Building flexible load regulation and control method based on modular strategy library and intelligent optimization

Abstract

The invention provides a building flexible load regulation and control method based on a modular strategy library and intelligent optimization, and relates to the field of building flexible loads. The method comprises the following steps: S1, acquiring energy consumption data, searching and removing abnormal data and missing data through a box plot, and interpolating the missing data through a linear interpolation method; s2, constructing a residential building flexible load collaborative regulation and control model; s3, verifying the flexible load coordinated regulation model of the residential building constructed in the step S2 through an actual residential case, and obtaining a prediction error of the total electricity consumption and the air conditioner electricity consumption; and S4, quantifying flexible resources of an actual building demand side to obtain an optimal scheduling scheme under different optimization targets. By adopting the steps, the problems of single research strategy and poor collaboration in the prior art are solved, and the comprehensive regulation and control capability of the building energy system is remarkably improved.

Description

A Building Flexible Load Control Method Based on Modular Strategy Library and Intelligent Optimization Technical Field

[0001] This invention relates to the field of building energy, and in particular to a building flexible load control method based on a modular strategy library and intelligent optimization. Background Technology

[0002] Buildings are the world's largest consumer of electricity, accounting for approximately 37% of global energy use. In China, building operations account for 21.7% of the country's total energy-related carbon emissions, with urban residential buildings accounting for as much as 38%. Meanwhile, electricity consumption by urban and rural residents continues to grow rapidly, and residential building electricity demand is expected to grow by about 10%. This increase in electricity consumption is leading to increasingly prominent peak loads on the power grid, while the rapid growth of renewable energy installed capacity is also bringing significant pressure to absorb the excess capacity.

[0003] Building energy flexibility refers to a building's ability to dynamically adjust its operation based on external conditions such as weather, while meeting users' basic needs for thermal comfort and productivity, thus supporting the stable operation of the power grid. Based on the energy consumption characteristics of electrical appliances and user behavior habits, residential building electrical loads can be divided into adjustable flexible loads, transferable flexible loads, and rigid loads. Adjustable flexible loads refer to loads that can be reduced by adjusting the operation of air conditioners and fans. These loads have high adjustment potential but are limited by user comfort and energy consumption habits. Transferable flexible loads refer to load transfer achieved by changing the operating times of water heaters, washing machines, etc., based on electricity price incentives, maintaining a constant total load within a certain time window. These loads are highly flexible and have a high adjustment capacity. Rigid loads refer to appliances such as lighting, refrigerators, and televisions whose operating time is strongly correlated with user demand, often operating only at rated power. Forced adjustment would significantly impact user experience, and they have almost no adjustment capacity.

[0004] Existing flexible control strategies for residential buildings mainly include two types: one is load reduction type and the other is load transfer type. By adjusting the operating time window of transferable appliances such as washing machines and dishwashers, and using incentives such as time-of-use pricing, electricity consumption is shifted from peak to off-peak hours.

[0005] Existing research on flexible control of residential buildings has two significant shortcomings: (1) Most existing research focuses on single equipment or single control methods, making it difficult to quickly combine and flexibly adjust the flexible control of building load according to dynamic electricity prices, climate fluctuations and diversified load demands. This limits the overall control capability and adaptability of residential buildings when facing complex scenarios with multiple time periods and multiple objectives.

[0006] (2) Existing single models often compromise between efficiency and interpretability. Although hybrid models offer potential solutions for balancing these performance aspects, further research is needed. These shortcomings limit the reliability, adaptability, and operational efficiency of flexible control models in practical applications.

[0007] Therefore, a building flexible load control method based on a modular strategy library and intelligent optimization is needed to solve the above problems. Summary of the Invention

[0008] The purpose of this invention is to provide a building flexible load control method based on a modular strategy library and intelligent optimization. Various control strategies are encapsulated into reusable standardized modules, enabling rapid combination and response of multiple strategy types, and significantly improving the building's adaptability to dynamic electricity prices and variable loads. Through a hybrid architecture of "simulation modeling-intelligent optimization," both computational efficiency and interpretability of the control process are ensured.

[0009] To achieve the above objectives, this invention provides a building flexible load control method based on a modular strategy library and intelligent optimization, comprising the following steps: S1: acquiring energy consumption data, identifying and eliminating abnormal and missing data through box plots, and imputing missing data using linear interpolation; S2: constructing a flexible load collaborative control model for residential buildings; S3: verifying the flexible load collaborative control model for residential buildings constructed in S2 through actual residential cases, obtaining the prediction errors of total electricity consumption and air conditioning electricity consumption; S4: quantifying the flexible resources on the actual building demand side, obtaining the optimal scheduling scheme under different optimization objectives.

[0010] Preferably, the energy consumption data in S1 includes historical total electricity consumption data, air conditioning electricity consumption data, historical meteorological data, and real-time operating parameters; historical total electricity consumption data and air conditioning electricity consumption data are exported through the Building Energy Management System (BEMS) or online channels; historical meteorological data are obtained from the National Meteorological Data Center or self-built meteorological stations, and historical meteorological data includes dry-bulb temperature, wet-bulb temperature, relative humidity, and dew point temperature; real-time operating parameters are dynamically collected through the building automation system, and real-time operating parameters include appliance start-up and shutdown data, operating power, and occupancy rate.

[0011] Preferably, S2 includes the following steps: S21: Construct a parameter set based on the energy consumption data in S1, and complete the parameterization definition of the building space and system using the EnergyPlus simulation tool; S22: Based on S21, use the external program Python and EMS for collaborative simulation to convert the total building power consumption into a function of the power and operating status of each device, customize the modular encapsulation and combined application of flexible strategies such as temperature setpoint adjustment, wind speed dynamic compensation, building pre-cooling / preheating, and adjustment window, and generate a building geometric model based on the basic physical parameters of the building. Based on the building geometric model, generate a building benchmark model for flexibility potential analysis through a household appliance model and custom flexible strategy control logic; S23: Based on the building benchmark model generated in S22, introduce the HOA (Hike-Only Algorithm) and set an electricity price model, with flexible yield, peak energy flexibility, and peak-valley reduction as objective functions respectively, to achieve optimization of flexible control strategies and complete the construction of a flexible load collaborative control model for residential buildings.

[0012] Preferably, the logical architecture of the EMS module in S22 is sensor-actuator-program, which is used for co-simulation with an external program through the FMI interface; a home appliance model is built using a custom Python script and the EnergyPlus-EMS module. The home flexible appliances include a split-type fixed-frequency air conditioner, electric fan, washing machine, water heater, and robot vacuum cleaner. The user operates within a specific time period... Total electrical energy consumed internally Represented as: In the formula, The table shows the power consumption status of household appliance a at time t. This indicates that appliance A is not working. This indicates that appliance a is working normally; This represents the power of household appliance a at time t; This represents the collection of all household appliances; This indicates the duration of each time period; the allowable operating time range for appliance a is... For electrical appliances with adjustable and transferable loads, the following are examples: In the formula, This represents the total number of time periods that the day is divided into, with a value of 144. This refers to the total number of working hours per day for appliance A; the fixed-frequency air conditioner uses the PTHP module in EnergyPlus to simulate the power consumption of a split-type air conditioning system. The operating schedule and set temperature of the fixed-frequency air conditioner are selected and set according to actual operating characteristics or specifications. The fixed-frequency air conditioner operates at its rated power. During operation, the fixed-frequency air conditioner operates within a certain time period. status As shown in the following formula: ; In the formula, Indoor temperature; Set the temperature for the air conditioner; the upper and lower limits for temperature adjustment are respectively... and Adjusted according to user comfort; To ensure accurate temperature control of the air conditioner, the temperature range is set between 0.25 and 0.5℃; the electric fan's output power is adjusted according to user needs. In the formula, These are the upper and lower limits for fan power adjustment, with the upper limit being the fan's rated power; transferable flexible loads include washing machines, water heaters, and robot vacuum cleaners, and their load attributes are described as follows: In the formula Indicates the time the device is turned on; Indicates the transferable time interval; rigid equipment includes refrigerators and lighting equipment, with operating power of: Basic physical parameters of the building include: indoor temperature. The upper limit of temperature adjustment is The minimum temperature adjustment step on the air conditioner remote control is 1℃, which is 0.5℃. The fan compensates for the indoor temperature rise. The maximum compensation for indoor temperature rise by the fan is... The pre-cooling temperature is The precooling window is The maximum precooling window is The permitted operating time range for household appliance A is: The transferable time interval is .

[0013] Preferably, in the HOA optimization algorithm in S23, the hiker is defined based on the terrain and slope. In iteration speed of time : In the formula The slope of the terrain is calculated as follows: In the formula, and hikers The altitude and horizontal distance traveled; The slope angle of the terrain. [0°, 50°]; hikers The speed after iteration is: In the formula, for Random numbers in the data; and These are the current speed and the initial speed, respectively. This refers to the location information of the team leader. For hikers The scan factor is set in the range [1,3]; hiker Location updated to : hikers initial position Lower bound of decision variables in optimization problems and the Upper Realm Sure: In the formula, The numbers are uniformly distributed between [0,1]. and To optimize the decision variable of the problem The lower and upper bounds of a dimension.

[0014] Preferably, in the objective function of S23, the flexible rate of return is the rate of change of costs before and after the flexible adjustment on the response day, and is calculated as follows: In the formula, Baseline Real-time power load; After flexible adjustment Real-time power load; The electricity price at time t; peak energy flexibility refers to the peak period before and after flexible adjustment. The change in total electricity consumption is calculated as follows: In the formula, For flexible adjustment of electricity prices during peak hours Power consumption for equipment operation; For peak electricity price periods after flexible adjustment Equipment operating power consumption; Peak-to-valley reduction rate is the rate of change of the difference between the peak and valley values ​​of the load before and after flexible regulation, and the calculation method is as follows: In the formula, The load difference between the peak and trough times serves as the baseline. The load difference between peak and valley times after flexible adjustment is used. Preferably, the baseline load and flexible adjustment day are determined in S4, and a flexible control strategy is formulated for actual residential cases. The flexible load collaborative control model of residential buildings constructed in S2 is applied to implement the flexible control strategy, quantify the flexible resources of residential buildings, and obtain the optimal scheduling scheme under different optimization objectives.

[0015] Therefore, the present invention adopts the above-mentioned building flexible load control method based on modular strategy library and intelligent optimization, and the technical effects are as follows: (1) The present invention realizes multi-strategy modular collaborative control. Based on EMS, the strategies such as temperature set point adjustment, wind speed compensation, and building pre-cooling are decoupled into basic modules that can be flexibly combined, supporting multi-objective collaborative optimization for dynamic electricity price and complex load, solving the problem of single strategy and poor synergy in existing research, and significantly improving the comprehensive control capability of building energy system.

[0016] (2) By integrating the high-precision energy management system (EMS) with the walking optimization algorithm, this invention significantly improves the optimization efficiency while ensuring prediction accuracy and ensuring a certain degree of transparency in the strategy optimization process.

[0017] (3) This invention provides a quantitative and transferable decision-making tool that quantifies the flexibility potential of buildings and outputs specific and operable optimal scheduling schemes. The model can be quickly transferred to other building types through parameter adaptation, providing efficient and reliable technical support for the large-scale development of demand-side resources. Attached Figure Description

[0018] Figure 1 is a flowchart of the building flexible load control method based on modular strategy library and intelligent optimization according to the present invention; Figure 2 is a logic diagram of temperature setpoint adjustment strategy in an embodiment of the present invention; Figure 3 is a logic diagram of wind speed compensation control strategy in an embodiment of the present invention; Figure 4 is a logic diagram of precooling strategy in an embodiment of the present invention; Figure 5 is a logic diagram of adjustment and usage window in an embodiment of the present invention; Figure 6 is a comparison diagram of measured data and predicted user data in an embodiment of the present invention; Figure 7 is a baseline load curve in an embodiment of the present invention; Figure 8 is a bar chart of time-of-use electricity price distribution for residential buildings in an embodiment of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0021] This invention provides a building flexible load control method based on a modular strategy library and intelligent optimization, as shown in Figure 1. Specifically, it includes the following steps: S1: Acquire energy consumption data, identify and remove abnormal and missing data using a box plot, and interpolate missing data using linear interpolation. In S1, the energy consumption data includes historical total electricity consumption data, air conditioning electricity consumption data, historical meteorological data, and real-time operating parameters. Historical total electricity consumption data and air conditioning electricity consumption data are exported through the Building Energy Management System (BEMS) or online channels. Historical meteorological data is obtained from the National Meteorological Data Center or a self-built meteorological station, and includes dry-bulb temperature, wet-bulb temperature, relative humidity, and dew point temperature. Real-time operating parameters are dynamically collected through the building automation system, and include appliance start / stop data, operating power, and occupancy rate.

[0022] S2: Construct a flexible load collaborative control model for residential buildings; specifically including the following steps: S21: Construct a parameter set based on the energy consumption data in S1, and complete the parameterization definition of building space and system using the EnergyPlus simulation tool; S22: Based on S21, use the external program Python and EMS for collaborative simulation to convert the total building power consumption into a function of the power and operating status of each device, customize the modular encapsulation and combination application of flexible strategies such as temperature setpoint adjustment, wind speed dynamic compensation, building precooling / preheating, and adjustment usage window, generate a building geometric model based on the basic physical parameters of the building, and generate a building benchmark model for flexible potential analysis based on the building geometric model through household appliance models and custom flexible strategy control logic; Based on EMS, decouple strategies such as temperature setpoint adjustment, wind speed compensation, and building precooling into flexibly combinable basic modules, support multi-objective collaborative optimization for dynamic electricity prices and complex loads, solve the problems of single strategies and poor collaboration in existing research, and significantly improve the comprehensive control capability of building energy systems.

[0023] The logical architecture of the EMS module in S22 is sensor-actuator-program, which is used for co-simulation with external programs through the FMI interface. Home appliance models are built using a custom Python script and the EnergyPlus-EMS module. These flexible home appliances include split-type fixed-frequency air conditioners, electric fans, washing machines, water heaters, and robot vacuum cleaners. Users can simulate these appliances within a specific time period. Total electrical energy consumed internally Represented as: In the formula, The table shows the power consumption status of household appliance a at time t. This indicates that appliance A is not working. This indicates that appliance a is working normally; This represents the power of household appliance a at time t; This represents the collection of all household appliances; This indicates the duration of each time period; the allowable operating time range for appliance a is... For electrical appliances with adjustable and transferable loads, the following are examples: In the formula, This represents the total number of time periods that the day is divided into, with a value of 144. This refers to the total number of working hours per day for appliance A; the fixed-frequency air conditioning equipment uses the PTHP module in EnergyPlus to simulate the power consumption of a split-type air conditioning system. The operating schedule and set temperature of the fixed-frequency air conditioner are selected and set according to actual operating characteristics or specifications. The fixed-frequency air conditioner operates at its rated power. During operation, the fixed-frequency air conditioner operates within a certain time period. status As shown in the following formula: ; In the formula, Indoor temperature; Set the temperature for the air conditioner; the upper and lower limits for temperature adjustment are respectively... and Adjusted according to user comfort; To ensure accurate temperature control of the air conditioner, the temperature range is set between 0.25 and 0.5℃; the electric fan's output power is adjusted according to user needs. In the formula, These are the upper and lower limits for fan power adjustment, with the upper limit being the fan's rated power; transferable flexible loads include washing machines, water heaters, and robot vacuum cleaners, and their load attributes are described as follows: In the formula Indicates the time the device is turned on; Indicates the transferable time interval; rigid equipment includes refrigerators and lighting equipment, with operating power of: Basic physical parameters of the building include: indoor temperature. The upper limit of temperature adjustment is The minimum temperature adjustment step of the air conditioner remote control is 1℃, which is 0.5℃. The temperature setpoint adjustment strategy control logic is shown in Figure 2. The fan compensates for the indoor temperature rise. The maximum compensation for indoor temperature rise by the fan is... The control logic of the wind speed compensation regulation strategy is shown in Figure 3, and the precooling temperature is... The precooling window is The maximum precooling window is The pre-cooling strategy control logic is shown in Figure 4. The time interval during which household appliance a is allowed to operate is... The transferable time interval is The window control logic is adjusted as shown in Figure 5.

[0024] S23: Based on the building baseline model generated in S22, the Hovering Optimization Algorithm (HOA) is introduced, and an electricity price model is set up. While ensuring prediction accuracy, the optimization efficiency is significantly improved, and a certain degree of transparency in the strategy optimization process is ensured. Flexible control strategies are optimized using flexible yield, peak energy flexibility, and peak-valley reduction as objective functions, respectively, thus completing the construction of a flexible load coordinated control model for residential buildings.

[0025] In the HOA optimization algorithm of S23, hikers are defined based on terrain and slope. In iteration speed of time : In the formula The slope of the terrain is calculated as follows: In the formula, and hikers The altitude and horizontal distance traveled; The slope angle of the terrain. [0°, 50°]; hikers The speed after iteration is: In the formula, for Random numbers in the data; and These are the current speed and the initial speed, respectively. This refers to the location information of the team leader. For hikers The scan factor is set in the range [1,3]; hiker Location updated to : hikers initial position Lower bound of decision variables in optimization problems and the Upper Realm Sure: In the formula, The numbers are uniformly distributed between [0,1]. and To optimize the decision variable of the problem The lower and upper bounds of a dimension.

[0026] In the objective function of S23, the flexible rate of return is the rate of change of costs before and after the flexible adjustment on the response date, and it is calculated as follows: In the formula, Baseline Real-time power load; After flexible adjustment Real-time power load; The electricity price at time t; peak energy flexibility refers to the peak period before and after flexible adjustment. The change in total electricity consumption is calculated as follows: In the formula, For flexible adjustment of electricity prices during peak hours Power consumption for equipment operation; For peak electricity price periods after flexible adjustment Equipment operating power consumption; Peak-to-valley reduction rate is the rate of change of the difference between the peak and valley values ​​of the load before and after flexible regulation, and the calculation method is as follows: In the formula, The load difference between the peak and trough times serves as the baseline. S2 represents the load difference between peak and valley times after flexible adjustment; S3: Validate the flexible load coordination and control model for residential buildings constructed in S2 using a real-world residential case study, obtaining the prediction errors for total electricity consumption and air conditioning electricity consumption; S4: Quantify the flexible resources on the actual building demand side to obtain the optimal scheduling scheme under different optimization objectives. In S4, the baseline load and flexible adjustment day are determined, flexible control strategies are formulated for the actual residential case study, and the flexible load coordination and control model for residential buildings constructed in S2 is applied to implement the flexible control strategy. The flexible resources of residential buildings are quantified to obtain the optimal scheduling scheme under different optimization objectives.

[0027] Example 1: This example describes a residential building in a certain city, built in 2011, with a floor area of ​​123 square meters. The user is located on a middle floor. The family consists of three members, including students and working professionals. Major household appliances include: three split-type fixed-frequency air conditioning systems, an electric fan, a washing machine, a water heater, and a robot vacuum cleaner. Using a power meter, smart socket, and a temperature and humidity recorder, invasive power load monitoring was employed to obtain the usage time, power parameters, and indoor temperature and humidity data of the household's electrical appliances. The data was collected at a frequency of 5 minutes. Outdoor meteorological parameters were obtained from the national meteorological website. Total electricity consumption and air conditioning electricity consumption data were obtained by exporting electricity bills online. The study selected data from 31 working days from July 1st to July 31st, 2023. Following actual work conditions and JGJ26-2010, a schedule for personnel, lighting, and appliance operation was established, and a day type was introduced as a time feature. Data preprocessing first used box plots to perform outlier analysis on the collected data, removing outliers and then filling them with interpolation.

[0028] In this embodiment, the above-mentioned scheme is used to predict the user's electricity consumption based on measured data and patented model. The comparison between the predicted value and the actual value is shown in Figure 6. The prediction error of total electricity consumption is 1.43%, and the prediction error of air conditioning electricity consumption is 2.29%, indicating that the model prediction accuracy is good.

[0029] The implementation and results of the actual building demand-side flexible resource quantification are as follows; the baseline load curve is formulated using "High X of Y" as shown in Figure 7. Dates with a daily maximum temperature ≥35℃ and daily electricity consumption not lower than 95% of the historical high are selected as flexible adjustment days.

[0030] July 9th was designated as the representative weekend day, and July 10th as the representative workday day. The time-of-use electricity pricing for residential buildings in Beijing is shown in Figure 8: peak hour price is 0.7060 yuan / kWh; mid-peak hour price is 0.5883 yuan / kWh; normal hour price is 0.5383 yuan / kWh; and off-peak hour price is 0.3383 yuan / kWh. Based on actual electricity prices, the peak period from 18:00 to 22:00 was identified as the key time for flexible temperature regulation. Specific strategies were formulated as follows: the setpoint adjustment range for air conditioners in the living room and bedrooms was 27℃-30℃, with an adjustment step of 0.5℃. With the fan and air conditioner operating in a combined mode, the indoor thermal environment was uniform. Assuming occupants were seated (1.0 m / s) and wearing short-sleeved T-shirts and shorts (0.36 m / s), the maximum permissible wind speed in the activity area was 0.8 m / s. Under the conditions of a set air conditioning temperature of 30.5℃ and relative humidity ≤72% at a given wind speed, the analysis shows that users meet thermal comfort requirements throughout all time periods. Under the conditions of a set air conditioning temperature of 30.1℃ and relative humidity ≤58%, the proportion of time periods meeting thermal comfort standards is 91%. The building pre-cooling temperature is 23℃. Window settings are 16:00-17:00 (2 hours); 14:00-17:00 (4 hours); and 12:00-17:00 (6 hours). The peak period temperature is set at 30℃ from 18:00-22:00. The load adjustment window strategy is based on the usage time of appliances such as washing machines, water heaters, and dishwashers, transferred according to incentives. A total of 22 flexible strategy modules are combined, as shown in Table 1.

[0031] Table 1

[0032] A flexible load collaborative optimization control model for residential buildings was applied to implement 22 strategies. The results show that, with flexibility return rate as the objective, the optimal strategy for both weekdays and weekends is S5-S2T4F, with returns of 27.60% and 26.38%, respectively. With peak-period energy flexibility as the objective, the optimal strategy for both weekdays and weekends is S6-S3P6, with reductions of 3.69kW (62.01%) and 4.44kW (67.79%), respectively. With peak-valley reduction rate as the objective, 7 strategies on weekdays achieved the highest reduction rate of 60.46%. Comprehensive multi-objective analysis shows that S5-S2T4F demonstrates a balanced performance in terms of return rate and peak shaving effect, exhibiting the best overall performance. On weekends, 3 strategies achieved the highest reduction rate of 56.83%. Comprehensive multi-objective analysis shows that S5-S2T4F also demonstrates a balanced performance in terms of return rate and peak shaving effect, exhibiting the best overall performance. Based on the above optimal strategy analysis, specific flexible appliance scheduling schemes corresponding to each optimal strategy are given in Table 2.

[0033] Table 2

[0034] This embodiment demonstrates that, while ensuring high prediction accuracy (total electricity consumption error of 1.43% and air conditioning electricity consumption error of 2.29%), the framework significantly improves modeling efficiency and strategy transparency. It can effectively tap into the flexibility potential of buildings, provide users with specific and operable optimal scheduling solutions, and has good cross-building portability, providing a systematic tool for the efficient development of demand-side resources.

[0035] Therefore, this invention adopts the aforementioned building flexible load control method based on a modular strategy library and intelligent optimization. Based on an EMS (Energy Management System), strategies such as temperature setpoint adjustment, wind speed compensation, and building precooling are decoupled into flexibly combinable basic modules. This supports multi-objective collaborative optimization for dynamic electricity prices and complex loads, solving the problems of single strategies and poor synergy in existing research, and significantly improving the comprehensive control capability of building energy systems. Various control strategies are encapsulated into reusable standardized modules, enabling rapid combination and response of multiple strategies, greatly improving the adaptability of buildings to dynamic electricity prices and variable loads. Through a hybrid architecture of "simulation modeling-intelligent optimization," both computational efficiency and interpretability of the control process are ensured.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A building flexible load control method based on a modular strategy library and intelligent optimization, characterized in that, Includes the following steps: S1: Obtain energy consumption data, find and remove abnormal and missing data through box plots, and imput missing data using linear interpolation. S2: Construct a flexible load collaborative control model for residential buildings; S3: Verify the flexible load collaborative control model for residential buildings constructed in S2 through actual residential cases, and obtain the prediction errors of total electricity consumption and air conditioning electricity consumption; S4: Quantify the flexible resources on the actual building demand side to obtain the optimal scheduling scheme under different optimization objectives.

2. The building flexible load control method based on modular strategy library and intelligent optimization according to claim 1, characterized in that, Energy consumption data in S1 includes historical total electricity consumption data, air conditioning electricity consumption data, historical meteorological data, and real-time operating parameters. Historical total electricity consumption data and air conditioning electricity consumption data are exported through the Building Energy Management System (BEMS) or online channels. Historical meteorological data is obtained from the National Meteorological Data Center or self-built meteorological stations, and includes dry-bulb temperature, wet-bulb temperature, relative humidity, and dew point temperature. Real-time operating parameters are dynamically collected through the building automation system, and include appliance start-up and shutdown data, operating power, and occupancy rate.

3. The building flexible load control method based on modular strategy library and intelligent optimization according to claim 1, characterized in that, S2 The process includes the following steps: S21: Construct a parameter set based on the energy consumption data in S1, and complete the parameterization definition of the building space and system using the EnergyPlus simulation tool; S22: Based on S21, use the external program Python and EMS for collaborative simulation to convert the total building power consumption into a function of the power and operating status of each device. Customize the modular encapsulation and combined application of flexible strategies such as temperature setpoint adjustment, wind speed dynamic compensation, building pre-cooling / preheating, and adjustment window. Based on the basic physical parameters of the building, generate a building geometric model. Based on the building geometric model, generate a building benchmark model for flexibility potential analysis using a household appliance model and custom flexible strategy control logic; S23: Based on the building benchmark model generated in S22, introduce the HOA (House Walking Optimization) algorithm, set an electricity price model, and use the flexibility rate of return, peak energy flexibility, and peak-valley reduction degree as objective functions to optimize the flexible control strategy and complete the construction of the flexible load collaborative control model for residential buildings.

4. The building flexible load control method based on modular strategy library and intelligent optimization according to claim 1, characterized in that, The logical architecture of the EMS module in S22 is sensor-actuator-program, which is used for co-simulation with external programs through the FMI interface. Home appliance models are built using a custom Python script and the EnergyPlus-EMS module. These flexible home appliances include split-type fixed-frequency air conditioners, electric fans, washing machines, water heaters, and robot vacuum cleaners. Users can simulate these appliances within a specific time period. Total electrical energy consumed internally Represented as: In the formula, The table shows the power consumption status of household appliance a at time t. This indicates that appliance A is not working. This indicates that appliance a is working normally; This represents the power of household appliance a at time t; This represents the collection of all household appliances; Indicates the duration of each time period; The allowable operating time range for appliance A is: For electrical appliances with adjustable and transferable loads, the following are examples: In the formula, This represents the total number of time periods that the day is divided into, with a value of 144. This refers to the total number of working hours per day for appliance A; the fixed-frequency air conditioner uses the PTHP module in EnergyPlus to simulate the power consumption of a split-type air conditioning system. The operating schedule and set temperature of the fixed-frequency air conditioner are selected and set according to actual operating characteristics or specifications. The fixed-frequency air conditioner operates at its rated power. During operation, the fixed-frequency air conditioner operates within a certain time period. status As shown in the following formula: ； In the formula, Indoor temperature; Set the temperature for the air conditioner; the upper and lower limits for temperature adjustment are respectively... and Adjusted according to user comfort; To ensure accurate temperature control of the air conditioner, the temperature range is set between 0.25 and 0.5℃; the electric fan's output power is adjusted according to user needs. In the formula, These are the upper and lower limits for fan power adjustment, with the upper limit being the fan's rated power; transferable flexible loads include washing machines, water heaters, and robot vacuum cleaners, and their load attributes are described as follows: In the formula Indicates the time the device is turned on; Indicates the transferable time interval; rigid equipment includes refrigerators and lighting equipment, with operating power of: ； Basic physical parameters of a building include: indoor temperature. The upper limit of temperature adjustment is The minimum temperature adjustment step on the air conditioner remote control is 1℃, which is 0.5℃. The fan compensates for the indoor temperature rise. ； The maximum compensation for indoor temperature rise by the fan is The pre-cooling temperature is The precooling window is The maximum precooling window is The permitted operating time range for household appliance A is: The transferable time interval is 。 5. The building flexible load control method based on modular strategy library and intelligent optimization according to claim 4, characterized in that, In the HOA optimization algorithm of S23, hikers are defined based on terrain and slope. In iteration speed of time ： In the formula The slope of the terrain is calculated as follows: In the formula, and hikers The altitude and horizontal distance traveled; The slope angle of the terrain. [0°, 50°]; hikers The speed after iteration is: In the formula, for Random numbers in the data; and These are the current speed and the initial speed, respectively. This refers to the location information of the team leader. For hikers The scan factor is set in the range [1,3]; hiker Location updated to ： hikers initial position Lower bound of decision variables in optimization problems and the Upper Realm Sure: In the formula, The numbers are uniformly distributed between [0,1]. and To optimize the decision variable of the problem The lower and upper bounds of a dimension.

6. The building flexible load control method based on modular strategy library and intelligent optimization according to claim 4, characterized in that, In the objective function of S23, the flexible rate of return is the rate of change of costs before and after the flexible adjustment on the response date, and it is calculated as follows: In the formula, Baseline Real-time power load; After flexible adjustment Real-time power load; The electricity price at time t; peak energy flexibility refers to the peak period before and after flexible adjustment. The change in total electricity consumption is calculated as follows: In the formula, For flexible adjustment of electricity prices during peak hours Power consumption for equipment operation; For peak electricity price periods after flexible adjustment Equipment operating power consumption; Peak-to-valley reduction rate is the rate of change of the difference between the peak and valley values ​​of the load before and after flexible regulation, and the calculation method is as follows: In the formula, The load difference between the peak and trough times serves as the baseline. This represents the load difference between the peak and trough times after flexible adjustment.

7. The building flexible load control method based on modular strategy library and intelligent optimization according to claim 1, characterized in that, In S4, the baseline load and flexible adjustment day are determined, and flexible control strategies are formulated for actual residential cases. The flexible load collaborative control model for residential buildings constructed in S2 is applied to implement the flexible control strategies, quantify the flexible resources of residential buildings, and obtain the optimal scheduling scheme under different optimization objectives.

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