Joint quantification method and system for the flexibility of adjustable energy systems in buildings

Abstract

The present invention discloses a joint quantification method and system for the flexibility of an adjustable energy system in a building. The method includes: establishing an adjustable energy system simulation platform; inputting flexible response parameters into the simulation platform, and performing parallel simulations on the baseline working condition, the independent response working condition, and the joint response working condition to obtain simulation data corresponding to each working condition; using the simulation data of the baseline working condition as a benchmark, comparing the energy consumption parameters in the simulation data under other response working conditions, and dividing the time periods; calculating the flexibility parameters of each system under each response working condition according to the divided time periods; and using the gain rate of the flexibility parameters of the air-conditioning system under the two response working conditions as a joint effect indicator. The present invention adopts a set of building energy flexibility quantification frameworks for two systems to perform multi-dimensional quantitative characterization of the joint effect. It can reflect the dynamic power changes of the air-conditioning system under the flexible action of internal disturbance.

Description

Technical Field

[0001] The present invention relates to the field of energy regulation technology, and in particular to a joint quantification method and system for adjusting the flexibility of an energy system in a building. Background Art

[0002] With the increasing adoption of renewable energy and the rise in peak electricity loads, demand-side energy flexibility is gaining increasing attention. Flexible regulation of electricity consumption helps maintain a balance between supply and demand, promotes the uptake of renewable energy, reduces pollution from excess power generation, and generates economic benefits for both the supply and demand sides. Buildings account for 30%-40% of global energy consumption and are considered a high-quality flexible energy source due to their high energy consumption and abundant flexible resources. However, the effective utilization of flexible energy scheduling in buildings currently accounts for less than 2% of total capacity. Quantifying building energy flexibility is key to unlocking and leveraging this flexibility.

[0003] Flexibility quantification is a method for assessing the variable capacity and temporal characteristics of adjustable resources within a building. This is often accomplished through computer modeling and simulation. Building operators use this information to develop flexibility strategies and participate in grid interactions. The power supply side needs to adjust power supply plans based on the building's energy flexibility quantification results. Building operators will implement flexible strategies to adjust power demand within the planned timeframe to meet planned targets. In this process, the comprehensiveness of the quantification determines the extent to which building energy flexibility is utilized, while its accuracy impacts both the power balance and the economic benefits for both the supply and demand sides.

[0004] Building flexibility resources are divided into four areas: air conditioning, lighting, miscellaneous electrical appliances, and non-specific components. Air conditioning system energy consumption accounts for over 50% of a building's operating energy consumption, and its flexibility stems from the thermal inertia of the building and system, as well as the range of thermal comfort. Lighting system energy consumption accounts for 25%-40% of public building energy consumption, and its flexibility stems from the range of visual comfort. Air conditioning and lighting systems are significant contributors to building energy flexibility, and their quantification is crucial. The two systems can also influence each other, leading to changes in overall flexibility. Comprehensive and accurate quantification of the flexibility of building air conditioning and lighting systems is a key challenge in establishing quantitative model mechanisms and calculating results.

[0005] The existing technology still has the following problems that need to be solved: (1) The flexibility quantification of the two systems is often considered separately. However, the cooling capacity brought by the change of lighting power will have an impact on the air-conditioning system. This joint effect has not been fully recognized and accurately quantified; (2) The current flexibility quantification model lacks the dynamic characteristics of the air-conditioning system, or the adjustment boundary of the lighting system is fuzzy, resulting in insufficient flexibility calculation accuracy, which will further cause measurement deviation of the joint effect, resulting in the lack of accuracy of the overall flexibility quantification result; (3) The quantitative indicators of the flexibility of the two systems and the joint effect only stay at the power difference level, which is insufficient to reflect the overall changes in system flexibility and cannot be effectively utilized by the power supply side. Summary of the Invention

[0006] Therefore, the present invention aims to provide a method and system for jointly quantifying the flexibility of adjustable energy systems within a building. This system employs a framework for quantifying the building energy flexibility of both systems to quantitatively characterize the combined effect across multiple dimensions. This method can reflect the dynamic power changes of the air conditioning system under the flexible operation of internal disturbances while also accounting for the impact of various boundary conditions, including daylight, on the dimming of the lighting system.

[0007] To achieve the above objectives, the present invention provides a joint quantification method for the flexibility of an adjustable energy system in a building, comprising the following steps:

[0008] S1. Establish an adjustable energy system simulation platform;

[0009] S2. Input flexible response parameters into the simulation platform, simulate the baseline working condition, independent response working condition, and joint response working condition in parallel, and obtain simulation data corresponding to each working condition;

[0010] S3. Using the simulation data of the baseline working condition as a benchmark, compare the energy consumption parameters in the simulation data under other response working conditions, and divide the response period and rebound period under the two response working conditions;

[0011] S4. Calculate the flexibility parameters of each system under each response condition based on the response period and rebound period of the two response conditions;

[0012] S5. The gain rate of the flexibility parameters of the air-conditioning system under two response conditions is used as the joint effect indicator.

[0013] Further preferably, in S1, the adjustable energy system simulation platform includes: a building thermal structure module, a building light structure module, an air conditioning system, a lighting system, and a simulation communication module;

[0014] The building light structure module obtains the real-time indoor sunlight illumination under the sunshade structure;

[0015] The lighting system adjusts the lighting power according to the requirements of indoor visual comfort;

[0016] The air conditioning system converts the lighting power into sensible heat and transmits the sensible heat, self-generated sensible heat and latent heat to the building thermal structure module using the simulation communication module;

[0017] The building thermal structure module obtains real-time indoor temperature and humidity data to control air conditioning cooling or heating.

[0018] Further preferably, in S2, the flexible response parameters include four parameters: indoor temperature setting value, visual comfort setting value, response start time, and response duration. S2 also includes processing the obtained simulation data in the following steps:

[0019] S201, extracting key information from each mat type simulation result file, wherein the key information includes energy consumption data and boundary data;

[0020] S202: Convert the extracted key information into a time series table with specific intervals.

[0021] Further preferably, in S3, taking the simulation data of the baseline working condition as a benchmark, comparing the energy consumption parameters in the simulation data under other response working conditions, and dividing the response period and the rebound period under the two response working conditions, includes:

[0022] S301. Compare the time series table of the baseline working condition with the time series tables of the independent response working condition and the combined response working condition; determine the start, end, and rebound recovery time points of the flexible response event;

[0023] S302 , in the time series table, the period from the start to the rebound recovery time point is defined as a flexible response period; and the period from the rebound recovery time point to the end is defined as a power rebound period.

[0024] Further preferably, in S301, dividing the response period and the rebound period under the two response conditions includes:

[0025] S3011, calculating the difference between the standard indoor set temperature in the time series table of the baseline working condition and the actual indoor set temperature in the time series table of the independent response working condition or the combined response working condition, and storing the result in a first list;

[0026] S3012, comparing the standard indoor temperature in the time series table of the baseline working condition with the actual indoor temperature in the time series table of any response working condition, and storing the result in a second list;

[0027] S3013: Mark the status according to the results of the first list and the second list, and determine the response period and the rebound period according to the marking results.

[0028] Furthermore, in S3013, the status marking according to the results of the first list and the second list includes:

[0029] When the difference between the standard indoor set temperature and the actual indoor set temperature is less than zero at any time in the first list, the state of the current time is marked as 1;

[0030] Otherwise, continue to determine the difference between the standard indoor temperature at the corresponding moment in the second list and the actual indoor temperature. If the difference is less than -0.05, mark the current state as 2; otherwise, mark the current state as 0.

[0031] Further preferably, in S3013, the step of determining the response period and the rebound period according to the marking result includes:

[0032] Traverse the state table. If the current state is 1 and the previous state value is 0, it means the response has started. Then set the response start time to the time corresponding to the current state.

[0033] If the current state is 2 and the previous state value is 1, it means the response is finished, and the response end time point is set to the time corresponding to the current state;

[0034] If the current state is 0 and the previous value is 2, then restore and set the rebound end time point to the time corresponding to the current state.

[0035] Further preferably, in S4 or S5, the flexibility parameters include:

[0036] Maximum curtailed power A (kW), the maximum difference between the response sequence and the baseline sequence during the response period;

[0037] Time to maximum power reduction TA (s), the time interval between the response start time and the maximum power reduction time;

[0038] Average power reduction a (kW), the average difference between the response sequence and the baseline sequence during the response period;

[0039] Maximum rebound power B (kW), the maximum difference between the rebound sequence and the baseline sequence during the rebound period;

[0040] Bounce time TB (s), the length of the entire rebound sequence;

[0041] Average rebound power b (kW), the average difference between the rebound sequence and the baseline sequence during the rebound period.

[0042] The present invention also provides a joint quantification system for the flexibility of an adjustable energy system in a building, which is used to implement the steps of the joint quantification method for the flexibility of an adjustable energy system in a building, comprising: an adjustable energy system simulation platform and a simulation data processing module;

[0043] The simulation data processing module is used to input flexible response parameters into the simulation platform, simulate the baseline working condition, independent response working condition and joint response working condition in parallel, and obtain simulation data corresponding to each working condition; using the simulation data of the baseline working condition as a benchmark, compare the energy consumption parameters in the simulation data under other response working conditions, and divide the response period and rebound period under the two response working conditions; calculate the flexibility parameters of each system under each response working condition according to the response period and rebound period of the two response working conditions; and use the gain rate of the flexibility parameters of the air-conditioning system under the two response working conditions as a joint effect indicator.

[0044] Further preferably, the adjustable energy system simulation platform includes: a building thermal structure module, a building light structure module, an air conditioning system, a lighting system, and a simulation communication module;

[0045] The architectural light structure module is built using Grasshopper software and includes a sunlight irradiance file, an optical structure file, and a sunshade structure file; it is used to obtain the real-time indoor sunlight irradiance under the sunshade structure.

[0046] The lighting system adjusts the lighting power according to the requirements of indoor visual comfort;

[0047] The air conditioning system is built using Dymola software, including air conditioning and cooling equipment. The lighting power is converted into sensible heat, and the sensible heat, self-generated sensible heat, and latent heat are sent to the building thermal structure module using a simulation communication module.

[0048] The building thermal structure module is built using EnergyPlus software and embedded with weather files and thermodynamic structure files to obtain real-time indoor temperature and humidity data and control air conditioning cooling or heating.

[0049] The joint quantification method and system for the flexibility of adjustable energy systems in buildings disclosed in this application simultaneously considers two typical flexible resources, air conditioning and lighting, and incorporates the cooling capacity generated by lighting system regulation into the flexibility calculation of the air conditioning system, further exploring the potential of building flexibility.

[0050] The air conditioning system model established in this application has good dynamic characteristics and nonlinear characterization capabilities. The lighting system model established can calculate visual comfort and take into account multiple boundary conditions including daylight and shading, greatly improving the accuracy of flexibility calculations.

[0051] This application divides the flexible response period into the power rebound period, and uses the obtained flexibility parameters to describe the impact of flexible strategies on the power grid and the impact of combined effects on flexibility from multiple perspectives, thereby improving the applicability and popularity of the quantitative results. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the process of the joint quantification method for the flexibility of adjustable energy systems in buildings according to the present invention.

[0053] Figure 2 Schematic diagram of the structure of the joint quantification system for adjustable energy system flexibility in buildings.

[0054] Figure 3 This is the structural block diagram of the air conditioning system and building thermal structure module.

[0055] Figure 4 This is an operation flow chart of the adjustable energy system simulation platform of the present invention. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] like Figure 1 As shown, an embodiment of one aspect of the present invention provides a joint quantification method for the flexibility of an adjustable energy system in a building, comprising the following steps:

[0058] S1. Establish an adjustable energy system simulation platform;

[0059] Among them, the adjustable energy system simulation platform includes: building thermal structure module, building light structure module, air conditioning system, lighting system, and simulation communication module;

[0060] Building light structure module, which obtains the real-time indoor daylight illumination under the sunshade structure;

[0061] Lighting system, adjust lighting power according to indoor visual comfort requirements;

[0062] The air conditioning system converts lighting power into sensible heat and uses the simulation communication module to send the sensible heat, self-generated sensible heat and latent heat to the building thermal structure module;

[0063] The building thermal structure module obtains real-time indoor temperature and humidity data to control air conditioning cooling or heating.

[0064] During the simulation, the building light structure module calculates the real-time indoor illuminance under the sunshade structure and transmits the results to the lighting system module via the simulation communication module. The lighting system then determines the appropriate indoor lighting system power adjustment based on the overall visual comfort requirements of the room. The lighting system's adjusted power is transmitted to the air conditioning system module via the simulation communication module, converted into sensible heat, and then transmitted to the building thermal structure module via the simulation communication module. The building thermal structure module calculates the real-time temperature and humidity of the indoor air under the influence of various internal and external heat sources and transmits this data to the air conditioning system module. The air conditioning system module then calculates the sensible and latent heat generated by the air conditioning based on the changes caused by sensor feedback and inputs it into the building thermal structure module.

[0065] S2. Input flexible response parameters into the simulation platform, simulate the baseline working condition, independent response working condition, and joint response working condition in parallel, and obtain simulation data corresponding to each working condition;

[0066] The flexible response parameters include four parameters: indoor temperature setting value, visual comfort setting value, response start time, and response duration. S2 also includes the following steps of processing the obtained simulation data:

[0067] S201, extracting key information from each mat type simulation result file, wherein the key information includes energy consumption data and boundary data;

[0068] S202: Convert the extracted key information into a time series table with specific intervals.

[0069] like Figure 4 As shown in the figure, the simulation platform is called for parallel simulation, and the flexible response parameters are input into three parallel simulation files to obtain three Mat files; after key information extraction, three DataFrame tables are obtained, and after time period division, four energy consumption series are obtained; after index calculation, the gain is finally calculated based on the air conditioning response flexibility parameter and the joint response flexibility parameter to obtain the joint effect index.

[0070] The baseline operating condition file performs a normal simulation without any response action within 24 hours of the same day based on the input response start time. The air conditioning-only independent response operating condition file, based on the former, changes the control parameters of the indoor thermostat of the air conditioning system within the specified response period according to the input indoor temperature set value and response time for simulation. The "air conditioning + lighting" joint response operating condition file, based on the air conditioning-only response file, changes the lighting system power within the response period according to the input visual comfort set value for simulation.

[0071] Extract key information from each MATLAB simulation result file and convert it into a 60-second time series DataFrame. This information includes energy consumption data (chiller power, chilled water pump power, cooling water pump power, cooling tower fan power, fan coil unit fan power, fresh air unit fan power, and indoor lighting power) and boundary data (indoor temperature, indoor setpoint temperature, cooling load, outdoor dry-bulb temperature, and outdoor relative humidity).

[0072] S3. Using the simulation data of the baseline working condition as a benchmark, compare the energy consumption parameters in the simulation data under other response working conditions, and divide the response period and rebound period under the two response working conditions;

[0073] The baseline operating condition table data was compared with the table data of the air conditioning independent response and the "air conditioning + lighting" joint response to determine the start, end and rebound recovery time points of the flexible response event. The flexible response period and power rebound period under the two response conditions were obtained, that is, four energy consumption sequences.

[0074] In S3, the simulation data of the baseline condition is used as a benchmark to compare the energy consumption parameters in the simulation data under other response conditions. When the response period and rebound period under the two response conditions are divided, the following are included:

[0075] S301. Compare the time series table of the baseline working condition with the time series tables of the independent response working condition and the combined response working condition; determine the start, end, and rebound recovery time points of the flexible response event; further, in S301, segmenting the response period and rebound period under the two response working conditions includes:

[0076] S3011, calculating the difference between the standard indoor set temperature in the time series table of the baseline working condition and the actual indoor set temperature in the time series table of the independent response working condition or the combined response working condition, and storing the result in a first list;

[0077] S3012, comparing the standard indoor temperature in the time series table of the baseline working condition with the actual indoor temperature in the time series table of any response working condition, and storing the result in a second list;

[0078] S3013: Mark the status according to the results of the first list and the second list, and determine the response period and the rebound period according to the marking results.

[0079] Furthermore, in S3013, the status marking according to the results of the first list and the second list includes:

[0080] When the difference between the standard indoor set temperature and the actual indoor set temperature is less than zero at any time in the first list, the state of the current time is marked as 1;

[0081] Otherwise, continue to determine the difference between the standard indoor temperature at the corresponding moment in the second list and the actual indoor temperature. If the difference is less than -0.05, mark the current state as 2; otherwise, mark the current state as 0.

[0082] Further preferably, in S3013, the step of determining the response period and the rebound period according to the marking result includes:

[0083] Traverse the state table. If the current state is 1 and the previous state value is 0, it means the response has started. Then set the response start time to the time corresponding to the current state.

[0084] If the current state is 2 and the previous state value is 1, it means the response is finished, and the response end time point is set to the time corresponding to the current state;

[0085] If the current state is 0 and the previous value is 2, then restore and set the rebound end time point to the time corresponding to the current state.

[0086] S302 , in the time series table, the period from the start to the rebound recovery time point is defined as a flexible response period; and the period from the rebound recovery time point to the end is defined as a power rebound period.

[0087] In S301, the specific determination procedure for time period division is as follows:

[0088] The genTimeIndex function accepts two parameters: database and datadr. These two parameters are the baseline DataFrame and the response DataFrame containing the time series data.

[0089] Calculate the difference between database[indoor set temperature] and datadr[indoor set temperature] and store it in cri.

[0090] Calculate the difference between database[room temperature] and datadr[room temperature] and store it in sub_Tro.

[0091] Use the if statement to loop through each row of data based on the difference values ​​cri and sub_Tro, and append the values ​​0, 1, and 2 to the list list according to the conditions.

[0092] i. When cri[i]<0, mark it as 1;

[0093] ii. Otherwise, when sub_Tro[i]<-0.05, mark it as 2;

[0094] iii. Under other conditions, mark it as 0.

[0095] ①Traverse the list and check the state transition conditions:

[0096] If the current value is 1 and the previous value is 0, the response starts and the response start time point is set to the current index i.

[0097] If the current value is 2 and the previous value is 1, the response ends and the response end time point is set to the current index i.

[0098] If the current value is 0 and the previous value is 2, then resume and set the rebound end time point to the current index i.

[0099] Each time point will be continuously updated during the traversal process, with the last updated time point taking precedence.

[0100] ②The code prints out the specific time points of the response start, end, and rebound end in the datadr dataset.

[0101] And return the index values ​​corresponding to these time points.

[0102] S4. Calculate the flexibility parameters of each system under each response condition based on the response period and rebound period of the two response conditions;

[0103] ① First, sum up the energy consumption indicators of each device to obtain the system-level energy consumption indicator:

[0104] 1. Air conditioning system energy consumption = chiller power + chilled water pump power + cooling water pump power + cooling tower fan power + fan coil fan power + fresh air unit fan power

[0105] 2. Lighting system energy consumption = indoor lighting power

[0106] 3. Total system energy consumption = air conditioning system energy consumption + lighting system energy consumption

[0107] ② Calculation of response indicators for the total system, air conditioning system, and lighting system, including the following indicators:

[0108] i. Maximum reduction power: the maximum difference between the response sequence and the baseline sequence during the response period, reflecting the maximum regulation capability of the system; A = (P base -P dr ) max

[0109] Among them, P base ,P dr Represent the system power during the baseline period and the flexible response period, respectively.

[0110] ii. Time to reach maximum power reduction: the time interval between the response start time and the maximum power reduction time,

[0111] Reflects the speed at which the system reaches the maximum response time;

[0112] t drst ,t drend ,t rebend They represent the start response time, response end time and rebound end time respectively.

[0113] iii. Average power reduction: The average difference between the response sequence and the baseline sequence during the response period, reflecting the overall reduction in demand during the system response period.

[0114] ③ Calculation of rebound indicators for the total system, air conditioning system, and lighting system, including the following indicators:

[0115] 1) Maximum rebound power: the maximum difference between the rebound sequence and the baseline sequence during the rebound period, reflecting the maximum impact of the system on the power grid during the rebound period; B = (P reb -P base ) max

[0116] 2) Rebound time: The duration of the entire rebound sequence, reflecting the time it takes for the system to recover to the baseline state where it can continue to participate in the response; TB = t rebend -t drend

[0117] 3) Average rebound power: The average difference between the rebound sequence and the baseline sequence during the rebound period, reflecting the overall increase in demand during the system rebound period. Represents the system power during power rebound.

[0118] S5. The gain ratio of the air conditioning system's flexibility parameters under the two response conditions is used as the joint effect index. ΔX (ΔA, ΔTA, Δa, ΔB, ΔTB, Δb): Six joint effect indices representing the gain of each air conditioning system flexibility index after considering the joint effect;

[0119] ΔX=X joint -X single

[0120] X single ,X joint They represent the flexibility index of the air-conditioning system under the response of the air-conditioning system only and the joint response of the two systems respectively.

[0121] ΔX% (ΔA%, ΔTA%, Δa%, ΔB%, ΔTB%, Δb%): Six percentage combined effect indicators, representing the gain rate of each flexibility indicator of the air conditioning system compared to the original after considering the combined effect.

[0122]

[0123] like Figure 2-3As shown, the present invention also provides a joint quantification system for the flexibility of an adjustable energy system in a building, which is used to implement the steps of the joint quantification method for the flexibility of an adjustable energy system in a building, including: an adjustable energy system simulation platform and a simulation data processing module;

[0124] The simulation data processing module is used to input flexible response parameters into the simulation platform, simulate the baseline working condition, independent response working condition and joint response working condition in parallel, and obtain simulation data corresponding to each working condition; using the simulation data of the baseline working condition as a benchmark, compare the energy consumption parameters in the simulation data under other response working conditions, and divide the response period and rebound period under the two response working conditions; calculate the flexibility parameters of each system under each response working condition according to the response period and rebound period of the two response working conditions; and use the gain rate of the flexibility parameters of the air-conditioning system under the two response working conditions as a joint effect indicator.

[0125] Further preferably, the adjustable energy system simulation platform includes: a building thermal structure module, a building light structure module, an air conditioning system, a lighting system, and a simulation communication module;

[0126] The building light structure module is used to obtain the real-time indoor sunlight illumination under the sunshade structure;

[0127] The lighting system adjusts the lighting power according to the requirements of indoor visual comfort;

[0128] The air conditioning system converts the lighting power into sensible heat, and uses the simulation communication module to send the sensible heat, the sensible heat generated by itself, and the latent heat to the building thermal structure module;

[0129] The building thermal structure module is used to obtain real-time indoor temperature and humidity data and control air conditioning cooling or heating.

[0130] like Figure 3 As shown, the air conditioning system was built using Dymola software, including air conditioning and cooling equipment (chillers, cooling towers, water pumps, electric water valves, fan coil units, and fresh air units), piping, and controllers (temperature controllers, flow controllers, and on-off controllers). This software uses the Modelica language as its computational core, and its variable-step-size solution algorithm can more accurately capture system dynamics and couple multiple physical domains to meet simulation requirements.

[0131] The building thermal structure module was built using EnergyPlus software, which includes weather files, thermodynamic structures (heat transfer coefficients and heat storage coefficients of the internal and external envelope structures), and internal disturbance parameters (heat dissipation from personnel, lighting, and equipment). The software, which uses C++ as its core and IDF as its programming format, can accurately describe the thermal inertia of the building itself and its internal thermal mass.

[0132] The architectural light structure module was built using Grasshopper software and includes a solar irradiance file, optical structure (reflectivity and transmittance of internal and external protective structures), and shading structure. The software includes a powerful solar simulation engine that allows for rapid configuration of complex simulation parameters and detailed analysis results. It also allows for functional expansion (such as a shading module) through custom scripts written in Python.

[0133] The lighting system was built using PyCharm software, including lighting fixtures and utility functions for converting illuminance to visual comfort. This software, compiled in Python, can easily describe the relationship between power and illuminance for different lighting systems and serves as the platform's control hub, facilitating communication with other software.

[0134] The simulation communication module includes three parts: thermal structure-air conditioning, light structure-lighting, and air conditioning-lighting, which are respectively implemented by the joint simulation interface FMI, the plug-in GHpython, and the program package buildingspy.

[0135] During the simulation, the dynamic calculation logic for lighting system power and the dynamic output of lighting heat dissipation is as follows: Grasshopper software calculates the indoor illuminance under daylight and transmits the results to PyCharm software using the Grasshopper plug-in GHpython. PyCharm then determines the appropriate lighting power adjustment based on overall visual comfort requirements. The lighting power adjustment is then transferred to Dymola software using the Modelica package buildingspy.

[0136] During simulation, the dynamic calculation logic for the air conditioning system power and the dynamic reception of lighting heat dissipation is as follows: EnergyPlus calculates the average indoor air temperature and humidity of the building through the co-simulation interface (FMI) as input to Dymola. Dymola then calculates the sensible and latent heat gain generated by the air conditioning system and returns it to EnergyPlus. Furthermore, Dymola receives power regulation signals from the lighting system model and transmits heat changes to EnergyPlus as sensible heat. Figure 2 The data interaction principle between EnergyPlus and Dymola is described.

[0137] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A joint quantification method for the flexibility of adjustable energy systems in buildings, characterized by: The following steps are involved: S1. Establish an adjustable energy system simulation platform; S2. Input flexible response parameters into the simulation platform, simulate the baseline working condition, independent response working condition, and joint response working condition in parallel, and obtain simulation data corresponding to each working condition; S3. Using the simulation data of the baseline condition as a benchmark, compare the energy consumption parameters in the simulation data under the independent response condition and the combined response condition, and segment the response period and rebound period under the two response conditions; including: S301. Compare the time series table of the baseline working condition with the time series tables of the independent response working condition and the combined response working condition; determine the start, end, and rebound recovery time points of the flexible response event; S302: In the time series table, the period from the start to the rebound recovery time point is defined as a flexible response period; and the period from the rebound recovery time point to the end is defined as a power rebound period; S4. Calculate the flexibility parameters of each system under each response condition based on the response period and rebound period of the two response conditions; S5. The gain rate of the flexibility parameters of the air-conditioning system under two response conditions is used as the joint effect indicator.

2. The joint quantification method for the flexibility of adjustable energy systems in buildings according to claim 1, characterized in that: In S1, the adjustable energy system simulation platform includes: a building thermal structure module, a building light structure module, an air conditioning system, a lighting system, and a simulation communication module; The building light structure module obtains the real-time indoor sunlight illumination under the sunshade structure; The lighting system adjusts the lighting power according to the requirements of indoor visual comfort; The air conditioning system converts the lighting power into sensible heat and transmits the sensible heat, self-generated sensible heat and latent heat to the building thermal structure module using the simulation communication module; The building thermal structure module obtains real-time indoor temperature and humidity data to control air conditioning cooling or heating.

3. The joint quantification method for the flexibility of adjustable energy systems in buildings according to claim 1, characterized in that: In S2, the flexible response parameters include four parameters: indoor temperature setting value, visual comfort setting value, response start time, and response duration. S2 also includes processing the obtained simulation data in the following steps: S201, extracting key information from each mat type simulation result file, wherein the key information includes energy consumption data and boundary data; S202: Convert the extracted key information into a time series table with specific intervals.

4. The joint quantification method for the flexibility of adjustable energy systems in buildings according to claim 3, characterized in that: In S301, dividing the response period and the rebound period includes: S3011, calculating the difference between the standard indoor set temperature in the time series table of the baseline working condition and the actual indoor set temperature in the time series table of the independent response working condition or the combined response working condition, and storing the result in a first list; S3012, comparing the standard indoor temperature in the time series table of the baseline working condition with the actual indoor temperature in the time series table of any response working condition, and storing the result in a second list; S3013: Mark the status according to the results of the first list and the second list, and determine the response period and the rebound period according to the marking results.

5. The joint quantification method for the flexibility of adjustable energy systems in buildings according to claim 4, characterized in that: In S3013, the status marking according to the results of the first list and the second list includes: When the difference between the standard indoor set temperature and the actual indoor set temperature is less than zero at any time in the first list, the state of the current time is marked as 1; Otherwise, continue to determine the difference between the standard indoor temperature at the corresponding moment in the second list and the actual indoor temperature. If the difference is less than -0.05, mark the current state as 2; otherwise, mark the current state as 0.

6. The joint quantification method for the flexibility of adjustable energy systems in buildings according to claim 4, characterized in that: In S3013, determining the response period and the rebound period according to the marking result includes: Traverse the state table. If the current state is 1 and the previous state value is 0, it means the response has started. Then set the response start time to the time corresponding to the current state. If the current state is 2 and the previous state value is 1, it means the response is finished, and the response end time point is set to the time corresponding to the current state; If the current state is 0 and the previous value is 2, then restore and set the rebound end time point to the time corresponding to the current state.

7. The joint quantification method for the flexibility of adjustable energy systems in buildings according to claim 1, characterized in that: In S4 or S5, the flexibility parameters include: Maximum curtailed power A (kW), the maximum difference between the response sequence and the baseline sequence during the response period; Time to maximum power reduction TA (s), the time interval between the response start time and the maximum power reduction time; Average power reduction a (kW), the average difference between the response sequence and the baseline sequence during the response period; Maximum rebound power B (kW), the maximum difference between the rebound sequence and the baseline sequence during the rebound period; Bounce time TB (s), the length of the entire rebound sequence; Average rebound power b (kW), the average difference between the rebound sequence and the baseline sequence during the rebound period.

8. A joint quantitative system for adjusting the flexibility of energy systems in buildings, characterized by: The steps for implementing the joint quantification method for the flexibility of an adjustable energy system in a building as recited in any one of claims 1 to 7 above include: an adjustable energy system simulation platform and a simulation data processing module; The simulation data processing module is used to input flexible response parameters into the simulation platform, simulate the baseline working condition, independent response working condition and joint response working condition in parallel, and obtain simulation data corresponding to each working condition; using the simulation data of the baseline working condition as a benchmark, compare the energy consumption parameters in the simulation data under other response working conditions, and divide the response period and rebound period under the two response working conditions; calculate the flexibility parameters of each system under each response working condition according to the response period and rebound period of the two response working conditions; and use the gain rate of the flexibility parameters of the air-conditioning system under the two response working conditions as a joint effect indicator.

9. The joint quantification system for adjustable energy system flexibility in a building according to claim 8, characterized in that: The adjustable energy system simulation platform includes: a building thermal structure module, a building light structure module, an air conditioning system, a lighting system, and a simulation communication module; The architectural light structure module is built using Grasshopper software and includes a sunlight irradiance file, an optical structure file, and a sunshade structure file; it is used to obtain the real-time indoor sunlight irradiance under the sunshade structure. The lighting system adjusts the lighting power according to the requirements of indoor visual comfort; The air conditioning system is built using Dymola software, including air conditioning and cooling equipment. The lighting power is converted into sensible heat, and the sensible heat, self-generated sensible heat, and latent heat are sent to the building thermal structure module using a simulation communication module. The building thermal structure module is built using EnergyPlus software and embedded with weather files and thermodynamic structure files to obtain real-time indoor temperature and humidity data and control air conditioning cooling or heating.

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