, the equipment operating parameters include the current start / stop status of the refrigeration unit, the cooling capacity adjustment rate, the current load rate, and the preset surge risk load range and high component stress load range.

4. The multi-type air conditioning collaborative control method based on user-equipment dual adjustment costs according to claim 1, characterized in that, The construction of the user satisfaction cost includes: A continuous indoor temperature model based on the equivalent thermal parameter method is established, and the model is discretized to obtain the dynamic temperature change within a time step determined by the current room temperature, the current cooling capacity, and the cooling capacity adjustment. Based on the current room temperature and the dynamic temperature change, the indoor temperature at the next moment is predicted, and the deviation between the predicted temperature and the user-set temperature is calculated. Based on the deviation value, an asymmetric penalty coefficient and a setpoint sensitivity coefficient are introduced to calculate the user satisfaction cost.

5. The multi-type air conditioning collaborative control method based on user-equipment dual adjustment costs according to claim 1 or 4, characterized in that, The user satisfaction cost is expressed as ,in, For the air conditioning equipment number, For air conditioning equipment The cost of user satisfaction For air conditioning equipment The amount of power regulation, This is the temperature deviation penalty adjustment coefficient. real numbers; Let the penalty function be expressed as: ,in, This refers to the temperature deviation index. The asymmetric penalty coefficient is used. Set point sensitivity adjustment factor, ,in, To set the sensitivity coefficient, real numbers; For general comfort reference temperature, The desired temperature set by the user.

6. The multi-type air conditioning collaborative control method based on user-equipment dual adjustment costs according to claim 1, characterized in that, The process of constructing the equipment adjustment loss cost includes: For split-type inverter air conditioners, the equipment adjustment loss cost includes the loss generated during the dynamic adjustment of the compressor frequency and the loss generated when the equipment operates in a non-optimal operating frequency range. For split-type fixed-frequency air conditioners, the equipment adjustment loss cost is triggered by the compressor start-stop switching event, and the generation of this loss cost is limited by the minimum start-stop time constraint. For central air conditioning systems, the equipment adjustment loss cost includes the start-up and shutdown losses of the refrigeration unit, the dynamic losses during the rapid adjustment of cooling capacity, and the losses during operation within a specific load range.

7. The multi-type air conditioning collaborative control method for user-equipment dual regulation costs according to claim 1, characterized in that, The objective function in step S3, which aims to minimize the user-device dual adjustment cost, is: , in, User satisfaction cost weighting coefficient This represents the total number of air conditioning units involved in the regulation. For air conditioning equipment The cost of user satisfaction For air conditioning equipment The amount of power regulation; The weighting coefficient for equipment adjustment loss costs. For air conditioning equipment Equipment adjustment and loss costs.

8. The multi-type air conditioning collaborative control method for user-equipment dual regulation costs according to claim 1, characterized in that, The constraints mentioned in step S3 include: Power balance constraints of the power grid: , In the formula, For the number of inverter air conditioners, For the first Taiwan inverter air conditioner power adjustment range For the number of fixed-frequency air conditioners, For the first Power adjustment of a fixed-frequency air conditioner For the number of central air conditioning units, For the first Taiwan central air conditioning power adjustment capacity Adjust power to the system target; The power adjustment range constraints of the three types of air conditioners: ,in, For the first The minimum operating power of a variable frequency air conditioner. For the first The maximum operating power of the inverter air conditioner For the current moment The actual power of the inverter air conditioner For the first The minimum power of a fixed-frequency air conditioner. For the first The maximum power of a fixed-frequency air conditioner For the current moment The actual power of a fixed-frequency air conditioner For the first The minimum operating power of a central air conditioning system in Taiwan. For the first The maximum operating power of the central air conditioning unit. For the current moment The actual power of the central air conditioning unit.

9. The multi-type air conditioning collaborative control method for user-equipment dual regulation costs according to claim 1, characterized in that, The distributed optimization algorithm adopts the adaptive alternating direction multiplier method, and dynamically updates the penalty parameter in each iteration through an adaptive penalty parameter adjustment mechanism; The adaptive penalty parameter adjustment mechanism includes: calculating the original residual and the dual residual of the current iteration step, comparing their relative magnitudes, and determining the penalty parameter for the next round based on the comparison result; based on the updated penalty parameter, continuing to perform local variable updates, global variable updates, and dual variable updates until the convergence criterion is met, and outputting the optimal power adjustment command for each type of air conditioning equipment.

10. A multi-type air conditioning collaborative control system based on user-equipment dual adjustment costs, characterized in that, The system performs the method described in any one of claims 1-9, specifically including: The status acquisition and preference setting module is used to acquire the operating status information of various types of air conditioners in the building and the user's comfort preference settings; The cost model construction module is used to construct a user-equipment dual adjustment cost model for each air conditioning device based on the acquired operating status information and comfort preference settings. The model includes user satisfaction cost and equipment adjustment loss cost. An optimization model construction module is used to establish a control optimization model. The control optimization model aims to minimize the user-equipment dual regulation cost of all air conditioning equipment involved in regulation, and the constraints of the control optimization model include power grid power balance constraints and operating constraints of various types of air conditioning. The collaborative control module is used to solve the control optimization model through a distributed optimization algorithm, generate and issue optimized control commands for various types of air conditioning equipment, and realize collaborative control of multiple types of air conditioning equipment.

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