Building group demand response potential multi-dimensional rapid evaluation method considering user will

Abstract

The invention discloses a building group demand response potential multi-dimensional rapid evaluation method considering user intentions. The method comprises the following steps: constructing an EnergyPlus calculation model and an RC initial model of a typical building type; respectively inputting the same input data into the EngyPlus calculation model and the RC model to obtain a first calculation result of the EngyPlus calculation model and a second calculation result of the RC model, fitting to obtain an error value, and adjusting model parameters of the RC initial model according to the error value to obtain an RC accurate model; and performing demand response on each single building based on an RC accurate model, analyzing a demand response result to obtain a physical attribute index, a social attribute index, an economic attribute index and a probability that users in the building group participate in response adjustment, and further calculating to obtain a demand response index of the building group. According to the method, more multivariate evaluation indexes are adopted, and user response intentions are taken into consideration, so that the demand response potential of the building group can be evaluated more scientifically and accurately.

Description

Technical Field

[0001] This invention belongs to the field of new energy system planning and design technology. Specifically, it relates to a multi-dimensional rapid assessment method for the demand response potential of building complexes that takes into account user preferences. Background Technology

[0002] Against the backdrop of energy transition and the construction of new power systems, demand response has become a key means to improve the power system's supply and demand balance capabilities and tap the potential for flexible regulation in buildings. Rapid, multi-dimensional assessment of the demand response potential of individual buildings and building complexes can provide crucial references for clarifying the characteristics of building demand response potential and formulating flexible regulation strategies for building groups.

[0003] Currently, building demand response potential assessments are mostly limited to a single evaluation dimension, focusing solely on the scale of load regulation or the amount of electricity regulated within a given time period, while lacking effective assessment of other core performance indicators of building demand response. Furthermore, most studies conduct assessments for specific building scenarios, resulting in insufficient methodological universality. At the modeling level, existing demand response potential assessments largely rely on complex physical models; although the application value of simplified physical models has gained attention, ensuring their assessment accuracy and determining reasonable parameters remains a critical technical bottleneck that needs to be overcome. Additionally, in assessments involving building complexes, the lack of consideration for user participation in demand response may lead to discrepancies between assessment results and actual conditions.

[0004] Existing patent 1: CN120087524A - A method and device for predicting the flexible adjustment potential of air conditioning in residential building complexes based on uncertainty, comprising five steps: data acquisition and stochastic prediction modeling, benchmark energy consumption prediction model construction, flexible adjustment strategy decision-making, post-adjustment energy consumption prediction, and demand potential evaluation. Data acquisition and stochastic prediction modeling involves collecting residents' energy usage habits through in-home measurements and questionnaires, extracting typical air conditioning usage characteristics using clustering algorithms, and constructing a stochastic prediction model using the Monte Carlo method to quantify various uncertainties. Benchmark energy consumption prediction model construction involves collecting data on the building layout and thermal performance of the building envelope, building a building performance calculation model using EnergyPlus, inputting the stochastic prediction parameters, and completing the benchmark energy consumption simulation. Flexible adjustment strategy decision-making: Based on the predicted results of residents' adjustment intentions, a decision is made on whether each air conditioner should implement an adjustment strategy daily. Post-adjustment energy consumption prediction involves predicting the cooling and heating energy consumption after adjustment. Potential evaluation involves selecting three core indicators: load flexibility, energy flexibility, and flexible benefit to calculate the adjustment potential. However, this patent focuses on load potential assessment based on the complex physics calculation tool (EnergyPlus), which may suffer from slow speed and complex modeling in the process of predicting relevant indicators of building complexes. In addition, this patent only covers residential buildings and does not cover other building types, so the assessment system has certain limitations.

[0005] Existing patent two: CN112990574B - An evaluation method and system based on building energy consumption flexibility adjustment potential index, includes the following steps: Step 1: Determine the building energy consumption flexibility adjustment potential index according to the target operating condition type. Step 2: Evaluate based on the potential index to determine the power grid peak-valley adjustment margin range. Step 1 calculates three adjustment potential models: the building's own energy consumption model, the energy storage device's adjustable capacity model, and the building's temperature control load adjustment potential model. Step 2 matches the operating condition with the model based on specific circumstances, calculates the adjustment amount of each module, and integrates the results to determine the power grid peak-valley adjustment margin range. However, the method for calculating building energy consumption in this patent is relatively simple, and its accuracy may be problematic due to inaccurate model parameters. Furthermore, the evaluation index of this method only involves one evaluation index: the physical attribute of energy consumption, making the evaluation system insufficiently comprehensive.

[0006] Existing Patent 3: CN120086948A - A Data-Model Jointly Driven Modeling Method for Building Flexible Resource Characteristic Models. The method includes: Step 1: Constructing a data-model jointly driven characteristic model for each flexible resource of the building; Step 2: Identifying model parameters based on actual operational datasets; Step 3: Regularly updating the dataset and adaptively adjusting the model parameters; Step 4: Quantifying flexibility potential and participating in grid demand response. In identifying model parameters using actual operational datasets, model parameters are autonomously determined using targeted methods based on historical operational data of flexible resources (such as high-resolution data collected by smart meters and sensors). Step 5: Quantifying flexibility potential and participating in grid demand response, based on the constructed characteristic model, calculating the flexible adjustment capability of each flexible resource, including load shedding service and load transfer service.

[0007] Although this method uses linear regression to determine simplified model parameters, it may lead to low computational efficiency when dealing with large datasets or numerous parameters. Furthermore, the evaluation scale is limited to individual buildings, potentially neglecting uncertainties at the scale of building complexes. Summary of the Invention

[0008] (a) The technical problem to be solved by the present invention

[0009] How can we provide a rapid, multi-dimensional assessment method for the demand response potential of building complexes that considers user preferences, can be evaluated from multiple evaluation dimensions, and reduces model complexity, thereby improving the accuracy of the assessment?

[0010] (II) The technical solution adopted in this invention

[0011] A multidimensional rapid assessment method for the demand response potential of building complexes that considers user preferences, the method comprising: Construct EnergyPlus computational models and RC initial models for typical building types; The same input data is input into the EnergyPlus computing model and the RC model respectively, and the first calculation result of the EnergyPlus computing model and the second calculation result of the RC model are obtained. The error value is obtained by fitting the first calculation result and the second calculation result. The model parameters of the initial RC model are adjusted according to the error value to obtain the accurate RC model. Based on the aforementioned RC precise model, demand response is performed on each individual building, and the demand response results are analyzed to obtain physical attribute indicators, social attribute indicators, and economic attribute indicators. Based on the RC precise model, the probability of users participating in response regulation within the building complex is calculated. The demand response index of the building complex is then calculated based on the probability of user participation in response regulation, physical attribute indicators, social attribute indicators, and economic attribute indicators.

[0012] Optionally, the typical building types include office buildings, commercial buildings, and residential buildings.

[0013] Optionally, the same input data can be input into both the EnergyPlus computational model and the RC model to obtain a first computational result from the EnergyPlus computational model and a second computational result from the RC model, including: Input weather information, indoor heat gain and indoor temperature setpoint into the EnergyPlus building model to calculate historical data of building load and indoor temperature, which are used as the first calculation result. Weather information, indoor heat gain, and indoor temperature setpoints are input into the RC model to calculate building load data, which serves as the second calculation result.

[0014] Optionally, the error value is obtained by fitting the first calculation result and the second calculation result, including: Calculate the mean absolute error between the first and second calculation results.

[0015] Optionally, the model parameters of the initial RC model are adjusted according to the error value to obtain the accurate RC model, including: Set a predetermined range for each model parameter, and use a non-dominated sorting genetic algorithm to iteratively update the model parameters of the initial RC model until the mean absolute error reaches the preset condition. Each model parameter includes: external wall heat transfer coefficient, external wall heat capacity, external window heat transfer coefficient, indoor heat capacity, indoor air quality, internal wall heat transfer coefficient, wall absorptivity, area coefficient, and wet volume coefficient.

[0016] Optionally, the physical property evaluation index is the energy stored and released per unit area. The expression is as follows: ; In the formula, For users to build loads at their preferred temperature, To implement the current load of the unit's demand response potential, This refers to the building's air-conditioned area.

[0017] Optionally, the social attribute evaluation index is the cost per unit of thermal comfort impact. The expression is as follows: ; In the formula, For users' thermal comfort at their preferred temperature, For thermal comfort under demand response implementation conditions, This indicates the energy stored and released per unit area.

[0018] Optionally, the economic attribute evaluation index is the unit economic return. The expression is as follows: ; In the formula, The peak electricity price is the time-of-use price. This refers to the electricity price during off-peak hours under the time-of-use pricing system. Indicates unit charge. Indicates the unit of energy released.

[0019] Optionally, the probability of the user participating in response adjustment The expression is as follows: , ; In the formula, PMV represents thermal comfort.

[0020] Optionally, the expression for the demand response index of the building complex is: ; In the formula, Indicates the first The first type of building Item unit area index, For the first Total area of ​​buildings of this type For the first The probability of building-type users participating in demand response. For the first building complex Item indicator.

[0021] (III) Beneficial Effects

[0022] The present invention discloses a multi-dimensional rapid assessment method for the demand response potential of building complexes that takes into account user preferences, which has the following technical advantages compared with existing methods: This method employs more diverse evaluation metrics and incorporates user willingness to respond, in order to more scientifically and accurately assess the demand response potential of building complexes. Attached Figure Description

[0023] Figure 1 This is an overall flowchart of a method for rapid multidimensional assessment of the demand response potential of building complexes, taking into account user preferences, according to one or more embodiments.

[0024] Figure 2 This is a schematic diagram of the multi-dimensional rapid assessment device for the demand response potential of building complexes that takes into account user preferences, as described in Embodiment 2 of the present invention.

[0025] Figure 3 This is a schematic diagram of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0027] Before describing the various embodiments of this application in detail, the technical concept of this application is first briefly described: Existing assessments of building demand response potential have a single evaluation dimension and lack consideration for user participation in demand response, which may lead to discrepancies between the assessment results and the actual situation. Therefore, this application provides a multi-dimensional rapid assessment method for the demand response potential of building clusters that considers user willingness. The key improvement lies in first constructing a simplified and accurate RC model. Based on this accurate RC model, three types of indicators—physical attribute indicators, social attribute indicators, and economic attribute indicators—are obtained, along with the probability of user participation in response adjustment. This further yields the demand response indicators for the building cluster. This method employs more diverse evaluation indicators and incorporates user willingness to respond, thus more scientifically and accurately assessing the demand response potential of building clusters. The specific principles of this application's multi-dimensional rapid assessment method for the demand response potential of building clusters that considers user willingness are described below with further examples.

[0028] Specifically, such as Figure 1 As shown, the multi-dimensional rapid assessment method for the demand response potential of building complexes that considers user preferences in this embodiment includes the following steps: Step S10: Construct the EnergyPlus calculation model and RC initial model for typical building types.

[0029] Step S20: Input the same input data into the EnergyPlus calculation model and the RC model respectively to obtain the first calculation result of the EnergyPlus calculation model and the second calculation result of the RC model.

[0030] Step S30: Fit the first calculation result and the second calculation result to obtain the error value, and adjust the model parameters of the initial RC model according to the error value to obtain the accurate RC model.

[0031] Step S40: Based on the RC precise model, conduct demand response for each individual building, analyze the demand response results, and obtain physical attribute indicators, social attribute indicators, and economic attribute indicators.

[0032] Step S50: Calculate the probability of users participating in response regulation within the building complex based on the RC precise model, and calculate the demand response index of the building complex based on the probability of users participating in response regulation, physical attribute indicators, social attribute indicators, and economic attribute indicators.

[0033] In one or more embodiments, typical building types include office buildings, commercial buildings, and residential buildings. Office, commercial, and residential building prototypes are extracted, and EnergyPlus building performance calculation models for these three typical building prototypes are constructed using Designbuilder. This includes modeling and setting parameters such as their geometry, floor plan, and building envelope type. This model can output performance data for building indoor temperature, building wall temperature, and building load. The structure and parameters to be identified in the initial RC model are clearly defined. The thermal balance model of each node is described using a system of differential equations. Simultaneously, the model parameters are analyzed to distinguish between known parameters and parameters to be identified (including external wall heat transfer coefficient, external wall heat capacity, external window heat transfer coefficient, indoor heat capacity, indoor air quality, internal wall heat transfer coefficient, wall absorptivity, area coefficient, and wet volume coefficient), preparing for subsequent optimization of the accuracy of the initial RC model.

[0034] In one or more embodiments, the same input data is input into the EnergyPlus calculation model and the RC model respectively to obtain a first calculation result of the EnergyPlus calculation model and a second calculation result of the RC model, including: inputting weather information, indoor heat gain and indoor temperature setpoint into the EnergyPlus building model to calculate historical data of building load and indoor temperature as the first calculation result; inputting weather information, indoor heat gain and indoor temperature setpoint into the RC model to calculate building load data as the second calculation result.

[0035] Furthermore, the mean absolute error between the first and second calculation results is calculated. That is, the mean absolute error (MAE) is used to quantify the degree of fit between the load calculation results of the initial RC model and the load calculation results of the EnergyPlus building model, and to evaluate the preliminary accuracy of the model.

[0036] In one or more embodiments, adjusting the model parameters of the initial RC model based on the error value to obtain an accurate RC model includes: setting predetermined ranges for each model parameter, iteratively updating the model parameters of the initial RC model using a non-dominated sorting genetic algorithm until the mean absolute error (MAE) reaches a preset condition. For example, a reasonable range is set for each parameter to be identified, and the parameter values ​​are iteratively updated using the NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm to continuously reduce the MAE error between the initial RC model calculation results and historical data. When the error meets the preset requirements, the final model parameters are determined, completing the establishment and verification of the accurate RC model, ensuring that the model accurately reflects the thermal properties of the building.

[0037] After obtaining the accurate RC model, demand response measures are implemented by setting outdoor meteorological data, indoor temperature setpoints, and their corresponding start and duration. Demand response potential analysis is conducted for three types of buildings, analyzing their physical attribute evaluation indicators, social attribute evaluation indicators, and economic attribute evaluation indicators.

[0038] In one or more embodiments, the physical property evaluation index uses energy storage and release per unit area. This characterizes the building's energy storage and release potential, among which... For users to build loads at their preferred temperature, To implement the current load of the unit's demand response potential, It describes the energy storage and release of a building based on changes in baseline load. This refers to the building's air-conditioned area.

[0039] , ; In one or more embodiments, the social attribute evaluation index is the cost per unit of thermal comfort impact. , defined as the thermal comfort (PMV) impact per unit of stored energy released, indicates that a lower cost value signifies a higher load regulation per unit area with smaller changes in thermal comfort (PMV). For the user's PMV at their preferred temperature, PMV under demand response implementation conditions.

[0040] ; In one or more embodiments, the economic attribute evaluation index is the unit economic benefit, that is, the load (energy release) saved during peak electricity price periods under a typical day's time-of-use electricity pricing scenario. The economic benefits, minus the increased load (charging) during off-peak electricity prices. The difference in economic losses represents the level of economic benefit per unit, with a larger value indicating better economic return per unit. and These are the peak-hour electricity price and the off-peak electricity price, respectively.

[0041] ; In the formula, Indicates unit charge. Indicates the unit of energy released.

[0042] In one or more embodiments, when assessing the demand response potential of an aggregated building complex, unlike assessing the demand response potential of a single building, it is necessary to consider users' willingness to participate in demand response. The uncertainty of user participation is transformed into a mathematically described binomial distribution, where 1 represents willingness to participate in demand response and 0 represents refusal to participate. The probability of users' willingness to participate in demand response is adjusted as follows: .

[0043] , .

[0044] In one or more embodiments, the area superposition method is used to assess the demand response potential of different building types within a building complex, resulting in a total demand response assessment index for all individual buildings within the target area. No. The first type of building Itemized unit area indicators (social, economic, and physical attribute indicators). For the first Total area of ​​buildings of this type For the first The probability of building-type users participating in demand response. For the first building complex The indicators are the social, economic, and physical attributes of the building complex.

[0045] .

[0046] This embodiment provides a multi-dimensional rapid assessment method for the demand response potential of building complexes that considers user willingness. At the modeling level, an RC (Responsive Response) model is first constructed to rapidly assess the demand response potential of typical building types. The NSGA-II algorithm is then used to optimize the model parameters, ultimately obtaining the RC model with optimal accuracy. Based on this, three categories of indicators—physical attributes, social attributes, and economic attributes—are further constructed to form a comprehensive assessment system for the demand response potential of individual buildings. For the assessment scenario of building complexes, based on the principle of area superposition and taking user willingness to respond into account, a comprehensive evaluation of the demand response potential indicators of the building complex is finally completed.

[0047] Embodiment 2 of this application also discloses a multi-dimensional rapid assessment device for the demand response potential of building complexes that takes into account user preferences. The device includes a model building module 100, a model calculation module 200, a model parameter update module 300, an individual building assessment module 400, and a building complex assessment module 500. The model building module 100 is configured to: build an EnergyPlus calculation model and an initial RC model for typical building types; the model calculation module 200 is configured to: input the same input data into the EnergyPlus calculation model and the RC model respectively, and obtain the first calculation result of the EnergyPlus calculation model and the second calculation result of the RC model; the model parameter update module 300 is configured to: fit the first calculation result and the second calculation result to obtain the error value, and adjust the model parameters of the initial RC model according to the error value to obtain the accurate RC model; the individual building evaluation module 400 is configured to: perform demand response on each individual building based on the accurate RC model, analyze the demand response results, and obtain physical attribute indicators, social attribute indicators, and economic attribute indicators; the building complex evaluation module 500 is configured to: calculate the probability of users participating in response adjustment within the building complex based on the accurate RC model, and calculate the demand response indicators of the building complex based on the probability of users participating in response adjustment, physical attribute indicators, social attribute indicators, and economic attribute indicators.

[0048] Embodiment 3 of this application also discloses a computer-readable storage medium storing a multi-dimensional rapid assessment program for the demand response potential of building complexes that takes into account user preferences. When the multi-dimensional rapid assessment program for the demand response potential of building complexes that takes into account user preferences is executed by a processor, it implements the above-described multi-dimensional rapid assessment method for the demand response potential of building complexes that takes into account user preferences.

[0049] This third embodiment also discloses a computer device, at the hardware level, such as... Figure 3 As shown, the computer device includes a processor 12, an internal bus 13, a network interface 14, and a computer-readable storage medium 11. The processor 12 reads the corresponding computer program from the computer-readable storage medium and runs it, forming a request processing device at the logical level. Of course, in addition to the software implementation, one or more embodiments of this specification do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices. The computer-readable storage medium 11 stores a multi-dimensional rapid assessment program for the demand response potential of building complexes that considers user preferences. When the processor executes the multi-dimensional rapid assessment program for the demand response potential of building complexes that considers user preferences, it implements the above-described multi-dimensional rapid assessment method for the demand response potential of building complexes that considers user preferences.

[0050] Computer-readable storage media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0051] The specific embodiments of the present invention have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that modifications and improvements can be made to these embodiments without departing from the principles and spirit of the present invention as defined by the claims and their equivalents, and such modifications and improvements should also be within the protection scope of the present invention.

Claims

1. A multi-dimensional rapid assessment method for the demand response potential of building complexes considering user preferences, characterized in that, The method includes: Construct EnergyPlus computational models and RC initial models for typical building types; The same input data is input into the EnergyPlus computing model and the RC model respectively, and the first calculation result of the EnergyPlus computing model and the second calculation result of the RC model are obtained. The error value is obtained by fitting the first calculation result and the second calculation result. The model parameters of the initial RC model are adjusted according to the error value to obtain the accurate RC model. Based on the aforementioned RC precise model, demand response is performed on each individual building, and the demand response results are analyzed to obtain physical attribute indicators, social attribute indicators, and economic attribute indicators. Based on the RC precise model, the probability of users participating in response regulation within the building complex is calculated. The demand response index of the building complex is then calculated based on the probability of user participation in response regulation, physical attribute indicators, social attribute indicators, and economic attribute indicators.

2. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences, as described in claim 1, is characterized in that... The typical building types include office buildings, commercial buildings, and residential buildings.

3. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences, as described in claim 1, is characterized in that... The same input data is input into the EnergyPlus computational model and the RC model respectively, resulting in the first calculation result of the EnergyPlus computational model and the second calculation result of the RC model, including: Input weather information, indoor heat gain and indoor temperature setpoint into the EnergyPlus building model to calculate historical data of building load and indoor temperature, which are used as the first calculation result. Weather information, indoor heat gain, and indoor temperature setpoints are input into the RC model to calculate building load data, which serves as the second calculation result.

4. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences, as described in claim 1, is characterized in that... The error value is obtained by fitting the first calculation result and the second calculation result, including: Calculate the mean absolute error between the first and second calculation results.

5. The multi-dimensional rapid assessment method for the demand response potential of building complexes considering user preferences, as described in claim 4, is characterized in that... Adjust the model parameters of the initial RC model based on the error value to obtain the accurate RC model, including: Set a predetermined range for each model parameter, and use a non-dominated sorting genetic algorithm to iteratively update the model parameters of the initial RC model until the mean absolute error reaches the preset condition. Each model parameter includes: external wall heat transfer coefficient, external wall heat capacity, external window heat transfer coefficient, indoor heat capacity, indoor air quality, internal wall heat transfer coefficient, wall absorptivity, area coefficient, and wet volume coefficient.

6. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences, as described in claim 1, is characterized in that... The physical property evaluation index is the energy storage and release per unit area. The expression is as follows: ； In the formula, For users to build loads at their preferred temperature, To implement the current load of the unit's demand response potential, This refers to the building's air-conditioned area.

7. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences as described in claim 1, characterized in that, The social attribute evaluation index is the unit thermal comfort impact cost. The expression is as follows: ； In the formula, For users' thermal comfort at their preferred temperature, For thermal comfort under demand response implementation conditions, This indicates the energy stored and released per unit area.

8. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences, as described in claim 1, is characterized in that... The economic attribute evaluation index is the unit economic return. The expression is as follows: ； In the formula, The peak electricity price is the time-of-use price. This refers to the electricity price during off-peak hours under the time-of-use pricing system. Indicates unit charge. Indicates the unit of energy released.

9. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences, as described in claim 1, is characterized in that... The probability of user participation in response adjustment The expression is as follows: ， ； In the formula, PMV represents thermal comfort.

10. The method for rapid multi-dimensional assessment of the demand response potential of building complexes considering user preferences as described in claim 1, characterized in that, The expression for the demand response index of a building complex is: ； In the formula, Indicates the first The first type of building Item unit area index, For the first Total area of ​​buildings of this type For the first The probability of building-type users participating in demand response. For the first building complex Item indicator.

Images