Building temperature control demand response optimization method based on min-max comfort zone deviation

Abstract

The application discloses a building temperature control demand response optimization method based on Min-max comfort zone deviation, relates to the technical field of building intelligent temperature control, divides a building area to be controlled into a plurality of user units, and sets an active temperature control terminal in each user unit; a user sets a temperature control demand through the active temperature control terminal, and the temperature control demand is a Min-max comfort zone temperature range; and a temperature control central server constructs a temperature control optimization model with the optimal power consumption cost and comfort zone deviation as an objective function. The application can provide the building temperature control demand response optimization method based on Min-max comfort zone deviation, can better cope with temperature fluctuations and the like in actual operation by taking the comfort zone deviation in the form of a penalty function into the objective function, and can avoid the problem of no feasible solution of the model. The constructed model supports linearization processing, can be solved by using an exact algorithm, can not only speed up the solving speed, but also can guarantee the global optimality of the solution, and balances the efficiency and the optimization effect.

Description

Technical Field

[0001] This invention relates to the field of building intelligent temperature control technology, and in particular to a method for optimizing building temperature control demand response based on the Min-max comfort zone deviation. Background Technology

[0002] Building temperature control demand response technology has received widespread attention as an effective means of balancing energy supply and demand and improving energy efficiency. In current technological systems, demand response primarily uses incentive mechanisms to encourage building users to adjust the usage patterns of their temperature control equipment in response to changes in demand from the power grid or energy supply side. For example, during peak electricity load periods, users are encouraged to appropriately increase air conditioning temperature settings to reduce cooling demand, thereby lowering overall building electricity consumption. The building's thermal inertia can be used to alleviate pressure on the power grid. Taking peak summer demand as an example, during periods of power shortage (which are usually also periods of higher electricity prices), participating in demand response by reducing heating / cooling power can both reduce electricity costs and promote grid safety and stability. Existing methods include:

[0003] This method is based on air conditioner setpoints. Different air conditioner setpoints are used at different times of the day. For example, during periods of high electricity prices, the air conditioner temperature is set to a higher value, such as 27°C, to reduce cooling power.

[0004] Temperature range constraint method. The building temperature is incorporated into the optimization model as a range constraint, such as 24-26℃. Due to the inviolability of constraints in the optimization model, temperatures exceeding this range will result in no feasible solution.

[0005] Temperature trajectory tracking method. A desired temperature change trajectory is preset, and the optimization objective is to minimize the deviation (e.g., sum of squares) between the predicted temperature and this preset trajectory. Due to the nonlinearity of the sum of squares formula, the model typically requires a long solution time. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for optimizing building temperature control demand response based on the Min-max comfort zone deviation. The technical solution adopted is as follows:

[0007] The building temperature control demand response optimization method based on the Min-max comfort zone deviation includes the following steps:

[0008] Step 1: Divide the building area to be controlled into several user units, and set up an active temperature control terminal in each user unit;

[0009] Step 2: The user sets the temperature control requirements through the active temperature control terminal. The temperature control requirements are the Min-max comfort zone temperature range.

[0010] Step 3: The central temperature control server constructs a temperature control optimization model with the objective function of maximizing electricity cost and comfort zone deviation.

[0011] Step 4: The central temperature control server collects user demand data, environmental data, power grid data, and equipment status data in real time and inputs them into the temperature control optimization model;

[0012] Step 5: Use the exact linear programming algorithm to solve the model and quickly obtain the global optimal solution. The global optimal solution is the power adjustment plan parameters of the air conditioners of each user unit within a set time period in the future.

[0013] By adopting the above technical solution, unlike the inviolability of constraints in the traditional temperature range constraint method (exceeding the range will lead to the model having no feasible solution), this solution incorporates the comfort zone deviation into the objective function in the form of a penalty function, which can better cope with temperature fluctuations and other situations in actual operation and avoid the problem of the model having no feasible solution.

[0014] By leveraging the building's thermal inertia, pre-cooling and other methods can lower the temperature to a lower level than the comfort zone during periods of low electricity prices, thereby reducing the air conditioning power during periods of high electricity prices. This maximizes the building's ability to respond to electricity demand while ensuring thermal comfort, and is particularly effective during peak summer periods when power supply is tight.

[0015] Since the constructed model supports linearization, it can be solved using an exact algorithm, which not only speeds up the solution process but also ensures the global optimality of the solution, thus balancing efficiency and optimization effect.

[0016] The objective function takes into account both electricity costs and comfort zone deviation. By optimizing the balance between the two, it can reduce users' electricity costs and make the temperature curve as close to the comfort zone as possible, thereby reducing the discomfort caused by temperature deviation and achieving the dual goals of economy and comfort.

[0017] Optionally, step 6 is also included, whereby the central temperature control server decodes the optimized power regulation plan parameters into real-time control commands and sends them to the active temperature control terminals of each user unit, and the terminals drive the air conditioning equipment to execute the corresponding control commands.

[0018] By adopting the above technical solution, compared with the traditional temperature range constraint method which may lead to infeasible model solutions due to hard constraints, this method incorporates the comfort zone deviation into the objective function in the form of a penalty function. Combined with the precise execution of real-time instructions in step 6, it can flexibly adjust to fluctuations in actual operating conditions, ensuring stable system operation. Utilizing the instruction issuance and equipment execution mechanism in step 6, the building's thermal inertia can be fully utilized through pre-cooling during low electricity price periods, while maintaining temperature through thermal inertia during high electricity price periods, reducing air conditioning power consumption and maximizing the ability to participate in electricity demand response while ensuring comfort.

[0019] Optionally, step 7 is also included: if the deviation between the actual temperature monitored by the temperature control central server and the comfort zone exceeds the preset deviation threshold, the temperature control central server will re-trigger the optimization process, update the user demand data, environmental data, power grid data, and equipment status data, and repeat step 5 to generate the corrected control strategy.

[0020] By adopting the above technical solution, when the deviation between the actual temperature and the comfort zone exceeds the preset threshold (such as due to sudden weather changes, temporary equipment failures, etc.), step 7 re-triggers the optimization process, updates various data and generates correction strategies, avoiding the problem in traditional methods where fixed strategies cannot cope with sudden situations, thus affecting comfort or economy, and enhancing the system's adaptability under complex working conditions.

[0021] During the re-optimization process, by combining updated grid data (such as real-time electricity price fluctuations) and environmental data, power regulation plans can be adjusted in a timely manner to ensure that buildings can flexibly participate according to grid supply and demand throughout the entire demand response cycle, give full play to the role of thermal inertia, and maintain the depth and effectiveness of demand response.

[0022] By monitoring deviations in real time and triggering secondary optimization, timely corrections can be made when the temperature deviates from the comfort zone, avoiding negative impacts on user experience due to excessive deviations. At the same time, by resolving the optimal solution based on updated data, it can be ensured that the goal of optimizing both electricity costs and comfort deviations is still followed during the adjustment process, achieving a dynamic balance between the two.

[0023] Optionally, user demand data includes the upper and lower limits of the Min-max comfort zone temperature; environmental data includes the current indoor temperature of the user unit, the predicted outdoor temperature for a future set time period, solar radiation intensity, and wind speed; grid data includes the time-of-use electricity price for a future set time period, which includes high-price periods and low-price periods, used to calculate electricity costs; and equipment status data includes the current operating status and fault information of the air conditioners in each user unit, used to analyze whether optimization instructions match the actual operating capacity of the equipment.

[0024] By adopting the above technical solution and incorporating user demand data such as the upper and lower limits of the Min-max comfort zone, combined with environmental, power grid, and equipment status data, the optimization model can better fit the actual scenario. Compared to the traditional temperature range constraint method, which is prone to having no feasible solution due to hard constraints, this solution can flexibly respond to various data changes and enhance the model's adaptability in actual operation.

[0025] By leveraging time-of-use pricing (high and low electricity price periods) from grid data and combining it with outdoor temperature forecasts from environmental data, the building's thermal inertia can be fully utilized through pre-cooling during low electricity price periods, while reducing air conditioning power during high electricity price periods. Simultaneously, based on equipment status data, the strategy ensures it aligns with equipment capabilities, maximizing demand response while prioritizing comfort.

[0026] The clear division and input of various data types provide a precise foundation for model linearization, and combined with precise algorithms, the global optimum can be solved quickly. Among them, data such as time-of-use electricity prices support the optimization of economic objectives, while data such as the upper and lower limits of the comfort zone ensure the achievement of comfort objectives, balancing solution speed and optimization quality.

[0027] Optionally, the objective function of the temperature control optimization model in step 3 is expressed as follows:

[0028] ;

[0029] in This indicates minimizing electricity costs. Indicates the air conditioner's power. This represents the time-of-use electricity price, where T represents the time domain for which electricity charges are considered. This indicates minimizing the maximum comfort zone deviation. Indicates building temperature. This represents the weighting coefficient for the temperature comfort zone, with a value range of [value missing]. A higher value indicates a greater emphasis on comfort; A represents the median value of the Min-max comfort zone. This represents the absolute value of the difference between A and the maximum or minimum value of the Min-max comfort zone.

[0030] By adopting the above technical solution, the part that minimizes electricity costs and the part that minimizes the deviation of the maximum comfort zone in the objective function are combined by weighting coefficients. The value of the weighting coefficients can reflect the importance attached to comfort, so that the optimization results can flexibly adjust the priority of the two according to actual needs, thereby reducing electricity costs while ensuring the thermal comfort of users.

[0031] Compared to the traditional temperature range constraint method, which often leads to infeasible models due to the inviolability of constraints, this objective function incorporates the maximum comfort zone deviation and treats it in a form similar to a penalty function. This allows the model to better adapt to various changes in actual operation and reduces the number of infeasible cases.

[0032] Optionally, the objective function can introduce auxiliary variables. and Perform linearization,

[0033] ;

[0034] ;

[0035] This represents the absolute deviation of temperature from the midpoint of the comfort zone. This represents the maximum deviation from the comfort zone at any given time.

[0036] Optionally, the final objective function expression after linearization is:

[0037] .

[0038] By adopting the above technical solution, the objective function supports linearization and can be solved quickly by combining it with the exact algorithm. This improves the solution speed while ensuring global optimality, making the optimization results both efficient and reliable.

[0039] Linearization allows the model to be solved using exact algorithms, which speeds up the solution process while ensuring global optimality. This avoids the problems of long solution time and easy getting trapped in local optima that traditional nonlinear models (such as models based on the sum of squares and deviations) have.

[0040] By quantifying the maximum comfort zone deviation using auxiliary variables, the objective function incorporates the comfort zone constraint in the form of a penalty function, rather than using an inviolable hard constraint. This solves the problem that the model becomes infeasible when the temperature exceeds the range in the traditional temperature range constraint method, thus improving the adaptability in actual operation.

[0041] The linearized model can more efficiently calculate the optimal strategy of pre-cooling during low electricity price periods and reducing power during high electricity price periods. Combined with the precise control of temperature deviation by auxiliary variables, it can fully utilize the building's thermal inertia while ensuring thermal comfort, and tap the demand response potential to a greater extent.

[0042] Optionally, in step 2, the user can simultaneously input the weighting coefficient of the temperature comfort zone through the active temperature control terminal. .

[0043] By adopting the above technical solution, users can independently adjust the weighting coefficient according to their own sensitivity to thermal comfort. (The value range is [0,1]). The larger the value, the more emphasis is placed on comfort, so that the optimization model can specifically balance the power cost and comfort deviation of the user unit and meet the personalized needs of different users.

[0044] The difference in weight coefficients among different user units can reflect the overall building's comprehensive preference for comfort and economy. The central temperature control server can adjust the temperature control strategy of each unit accordingly during optimization. For example, units with high weight coefficients can prioritize ensuring the stability of the comfort zone, while units with low weight coefficients can participate more in demand response, thereby improving the overall flexibility and adaptability of the system.

[0045] A memory that stores an optimization program designed using a building temperature control demand response optimization method based on the Min-max comfort zone deviation.

[0046] A computer runs an optimization program designed based on a building temperature control demand response optimization method using the Min-max comfort zone deviation, and outputs power regulation plan parameters for the air conditioning of each user unit within a future set time period.

[0047] In summary, the present invention has at least one of the following beneficial technical effects:

[0048] This invention provides a building temperature control demand response optimization method based on the Min-max comfort zone deviation. By incorporating the comfort zone deviation into the objective function in the form of a penalty function, it can better cope with temperature fluctuations and other situations in actual operation, avoiding the problem of the model having no feasible solution.

[0049] By leveraging the building's thermal inertia, pre-cooling and other methods can lower the temperature to a lower level than the comfort zone during periods of low electricity prices, thereby reducing the air conditioning power during periods of high electricity prices. This maximizes the building's ability to respond to electricity demand while ensuring thermal comfort, and is particularly effective during peak summer periods when power supply is tight.

[0050] The constructed model supports linearization and can be solved using exact algorithms, which not only speeds up the solution process but also ensures the global optimality of the solution, balancing efficiency and optimization effect.

[0051] The objective function takes into account both electricity costs and comfort zone deviation. By optimizing the balance between the two, it can reduce users' electricity costs and make the temperature curve as close to the comfort zone as possible, thereby reducing the discomfort caused by temperature deviation and achieving the dual goals of economy and comfort. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the building temperature control demand response optimization method based on the Min-max comfort zone deviation of the present invention.

[0053] Figure 2 This is a schematic diagram of the Min-max comfort zone deviation in the building temperature control demand response optimization method based on the Min-max comfort zone deviation of this invention. Detailed Implementation

[0054] The present invention will be further described in detail below with reference to the accompanying drawings.

[0055] This invention discloses a method for optimizing building temperature control demand response based on the Min-max comfort zone deviation.

[0056] Reference Figure 1 and Figure 2Example 1, a building temperature control demand response optimization method based on Min-max comfort zone deviation, includes the following steps:

[0057] Step 1: Divide the building area to be controlled into several user units, and set up an active temperature control terminal in each user unit;

[0058] Step 2: The user sets the temperature control requirements through the active temperature control terminal. The temperature control requirements are the Min-max comfort zone temperature range.

[0059] Step 3: The central temperature control server constructs a temperature control optimization model with the objective function of maximizing electricity cost and comfort zone deviation.

[0060] Step 4: The central temperature control server collects user demand data, environmental data, power grid data, and equipment status data in real time and inputs them into the temperature control optimization model;

[0061] Step 5: Use the exact linear programming algorithm to solve the model and quickly obtain the global optimal solution. The global optimal solution is the power adjustment plan parameters of the air conditioners of each user unit within a set time period in the future.

[0062] Unlike the traditional temperature range constraint method where the constraints are inviolable (exceeding the range will lead to the model having no feasible solution), this scheme incorporates the comfort zone deviation into the objective function in the form of a penalty function, which can better cope with temperature fluctuations and other situations in actual operation and avoid the problem of the model having no feasible solution.

[0063] By leveraging the building's thermal inertia, pre-cooling and other methods can lower the temperature to a lower level than the comfort zone during periods of low electricity prices, thereby reducing the air conditioning power during periods of high electricity prices. This maximizes the building's ability to respond to electricity demand while ensuring thermal comfort, and is particularly effective during peak summer periods when power supply is tight.

[0064] Since the constructed model supports linearization, it can be solved using an exact algorithm, which not only speeds up the solution process but also ensures the global optimality of the solution, thus balancing efficiency and optimization effect.

[0065] The objective function takes into account both electricity costs and comfort zone deviation. By optimizing the balance between the two, it can reduce users' electricity costs and make the temperature curve as close to the comfort zone as possible, thereby reducing the discomfort caused by temperature deviation and achieving the dual goals of economy and comfort.

[0066] Example 2 also includes step 6, whereby the central temperature control server decodes the optimized power regulation plan parameters into real-time control commands and sends them to the active temperature control terminals of each user unit, and the terminals drive the air conditioning equipment to execute the corresponding control commands.

[0067] Compared to the traditional temperature range constraint method, which may lead to infeasible model solutions due to hard constraints, this method incorporates comfort zone deviations into the objective function as a penalty function. Combined with the precise execution of real-time instructions in step 6, it can flexibly adjust to fluctuations in actual operating conditions, ensuring stable system operation. Utilizing the instruction issuance and equipment execution mechanism in step 6, the building's thermal inertia can be fully utilized through pre-cooling during low electricity price periods, while maintaining temperature through thermal inertia during high electricity price periods, reducing air conditioning power consumption and maximizing the ability to participate in electricity demand response while ensuring comfort.

[0068] Example 3 also includes step 7: if the deviation between the actual temperature monitored by the temperature control central server and the comfort zone exceeds the preset deviation threshold, the temperature control central server re-triggers the optimization process, updates the user demand data, environmental data, power grid data, and equipment status data, and repeats step 5 to generate the corrected control strategy.

[0069] When the deviation between the actual temperature and the comfort zone exceeds the preset threshold (such as due to sudden weather changes, temporary equipment failures, etc.), step 7 re-triggers the optimization process, updates various data and generates correction strategies, avoiding the problem in traditional methods where fixed strategies cannot cope with sudden situations, thus affecting comfort or economy, and enhancing the system's adaptability under complex working conditions.

[0070] During the re-optimization process, by combining updated grid data (such as real-time electricity price fluctuations) and environmental data, power regulation plans can be adjusted in a timely manner to ensure that buildings can flexibly participate according to grid supply and demand throughout the entire demand response cycle, give full play to the role of thermal inertia, and maintain the depth and effectiveness of demand response.

[0071] By monitoring deviations in real time and triggering secondary optimization, timely corrections can be made when the temperature deviates from the comfort zone, avoiding negative impacts on user experience due to excessive deviations. At the same time, by resolving the optimal solution based on updated data, it can be ensured that the goal of optimizing both electricity costs and comfort deviations is still followed during the adjustment process, achieving a dynamic balance between the two.

[0072] Example 4: User demand data includes the upper and lower limits of the Min-max comfort zone temperature; environmental data includes the current indoor temperature of the user unit, the predicted outdoor temperature for a future set time period, solar radiation intensity, and wind speed; power grid data includes the time-of-use electricity price for a future set time period, which includes high-price periods and low-price periods, used to calculate electricity costs; equipment status data includes the current operating status and fault information of the air conditioners in each user unit, used to analyze whether the optimization instructions match the actual operating capacity of the equipment.

[0073] By incorporating user demand data such as the upper and lower limits of the Min-max comfort zone, combined with environmental, power grid, and equipment status data, the optimization model can better reflect real-world scenarios. Compared to traditional temperature range constraint methods, which are prone to infeasible solutions due to hard constraints, this approach can flexibly respond to various data changes, enhancing the model's adaptability in actual operation.

[0074] By leveraging time-of-use pricing (high and low electricity price periods) from grid data and combining it with outdoor temperature forecasts from environmental data, the building's thermal inertia can be fully utilized through pre-cooling during low electricity price periods, while reducing air conditioning power during high electricity price periods. Simultaneously, based on equipment status data, the strategy ensures it aligns with equipment capabilities, maximizing demand response while prioritizing comfort.

[0075] The clear division and input of various data types provide a precise foundation for model linearization, and combined with precise algorithms, the global optimum can be solved quickly. Among them, data such as time-of-use electricity prices support the optimization of economic objectives, while data such as the upper and lower limits of the comfort zone ensure the achievement of comfort objectives, balancing solution speed and optimization quality.

[0076] Example 5, the objective function of the temperature control optimization model in step 3 is expressed as follows:

[0077] ;

[0078] in This indicates minimizing electricity costs. Indicates the air conditioner's power. This represents the time-of-use electricity price, where T represents the time domain for which electricity charges are considered. This indicates minimizing the maximum comfort zone deviation. Indicates building temperature. This represents the weighting coefficient for the temperature comfort zone, with a value range of [value missing]. A higher value indicates a greater emphasis on comfort; A represents the median value of the Min-max comfort zone. This represents the absolute value of the difference between A and the maximum or minimum value of the Min-max comfort zone.

[0079] In the objective function, the part that minimizes electricity costs and the part that minimizes the deviation of the maximum comfort zone are combined through weighting coefficients. The value of the weighting coefficients can reflect the importance attached to comfort, so that the optimization results can flexibly adjust the priority of the two according to actual needs, thereby reducing electricity costs while ensuring the thermal comfort of users.

[0080] Compared to the traditional temperature range constraint method, which often leads to infeasible models due to the inviolability of constraints, this objective function incorporates the maximum comfort zone deviation and treats it in a form similar to a penalty function. This allows the model to better adapt to various changes in actual operation and reduces the number of infeasible cases.

[0081] Example 6: Introducing auxiliary variables into the objective function and Perform linearization,

[0082] ;

[0083] ;

[0084] This represents the absolute deviation of temperature from the midpoint of the comfort zone. This represents the maximum deviation from the comfort zone at any given time.

[0085] Example 7: The final objective function expression after linearization is:

[0086] .

[0087] The objective function supports linearization and can be solved quickly by combining it with an exact algorithm. This improves the solution speed while ensuring global optimality, making the optimization results both efficient and reliable.

[0088] Linearization allows the model to be solved using exact algorithms, which speeds up the solution process while ensuring global optimality. This avoids the problems of long solution time and easy getting trapped in local optima that traditional nonlinear models (such as models based on the sum of squares and deviations) have.

[0089] By quantifying the maximum comfort zone deviation using auxiliary variables, the objective function incorporates the comfort zone constraint in the form of a penalty function, rather than using an inviolable hard constraint. This solves the problem that the model becomes infeasible when the temperature exceeds the range in the traditional temperature range constraint method, thus improving the adaptability in actual operation.

[0090] The linearized model can more efficiently calculate the optimal strategy of pre-cooling during low electricity price periods and reducing power during high electricity price periods. Combined with the precise control of temperature deviation by auxiliary variables, it can fully utilize the building's thermal inertia while ensuring thermal comfort, and tap the demand response potential to a greater extent.

[0091] Example 8: In step 2, the user simultaneously inputs the weighting coefficient of the temperature comfort zone through the active temperature control terminal. .

[0092] Users can adjust the weighting coefficient according to their own sensitivity to thermal comfort. (The value range is [0,1]). The larger the value, the more emphasis is placed on comfort, so that the optimization model can specifically balance the power cost and comfort deviation of the user unit and meet the personalized needs of different users.

[0093] The difference in weight coefficients among different user units can reflect the overall building's comprehensive preference for comfort and economy. The central temperature control server can adjust the temperature control strategy of each unit accordingly during optimization. For example, units with high weight coefficients can prioritize ensuring the stability of the comfort zone, while units with low weight coefficients can participate more in demand response, thereby improving the overall flexibility and adaptability of the system.

[0094] Example 9: A memory that stores an optimization program designed using a building temperature control demand response optimization method based on the Min-max comfort zone deviation.

[0095] Example 10: A computer runs an optimization program designed based on a building temperature control demand response optimization method for Min-max comfort zone deviation, and outputs power adjustment plan parameters for the air conditioners of each user unit within a future set time period.

[0096] The following specific embodiments illustrate the implementation principle of the present invention:

[0097] A three-story office building (approximately 2000 square meters in total area) is participating in the summer peak electricity demand response program. The goal is to reduce electricity costs during peak electricity price periods and alleviate pressure on the power grid, while ensuring the thermal comfort of office workers. The building is divided into 12 user units (4 offices per floor).

[0098] A temperature control optimization method based on the Min-max comfort zone deviation is adopted, and the specific implementation process is as follows:

[0099] Step 1: User Unit Division and Terminal Configuration. The office building is divided into 12 independent user units (numbered U1-U12) by floor and office area. Each unit is equipped with a smart temperature control panel (active temperature control terminal), supporting user input of temperature control requirements and communication with the central temperature control server. The server pre-loads the thermal characteristic parameters of each unit (e.g., U1-U4 are west-facing offices with higher heat transfer coefficients; U5-U8 are south-facing offices with significant solar radiation impact) and air conditioning parameters (maximum cooling power of 3kW per unit). Step 2: User Input of Temperature Control Requirements and Weighting Coefficients. Personnel in each office input the following through the smart panel: Min-max comfort zone: all set to 24-26℃ (summer cooling comfort zone);

[0100] Temperature comfort zone weighting coefficient The R&D department (U1-U4) has long working hours, so input 0.8 (more emphasis on comfort); the administrative department (U5-U8) input 0.6; the meeting room (U9-U12) input 0.5 (some comfort can be sacrificed to participate in demand response).

[0101] Step 3: Server collects multi-dimensional data. The central temperature control server collects data for the next 24 hours at 20:00 on the same day: User demand data: comfort zone (24-26℃) and weighting coefficients (0.5 / 0.6 / 0.8) for 12 units; Environmental data: current indoor temperature of each unit is 25℃; outdoor temperature for the next 24 hours is 28-36℃ (peaking at 36℃ from 14:00 to 18:00), solar radiation intensity is 800-1200W / ㎡ (strongest from 12:00 to 15:00), wind speed is 1-3m / s; Power grid data: time-of-use electricity price (low price of 0.5 yuan / kWh from 0:00 to 8:00, high price of 1.5 yuan / kWh from 14:00 to 18:00, and flat price of 0.8 yuan / kWh for other times); Equipment status data: all air conditioners are operating normally and there is no fault information.

[0102] Step 4: Construct a temperature control optimization model. The server constructs an objective function based on the collected data, with the following expression: ;

[0103] in, For air conditioner power, For time-of-use electricity pricing, Indoor temperature, Weighting coefficients input by the user; comfort zone midpoint A = 25℃, absolute value of the difference between A and the upper and lower limits is 1℃ (25-24=1, 26-25=1); auxiliary variables are introduced. and Perform linearization;

[0104] The final linearization objective function is: .

[0105] Step 5: Model Solving and Optimization Strategy Generation. A linear programming algorithm (simplex method) is used to solve the model, yielding the power adjustment plan for each unit for the next 24 hours: Low electricity price period (0:00-8:00): U1-U4 ( =0.8) Pre-cooled to 24℃, power maintained at 2.5kW; U5-U8 ( =0.6) Pre-cooled to 23.5℃, power 3kW; U9-U12 ( =0.5) Pre-cool to 23℃, power 3kW (fully utilize thermal inertia); During high electricity price periods (14:00-18:00): all units reduce power to 1kW, using thermal inertia to maintain temperature: U1-U4≤26℃, U5-U8≤26.5℃, U9-U12≤27℃ (maximum deviation does not exceed the user's acceptable range); During other periods: power is adjusted to 1.5kW, maintaining temperature at around 25℃.

[0106] Step 6: Command Issuance and Execution The central temperature control server breaks down the power adjustment plan into real-time commands and issues them to the intelligent temperature control terminals of each unit: 0:00 triggers the power of U1-U4 air conditioners to be adjusted to 2.5kW, with a target temperature of 24℃; 14:00 triggers the power of all air conditioners to be reduced to 1kW, allowing the temperature to rise slowly; the terminals drive the air conditioners to execute the commands precisely and provide real-time feedback on the operating status.

[0107] Step 7: Dynamic Monitoring and Correction 15:00 Monitoring: Due to afternoon sun exposure, the actual temperature of U7 reached 27℃, exceeding the preset deviation threshold (0.5℃). The server immediately triggered re-optimization. Data Update: The measured outdoor temperature was 36℃ (1℃ higher than predicted), and the current temperature of U7 was 27℃. The air conditioner was functioning correctly. Secondary Solution: The power of U7 during peak electricity price periods was adjusted to 1.2kW. After 1 hour, the temperature dropped to 26.3℃, returning to an acceptable range, while maintaining the optimal balance between electricity cost and comfort deviation. Implementation Results: Economic Efficiency: Total power was reduced by 60% during peak electricity price periods, and the daily electricity cost was reduced by 28% compared to the traditional setpoint method (fixed at 26℃). Comfort: The maximum temperature deviation of all units was ≤1℃, and user satisfaction reached 95%. Demand Response: The grid load was reduced by approximately 15kW during peak electricity price periods, effectively participating in peak summer demand response.

[0108] The effectiveness of the method in personalized comfort assurance, economic cost reduction and power grid demand response was verified through real-world scenarios, demonstrating its core advantages such as linear solution, dynamic correction and data fusion.

[0109] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing building temperature control demand response based on Min-max comfort zone deviation, characterized in that, Includes the following steps: Step 1: Divide the building area to be controlled into several user units, and set up an active temperature control terminal in each user unit; Step 2: The user sets the temperature control requirements through the active temperature control terminal. The temperature control requirements are the Min-max comfort zone temperature range. Step 3: The central temperature control server constructs a temperature control optimization model with the objective function of maximizing electricity cost and comfort zone deviation. Step 4: The central temperature control server collects user demand data, environmental data, power grid data, and equipment status data in real time and inputs them into the temperature control optimization model; Step 5: Use the exact linear programming algorithm to solve the model and quickly obtain the global optimal solution. The global optimal solution is the power adjustment plan parameters of the air conditioners of each user unit within a set time period in the future. The objective function of the temperature control optimization model in step 3 is expressed as follows: ； in This indicates minimizing electricity costs. Indicates the air conditioner power. This represents the time-of-use electricity price, where T represents the time domain for which electricity charges are considered. This represents minimizing the maximum comfort zone deviation. Indicates building temperature. This represents the weighting coefficient for the temperature comfort zone, with a value range of [value missing]. A higher value indicates a greater emphasis on comfort; A represents the median value of the Min-max comfort zone. This represents the absolute value of the difference between A and the maximum or minimum value of the Min-max comfort zone; introducing auxiliary variables into the objective function and Perform linearization, ； ； This indicates the absolute deviation of temperature from the midpoint of the comfort zone. This represents the maximum deviation from the comfort zone at any given time. The final objective function expression after linearization is: 。 2. The building temperature control demand response optimization method based on Min-max comfort zone deviation according to claim 1, characterized in that, It also includes step 6, where the central temperature control server decodes the optimized power regulation plan parameters into real-time control commands and sends them to the active temperature control terminals of each user unit. The terminals then drive the air conditioning equipment to execute the corresponding control commands.

3. The building temperature control demand response optimization method based on Min-max comfort zone deviation according to claim 2, characterized in that, It also includes step 7, in which if the deviation between the actual temperature monitored by the temperature control central server and the comfort zone exceeds the preset deviation threshold, the temperature control central server will re-trigger the optimization process, update the user demand data, environmental data, power grid data, and equipment status data, and repeat step 5 to generate the corrected control strategy.

4. The building temperature control demand response optimization method based on Min-max comfort zone deviation according to claim 3, characterized in that, User demand data includes the upper and lower limits of the Min-max comfort zone temperature; environmental data includes the current indoor temperature of the user unit, the predicted outdoor temperature for a future set time period, solar radiation intensity, and wind speed; power grid data includes the time-of-use electricity price for a future set time period, including high-price and low-price periods, used to calculate electricity costs; equipment status data includes the current operating status and fault information of the air conditioners in each user unit, used to analyze whether optimization instructions match the actual operating capacity of the equipment.

5. The building temperature control demand response optimization method based on Min-max comfort zone deviation according to claim 4, characterized in that, In step 2, the user simultaneously inputs the weighting coefficient of the temperature comfort zone through the active temperature control terminal. .

6. A memory, characterized in that, The storage employs an optimization program designed using the building temperature control demand response optimization method based on the Min-max comfort zone deviation as described in any one of claims 1-5.

7. A computer, characterized in that, The optimization program designed by the building temperature control demand response optimization method based on the Min-max comfort zone deviation as described in any one of claims 1-5 outputs the power adjustment plan parameters of the air conditioners of each user unit within a future set time period.

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