Joint quantification method and system for flexibility of adjustable energy system in building

Abstract

The invention discloses a joint quantification method and system for flexibility of an adjustable energy system in a building. The method comprises the following steps: establishing an adjustable energy system simulation platform; flexible response parameters are input into the simulation platform, parallel simulation is carried out on the baseline working condition, the independent response working condition and the combined response working condition, and simulation data corresponding to each working condition are obtained; comparing energy consumption parameters in the simulation data under other response working conditions by taking the simulation data of the baseline working condition as a reference, and segmenting time periods; respectively calculating flexibility parameters of each system under each response working condition according to the segmented time periods; and the gain rates of the flexibility parameters of the air conditioning system under the two response working conditions serve as joint effect indexes. According to the method, a set of building energy flexibility quantification framework for two systems is adopted, and multi-dimensional quantitative characterization is carried out on the joint effect. And the dynamic power change of the air conditioning system under the internal disturbance flexible action can be reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of energy regulation and control, 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 popularization of renewable energy and the increase in peak power load, energy flexibility on the demand side has gradually received attention. Flexible adjustment of electricity consumption levels is conducive to maintaining the balance between electricity supply and demand, promoting the consumption of new energy, reducing more pollution caused by additional power generation in power plants, and bringing economic benefits to both supply parties. Building energy consumption accounts for 30%-40% of the world's total energy consumption. Due to its high energy consumption level and rich flexible resources, it is considered to be a high-quality flexible energy. However, the current effective utilization of flexible scheduling of building energy is less than 2% of the total capacity. Quantifying building energy flexibility is the key to exploring and utilizing its flexibility.

[0003] Flexibility quantification is a method to evaluate the variable capacity and time characteristics of adjustable resources in buildings. It is often solved through computer modeling and simulation. Building operators formulate flexible strategies and participate in grid interaction based on this. The power supply side needs to adjust the power supply plan according to the results of building energy flexibility quantification. Building operators will implement flexible strategies to change the demand power within the planned time to meet the planned goals. In this process, the comprehensiveness of quantification determines the utilization of building energy flexibility, and the accuracy of quantification will affect the power balance and the economic benefits of both parties.

[0004] Building flexibility resources are divided into four areas, namely air conditioning, lighting, miscellaneous electrical appliances, and non-specific components. The energy consumption of the air conditioning system accounts for more than 50% of the building's operating energy consumption, and its flexibility comes from the thermal inertia of the building and the system and the range of thermal comfort. The energy consumption of the lighting system accounts for 25%-40% of the energy consumption of public buildings, and its flexibility comes from the range of visual comfort. Air conditioning and lighting systems are high contributors to building energy flexibility, and their quantification is extremely critical. The two systems may affect each other and cause changes in overall flexibility. In the process of building a quantitative model mechanism and calculating results, how to achieve comprehensive and accurate quantification of the flexibility of the building's air conditioning system and lighting system is an important problem.

[0005] The existing technology still has the following problems to be solved: (1) The flexibility quantification of the two systems is often considered separately. However, the cooling capacity brought by the change in lighting power will affect the air-conditioning system. This combined effect has not been fully recognized and accurately quantified; (2) The existing flexibility quantification models lack the dynamic characteristics of the air-conditioning system, or the adjustment boundaries of the lighting system are blurred, resulting in insufficient flexibility calculation accuracy, which will further cause measurement deviations of the combined effect, resulting in the lack of accuracy in the overall flexibility quantification results; (3) The quantitative indicators of the flexibility of the two systems and the combined effect only remain 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 purpose of the present invention is to provide a joint quantification method and system for the flexibility of adjustable energy systems in buildings. A set of building energy flexibility quantification frameworks for two systems is used to quantitatively characterize the joint effect in multiple dimensions. The dynamic power changes of the air-conditioning system under the flexible action of internal disturbances can be reflected, and the influence of various boundary conditions including daylight on the dimming of the lighting system is considered.

[0007] In order to achieve the above object, 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 condition, independent response condition and joint response condition in parallel, and obtain simulation data corresponding to each condition;

[0010] 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;

[0011] S4. Calculate the flexibility parameters of each system under each response condition according to 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 a 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 indoor real-time 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 from illumination, and uses the simulation communication module to send the sensible heat from illumination, the sensible heat generated by itself, and the latent heat to the building thermal structure 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 condition as a benchmark, comparing the energy consumption parameters in the simulation data under other response conditions, and dividing the response period and the rebound period under the two response conditions, includes:

[0022] S301, comparing 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; determining 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, performing difference calculation 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 the 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 time in the second list and the actual indoor temperature. If it is less than -0.05, mark the current state as 2; otherwise, mark the current state as 0.

[0031] Further preferably, in S3013, judging 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 the response start time is set 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 parameter includes:

[0036] Maximum power reduction 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 time 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, including: 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 in 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 building 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 indoor real-time 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 cold source equipment; the lighting power is converted into apparent heat from illumination, and the apparent heat from illumination, the sensible heat generated by itself, and the latent heat are sent to the building thermal structure module using the simulation communication module;

[0048] The building thermal structure module is built using EnergyPlus software, embedded with weather files and thermodynamic structure files, and is used 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 considers two typical flexible resources, air conditioning and lighting, and adds the cooling capacity brought by the adjustment of the lighting system to the flexibility calculation of the air conditioning system, further exploring the potential of building flexibility that can be utilized;

[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 a variety of boundary conditions including daylight and shading, which greatly improves the accuracy of flexibility calculation;

[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 It is a schematic flow chart of the joint quantification method of the flexibility of the adjustable energy system in the building 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 through 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, 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 the lighting power into apparent heat, and uses the simulation communication module to send the apparent heat, the sensible heat and latent heat generated by itself 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 illumination under the sunshade structure, and transmits the result to the lighting system module based on the simulation communication module. The lighting system determines the appropriate indoor lighting system adjustment power according to the requirements of the overall visual comfort of the room. The adjustment power of the lighting system is transmitted to the air-conditioning system module through the simulation communication module, converted into sensible heat, and then transmitted to the building thermal structure module through the simulation communication module. The building thermal structure module calculates the real-time temperature and humidity of the indoor air under the action of various internal and external heat sources, and transmits the data to the air-conditioning system module. The air-conditioning system module then calculates the sensible heat and latent heat generated by the air-conditioning according to the changes brought about by the sensor feedback, and inputs it into the building thermal structure module.

[0065] S2. Input flexible response parameters into the simulation platform, simulate the baseline condition, independent response condition and joint response condition in parallel, and obtain simulation data corresponding to each 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; three DataFrame tables are obtained after key information extraction, and four energy consumption sequences are obtained after time period segmentation; after the 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 response action within 24 hours of the same day according to the input response start time; the air-conditioning independent response operating condition file is based on the former, and changes the control parameters of the indoor thermostat of the air-conditioning system within the specified response period according to the input indoor temperature setting value and response time for simulation; the "air-conditioning + lighting" joint response operating condition file is based on the air-conditioning response file, and changes the lighting system power within the response period according to the input visual comfort setting value for simulation.

[0071] Extract key information from each mat-type simulation result file and convert it into a time series DataFrame table with an interval of 60 seconds. The extracted information includes: energy consumption data (chiller power, chilled water pump power, cooling water pump power, cooling tower fan power, fan coil fan power, fresh air unit fan power, indoor lamp power), boundary data (indoor temperature, indoor set temperature, cooling load, outdoor dry bulb temperature, outdoor relative humidity).

[0072] 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;

[0073] The table data of the baseline operating condition are 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, and the flexible response period and power rebound period under the two response conditions, that is, 4 energy consumption sequences, are obtained.

[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, and the response period and rebound period under the two response conditions are divided, including:

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

[0076] S3011, performing difference calculation 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 the 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 time in the second list and the actual indoor temperature. If it is less than -0.05, mark the current state as 2; otherwise, mark the current state as 0.

[0082] Further preferably, in S3013, judging 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 the response start time is set 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 function genTimeIndex receives 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 and conditionally append values ​​0, 1, and 2 to the list list based on the difference values ​​cri and sub_Tro.

[0092] i. When cri[i]<0, it is marked 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, and the last updated time point will prevail.

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

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

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

[0103] ① First, add up the energy consumption indicators of each device to get 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 power reduction: the maximum difference between the response sequence and the baseline sequence during the response period, reflecting the maximum regulation capacity 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, the end response time and the end rebound 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 level of demand during the system response period.

[0114] ③ Calculation of rebound index of 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 length 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 rate of the flexibility parameters of the air-conditioning system under the two response conditions is used as the joint effect index. ΔX (ΔA, ΔTA, Δa, ΔB, ΔTB, Δb): six joint effect indexes, representing the gain of each flexibility index of the air-conditioning system 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 in 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 from illumination, and uses the simulation communication module to send the sensible heat from illumination, 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 is built using Dymola software, including air conditioning and cold source equipment (chillers, cooling towers, water pumps, electric water valves, fan coils, fresh air units), pipelines, and controllers (temperature controllers, flow controllers, switch-type controllers). The software uses the Modelica language as the computing core, and its variable step-size solution algorithm can more accurately capture system dynamics and can couple multiple physical fields to meet simulation requirements;

[0131] The building thermal structure module is built using EnergyPlus software, including weather files, thermodynamic structures (heat transfer coefficients and heat storage coefficients of internal and external envelope structures), and internal disturbance parameters (heat dissipation of personnel, lights, and equipment). The software uses C++ language as the calculation core and IDF as the writing format, which can accurately describe the thermal inertia of the building body and its internal thermal mass;

[0132] The building light structure module is built using Grasshopper software, which includes a solar irradiance file, an optical structure (reflectivity and transmittance of internal and external maintenance structures), and a sunshade structure. The software includes a powerful daylight simulation engine that can quickly set complex simulation parameters and obtain detailed analysis results. It can also implement functional expansion (such as sunshade module) by writing custom scripts in Python.

[0133] The lighting system is built using PyCharm software, including lighting fixtures and utility functions for illuminance-visual comfort conversion. The software is a python language compilation platform that can conveniently describe the relationship between power and illuminance of different lighting systems, and serves as the platform control center to facilitate communication with other software;

[0134] The simulation communication module includes three parts: thermal structure-air conditioning, optical 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 of the lighting system power and the dynamic output logic of the lighting heat dissipation are as follows: Grasshopper software calculates the indoor illumination under daylight, and transmits the result to PyCharm software based on the plug-in GHpython in Grasshopper. PyCharm determines the appropriate lighting adjustment power according to the requirements of overall visual comfort. The lighting adjustment power is transmitted to Dymola software through Modelica calling the buildingspy package.

[0136] During the simulation, the dynamic calculation logic of the air conditioning system power and the dynamic reception logic of the lighting heat dissipation are as follows: through the joint simulation interface FMI, EnergyPlus calculates the average air temperature and humidity in the building as input to Dymola, and the sensible and latent heat generated by the air conditioning system is calculated by Dymola and then returned to EnergyPlus. In addition, Dymola receives the power regulation signal from the lighting system model and transmits the heat change to EnergyPlus in the form of sensible heat. Figure 2 The data interaction principle between EnergyPlus and Dymola is described.

[0137] Obviously, the above embodiments are only examples for clear explanation, and are not intended to limit the implementation methods. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from them are still within the protection scope of the invention.

Claims

1. A joint quantification method for the flexibility of adjustable energy systems in buildings, characterized in that: 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 condition, independent response condition and joint response condition in parallel, and obtain simulation data corresponding to each condition; 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; S4. Calculate the flexibility parameters of each system under each response condition according to 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 a joint effect indicator.

2. The method for joint quantification of 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 indoor real-time 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 from illumination, and uses the simulation communication module to send the sensible heat from illumination, the sensible heat generated by itself, and the latent heat to the building thermal structure 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 the following steps of processing the obtained simulation data: 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 method for joint quantification of the flexibility of adjustable energy systems in buildings according to claim 3, characterized in that: 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, and the response period and rebound period under the two response conditions are divided, including: S301, comparing 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; determining 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.

5. The method for joint quantification of the flexibility of adjustable energy systems in buildings according to claim 4, characterized in that: In S301, dividing the response period and the rebound period under the two response conditions includes: S3011, performing difference calculation 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 the 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.

6. The method for joint quantification of the flexibility of adjustable energy systems in buildings according to claim 5, characterized in that: In S3013, the state marking is performed according to the results of the first list and the second list, including: 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 time in the second list and the actual indoor temperature. If it is less than -0.05, mark the current state as 2; otherwise, mark the current state as 0.

7. The method for joint quantification of the flexibility of adjustable energy systems in buildings according to claim 5, characterized in that: In S3013, judging 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 the response start time is set 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.

8. The method for joint quantification of the flexibility of adjustable energy systems in buildings according to claim 1, characterized in that: In S4 or S5, the flexibility parameter includes: Maximum power reduction 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 time 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.

9. A joint quantification system for the flexibility of adjustable energy systems in buildings, characterized in that: The steps for implementing the joint quantification method of the flexibility of the adjustable energy system in a building as described in any one of claims 1 to 8 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 in 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.

10. The combined quantification system for the flexibility of adjustable energy systems in buildings according to claim 9, 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 building 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 indoor real-time 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 cold source equipment; the lighting power is converted into apparent heat from illumination, and the apparent heat from illumination, the sensible heat generated by itself, and the latent heat are sent to the building thermal structure module using the simulation communication module; The building thermal structure module is built using EnergyPlus software, embedded with weather files and thermodynamic structure files, and is used to obtain real-time indoor temperature and humidity data and control air conditioning cooling or heating.

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