Air conditioner cluster optimization decision method and device considering user regulation characteristics

Abstract

The application discloses an air conditioner cluster optimization decision method and device considering user regulation characteristics, and relates to the technical field of demand response. The air conditioner cluster optimization decision method considering user regulation characteristics comprises the following steps: obtaining user basic information, power consumption operation data and regulation sensitivity parameters; constructing an air conditioner cluster load model considering regulation sensitivity based on the regulation sensitivity parameters, which is used for differentiating indoor temperature, adjustable time length and operation constraints of different users; constructing a multi-objective regulation optimization model considering both the economy of an aggregator and the comfort of a user based on the air conditioner cluster load model under the premise of meeting the regulation demand of a power grid load; and solving the multi-objective optimization model to obtain a load regulation decision scheme. The application can effectively distinguish the regulation capacity and bearing level of different users while ensuring the safe operation of a power grid, improve the execution effect of a load optimization scheme and the participation enthusiasm of a user, and has good engineering application value.

Description

Technical Field

[0001] This invention relates to an air conditioning cluster optimization decision-making method and apparatus that takes into account user control characteristics, belonging to the field of demand response technology. Background Technology

[0002] With the continuous widening of the peak-valley difference in the power system and the high proportion of new energy sources such as wind power and photovoltaics connected to the grid, the power system's supply and demand balance faces severe challenges. Air conditioning load, as a crucial component of peak grid load, possesses significant potential for flexible regulation after large-scale aggregation. Deeply exploring the adjustable potential of air conditioning clusters and guiding their participation in peak-shaving interactions has become an important means to smooth grid peak-valley fluctuations and improve operational safety and economy.

[0003] The National Development and Reform Commission (NDRC) requires that power grid operation prioritize ensuring a safe and reliable power supply and determine load management schemes based on the actual balance of power supply and demand. Currently, the following are publicly available solutions for air conditioning load participation in peak shaving and regulation: Application No. CN119022407A discloses a method for renewable energy consumption of aggregated air conditioning loads that considers building heat transfer characteristics. This method quantifies comfort by constructing a building RC heat transfer model and introducing the Predicted Average Votes (PMV) index, achieving precise regulation of air conditioning loads. Application No. CN119222719B discloses a method for cluster control and scheduling optimization of air conditioning loads. This method constructs a dynamic response model of the air conditioning load state queue model, comprehensively considering the consistency between rebound load smoothing and user comfort, and provides a scheduling strategy that allows users to participate in the response multiple times. While existing solutions have made progress in improving regulation accuracy and reducing aggregation costs, they do not fully consider the deep participation of users in the peak shaving process. For example, the differences in sensitivity of different users to regulation duration and temperature adjustment amplitude are not fully represented, and the linkage between regulation strategies, hierarchical compensation mechanisms, and user subjective satisfaction needs to be strengthened. This may lead to a mismatch between the control granularity and the actual thermal inertia characteristics, resulting in problems such as decreased user satisfaction and low willingness to respond, making it difficult to effectively support the large-scale and efficient dispatch of air conditioning loads. Summary of the Invention

[0004] The purpose of this invention is to provide an air conditioning cluster optimization decision-making method and device that considers user control characteristics. By constructing an air conditioning cluster load model that integrates sensitivity coefficients and equivalent thermal parameter models, and using the Sigmoid function to quantify the loss of user satisfaction in the user behavior dimension, a dual-objective optimization decision-making model is constructed with the goals of maximizing aggregator economic benefits and maximizing user satisfaction. The Pareto front solution and knee point decision method are combined for scheme selection, so as to effectively balance peak-shaving economy and user thermal comfort while meeting the grid control requirements, thereby improving the scientific nature and execution efficiency of the air conditioning cluster optimization decision-making scheme.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution.

[0006] On the one hand, the present invention provides an air conditioning cluster optimization decision-making method that considers user control characteristics, including:

[0007] Acquire basic user information, electricity consumption data, and control sensitivity parameters;

[0008] Based on the aforementioned regulation sensitivity parameters, an air conditioning cluster load model that takes regulation sensitivity into account is constructed to differentiate the indoor temperature, adjustable duration, and operating constraints of different users.

[0009] Based on the aforementioned air conditioning cluster load model, a multi-objective control optimization model is constructed that takes into account both the aggregator's economic efficiency and the user's comfort, while meeting the power grid load control requirements.

[0010] The multi-objective regulation optimization model is solved to obtain the load regulation decision scheme.

[0011] Optionally, the user basic information, electricity consumption operation data, and regulation sensitivity parameters are obtained by collecting basic data of air conditioning user groups participating in peak shaving within the target area;

[0012] The user's basic information includes the air conditioner's rated power, equivalent thermal resistance, equivalent heat capacity, initial set temperature, and upper and lower limits of room temperature.

[0013] The power consumption data includes air conditioner on / off status, indoor and outdoor temperatures, and load baseline curves.

[0014] The control sensitivity is used to characterize the user's tolerance for load control duration and temperature adjustment range; it is set in levels according to the user's tolerance for load control duration and temperature adjustment range, and the user's tolerance for air conditioning control duration and temperature adjustment range is characterized by control sensitivity, and a system is established based on the sensitivity level. User classification system:

[0015] ;

[0016] In the formula, These are user sensitivity classification levels;

[0017] The higher the control sensitivity, the shorter the duration for which users are willing to participate in control, and the smaller the allowable range of indoor temperature adjustment; the lower the control sensitivity, the longer the duration for which users are willing to participate in control, and the larger the allowable range of indoor temperature adjustment.

[0018] Optionally, the process of constructing the air conditioning cluster load model includes:

[0019] A first-order equivalent thermal parameter (ETP) model is used to describe the thermal dynamics of a building room under air conditioning control. The thermal characteristics of the building envelope are simplified into units composed of equivalent thermal resistance and equivalent heat capacity. A mathematical expression characterizing the dynamic response of indoor temperature to natural outdoor temperature changes is then established.

[0020] ;

[0021] In the formula, for Real-time room temperature; for The ambient temperature at all times; This is for simulating step size; and These are the equivalent thermal resistance and equivalent heat capacity of the building's rooms, respectively.

[0022] air conditioner The changes in indoor temperature and switch status after the addition are represented as follows:

[0023] ;

[0024] In the formula, for Air conditioning The on / off status is 0 when the air conditioner is off and 1 when the air conditioner is on. For air conditioning The equivalent thermal resistance; For air conditioning The equivalent heat capacity; for Air conditioning The electrical power; This represents the maximum power consumption of a single air conditioner unit. For air conditioning exist The indoor temperature at any given time; , The upper and lower limits of the set room temperature; For air conditioning The initial set temperature;

[0025] Based on the acquired control sensitivity, the indoor temperature and adjustable duration are adjusted for different users:

[0026] Indoor temperature based on user sensitivity coefficient Set differentiated indoor temperature comfort ranges:

[0027] ;

[0028] In the formula: For the first User-specific sensitivity coefficient; and They are respectively Air conditioning Minimum and maximum permissible indoor temperatures; The average of the allowable temperature adjustment range;

[0029] The adjustable duration By setting the maximum control time for users with different levels of sensitivity. Make corrections:

[0030] ;

[0031] In the formula, For air conditioning exist The duration has been adjusted.

[0032] consider A cluster load model for air conditioning systems, taking into account regulation sensitivity, is obtained from the analysis of individual air conditioning loads. :

[0033] ;

[0034] In the formula, This represents the total duration of demand response regulation.

[0035] Optionally, in the construction of the multi-objective regulation and optimization model that takes into account both the aggregator's economic efficiency and user comfort, the calculation process for the aggregator's economic efficiency is as follows: peak-shaving revenue on the grid side. The product of the aggregator's actual peak-shaving volume and the peak-shaving unit price is expressed as:

[0036] ;

[0037] In the formula, for Real-time electricity prices in the peak-shaving market; This is for simulating step size; To adjust the peak shaving capacity of aggregators, the following provisions are made regarding their peak shaving capabilities:

[0038] ;

[0039] In the formula, for Real-time aggregation of peak shaving volume. This refers to the peak-shaving demand on the power grid side. , , This is the confirmation coefficient for peak-shaving capacity; when the peak-shaving amount Below When this occurs, it indicates that the peak-shaving effect is significantly insufficient, and the power grid side considers it as failing to meet the peak-shaving requirements, and penalizes the peak-shaving behavior at that moment; when In When the peak-shaving range is within a certain interval, the peak-shaving amplitude is considered low, and this portion of the peak-shaving contribution is not included in the effective peak-shaving amount; that is, the effective peak-shaving amount is recorded as zero. achieve During the interval, it is assumed that the peak-shaving behavior meets the grid's peak-shaving requirements, and the grid side performs metering and settlement based on the actual peak-shaving amount; when Exceed To avoid excessive peak shaving from adversely affecting system operation and user comfort, the grid side sets an upper limit on the measurable peak shaving amount, with a maximum value of [value missing]. Through the aforementioned segmented confirmation mechanism, air conditioning aggregators are guided to meet the grid's peak-shaving needs while avoiding ineffective or excessive peak-shaving activities.

[0040] The impact of temperature regulation amplitude and duration on user comfort during the regulation process is quantified using regulation factors, and a peak-shaving subsidy model on the user side is further constructed; regulation factors The product of the controlled temperature and the duration is used as the metric, as shown in the following formula:

[0041] ;

[0042] In the formula, for Air conditioning The on / off state; For air conditioning The initial set temperature; for Air conditioning Temperature;

[0043] The physical meaning can be interpreted as follows: 3℃·h represents a temperature adjustment range of 3℃ for 1 hour, or a temperature adjustment range of 2℃ for 1.5 hours. It reflects the cumulative thermal comfort impact on users during the temperature adjustment process. The larger the value, the more obvious the negative impact of peak-shaving behavior on user comfort, and more subsidies should be given.

[0044] To ensure the targeted incentive of peak-shaving subsidies, define air conditioning Subsidized unit price In order to regulate factors Related piecewise functions This means that the greater the sacrifice in comfort a user makes during peak hours, the higher the subsidized unit price they receive, expressed as:

[0045] ;

[0046] In the formula, , , The subsidized unit price corresponds to different levels of user comfort sacrifice, satisfying... ;

[0047] User-controlled subsidies are represented as follows:

[0048] ;

[0049] In the formula, Adjustments and subsidies for user income; For regulation; This represents the total number of air conditioners.

[0050] Optionally, in the construction of the multi-objective regulation and optimization model that takes into account both the aggregator's economic efficiency and user comfort, the calculation process for user comfort is as follows:

[0051] Taking into account both objective regulatory factors and subjective regulatory sensitivity, the Sigmoid function is used to characterize the differentiated user satisfaction level, as shown in the following formula:

[0052] ;

[0053] In the formula, Let the satisfaction function be used. For conversion parameters; The minimum satisfaction coefficient; , The coefficients are constants. As a regulatory factor; for The maximum control factor for a user class represents the maximum acceptable sacrifice threshold for that user class. The maximum acceptable sacrifice threshold varies for users with different sensitivity types; the higher the sensitivity, the smaller the maximum sacrifice threshold.

[0054] The higher the user's control factor, the greater the sacrifice of comfort, and the lower the user's satisfaction level. The satisfaction level is affected by the user's control sensitivity; the higher the control sensitivity, the lower the user's satisfaction level.

[0055] Optionally, the multi-objective regulation optimization model is expressed as:

[0056] ;

[0057] In the formula, For the aggregator's economic objectives; To achieve user satisfaction goals; This represents the average user satisfaction level.

[0058] Optionally, the multi-objective optimization model is solved using a multi-objective iterative optimization algorithm. The input includes the power grid peak-shaving signal, environmental parameters, and user sensitivity distribution. The solution set under different temperature control strategies is searched to generate the Pareto front. The knee method is used to select the optimal compromise solution from the Pareto front. Based on the selected knee solution, the final load control scheme is output.

[0059] The peak-shaving architecture adopts a time scale design that combines medium and fine granularity: the medium granularity layer has a control cycle of 5 minutes and is responsible for peak-shaving decisions and command updates; the fine granularity layer has a time step of 1 minute and performs fine modeling and power tracking of the air conditioning load response process to ensure the continuity and controllability of the peak-shaving effect.

[0060] Secondly, the present invention provides an air conditioning cluster optimization decision-making device that considers user control characteristics, comprising:

[0061] The data acquisition module is used to: acquire basic user information, electricity consumption data, and control sensitivity parameters;

[0062] The control decision module is used to: construct an air conditioning cluster load model that takes into account the control sensitivity based on the control sensitivity parameter, and to differentiate the indoor temperature, adjustable duration and operating constraints of different users.

[0063] Based on the aforementioned air conditioning cluster load model, a multi-objective control optimization model is constructed that takes into account both the aggregator's economic efficiency and the user's comfort, while meeting the power grid load control requirements.

[0064] The multi-objective regulation optimization model is solved to obtain the load regulation decision scheme.

[0065] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the air conditioning cluster optimization decision-making method considering user control characteristics as described in any of the first aspects.

[0066] Fourthly, the present invention provides a computer device / equipment / system, characterized in that it comprises:

[0067] Memory, used to store computer programs / instructions;

[0068] A processor for executing the computer program / instructions to implement the steps of the air conditioning cluster optimization decision-making method that takes into account user control characteristics as described in any of the first aspects.

[0069] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0070] 1. This invention provides an innovative approach for the large-scale and efficient use of flexible resources in the new power system through refined characterization of user characteristics and dual-objective collaborative optimization. While ensuring user comfort, it significantly improves peak-shaving revenue and achieves reasonable allocation of subsidies.

[0071] 2. The load model constructed in this invention integrates the physical-level ETP model with the behavioral-level regulation sensitivity characteristics, which can accurately characterize the dynamic response of indoor temperature and the regulation duration boundary of users with different sensitivities during peak shaving. It adopts a time scale design combining medium and fine granularity, using a 5-minute control cycle for peak shaving decisions and a 1-minute step size for power tracking, significantly improving the regulation accuracy of air conditioning clusters during peak shaving periods.

[0072] 3. This invention breaks through the limitations of traditional static comfort constraints by constructing a categorized satisfaction function using the Sigmoid function, setting differentiated extreme sacrifice thresholds for users with high, medium, and low sensitivity. This quantitative approach can more objectively reflect users' subjective psychological perception, avoiding a significant decline in user experience caused by solely pursuing economic benefits, and ensuring the long-term sustainability of the control scheme.

[0073] 4. This invention introduces a unified quantitative control factor to quantify the combined impact of temperature adjustment amplitude and duration, and designs a matching segmented subsidy mechanism to reflect the incentive principle of "more adjustment, more reward; less adjustment, less subsidy." This mechanism guides control resources to be rationally concentrated on low-sensitivity users, effectively reducing the difficulty for aggregators to access subsidies while ensuring reasonable allocation of subsidies on the user side.

[0074] 5. This invention selects the optimal solution through the Pareto front and knee point decision method, which can accurately identify the positions where the objective function trade-offs change significantly, providing aggregators with decision support that takes into account the interests of multiple parties. Attached Figure Description

[0075] Figure 1 The flowchart shows the air conditioning cluster optimization decision-making method considering user control characteristics according to the present invention.

[0076] Figure 2 This is a schematic diagram of the load baseline and outdoor temperature without considering peak shaving in this invention;

[0077] Figure 3 This is a schematic diagram of the Pareto front obtained by this invention;

[0078] Figure 4 This is a schematic diagram of the demand response peak shaving results of the present invention;

[0079] Figure 5 This is a schematic diagram illustrating the number of demand response peak-shaving air conditioners put into operation and deployed according to the present invention.

[0080] Figure 6This is a schematic diagram illustrating the average set temperature variation of the air conditioner according to the present invention. Detailed Implementation

[0081] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0082] Example 1:

[0083] This embodiment introduces an optimization decision-making method for air conditioning clusters that considers user control characteristics, such as... Figure 1 As shown, it includes:

[0084] Acquire basic user information, electricity consumption data, and control sensitivity parameters;

[0085] Based on the aforementioned regulation sensitivity parameters, an air conditioning cluster load model that takes regulation sensitivity into account is constructed to differentiate the indoor temperature, adjustable duration, and operating constraints of different users.

[0086] Based on the aforementioned air conditioning cluster load model, a multi-objective control optimization model is constructed that takes into account both the aggregator's economic efficiency and the user's comfort, while meeting the power grid load control requirements.

[0087] The multi-objective regulation optimization model is solved to obtain the load regulation decision scheme.

[0088] This embodiment uses a peak-shaving demand response as an example, simulating 5000 air conditioners and user sensitivity classification levels. =3, high, medium and low sensitivity users are called H type, M type and L type users respectively, with a ratio of 3:4:3. The basic setting temperature of air conditioner is 24℃, the upper and lower limits of the reference room temperature are 25℃ and 23℃ respectively, the peak shaving period of the grid side is set from 18:00 to 20:00, and the peak shaving range recognized by the grid is 80%-120%.

[0089] Other parameters are shown in Table 1. The load baseline and outdoor temperature, which do not consider peak shaving, are as follows: Figure 2 As shown.

[0090] Table 1 Air Conditioner Related Parameters

[0091]

[0092] I. The multi-objective regulation optimization function that balances the economic benefits for aggregators and the comfort of users is as follows:

[0093] Objective 1: Economic benefits for aggregators

[0094] Grid-side peak shaving revenue This is the product of the aggregator's actual peak-shaving volume and the peak-shaving unit price.

[0095] ;

[0096] In the formula, for The real-time electricity price for the peak-shaving market is set at 4.8 yuan / kWh; This is for simulating step size; This refers to the revised peak-shaving capacity of aggregators. According to the Jiangsu Province Electricity Demand Response Implementation Rules, the peak-shaving capacity of aggregators is stipulated as follows:

[0097] ;

[0098] In the formula: for Real-time aggregation of peak shaving volume. The peak-shaving demand on the grid side is taken as 1500kW; , , The confirmation coefficients for peak-shaving capacity are set to 0.5, 0.8, and 1.2, respectively.

[0099] The impact of temperature adjustment amplitude and duration on user comfort during the regulation process is quantified by using regulation factors, and a peak-shaving subsidy model on the user side is further constructed.

[0100] ;

[0101] To ensure the targeted incentive of peak-shaving subsidies, define air conditioning Subsidized unit price In order to regulate factors Related piecewise functions This means that the greater the sacrifice in comfort users make during peak hours, the higher the subsidized unit price they will receive, such as:

[0102] ;

[0103] In the formula, , , The subsidy unit prices corresponding to different levels of user comfort sacrifice are 0.1, 0.2, and 0.3 yuan / kWh, respectively.

[0104] ;

[0105] In the formula, Adjustments and subsidies for user income; For regulation; This represents the total number of air conditioners.

[0106] Objective 2: Average user satisfaction

[0107] Implementing peak-shaving subsidies can compensate users for the loss of comfort caused by participating in peak-shaving to some extent, but there will still be subjective dissatisfaction. For example, the higher the user's sensitivity to regulation, the faster their marginal dissatisfaction increases.

[0108] To accurately quantify this subjective psychological perception effect, the Sigmoid function is used to characterize the differentiated user satisfaction level, taking into account both objective regulatory factors and subjective regulatory sensitivity.

[0109] ;

[0110] In the formula, Let the satisfaction function be used. For conversion parameters; The minimum satisfaction coefficient is set to 0.4; , These are constant coefficients, taken as 8 and 0.5 respectively; for The maximum control factor for a user class represents the maximum acceptable sacrifice threshold for that user class, which is respectively... , , .

[0111] In summary, the multi-objective function of the proposed cluster air conditioning peak-shaving model is:

[0112] ;

[0113] In the formula, For the aggregator's economic objectives; To achieve user satisfaction goals; This represents the average user satisfaction level.

[0114] II. Solving the cluster air conditioning peak-shaving model considering user control characteristics in two scenarios.

[0115] Scenario 1: The peak shaving model only considers economic optimization, with user satisfaction as a constraint, and its value is not lower than 0.8;

[0116] Scenario 2: The peak-shaving model takes into account both economic optimization and satisfaction optimization, which is the model of this invention.

[0117] In this model, there are two objective functions. Multi-objective particle swarm optimization cannot compute a unique optimal solution; instead, it generates a Pareto front consisting of nine optimal solutions, such as... Figure 3 As shown in Table 2, the simulation results under different scenarios are as follows.

[0118] In multi-objective optimization problems, since each solution to the Pareto front is a feasible solution for system planning, the knee method is used to select the Pareto front. The knee solution corresponds to the position where the trade-off between objective functions changes significantly; that is, near this point, further improving economic benefits will come at the cost of significantly sacrificing user satisfaction, and vice versa.

[0119] Table 2 Simulation results for each scenario

[0120]

[0121] In Scenario 1, the proposed model uses an average user satisfaction score of at least 0.8 as a constraint, aiming to maximize economic benefits while ensuring comfort. Table 2 shows that the average user satisfaction score in Scenario 1 is relatively high at 0.84. However, because the satisfaction constraint significantly limits the temperature adjustment range and the adjustable air conditioning range, the overall system economy is low, with economic benefits of only 3086.77 yuan. Meanwhile, to ensure high satisfaction, the average subsidy level is relatively high, reaching 1.93 yuan per household, with subsidies for low-sensitivity users significantly higher than those for medium- and high-sensitivity users.

[0122] In Scenario 2, the proposed model balances the goals of maximizing economic benefits and user satisfaction. Table 2 shows that the economic benefits for aggregators for each optimized solution range from 750 to 7200 yuan, and the overall economic efficiency shows a significant upward trend as the temperature adjustment range for low-sensitivity users increases. When the temperature adjustment range for low-sensitivity users is 2-3℃, the aggregator's economic benefits can increase to over 4600 yuan, significantly higher than solutions with lower temperature adjustment ranges.

[0123] The knee point solution was used to select the optimal solution. The aggregator's economic benefit was 5655.68 yuan, the average user satisfaction was 0.88, and the average user subsidy was 1.83 yuan. Among them, the average satisfaction of low, medium and high sensitive users were 0.76, 0.93 and 0.97, respectively, and the average subsidies were 4.03, 1.10 and 0.15 yuan.

[0124] It can be seen that Scenario 1 emphasizes user comfort, which limits the potential for economic gains; Scenario 2 balances satisfaction and economy, achieving higher peak-shaving revenue and a more reasonable subsidy allocation.

[0125] The results of peak shaving by air conditioning clusters are as follows Figure 4 As shown, during the peak-shaving period from 18:00 to 20:00, the actual reduction was entirely within the confidence range of ±20% of the target reduction, indicating a good peak-shaving effect. Specifically, the average peak-shaving reduction was 1492.86 kW, with the minimum reduction occurring at 19:56 (1202.49 kW) and the maximum reduction occurring at 19:45 (2245.83 kW).

[0126] like Figure 5 As shown, at the start of peak shaving, 550 air conditioners are sufficient to meet the reduction demand; at 18:35, all low-sensitivity air conditioners are put into operation, followed by medium-sensitivity air conditioners; at 19:40, all medium-sensitivity air conditioners are put into operation, and high-sensitivity air conditioners begin to be put into operation. At 19:35, the first batch of medium-sensitivity air conditioners that were put into operation reached their control duration and exited peak shaving.

[0127] like Figure 6 As shown, with the peak-shaving process, the average set temperature of the three types of air conditioners slowly increased. Because low-sensitivity users started using their systems earlier, their average set temperature curves began to rise first. By 18:40, all low-sensitivity users had activated their systems, and the average set temperature stabilized at 26.5℃. Medium-sensitivity users started using their systems at 18:35, and their average set temperature curves began to rise, eventually stabilizing at close to 25.5℃. High-sensitivity users only activated their systems at 19:40, and their set temperature curves rose the latest, with a lower temperature adjustment range.

[0128] Example 2:

[0129] Based on the same inventive concept as Embodiment 1, this embodiment introduces an air conditioning cluster optimization decision-making device that considers user control characteristics, comprising:

[0130] The data acquisition module is used to: acquire basic user information, electricity consumption data, and control sensitivity parameters;

[0131] The control decision module is used to: construct an air conditioning cluster load model that takes into account the control sensitivity based on the control sensitivity parameter, and to differentiate the indoor temperature, adjustable duration and operating constraints of different users.

[0132] Based on the aforementioned air conditioning cluster load model, a multi-objective control optimization model is constructed that takes into account both the aggregator's economic efficiency and the user's comfort, while meeting the power grid load control requirements.

[0133] The multi-objective regulation optimization model is solved to obtain the load regulation decision scheme.

[0134] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0135] Example 3:

[0136] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the air conditioning cluster optimization decision-making method considering user control characteristics as described in any of the embodiments.

[0137] Example 4:

[0138] Based on the same inventive concept as other embodiments, this embodiment introduces a computer device / apparatus / system, characterized in that it includes:

[0139] Memory, used to store computer programs / instructions;

[0140] A processor is configured to execute the computer program / instructions to implement the steps of the air conditioning cluster optimization decision-making method considering user control characteristics as described in any of Embodiment 1.

[0141] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0145] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. An optimal decision-making method for air conditioning clusters considering user control characteristics, characterized in that, include: Acquire basic user information, electricity consumption data, and control sensitivity parameters; Based on the aforementioned regulation sensitivity parameters, an air conditioning cluster load model that takes regulation sensitivity into account is constructed to differentiate the indoor temperature, adjustable duration, and operating constraints of different users. Based on the aforementioned air conditioning cluster load model, a multi-objective control optimization model is constructed that takes into account both the aggregator's economic efficiency and the user's comfort, while meeting the power grid load control requirements. The multi-objective regulation optimization model is solved to obtain the load regulation decision scheme.

2. The air conditioning cluster optimization decision-making method considering user control characteristics according to claim 1, characterized in that, The user basic information, electricity consumption data and control sensitivity parameters are obtained by collecting basic data of air conditioning users participating in peak shaving in the target area. The user's basic information includes the air conditioner's rated power, equivalent thermal resistance, equivalent heat capacity, initial set temperature, and upper and lower limits of room temperature. The power consumption data includes air conditioner on / off status, indoor and outdoor temperatures, and load baseline curves. The control sensitivity is used to characterize the user's tolerance for load control duration and temperature adjustment range; it is set in levels according to the user's tolerance for load control duration and temperature adjustment range, and the user's tolerance for air conditioning control duration and temperature adjustment range is characterized by control sensitivity, and a system is established based on the sensitivity level. User classification system: ； In the formula, These are user sensitivity classification levels; The higher the sensitivity of the control, the shorter the duration for which users are willing to participate in the control, and the smaller the allowable range of indoor temperature adjustment; The lower the sensitivity of temperature regulation, the longer users are willing to participate in regulation, and the greater the range of indoor temperature adjustment allowed.

3. The air conditioning cluster optimization decision-making method considering user control characteristics according to claim 1, characterized in that, The process of constructing the air conditioning cluster load model includes: A first-order equivalent thermal parameter model is used to describe the thermal dynamics of a building room under air conditioning control. The thermal characteristics of the building envelope are simplified into units composed of equivalent thermal resistance and equivalent heat capacity. A mathematical expression characterizing the dynamic response of indoor temperature to natural outdoor temperature changes is then established. ； In the formula, for Real-time room temperature; for The ambient temperature at all times; This is for simulating step size; and These are the equivalent thermal resistance and equivalent heat capacity of the building's rooms, respectively. air conditioner The changes in indoor temperature and switch status after the addition are represented as follows: ； In the formula, for Air conditioning The on / off status is 0 when the air conditioner is off and 1 when the air conditioner is on. For air conditioning The equivalent thermal resistance; For air conditioning The equivalent heat capacity; for Air conditioning The electrical power; This represents the maximum power consumption of a single air conditioner unit. For air conditioning exist The indoor temperature at any given time; , The upper and lower limits of the set room temperature; For air conditioning The initial set temperature; Based on the acquired control sensitivity, the indoor temperature and adjustable duration are adjusted for different users: Indoor temperature based on user sensitivity coefficient Set differentiated indoor temperature comfort ranges: ； In the formula: For the first User-specific sensitivity coefficient; and They are respectively Air conditioning Minimum and maximum permissible indoor temperatures; The average of the allowable temperature adjustment range; The adjustable duration By setting the maximum control time for users with different levels of sensitivity. Make corrections: ； In the formula, For air conditioning exist The duration has been adjusted. consider A cluster load model for air conditioning systems, taking into account regulation sensitivity, is obtained from the analysis of individual air conditioning loads. : ； In the formula, This represents the total duration of demand response regulation.

4. The air conditioning cluster optimization decision-making method considering user control characteristics according to claim 1, characterized in that, In the construction of the multi-objective regulation and optimization model that takes into account both the aggregator's economics and user comfort, the calculation process for the aggregator's economics is as follows: peak-shaving revenue on the grid side. The product of the aggregator's actual peak-shaving volume and the peak-shaving unit price is expressed as: ； In the formula, for Real-time electricity prices in the peak-shaving market; This is for simulating step size; To adjust the peak shaving capacity of aggregators, the following provisions are made regarding their peak shaving capabilities: ； In the formula, for Real-time aggregation of peak shaving volume. This represents the peak-shaving demand on the power grid side. , , This is the confirmation coefficient for peak-shaving capacity; The impact of temperature regulation amplitude and duration on user comfort during the regulation process is quantified using regulation factors, and a peak-shaving subsidy model on the user side is further constructed; regulation factors The product of the controlled temperature and the duration is used as the metric, as shown in the following formula: ； In the formula, for Air conditioning The on / off state; For air conditioning The initial set temperature; for Air conditioning Temperature; This reflects the cumulative impact on user thermal comfort during the temperature adjustment process. The higher the value, the more significant the negative impact of peak-shaving behavior on user comfort, and more subsidies should be provided. Define air conditioner Subsidized unit price In order to regulate factors Related piecewise functions This means that the greater the sacrifice in comfort a user makes during peak hours, the higher the subsidized unit price they receive, expressed as: ； In the formula, , , The subsidized unit price corresponds to different levels of user comfort sacrifice, satisfying... ; User-controlled subsidies are represented as follows: ； In the formula, Adjustments and subsidies for user income; For regulation; This represents the total number of air conditioners.

5. The air conditioning cluster optimization decision-making method considering user control characteristics according to claim 1, characterized in that, In the construction of the multi-objective regulation and optimization model that takes into account both the aggregator's economic efficiency and user comfort, the calculation process for user comfort is as follows: Taking into account both objective regulatory factors and subjective regulatory sensitivity, the Sigmoid function is used to characterize the differentiated user satisfaction level, as shown in the following formula: ； In the formula, For satisfaction function; For conversion parameters; The minimum satisfaction coefficient; , The coefficients are constants. As a regulatory factor; for The maximum control factor for a user class represents the maximum acceptable sacrifice threshold for that user class. The maximum acceptable sacrifice threshold varies for users with different sensitivity types; the higher the sensitivity, the smaller the maximum sacrifice threshold. The higher the user's control factor, the greater the sacrifice of comfort, and the lower the user's satisfaction level. The satisfaction level is affected by the user's control sensitivity; the higher the control sensitivity, the lower the user's satisfaction level.

6. The air conditioning cluster optimization decision-making method considering user control characteristics according to claim 1, characterized in that, The multi-objective regulation optimization model is expressed as follows: ； In the formula, For the aggregator's economic objectives; To achieve user satisfaction goals; This represents the average user satisfaction level.

7. The air conditioning cluster optimization decision-making method considering user control characteristics according to claim 1, characterized in that, The multi-objective optimization model is solved using a multi-objective iterative optimization algorithm. The input includes the power grid peak-shaving signal, environmental parameters, and user sensitivity distribution. The solution set under different temperature control strategies is searched to generate the Pareto front. The knee method is used to select the optimal compromise solution from the Pareto front. Based on the selected knee solution, the final load control scheme is output. The peak-shaving architecture adopts a time scale design that combines medium and fine granularity: the medium granularity layer has a control cycle of 5 minutes and is responsible for peak-shaving decisions and command updates; the fine granularity layer has a time step of 1 minute and performs fine modeling and power tracking of the air conditioning load response process to ensure the continuity and controllability of the peak-shaving effect.

8. An air conditioning cluster optimization decision-making device considering user control characteristics, characterized in that, include: The data acquisition module is used to: acquire basic user information, electricity consumption data, and control sensitivity parameters; The control decision module is used to: construct an air conditioning cluster load model that takes into account the control sensitivity based on the control sensitivity parameter, and to differentiate the indoor temperature, adjustable duration and operating constraints of different users. Based on the aforementioned air conditioning cluster load model, a multi-objective control optimization model is constructed that takes into account both the aggregator's economic efficiency and the user's comfort, while meeting the power grid load control requirements. The multi-objective regulation optimization model is solved to obtain the load regulation decision scheme.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the air conditioning cluster optimization decision-making method that takes into account user control characteristics as described in any one of claims 1 to 7.

10. A computer device / equipment / system, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the air conditioning cluster optimization decision-making method considering user control characteristics as described in any one of claims 1 to 7.

Images