Central air conditioner control method and control system considering time lag effect

Abstract

The invention discloses a central air conditioner control method and system considering the time-lag effect, and the control method comprises the steps: constructing a time-lag effect model which is used for reflecting the time-lag effect characteristics of a central air conditioner system; formulating a central air conditioner demand response strategy according to the time-delay effect model; on the basis of the time-lag effect model and the demand response strategy, key indexes used for evaluating the interaction effect of the central air conditioning system are determined; integrating the key indexes, and constructing a demand response interaction capability evaluation system; and integrating the time-lag effect model, the demand response strategy and the demand response interaction ability evaluation system into the central air-conditioning control system to obtain the central air-conditioning control system considering the time-lag effect. According to the control method and the control system, the energy utilization efficiency and the demand response capability of the central air conditioning system can be effectively improved, meanwhile, the user comfort and the economic benefit are considered, and an important guarantee is provided for building energy conservation and power grid stability.

Description

Technical Field

[0001] The present invention relates to the technical field of power system management, and in particular to a central air-conditioning control method and a control system taking time lag effect into consideration. Background Art

[0002] With the sharp increase in global energy consumption and increasingly severe environmental problems, building energy conservation has become a top priority in current research. Building energy consumption accounts for 30% to 40% of the total social energy consumption, among which central air-conditioning systems are the main contributors to building energy consumption, accounting for about 40% to 60% of the total building energy consumption. Therefore, improving the energy efficiency and demand response capabilities of central air-conditioning systems is of great practical significance for achieving building energy conservation and intelligent management.

[0003] The control strategy of central air-conditioning system has evolved from simple to complex, from experience to intelligence. Early control methods mainly relied on classical control theories such as PID. Although these methods are simple and intuitive, they are difficult to deal with the nonlinear and time-varying characteristics of the system, and their performance under complex working conditions is often unsatisfactory. With the development of computer technology and control theory, predictive control technology has begun to be applied in central air-conditioning systems. This type of method establishes a system model to predict future load changes, thereby adjusting system parameters in advance, which improves the foresight of control to a certain extent. However, its performance is heavily dependent on the accuracy of load prediction, and the calculation complexity is high, and the real-time performance needs to be improved.

[0004] In recent years, with the vigorous development of artificial intelligence technology, some researchers have begun to try to apply advanced algorithms such as deep learning and reinforcement learning to central air-conditioning control. These methods optimize control strategies through continuous interaction with the environment, showing strong adaptive capabilities and optimization potential. However, such methods often require a large amount of data for training, and it is difficult to ensure stability and interpretability in all scenarios, which may face challenges in practical engineering applications. On the other hand, with the proposal and development of the concept of smart grid, central air-conditioning, as an important adjustable load, has received increasing attention for its demand response capability. Researchers have proposed various demand response methods based on price signals or direct load control, aiming to respond to the dynamic demand of the power grid by adjusting the operating parameters of the air conditioner. These methods have achieved certain results in improving the stability and economy of the power grid, but often ignore the user's comfort, which may lead to a decline in indoor environmental quality and affect the user experience.

[0005] In view of the above problems and limitations, it has become an urgent problem to propose a central air-conditioning control method and control system considering the time lag effect, so as to effectively integrate advanced control algorithms, intelligent optimization technology and demand response strategies to achieve efficient, flexible and intelligent central air-conditioning management. Summary of the invention

[0006] The purpose of the present invention is to provide a central air-conditioning control method and control system that takes time lag effects into consideration. By establishing a time lag effect model, designing a demand response strategy, developing interactive capability evaluation indicators, constructing an evaluation system and realizing system integration, the energy utilization efficiency can be effectively improved, the system's adaptability to changes in grid demand can be enhanced, and user comfort and economic benefits can be guaranteed at the same time.

[0007] The technical solution of the present invention to solve the above technical problem is as follows: a central air conditioning control method considering the time lag effect, comprising:

[0008] Construct a time-lag effect model to reflect the time-lag effect characteristics of the central air-conditioning system;

[0009] Formulate a central air conditioning demand response strategy based on the time lag effect model;

[0010] Based on the time lag effect model and the demand response strategy, determine key indicators for evaluating the interactive effect of the central air conditioning system;

[0011] Integrate the key indicators to build a demand response interactive capability evaluation system;

[0012] The time lag effect model, demand response strategy and demand response interactive capability evaluation system are integrated into a central air-conditioning control system to obtain a central air-conditioning control system that takes time lag effect into consideration.

[0013] Preferably, the time lag effect model includes a delay module and a temperature rise module, which are used to reflect the influence of the time lag effect on the air-conditioning terminal.

[0014] Further preferably, the demand response strategy is optimized through multi-dimensional feature extraction and machine learning algorithms.

[0015] Further optimization, in terms of real-time control strategy, by reducing the cooling capacity of the air-conditioning system or adjusting the operating time, the pressure on the power grid is reduced during the peak load period of the power grid; during the demand response period, by keeping the water pump running and the valve fully open, the residual coolness in the pipeline is used to provide cooling, thereby reducing energy consumption; after the demand response is over, the chiller is quickly restored to operation, and the delay value calculated by the time-lag effect model is used to ensure that the water output from the chiller is quickly transmitted to the terminal, thereby improving the recovery efficiency.

[0016] Further preferably, the key indicators are divided into social indicators and physical indicators, the social indicators include social benefits of user-side resources, user risk preference value, user willingness to participate in demand response, user-side resource economic benefits, user economic income, time-of-use electricity price difference and electricity price sensitivity; the physical indicators include daily load rate, load peak-to-valley difference rate, maximum peak shaving value, maximum valley filling value, load response degree, outdoor temperature, season and date type.

[0017] Further optimization, the method of constructing a demand response interactive capability evaluation system is as follows:

[0018] Construct a multidimensional grey number decision matrix for demand response capability evaluation;

[0019] Standardize the multi-dimensional evaluation indicators of user-side resource demand response capability;

[0020] Construct a profit-loss decision matrix for demand responsiveness assessment;

[0021] The demand response capability evaluation profit and loss decision matrix is ​​used to calculate the positive and negative bullseye vectors of the demand response capability evaluation, and to measure the influence of different indicators on the user-side resource demand response capability evaluation;

[0022] The weights of demand responsiveness evaluation indicators are calculated based on prospect theory.

[0023] Further preferably, the method for calculating the weights of demand response capability evaluation indicators based on prospect theory is as follows:

[0024] Calculate the comprehensive prospect value of the demand response capability assessment index;

[0025] The weights of demand response capability evaluation indicators are solved based on the comprehensive prospect value.

[0026] The present invention also provides a central air-conditioning control system taking into account the time lag effect, comprising:

[0027] A time lag effect model building unit, used for building a time lag effect model for reflecting the time lag effect characteristics of a central air conditioning system;

[0028] A central air-conditioning demand response strategy formulation unit, used to formulate a central air-conditioning demand response strategy according to the time lag effect model;

[0029] A key indicator determination unit, used to determine key indicators for evaluating the interaction effect of the central air-conditioning system based on the time lag effect model and the demand response strategy;

[0030] A demand response interactive capability evaluation system construction unit, used to integrate the key indicators and construct a demand response interactive capability evaluation system;

[0031] The integration unit is used to integrate the time lag effect model, demand response strategy and demand response interactive capability evaluation system into the central air-conditioning control system to obtain a central air-conditioning control system that takes the time lag effect into consideration.

[0032] Preferably, the demand response interactive capability evaluation system construction unit includes:

[0033] A multidimensional grey number decision matrix construction unit for evaluating demand response capability, used for constructing a multidimensional grey number decision matrix for evaluating demand response capability;

[0034] A normalization unit, used to normalize the multi-dimensional evaluation indicators of the user-side resource demand response capability;

[0035] Demand response capability evaluation profit and loss decision matrix unit, used to construct a demand response capability evaluation profit and loss decision matrix;

[0036] A demand response capability assessment positive and negative bullseye vector calculation unit is used to calculate the demand response capability assessment positive and negative bullseye vectors using the demand response capability assessment profit and loss decision matrix, and measure the influence of different indicators on the user-side resource demand response capability assessment;

[0037] The demand response capability evaluation index weight calculation unit is used to calculate the demand response capability evaluation index weight based on prospect theory.

[0038] Further preferably, the demand response capability evaluation index weight calculation unit includes:

[0039] A demand response capability evaluation index comprehensive prospect value calculation unit, used to calculate the demand response capability evaluation index comprehensive prospect value;

[0040] The demand response capability evaluation index weight solving unit is used to solve the demand response capability evaluation index weight based on the comprehensive prospect value.

[0041] The central air-conditioning control method and control system considering the time lag effect provided by the present invention can not only improve the energy efficiency and demand response capability of the central air-conditioning system, but also provide important support for the stable operation of the power grid. By establishing a time lag effect model that reflects the time lag characteristics of the central air-conditioning system, it can be ensured that the system can accurately simulate and respond to different operating states; in terms of demand response strategy design, a highly adaptable control strategy is adopted, and real-time adjustment is made according to the time lag effect model, which can ensure that the system maintains the user's comfort while responding to the power grid demand; by determining the key indicators for evaluating the interactive effect of the air-conditioning system demand response, the system's operating efficiency, economic benefits and user experience can be fully reflected; through the cloud platform interface and mobile terminal applications, remote monitoring and management of the central air-conditioning system can be achieved, and users can view the system status and adjust the control strategy at any time to improve management efficiency and convenience. The system integrates real-time data acquisition and processing functions, and collects and transmits operating data in real time through a distributed data acquisition system, ensuring that the central control unit can quickly respond to demand changes and improve the system's operating stability and reliability. At the same time, the big data analysis and interactive capability evaluation system provides scientific decision-making support to assist managers in formulating optimized demand response strategies. In summary, the present invention not only effectively improves the energy utilization efficiency and demand response capability of the central air-conditioning system, but also takes into account user comfort and economic benefits, providing important guarantees for building energy conservation and power grid stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of a central air conditioning control method considering time lag effect provided by the present invention;

[0043] Figure 2 Flowchart for demand response simulation for time-lag effect model. DETAILED DESCRIPTION

[0044] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0045] like Figure 1 As shown, the present invention provides a central air conditioning control method considering the time lag effect, comprising:

[0046] S1: Construct a time lag effect model to reflect the time lag effect characteristics of the central air conditioning system;

[0047] The time-lag effect model reflects the impact of the time-lag effect on the air conditioning terminal by adding delay and temperature rise values ​​on the basis of the traditional model. During the demand response period, the chiller stops and no longer provides cooling, but the water pump keeps running, the valves of each branch remain fully open, and the cold water continues to circulate in the pipeline. The terminal uses the residual cold in the chilled water pipeline for cooling; after the demand response ends, the chiller immediately starts to supply cold water. The time-lag effect is taken into account in the process from the chiller stopping to the chiller discharging water to the terminal cooling process. The process of demand response simulation by the time-lag effect model is as follows: Figure 2 As shown;

[0048] In order to enable the model to simulate the above scenarios, the traditional model needs to be modified, that is, to add a temperature rise and delay module to the program. When the temperature rise of the water pump remains unchanged, the change of the terminal temperature rise value is determined by the chilled water pipe. The longer the chilled water pipe, the greater the terminal temperature rise value. The chilled water temperature rise at the same terminal is the same at any time. The calculation formula of the chilled water temperature rise in the time lag effect model is as follows:

[0049]

[0050] Where: ——water inlet temperature at the nth terminal, °C; t c h iller,out ——Chiller outlet water temperature, °C; Δt n ; Total temperature rise at the nth end, °C.

[0051] The change of the terminal delay value is determined by the chilled water pipe. The longer the chilled water pipe, the larger the terminal delay value. The transmission delay caused by the time lag effect will make the terminal water temperature change later than the chiller action time. The delay from the chiller to the terminal is u. If the chiller stops at time i, the chiller temperature change it generates will be transmitted to the terminal after a time of u, as shown in formula (2):

[0052] terminal(i)=chiller(iu) (2)

[0053] In the formula: terminal(i)——the state of the terminal at time i; chiller(iu)——the state of the chiller at time iu.

[0054] After considering the temperature rise and delay, the water temperature at a specific time at the end is calculated as follows:

[0055]

[0056] Where: ——water inlet temperature at the nth end time i, °C; t chiller(i-u),out ——Water outlet temperature of the refrigeration machine at iu, ℃.

[0057] S2: Formulate a central air conditioning demand response strategy based on the time lag effect model;

[0058] When designing the demand response strategy, a central air-conditioning demand response strategy that can flexibly adapt to different operating environments is formulated based on the established time lag effect model.

[0059] Firstly, the key parameters in the time-lag effect model (such as chiller outlet water temperature, terminal inlet water temperature, delay value, etc.) are used to extract the multidimensional features that affect the air conditioning demand response, including load change rate, ambient temperature fluctuation, user demand preference, etc.

[0060] Then, advanced machine learning algorithms (such as deep learning, reinforcement learning, etc.) are used for optimization. Through training with a large amount of historical data and real-time data, an accurate load forecasting model is formed. The trained model is used to predict future load demand, and the operating parameters of the air-conditioning system are adjusted in real time according to the prediction results to ensure that the system can respond to grid demand and maintain user comfort during the demand response process.

[0061] In terms of real-time control strategy, by reducing the cooling capacity of the air-conditioning system or adjusting the operating time, the pressure on the power grid can be reduced during peak load periods. During the demand response period, by keeping the water pump running and the valve fully open, the residual cold in the pipeline is used to provide cooling and reduce energy consumption. After the demand response is over, the chiller quickly resumes operation. According to the delay value calculated by the time-lag effect model, the water output from the chiller is ensured to be quickly transmitted to the terminal, thereby improving the recovery efficiency. The user's comfort and demand feedback is collected through the user interaction interface, and the control strategy is adjusted in real time according to the user feedback to ensure that the user experience is not significantly affected during the demand response process;

[0062] S3: Based on the time lag effect model and the demand response strategy, determine key indicators for evaluating the interactive effect of the central air-conditioning system;

[0063] Among them, the key indicators are divided into social indicators and physical indicators.

[0064] Among them, social indicators include social benefits of user-side resources, user risk preference value, user willingness to participate in demand response, economic benefits of user-side resources, user economic income, time-of-use electricity price difference, and electricity price sensitivity. The social benefits of user-side resources include benefits in terms of reducing energy consumption and carbon emissions. The higher the social benefits of user-side resources, the stronger their ability to participate in demand response. The larger the user risk preference value, the greater the risk they can bear in the process of participating in demand response, and the stronger the ability of the user-side resources held by the user to participate in demand response. The higher the user's willingness to participate in demand response, the greater the possibility of participating in demand response, and the stronger the ability of the user-side resources held by the user to participate in demand response. The higher the economic benefits of user-side resources, the stronger the user's willingness to participate in demand response, and the stronger the user's ability to hold user-side resource demand response. Users with higher economic income are less sensitive to cost-effectiveness, less likely to participate in demand response to reduce costs, and have weaker ability to hold user-side resources to participate in demand response. The user-side resource demand response capability value increases with the increase of the peak-valley electricity price difference, because time-of-use electricity prices prompt users to change their electricity consumption behavior and save costs. When electricity prices rise, the upward trend of user electricity consumption slows down or the user's electricity consumption decreases, which means that the user is more sensitive to changes in electricity prices, and the user's ability to respond to user-side resource demand is stronger.

[0065] Physical indicators include daily load rate, load peak-to-valley difference rate, maximum peak shaving value, maximum valley filling value, load response degree, outdoor temperature, season and date type. Daily load rate refers to the ratio of the average load to the maximum load during the day, which can reflect the changes in the user's electricity load. The larger the value, the greater the load's ability to participate in demand response. Load peak-to-valley difference rate refers to the ratio of the peak-to-valley difference to the peak load, which can reflect the peak-shaving level of the power grid. The larger the value, the greater the load's ability to participate in demand response. Maximum peak shaving refers to the difference between the user load and the minimum load at the peak moment of the power grid, which measures the maximum ability of the load to participate in demand response peak shaving. The larger the value, the greater the load's ability to participate in demand response. Maximum valley filling value refers to the difference between the user load and the maximum load at the low moment of the power grid, which measures the maximum ability of the load to participate in demand response valley filling. The larger the value, the greater the load's ability to participate in demand response. Load response degree refers to the ratio of the actual response capacity of the load to the maximum response capacity of the load. The larger the value, the greater the load's ability to participate in demand response. The outdoor temperature has the greatest impact on the demand response capacity of air conditioners and HVAC equipment. When the outdoor temperature is higher than 28°C, air conditioners are used more frequently, and the air conditioner load demand response capacity is stronger. When the outdoor temperature is lower than 10°C, HVAC equipment is used more frequently, and the HVAC equipment load demand response capacity is stronger. For residential users, the summer load reaches the peak of the year, and the spring and autumn loads are at the valley of the year. Therefore, the user-side resource demand response capacity is the greatest in summer, followed by winter, while the demand response capacity in spring and autumn is relatively small. The load of industrial and commercial users is higher on weekdays, while the load of residential users is lower, and the load characteristics on holidays are opposite to those on weekdays. Therefore, industrial and commercial loads have a greater ability to participate in demand response on weekdays, and residential loads have a greater ability to participate in demand response on holidays;

[0066] S4: Integrate the key indicators to build a demand response interactive capability evaluation system;

[0067] The demand response capability evaluation based on social-physical integration is a complex system decision-making problem involving multi-dimensional indicators. The indicator set evaluation space vector is defined as I = {I 1 ,I 2 ,...,I N}, and the multidimensional evaluation index set of demand response capability is divided into benefit type and cost type, and the total number of indicators is represented as Ω P and Ω N .

[0068] (1) Constructing a multidimensional grey number decision matrix for demand response capability evaluation

[0069] Social indicator information is difficult to collect and is affected by multiple subjective factors such as user behavior habits and personal experience. When determining the value of social indicators, in most cases, only the value range of the information can be obtained. In order to more accurately describe the social indicators for demand response capability evaluation and avoid information loss during the quantification process, gray numbers are used to represent the multidimensional evaluation indicators of demand response capability.

[0070] Assume that there are M user-side resources in a certain area that need to be evaluated, and their set is represented by S = (S 1 ,S 2 ,...,S M ). Each user-side resource includes N evaluation indicators, so the demand response capability evaluation gray number decision matrix of all user-side resources and evaluation indicators in the area is expressed as:

[0071]

[0072] Among them, S m Represents the evaluation space vector of the resource demand response capability of the mth user side. The element a in the matrix A mn ,m∈[1,M],n∈[1,N] is the evaluation result of the nth indicator of the mth user-side resource in the form of gray number representation;

[0073] Based on the preset reference points of the evaluation results of each indicator, the expected reference vector of the indicator is defined, expressed as Q = [q 1 ,...,q n ,...,q N ], where q n It is the gray number expression of the decision maker's expectation of the indicator;

[0074] (2) Standardizing multi-dimensional evaluation indicators of user-side resource demand response capability

[0075] Normalization can eliminate the differences in dimensions and units between different social and physical indicators, so that all evaluation indicators are in the same dimension for comprehensive evaluation, and more accurately evaluate the overall level of user-side resource demand response capability;

[0076] The expected index reference vector Q and the gray number decision matrix A of the demand response capability evaluation are normalized to obtain the normalized expected index reference vector E = [e 1 ,e 2 ,...,e N ] and the normalized demand response capability evaluation gray number decision matrix B = [b mn ] M×N Taking the nth evaluation indicator as an example, the specific standardization process is as follows:

[0077]

[0078] in, It represents the maximum value of the upper bound of the gray number interval in the gray number decision matrix of the indicator expectation reference vector and the demand response capability evaluation. Represents the minimum value of the lower bound of the gray number interval, respectively expressed as:

[0079]

[0080] (3) Constructing a profit-loss decision matrix for demand response capability assessment

[0081] Compared with the normalized matrix, the profit-and-loss decision matrix can more intuitively show the mutual influence and trade-off relationship between various evaluation indicators, which helps decision makers to more comprehensively understand the correlation and weight between various indicators. The construction process of the profit-and-loss decision matrix for user-side resource demand response capability assessment is as follows:

[0082] First, obtain the normalized index expected reference vector E = [e 1 ,e 2 ,...,e N ] and the normalized demand response capability evaluation gray number decision matrix B = [b mn ] M×N ;

[0083] Secondly, the distance between each element in the gray number decision matrix of the standardized demand response capability evaluation and the expected reference point of the standardized index is calculated. Taking the nth evaluation index of the mth user-side resource as an example, the distance d mn It can be expressed as:

[0084]

[0085] Finally, the profit and loss decision matrix F = [f mn ] M×N , where f mn is the profit and loss decision matrix element, expressed as:

[0086]

[0087] (4) using the demand response capability evaluation profit and loss decision matrix to calculate the positive and negative bullseye vectors of the demand response capability evaluation, and measuring the influence of different indicators on the user-side resource demand response capability evaluation;

[0088] 1) Define the positive bullseye vector of demand response capability evaluation as f + , then the corresponding element of each indicator of the positive bull's eye vector is the maximum value of the corresponding indicator column of the profit and loss decision matrix, expressed as:

[0089]

[0090] in,

[0091] 2) Define the negative bullseye vector of demand response capability evaluation as f - , then the corresponding element of each indicator of the negative bull's-eye vector is the minimum value of the corresponding indicator column of the profit and loss decision matrix, expressed as:

[0092]

[0093] in,

[0094] (5) Calculate the weights of demand response capability evaluation indicators based on prospect theory;

[0095] 1) Calculate the comprehensive prospect value of demand response capability evaluation indicators;

[0096] Prospect theory is a decision-making theory used to describe people's asymmetric treatment of risks and benefits in the decision-making process. The theory believes that people are more inclined to avoid losses rather than pursue gains. Since the contribution of each indicator of demand responsiveness assessment to system performance is often different, and the determination of weights involves risk perception and value judgment of each indicator. Therefore, prospect theory provides a theoretical basis for more accurately analyzing the preferences and weight distribution of different indicators and improving the accuracy of the user-side resource demand responsiveness assessment results.

[0097] Based on the prospect utility value function, the profit and loss value of demand response capability assessment is converted into the prospect utility value of demand response capability assessment, which is specifically expressed as:

[0098]

[0099] Among them, α and β represent the degree of concavity of the power function of the value of the gain and loss areas, respectively, and both values ​​are less than 1; θ indicates that the loss area is steeper than the gain area. When θ>1, it indicates loss aversion, that is, more attention is paid to avoiding the impact of cost-based indicators on demand response capabilities;

[0100] Combining the obtained positive and negative bullseye vectors, the positive and negative prospect evaluation matrix of demand response capability evaluation is obtained. The specific method is as follows:

[0101] If we use the negative bull's-eye vector f - As the reference point of prospect theory, the user's return at this time is better than the return of the reference point, and the user is risk-averse. At this time, the positive prospect value is Construct the positive prospect evaluation matrix V + =[v + (f mn )] M×N ;

[0102] If we use the positive bull's-eye vector f + As the reference point of prospect theory, the user's income at this time is lower than the income of the reference point, and the user shows risk preference. At this time, the negative prospect value is Construct the negative prospect evaluation matrix V - =[v - (f mn )] M×N ;

[0103] Based on the positive and negative demand response capability evaluation prospect evaluation matrix, the comprehensive prospect value of the mth user-side resource is calculated, which is expressed as the sum of the positive prospect value and the negative prospect value. The specific calculation is:

[0104]

[0105] Among them, w n represents the weight of the nth evaluation indicator, π + (w n ),π - (w n ) represent the weight functions of the gains and losses faced by user-side resources, respectively, and the calculation formula is:

[0106]

[0107] Among them, γ and σ are weight coefficients, and the values ​​are usually γ=0.61 and σ=0.69.

[0108] 2) Solve the weights of demand response capability evaluation indicators based on the comprehensive prospect value;

[0109] The optimization goal of the present invention is to minimize the distance between each element and the expected reference point of the standardized index while ensuring the maximum comprehensive prospect value of the demand response capability evaluation result. The optimization variable is the weight distribution w={w 1 ,…,w n ,…,w N}, the specific model is:

[0110]

[0111] Among them, μ represents the decision maker's preference coefficient for the two factors.

[0112] The above optimization problem is a typical convex optimization problem, and the best evaluation index weight distribution result can be solved by MATLAB.

[0113] (6) Conduct credibility analysis on the user-side resource demand response capability assessment results;

[0114] There are large differences in the degree of influence of different indicators on the evaluation of user-side resource demand response capability. If the weight of a certain indicator n is large, the change of this indicator will lead to a positive target distance To explain this phenomenon, the positive target distance and negative target distance of the user-side resource m participating in the demand response can be adjusted by the required evaluation weight coefficient to obtain the weighted positive target distance and weighted negative target distance It is expressed as:

[0115]

[0116] In order to comprehensively evaluate the demand response capability of user-side resources, it is necessary to consider and The relative deviation distance of the user-side resource m from the target is defined as:

[0117]

[0118] In order to effectively measure the impact of a single-dimensional evaluation index on the user-side resource demand response capability, the single-dimensional weighted positive target distance of the user-side resource m on the index n is defined as and weighted negative target distance for:

[0119]

[0120] Then the relative deviation of a single-dimensional indicator from the target can be expressed as:

[0121]

[0122] Relative deviation from target C mn The value range is [0,1]. mn When it approaches 0, it means the evaluation result is closer to This means that the evaluation results of user-side resource demand response capability are poor.

[0123] Based on the relative deviation from the target distance of each evaluation indicator of user-side resources, the demand response capability is further analyzed, and the key evaluation indicators that affect the user-side resource demand response capability are identified. When the social-physical fusion indicator system is used to evaluate the user-side resource demand response capability, if the relative deviation from the target distance of each indicator is large, the relative deviation from the target distance is difficult to reflect the actual situation of the user-side resource demand response capability, resulting in low credibility of the evaluation results. Therefore, the confidence of the evaluation results of the user-side resource demand response capability is measured by fuzzy entropy, which is defined as:

[0124]

[0125] Among them, η is the standardized coefficient, which is calculated as follows:

[0126]

[0127] Among them, δ is the user-side resource demand response capability evaluation level.

[0128] Confidence interval of user-side resource demand response capability assessment results The value range of is [0,1]. When it is close to 0, it means that the confidence level of the demand response capability evaluation result is low. When it is close to 1, it means that the confidence of the evaluation result is high.

[0129] On this basis, the confidence and relative offset target distance are comprehensively considered to calculate the confidence relative offset target distance P m , used to measure demand responsiveness, expressed as:

[0130]

[0131] in, To evaluate the weight coefficient, its size depends on the final decision maker's preference for the overall evaluation results of the user-side resource demand response capability and the influence of each evaluation indicator. By conducting a credibility analysis on the demand response capability evaluation results, the calculation accuracy of the proposed algorithm for social physical indicators can be improved.

[0132] S5: Integrate the time lag effect model, demand response strategy and demand response interactive capability evaluation system into the central air conditioning control system to obtain a central air conditioning control system that takes time lag effect into consideration and can automatically adjust, optimize performance and continuously improve user comfort.

[0133] Here, in order to realize the system integration of models, strategies and evaluation systems, it is necessary to organically combine each module into the central air-conditioning control system. The specific implementation methods are as follows:

[0134] (1) Central Control Unit

[0135] The modules such as time-lag effect model, demand response strategy and interactive capability evaluation system are integrated into the central control unit. The central control unit is responsible for coordinating the operation of each module to ensure that the system responds to demand according to the predetermined strategy. At the same time, the central control unit receives and processes the data transmitted by each module in real time, and dynamically adjusts the operating parameters of the air-conditioning system according to the demand response strategy.

[0136] (2) Distributed Data Acquisition System

[0137] Sensors and data acquisition devices deployed at each air conditioning terminal and key nodes are responsible for collecting real-time operating data, including parameters such as temperature, humidity, and energy consumption. These data are transmitted to the central control unit through the network to ensure that the system can obtain operating status information in real time and respond to grid needs in a timely manner.

[0138] (3) Cloud platform interface

[0139] The cloud platform enables remote monitoring and management of the central air conditioning system, allowing managers to view system status and adjust control strategies anytime and anywhere through the Internet. The cloud platform provides big data storage and analysis functions, stores and analyzes historical data, and assists in formulating better demand response strategies.

[0140] (4) Mobile Application

[0141] Provide managers with mobile applications to check the operating status, evaluation results and strategy adjustments of the air conditioning system at any time. The mobile application provides a friendly user interface, allowing managers to adjust response strategy parameters and evaluation index weights according to actual needs, thereby improving the flexibility and adaptability of the system.

[0142] Through the above integration steps, intelligent control of the central air-conditioning system can be achieved, energy utilization efficiency can be improved, and the adaptability of the central air-conditioning system to changes in power grid demand can be enhanced, while ensuring user comfort and economic benefits.

[0143] The present invention also provides a central air conditioning control system considering the time lag effect, which is used to execute the central air conditioning control method considering the time lag effect. The central air conditioning control system considering the time lag effect includes:

[0144] A time lag effect model building unit, used for building a time lag effect model for reflecting the time lag effect characteristics of a central air conditioning system;

[0145] A central air-conditioning demand response strategy formulation unit, used to formulate a central air-conditioning demand response strategy according to the time lag effect model;

[0146] A key indicator determination unit, used to determine key indicators for evaluating the interaction effect of the central air-conditioning system based on the time lag effect model and the demand response strategy;

[0147] A demand response interactive capability evaluation system construction unit, used to integrate the key indicators and construct a demand response interactive capability evaluation system;

[0148] The integration unit is used to integrate the time lag effect model, demand response strategy and demand response interactive capability evaluation system into the central air-conditioning control system to obtain a central air-conditioning control system that takes the time lag effect into consideration.

[0149] The demand response interactive capability evaluation system construction unit includes:

[0150] A multidimensional grey number decision matrix construction unit for evaluating demand response capability, used for constructing a multidimensional grey number decision matrix for evaluating demand response capability;

[0151] A normalization unit, used to normalize the multi-dimensional evaluation indicators of the user-side resource demand response capability;

[0152] Demand response capability evaluation profit and loss decision matrix unit, used to construct a demand response capability evaluation profit and loss decision matrix;

[0153] A demand response capability assessment positive and negative bullseye vector calculation unit is used to calculate the demand response capability assessment positive and negative bullseye vectors using the demand response capability assessment profit and loss decision matrix, and measure the influence of different indicators on the user-side resource demand response capability assessment;

[0154] The demand response capability evaluation index weight calculation unit is used to calculate the demand response capability evaluation index weight based on prospect theory.

[0155] Wherein, the demand response capability evaluation index weight calculation unit includes:

[0156] A demand response capability evaluation index comprehensive prospect value calculation unit, used to calculate the demand response capability evaluation index comprehensive prospect value;

[0157] The demand response capability evaluation index weight solving unit is used to solve the demand response capability evaluation index weight based on the comprehensive prospect value.

[0158] The central air-conditioning control method and control system considering the time lag effect provided by the present invention can not only improve the energy efficiency and demand response capability of the central air-conditioning system, but also provide important support for the stable operation of the power grid. By establishing a time lag effect model that reflects the time lag characteristics of the central air-conditioning system, it can be ensured that the system can accurately simulate and respond to different operating states; in terms of demand response strategy design, a highly adaptable control strategy is adopted, and real-time adjustment is made according to the time lag effect model, which can ensure that the system maintains the user's comfort while responding to the power grid demand; by determining the key indicators for evaluating the interactive effect of the air-conditioning system demand response, the system's operating efficiency, economic benefits and user experience can be fully reflected; through the cloud platform interface and mobile terminal applications, remote monitoring and management of the central air-conditioning system can be achieved, and users can view the system status and adjust the control strategy at any time to improve management efficiency and convenience. The system integrates real-time data acquisition and processing functions, and collects and transmits operating data in real time through a distributed data acquisition system, ensuring that the central control unit can quickly respond to demand changes and improve the system's operating stability and reliability. At the same time, the big data analysis and interactive capability evaluation system provides scientific decision-making support to assist managers in formulating optimized demand response strategies. In summary, the present invention not only effectively improves the energy utilization efficiency and demand response capability of the central air-conditioning system, but also takes into account user comfort and economic benefits, providing important guarantees for building energy conservation and power grid stability.

[0159] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the application can adopt the form of complete hardware embodiment, complete software embodiment, or the embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0160] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0161] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0162] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0163] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0164] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A central air conditioning control method considering time lag effect, characterized in that: include: Construct a time-lag effect model to reflect the time-lag effect characteristics of the central air-conditioning system; Formulate a central air conditioning demand response strategy based on the time lag effect model; Based on the time lag effect model and the demand response strategy, determine key indicators for evaluating the interactive effect of the central air conditioning system; Integrate the key indicators to build a demand response interactive capability evaluation system; The time lag effect model, demand response strategy and demand response interactive capability evaluation system are integrated into a central air-conditioning control system to obtain a central air-conditioning control system that takes time lag effect into consideration.

2. A central air conditioning control method considering time lag effect according to claim 1, characterized in that: The time lag effect model includes a delay module and a temperature rise module, which are used to reflect the influence of the time lag effect on the air conditioning terminal.

3. A central air conditioning control method considering time lag effect according to claim 1, characterized in that: The demand response strategy is optimized through multi-dimensional feature extraction and machine learning algorithms.

4. A central air conditioning control method considering time lag effect according to claim 1, characterized in that: In terms of real-time control strategies, the pressure on the power grid can be reduced during peak load periods by reducing the cooling capacity of the air-conditioning system or adjusting the operating hours; during demand response periods, the residual cooling in the pipeline can be used to provide cooling and reduce energy consumption by keeping the water pump running and the valve fully open; After the demand response ends, the chiller quickly resumes operation. The delay value calculated by the time-lag effect model ensures that the chiller outlet water is quickly transmitted to the terminal, thereby improving the recovery efficiency.

5. A central air conditioning control method considering time lag effect according to claim 1, characterized in that: The key indicators are divided into social indicators and physical indicators. The social indicators include the social benefits of user-side resources, user risk preference value, user willingness to participate in demand response, user-side resource economic benefits, user economic income, time-of-use electricity price difference and electricity price sensitivity; the physical indicators include daily load rate, load peak-to-valley difference rate, maximum peak shaving value, maximum valley filling value, load response degree, outdoor temperature, season and date type.

6. A central air conditioning control method considering time lag effect according to claim 1, characterized in that: The method of constructing a demand response interactive capability evaluation system is as follows: Construct a multidimensional grey number decision matrix for demand response capability evaluation; Standardize the multi-dimensional evaluation indicators of user-side resource demand response capability; Construct a profit-loss decision matrix for demand responsiveness assessment; The demand response capability evaluation profit and loss decision matrix is ​​used to calculate the positive and negative bullseye vectors of the demand response capability evaluation, and to measure the influence of different indicators on the user-side resource demand response capability evaluation; The weights of demand responsiveness evaluation indicators are calculated based on prospect theory.

7. A central air conditioning control method considering time lag effect according to claim 6, characterized in that: The method for calculating the weights of demand response capability evaluation indicators based on prospect theory is as follows: Calculate the comprehensive prospect value of the demand response capability assessment index; The weights of demand response capability evaluation indicators are solved based on the comprehensive prospect value.

8. A central air conditioning control system taking into account the time lag effect, characterized in that: include: A time lag effect model building unit, used for building a time lag effect model for reflecting the time lag effect characteristics of a central air conditioning system; A central air-conditioning demand response strategy formulation unit, used to formulate a central air-conditioning demand response strategy according to the time lag effect model; A key indicator determination unit, used to determine key indicators for evaluating the interaction effect of the central air-conditioning system based on the time lag effect model and the demand response strategy; A demand response interactive capability evaluation system construction unit, used to integrate the key indicators and construct a demand response interactive capability evaluation system; The integration unit is used to integrate the time lag effect model, demand response strategy and demand response interactive capability evaluation system into the central air-conditioning control system to obtain a central air-conditioning control system that takes the time lag effect into consideration.

9. A central air conditioning control system considering time lag effect according to claim 8, characterized in that: The demand response interactive capability evaluation system construction unit includes: A multidimensional grey number decision matrix construction unit for evaluating demand response capability, used for constructing a multidimensional grey number decision matrix for evaluating demand response capability; A normalization unit, used to normalize the multi-dimensional evaluation indicators of the user-side resource demand response capability; Demand response capability evaluation profit and loss decision matrix unit, used to construct a demand response capability evaluation profit and loss decision matrix; A demand response capability assessment positive and negative bullseye vector calculation unit is used to calculate the demand response capability assessment positive and negative bullseye vectors using the demand response capability assessment profit and loss decision matrix, and measure the influence of different indicators on the user-side resource demand response capability assessment; The demand response capability evaluation index weight calculation unit is used to calculate the demand response capability evaluation index weight based on prospect theory.

10. A central air conditioning control system considering time lag effect according to claim 9, characterized in that: The demand response capability evaluation index weight calculation unit includes: A demand response capability evaluation index comprehensive prospect value calculation unit, used to calculate the demand response capability evaluation index comprehensive prospect value; The demand response capability evaluation index weight solving unit is used to solve the demand response capability evaluation index weight based on the comprehensive prospect value.

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