Demand response performance evaluation method based on multi-dimensional uncertainty parameters

Abstract

The invention provides a demand response performance evaluation method based on multi-dimensional uncertainty parameters. The objective of the invention is to evaluate the response performance of a building under the influence of uncertain parameters by identifying the uncertain parameters influencing the response performance. The method comprises the following steps: constructing an evaluation index system covering different time stages of demand response, wherein the evaluation index system comprises an intensity index, a capacity index and a rebound rate index; key influence parameters are identified and classified, a reasonable value range of the key influence parameters is defined, and the key influence parameters include eight key building design variables and three types of control variables including response duration, control temperature and response occurrence time; establishing an accurate building numerical simulation model, formulating a demand response strategy, and aiming at different design variables and control variables, performing simulation of the corresponding response strategy by utilizing EnergyPlus; the electrical load of the building is obtained through simulation, and the comprehensive response performance of the building is obtained in combination with the evaluation index system.

Description

Technical Field

[0001] The present invention relates to the technical field of building power management, and in particular to a demand response performance evaluation method based on multi-dimensional uncertainty parameters. Background Art

[0002] As renewable energy penetration increases in the power system, the power structure of the new power system will shift from being dominated by coal-fired power generation, which has a continuously controllable output, to being dominated by renewable energy generation, which has greater uncertainty and less controllability. This requires the power system to not only maintain a balanced power supply but also have sufficient flexibility to cope with the strong randomness and volatility of renewable energy generation. Building HVAC systems are a key source of this flexibility.

[0003] Driven by the availability of renewable energy, response events are unpredictable. To meet the needs of aggregation centers, it is necessary to evaluate the response performance of buildings under different operating environments. Building design parameters, demand response control parameters, and meteorological parameters are the main factors affecting building response performance, and these parameters all have certain uncertainties. The interaction of different design parameters can form a complex design space, resulting in huge uncertainty in response performance. Related variables such as control parameters and meteorological parameters vary widely in actual operation. However, current research mainly relies on simulations or experiments to evaluate the response performance of buildings under certain building thermal parameters or specific control strategies, which limits our comprehensive understanding and quantitative analysis of the building response potential based on different design variables and control variables.

[0004] The participation of air conditioning loads in power demand response can effectively alleviate the imbalance between power supply and demand. However, after the response period, changes in the building's thermal balance can lead to a rebound effect, posing risks to grid operations. Existing quantification and evaluation methods primarily focus on the response potential during the response phase, ignoring the impact of the rebound effect. This results in incomplete quantification and evaluation of demand response characteristics.

[0005] Based on the above problems and shortcomings, the present invention proposes a demand response performance evaluation method based on multi-dimensional uncertainty parameters. Summary of the Invention

[0006] The purpose of the present invention is to evaluate the response performance of a building under the influence of uncertain parameters by identifying the uncertain parameters that affect the response performance.

[0007] To achieve this objective, this paper proposes a demand response performance evaluation method based on multidimensional uncertainty parameters. This method first constructs an evaluation index system covering different time stages of demand response; identifies and classifies key influencing parameters, and defines their reasonable value ranges; then establishes an accurate building numerical simulation model; and finally, through simulation, determines the building's response performance.

[0008] The present invention provides the following technical solution, which is characterized by comprising the following steps:

[0009] S1: Construct an evaluation index system covering different time stages of demand response.

[0010] S2: Identify and classify key influencing parameters and clarify their reasonable value ranges.

[0011] S3: Establish accurate numerical simulation models of buildings and formulate demand response strategies.

[0012] S4: Obtain the response performance of the building through simulation.

[0013] Furthermore, the construction of the evaluation index system covering different time stages of demand response in step S1 specifically includes the following steps:

[0014] S11. Construct strength index, which mainly includes the maximum adjustable power ratio and maximum rebound power ratio They represent the proportion of the maximum load reduction in the response phase and the proportion of the maximum rebound load in the recovery phase compared to the baseline scenario, respectively. and The calculation formula is as follows:

[0015]

[0016]

[0017] Among them, P b (t) is the baseline power at time t in baseline operating mode, W; is the maximum load reduction in the response phase, W; is the maximum rebound load during the recovery phase, W.

[0018] S12. Construct capacity indicators. Capacity indicators mainly include response capacity ratio (γ f ) and rebound capacity ratio (γ r ), which represent the proportion of the cumulative load reduction in the response phase and the proportion of the cumulative rebound load in the recovery phase relative to the baseline scenario. f and γ r The calculation formula is as follows:

[0019]

[0020]

[0021] Among them, E f is the cumulative load reduction in the response phase, kJ; Er is the cumulative rebound load during the recovery phase, kJ.

[0022] S13. Construct the rebound rate index. The rebound rate index mainly includes the power rebound rate (λ) and the capacity rebound rate (η), which comprehensively considers the integrated effect of the performance of the two stages. The calculation formulas of λ and η are as follows:

[0023]

[0024]

[0025] Where λ is the ratio of the maximum rebound load to the maximum adjustable power, and η is the ratio of the rebound capacity to the response capacity.

[0026] Furthermore, the step S2 of identifying and classifying key influencing parameters and clarifying their reasonable value ranges specifically includes the following steps:

[0027] S21. Identify building design variables. Key design variables that influence building response performance include: exterior window solar heat gain coefficient, interior furniture area, interior furniture density, interior wall density, interior wall specific heat, floor density, floor specific heat, and indoor equipment emissivity. Determine the value range based on relevant building design specifications.

[0028] S22. Identify the response control variables. Response duration, indoor control temperature, and response occurrence time are typical characteristics of a response event. Determine the value range based on relevant design specifications and academic research.

[0029] Furthermore, the step S3 of establishing an accurate building numerical simulation model and obtaining the building's response performance through simulation specifically includes the following steps:

[0030] S31. Use EnergyPlus to establish accurate modeling of building heat transfer and air conditioning systems. The building thermal parameter loads must comply with national standards. The building internal disturbance and operation schedule must conform to China's building characteristics.

[0031] S32. For design parameters, formulate response strategies separately.

[0032] S33. For control parameters, the corresponding control strategy is obtained by changing the control variables in sequence through the control variable method.

[0033] Furthermore, in step S4, the response performance of the building is obtained through simulation.

[0034] S4. Input the variables in step S2 and the control strategy in step S3 into EncrgyPlus for simulation in sequence to obtain the building power load under the two scenarios, and combine the evaluation indicators to obtain the comprehensive response performance of the building.

[0035] Compared with existing technologies, the present invention offers the following advantages: the established evaluation indicators comprehensively cover both the response and rebound phases of demand response, ensuring a complete description of building response performance. Through scientific screening, the building design and control variables that have a decisive impact on response performance are identified, and their value ranges are clarified. Through simulation, a quantitative analysis of building response performance under multidimensional uncertain parameters is achieved. This method has important practical significance for promoting better building participation in electricity demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 , a flowchart of a method for evaluating demand response performance based on multi-dimensional uncertainty parameters according to an example of the present invention;

[0037] Figure 2 , flow chart of the demand response key parameter identification and strategy formulation method of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] like Figure 1 As shown, the present invention provides a technical solution: a demand response performance evaluation method based on multi-dimensional uncertainty parameters, comprising the following steps:

[0040] S1: Construct an evaluation index system covering different time stages of demand response.

[0041] S2: Identify the key influencing parameters of the classification and clarify their reasonable value ranges.

[0042] S3: Establish accurate numerical simulation models of buildings and formulate demand response strategies.

[0043] S4: Obtain the response performance of the building through simulation.

[0044] The step S1 constructs an evaluation index system covering different time stages of demand response, including the following steps:

[0045] S11. Construct strength index, which mainly includes the maximum adjustable power ratio and maximum rebound power ratio They represent the proportion of the maximum load reduction in the response phase and the proportion of the maximum rebound load in the recovery phase compared to the baseline scenario, respectively. and The calculation formula is as follows:

[0046]

[0047]

[0048] Among them, P b (t) is the baseline power at time t in baseline operating mode, W; is the maximum load reduction in the response phase, W; is the maximum rebound load during the recovery phase, W.

[0049] S12. Construct capacity indicators. Capacity indicators mainly include response capacity ratio (γ f ) and rebound capacity ratio (γ r ), which represent the proportion of the cumulative load reduction in the response phase and the proportion of the cumulative rebound load in the recovery phase relative to the baseline scenario. f and γ r The calculation formula is as follows:

[0050]

[0051]

[0052] Among them, E f is the cumulative load reduction in the response phase, kJ; E r is the cumulative rebound load during the recovery phase, kJ.

[0053] S13. Construct the rebound rate index. The rebound rate index mainly includes the power rebound rate (λ) and the capacity rebound rate (η), which comprehensively considers the integrated effect of the performance of the two stages. The calculation formulas of λ and η are as follows:

[0054]

[0055]

[0056] Where λ is the ratio of the maximum rebound load to the maximum adjustable power, and η is the ratio of the rebound capacity to the response capacity.

[0057] Step S2 identifies and classifies key influencing parameters and clarifies their reasonable value ranges, including the following steps:

[0058] S21. Identify building design variables. Key design variables that influence building response performance include: exterior window solar heat gain coefficient, interior furniture area, interior furniture density, interior wall density, interior wall specific heat, floor density, floor specific heat, and indoor equipment emissivity. Determine the value range based on the General Specification for Energy Efficiency and Renewable Energy Utilization in Buildings.

[0059] S22. Identify the response control variables. Response duration, indoor controlled temperature, and response time are typical characteristics of a response event. Response time: 1 hour, 2 hours, 3 hours, 4 hours; controlled temperature: 25°C, 26°C, 27°C, 28°C; response time is determined by the building's function.

[0060] The step S3 of establishing an accurate building numerical simulation model and formulating a demand response strategy includes the following steps:

[0061] S31. Use EnergyPlus to establish accurate modeling of building heat transfer and air conditioning systems. The building thermal parameter loads must comply with national standards. The building internal disturbance and operation schedule must conform to China's building characteristics.

[0062] S32. For the design parameter study, the indoor temperature set point was adjusted upward by 2°C during the peak load period of 14:00-16:00 during the cooling period using the EnergyPlus thermostat.

[0063] S33. For the study of control parameters, the control strategy is shown in Table 1:

[0064] Table 1 Demand response control strategies for different research objects

[0065]

[0066] The step S4 obtains the response performance of the building through simulation, including the following steps:

[0067] S4. Input the variables in step S2 and the control strategy in step S3 into EnergyPlus for simulation in sequence to obtain the building power load under the two scenarios, and combine the evaluation indicators to obtain the comprehensive response performance of the building.

Claims

1. A demand response performance evaluation method based on multi-dimensional uncertainty parameters, characterized by: The following steps are involved: S1: Construct an evaluation index system covering different time stages of demand response; S2: Identify and classify key influencing parameters and clarify their reasonable value ranges; S3: Build accurate building numerical simulation models and formulate demand response strategies; S4: Obtain the response performance of the building through simulation.

2. The demand response performance evaluation method based on multidimensional uncertainty parameters according to claim 1, characterized in that: The first step is to construct an evaluation index system covering the response and rebound phases of demand response. Specifically, the intensity index is constructed based on the proportion of the maximum load reduction in the response phase and the proportion of the maximum rebound load in the recovery phase; the capacity index is constructed based on the proportion of the cumulative load reduction in the response phase and the proportion of the cumulative rebound load in the recovery phase. Based on the integrated effect of the performance of the two stages of response and rebound, a rebound rate index is constructed.

3. The demand response performance evaluation method based on multidimensional uncertainty parameters according to claim 1 is characterized in that: The second step is to identify and classify key influencing parameters and clarify their reasonable value ranges. Specifically, identify the response key design variables and determine the value range based on existing research and specifications; identify the response control variables and determine the corresponding control variable values ​​according to different building functions.

4. The method of identifying and classifying key influencing parameters and clarifying their reasonable value ranges according to claim 3 is characterized in that: The selection and identification of key influencing parameters of demand response include: identifying key design variables that affect building response performance, such as solar heat gain coefficient of external windows, indoor furniture area, indoor furniture density, interior wall density, interior wall specific heat, floor density, floor specific heat, and indoor equipment radiation coefficient; and identifying three response control variables: response duration, indoor control temperature, and response occurrence time.

5. The demand response performance evaluation method based on multidimensional uncertainty parameters according to claim 1, characterized in that: The third step is to establish an accurate numerical simulation model of the building and formulate a demand response strategy. Specifically, EnergyPlus is used to establish an accurate model of the building's heat transfer and air-conditioning system, and key design parameters and control parameters are simulated separately.

6. The method of claim 5, wherein: Different demand response strategies were developed for the design parameters and control parameters. Specifically, for the design parameter study, the EnergyPlus thermostat was used to increase the indoor temperature set point by 2°C during the peak load period of 14:00-16:00 during the cooling period. For the control parameter study, control strategies were developed for different response times, control temperatures, and different building types. The specific control strategies are shown in Table 1. Table 1 Demand response control strategies for different research objects 7. The demand response performance evaluation method based on multidimensional uncertainty parameters according to claim 1, characterized in that: The fourth step combines the evaluation indicators to derive the comprehensive response performance of the building. Specifically, the key design parameters and control parameters identified are coupled with the control strategy, and simulation is performed using EnergyPlus to derive the building power load under different scenarios. The comprehensive response performance of the building is then derived by combining the evaluation indicators.

Images