Public building power demand response potential assessment method

Abstract

The invention discloses a public building power demand response potential evaluation method, which comprises the steps of S1, selecting a plurality of public buildings, and taking demand response energy acting force of each public building as an evaluation object, setting the adjustable load control capability of the public building, the estimated adjustable quantity and the subjective willingness of the user as a control capability index, an adjustment capability index and an adjustment willingness index; s2, constructing a standardized matrix for the three evaluation indexes of each evaluation object; s3, calculating the difference between each evaluation index and the optimal and worst vectors; s4, calculating the closeness degree of the evaluation object and the optimal scheme, and obtaining the comprehensive evaluation index of the public building demand response capability. Through the method, public buildings with high demand response potential can be fully screened as response resources, energy resources are more efficiently distributed and utilized, waste is reduced, the overall power grid operation efficiency is improved, the carbon energy utilization efficiency of an energy system is reduced, and the peak-valley difference emission of the power grid is effectively reduced.

Description

Technical Field

[0001] The invention relates to the field of building science, and in particular to a method for evaluating the electric power demand response potential of a public building. Background Art

[0002] With the advent of the renewable energy era, the resource allocation capacity of traditional energy networks has gradually failed to meet the development needs of energy systems. Virtual power plants aggregate different distributed energy sources to achieve coordinated optimization control of distributed energy sources and participate in the operation of the power market and power system. Demand response plays an important role in virtual power plants. Power users adjust their electricity consumption habits and energy consumption during the demand response stage according to dynamic price signals or incentive mechanisms in the power market. Demand response potential assessment methods can be roughly divided into model-based demand response potential assessment methods and data-based demand response potential assessment methods. Model-based demand response potential assessment methods refer to establishing a physical model of an adjustable object and analyzing the potential of the object under different adjustment strategies under certain constraints. Model-based demand response potential assessment methods require collecting detailed physical parameters of the adjustable object and establishing its physical model, which is a relatively cumbersome process. Data-based demand response potential assessment methods refer to methods that use certain data analysis methods to directly mine the demand response potential from actual power load data. Data-based demand response potential assessment methods require relatively detailed power load data and do not require the establishment of complex physical models, but it is difficult to directly explain its demand response potential from a physical level.

[0003] Although the existing demand response evaluation models have made some progress, there are still some problems and shortcomings. First, some models may be too complex and require a lot of data input and computing resources, which may be impractical for many public buildings. In addition, some models may be too dependent on specific scenarios or data, lack universality, and cannot adapt to different types of public buildings or electricity markets. Finally, the implementation of demand-side management has changed the user's original energy use behavior pattern, resulting in a decrease in the user's energy satisfaction. Although existing studies have considered the flexible load on the user side for demand-side management, they pay less attention to the feasibility of these response measures and the subjective willingness of users on the demand side, resulting in a situation where the ideal response potential is high but it is difficult to control in practice.

[0004] Therefore, a new comprehensive evaluation model for the demand response potential of public buildings that integrates subjective and objective factors is studied in order to solve the problem of quickly discovering high-quality response resources in the initial promotion stage of public virtual power plants and to improve the accuracy, universality, interpretability and practicality of the model. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned defects. The present invention provides a method for evaluating the potential of public building electricity demand response, and studies the influence of different index calculation methods and different scoring settings respectively. An evaluation index system based on the adjustable load control capacity of public buildings, the estimated adjustable amount and the subjective willingness of users is established, an index calculation method is constructed, a comprehensive evaluation model of building response capacity is established, and the quality of response resources is estimated, laying a foundation for the screening of high-quality resources for public building demand response and the formulation of response strategies. To achieve the above purpose, the technical solution of the present invention includes:

[0006] A method for evaluating the potential of public building electricity demand response, comprising:

[0007] S1: Select several public buildings, take the demand response capability of each public building as the evaluation object, and set the adjustable load control capacity of the public building, the estimated adjustable amount and the user's subjective willingness as the control capacity index, the adjustment capacity index and the adjustment willingness index;

[0008] S2: construct a standardized matrix of the three evaluation indicators of each evaluation object;

[0009] S3: Calculate the gap between each evaluation index and the optimal and worst vectors;

[0010] S4: Calculate the degree of proximity between the evaluation object and the optimal solution to obtain a comprehensive evaluation index of the public building demand response capability.

[0011] A further improvement of the present invention is that in step S1, the control capability index is assigned in the form of a score between 0 and 1, and the scoring method is as follows: for buildings where the air-conditioning equipment system uses water-cooled chillers and air-cooled chillers, if the equipment can be frequency-variable and remotely controlled, 1 point is awarded, if it can only be frequency-variable or remotely controlled, 0.5 points are awarded, and if it cannot be frequency-variable and remotely controlled, 0.2 points are awarded; for buildings where the air-conditioning equipment system uses a multi-split system, if the system can be frequency-variable and remotely controlled, 1 point is awarded, if it can be frequency-variable but not remotely controlled, 0.5 points are awarded, and if it cannot be frequency-variable and remotely controlled, 0.2 points are awarded; for buildings that use multiple air-conditioning equipment systems, the average of the scoring values ​​of each air-conditioning equipment system is used as the control capability index of the building. The scoring standards for different air-conditioning equipment systems are as shown in the following table.

[0012]

[0013] The further improvement of the present invention is that: in step S1, the adjustment capacity index is the responsive load value estimated based on the basic information of the equipment of the load system; the load system includes a refrigeration system, a fresh air system, a lighting system, and an elevator system; the value selection method is as follows: for chillers and air-cooled units, if the number of chillers is two or less, the index is 5% of the rated total power of a peak-on device; if the number of chillers is more than two, the index is the rated total power of a peak-on device; for multi-connected systems, 10% of the total system power is taken; for fresh air systems, when the fresh air system is normally open, the total rated power of the fresh air unit is taken, if it is normally closed, 0 is taken, and if it is intermittently opened, 10% of the power of the fresh air unit is taken; for lighting systems, 20% of the total lighting power is taken for remotely controllable lighting systems, and 1% of the total lighting power is taken for non-remotely controllable lighting systems because of their poor control ability and the estimated response amount is small; for elevator systems, the power of one elevator is multiplied by the number of elevators that can be closed; and finally, the values ​​of all load systems are summed to obtain the adjustment capacity index. The adjustment capacity index setting is detailed in the table below.

[0014]

[0015] A further improvement of the present invention is that in step S1, the adjustment willingness index is assigned in the form of a score, where the index is 1 if very willing, 0.8 if relatively willing, 0.6 if average, 0.4 if not very willing, and 0.2 if very unwilling, and finally the adjustment willingness index is obtained. See the table below for details.

[0016] Indicator system Very willing More willing Generally willing Not willing Very unwilling Subjective Will 1 0.8 0.6 0.4 0.2

[0017] A further improvement of the present invention is that step S2 specifically includes: calculating the standardized value of each indicator according to the following formula: ij Indicates the standardized value of the jth index of the i-th evaluation object:

[0018]

[0019] Where: j ranges from 1 to 3; when j is 1, X ij represents the control capability index of the i-th evaluation object; when j is 2, X ij represents the adjustment ability index of the i-th evaluation object; when j is 3, X ij It represents the adjustment willingness index of the i-th evaluation object.

[0020] A further improvement of the present invention is that step S3 specifically includes:

[0021] Define the distance between the object of the i-th public building evaluation and the maximum value:

[0022]

[0023] Define the distance between the evaluation object of the i-th public building and the minimum value:

[0024]

[0025] Where: m = 3, is the maximum value of the jth index after normalization, is the minimum value of the jth index after standardization; z ij It is the standardized value of the jth indicator of the i-th evaluation object; when j is 1, it indicates the control ability indicator, when j is 1, it indicates the adjustment ability indicator, and when j is 1, it indicates the adjustment willingness indicator. The actual meaning of D+ and D- values ​​is the distance between the evaluation object and the optimal or worst solution. The larger the value, the farther the distance. The larger the D+ value of the research object, the farther the distance from the optimal solution; the larger the D- value, the farther the distance from the worst solution. The most understandable research object is the one with a smaller D+ value and a larger D- value.

[0026] A further improvement of the present invention is that: Step S4 specifically includes: the calculation formula of the comprehensive evaluation index of public building demand response capability is as follows:

[0027]

[0028] Where: C i is the evaluation object of the ith public building. The larger the D-value is, the farther the research object is from the worst solution, and the better the research object is; the larger the C value is, the better the evaluation object is, that is, the better the demand response ability of the public building is.

[0029] The beneficial effects of the present invention include: selecting appropriate indicators from multiple indicators to evaluate the potential of virtual power plants, and analyzing the rationality and scientificity of different combinations of evaluation indicators. Finally, the three factors of control capability, estimated response amount and user subjective willingness are combined to construct a comprehensive evaluation model of demand response potential. This method quantifies the influence of control capability, estimated response amount and user subjective willingness on the demand response potential of virtual power plants. The calculation process and data acquisition of the constructed comprehensive evaluation model of demand response potential of virtual power plants are simple, with certain universality, and fully consider the implementation difficulty and implementation effect of demand response management. The estimated response amount at the same control level and subjective willingness can be compared vertically, and the response potential of buildings under different subjective willingness and control capabilities can also be compared horizontally. Through this method, public buildings with high demand response potential can be fully screened as response resources, energy resources can be allocated and utilized more efficiently, waste can be reduced, the overall efficiency of power grid operation can be improved, the carbon energy utilization efficiency of the energy system can be reduced, and the peak-to-valley difference emissions of the power grid can be effectively reduced. Based on the current networked buildings of Shanghai's national government office buildings and large public buildings energy consumption monitoring platform, assuming that 30% of commercial buildings (about 500 buildings) participate in demand-side response projects, and referring to the peak-shaving capacity in Huangpu District, Shanghai, it can form a peak-shaving capacity of about 250,000 kilowatts, equivalent to saving 4 billion yuan in investment in power generation, transmission and transformation construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The present invention is a flowchart of a method for evaluating the potential of public building electricity demand response. DETAILED DESCRIPTION

[0031] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0032] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the potential of electric power demand response in a public building. The method comprises the following steps in a specific implementation process:

[0033] (1) 68 public buildings were selected as the evaluation objects for demand response capabilities, and the adjustable load control capability, regulation capability and user subjective willingness of public buildings were set as control capability indicators, regulation capability indicators and regulation willingness indicators. Taking public building sample 1 as an example:

[0034] Control capability index: Sample 1 water-cooled chiller can be variable frequency but cannot be remotely controlled, so the value is 0.5. There is no air-cooled chiller, so it is not calculated. The multi-split unit can be variable frequency but cannot be remotely controlled, so the value is 0.5. The average value of control capability is (0.5+0.5) / 2=0.5, so the control capability index is 0.5.

[0035] Adjustment capacity index: Sample 1 has 3 water-cooled chillers in use during the summer peak. The total rated power of the units in use during the summer peak is 755Kw. The adjustment capacity of the water-cooled chillers is 1 unit power 755 / 3=251.6kW. The rated total power of the multi-split unit is 790kW. The adjustment capacity of the multi-split unit is 10% of the rated total power of the multi-split unit, which is 7.9kW. The total lighting power is 200kW. The lighting adjustment capacity is 10% of the total lighting power, which is 2Kw. There are 9 elevators with a total power of 200kW. 2 elevators can be turned off during peak hours. The elevator adjustment capacity is the power of 2 elevators, which is 44.4Kw. The adjustment capacity is the sum of all adjustable loads (251.6+7.9+2+44.4)=305.9. The adjustment capacity index is 305.9.

[0036] Adjustment willingness index: Sample 1 is more willing to participate in demand response in terms of subjective willingness, and the adjustment willingness index is 0.8.

[0037] Calculate and summarize the distribution of the three indicators of control ability, adjustment ability and subjective willingness of other samples.

[0038] (2) The three indicators of adjustable load control capacity, regulation capacity and user subjective willingness of 68 public buildings are standardized according to formula 2-1. Take public building sample 1 as an example: the square root of the sum of the 68 buildings in the control capacity index of sample 1 is 4.02, and the standardized value of the control capacity index of sample 1 is 0.5 / 4.02=0.12. Similarly, the standardized value of the regulation capacity index is 0.05, and the standardized value of the regulation willingness index is 0.12.

[0039] Calculate and summarize the distribution of the three indicators of control ability, adjustment ability and subjective willingness of other samples after standardized processing.

[0040] (3) Calculate the gap between each evaluation index and the optimal and worst vectors. Taking sample 1 as an example, the optimal vector of the three indicators is (0.24, 0.47, 0.15), and the worst vector is (0, 0, 0.03). Therefore, according to Formula 2-2 and Formula 2-3, the gap between sample 1 and the optimal vector is 0.44, and the gap between sample 1 and the worst vector is 0.16.

[0041] Calculate and summarize the distribution of the gaps between other samples and the best and worst vectors.

[0042] (4) Calculate the comprehensive evaluation of the demand response capability of each sample. Taking sample 1 as an example, according to formula 2-4, the comprehensive evaluation index of sample 1 is 0.16 / (0.44+0.16)=0.27.

[0043] Calculate and summarize the comprehensive evaluation indicators of all samples. And rank the demand response capabilities of all samples. The higher the demand response potential, the smaller the order. Finally, the ranking distribution of the demand response capabilities of all samples is shown in the following table. Among them, indicator 1, indicator 2, and indicator 3 are control ability indicators, adjustment ability indicators, and adjustment willingness indicators respectively.

[0044]

[0045]

[0046] In the above technical solution, the air conditioning control capacity index takes into account the impact of different air conditioning systems' different adjustment capabilities on the demand response effect, and therefore gives different scores. At the same time, considering that the building uses multiple air conditioning systems, a comprehensive scoring method is given. The adjustment capacity index takes into account the estimated response load that may be achieved by different control schemes, which is closer to the actual effect rather than a purely ideal value.

[0047] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for evaluating the potential of public building electricity demand response, characterized in that include: S1: Select several public buildings, take the demand response capability of each public building as the evaluation object, and set the adjustable load control capability of the public building, the estimated adjustable amount and the user's subjective willingness as the control capability index, the adjustment capability index and the adjustment willingness index; S2: construct a standardized matrix of the three evaluation indicators of each evaluation object; S3: Calculate the gap between each evaluation index and the optimal and worst vectors; S4: Calculate the degree of proximity between the evaluation object and the optimal solution to obtain a comprehensive evaluation index of the public building demand response capability.

2. A method for evaluating the potential of public building power demand response according to claim 1, characterized in that: In step S1, the control capability index is assigned in the form of a score between 0 and 1, and the scoring method is as follows: for buildings whose air-conditioning equipment systems use water-cooled chillers and air-cooled chillers, if the equipment can be frequency-controlled and remotely controlled, 1 point is given, if it can only be frequency-controlled or only remotely controlled, 0.5 point is given, and if it cannot be frequency-controlled and cannot be remotely controlled, 0.2 point is given; for buildings whose air-conditioning equipment systems use multi-split systems, if the system can be frequency-controlled and remotely controlled, 1 point is given, if it can be frequency-controlled but not remotely controlled, 0.5 point is given, and if it cannot be frequency-controlled and cannot be remotely controlled, 0.2 point is given; for buildings that use multiple air-conditioning equipment systems, the average of the scoring values ​​of each air-conditioning equipment system is used as the control capability index of the building.

3. A method for evaluating the potential of public building power demand response according to claim 2, characterized in that: In step S1, the adjustment capacity index is the responsive load value estimated based on the basic information of the equipment in the load system; the load system includes the refrigeration system, the fresh air system, the lighting system, and the elevator system; the value selection method is as follows: for chillers and air-cooled units, if there are two or less chillers, the index is 5% of the total rated power of one peak-on device; if there are more than two chillers, the index is the total rated power of one peak-on device; for multi-split systems, 10% of the total system power is taken; for fresh air systems, when the fresh air system is normally open, the total rated power of the fresh air unit is taken; if it is normally closed, 0 is taken; if it is intermittently opened, 10% of the power of the fresh air unit is taken; For lighting systems, 20% of the total lighting power is taken for remotely controllable lighting systems, and 1% of the total lighting power is taken for non-remotely controllable lighting systems; For the elevator system, take the power of one elevator and multiply it by the number of elevators that can be shut down; finally, sum the values ​​of all load systems to get the regulation capacity index.

4. A method for evaluating the potential of public building power demand response according to claim 3, characterized in that: In step S1, the adjustment willingness index is assigned in the form of a score. If the person is very willing, the index is 1; if the person is relatively willing, the index is 0.8; if the person is average, the index is 0.6; if the person is not very willing, the index is 0.4; if the person is very unwilling, the index is 0.2, and finally the adjustment willingness index is obtained.

5. A method for evaluating the potential of public building electricity demand response according to claim 1, characterized in that: Step S2 specifically includes: calculating the standardized value of each indicator according to the following formula: ij Represents the standardized value of the jth indicator of the i-th evaluation object: Where: j ranges from 1 to 3; when j is 1, X ij represents the control capability index of the i-th evaluation object; when j is 2, X ij represents the adjustment ability index of the i-th evaluation object; when j is 3, X ij It represents the adjustment willingness index of the i-th evaluation object.

6. A method for evaluating the potential of public building electricity demand response according to claim 5, characterized in that: Step S3 specifically includes: Define the distance between the object of the i-th public building evaluation and the maximum value Define the distance between the evaluation object of the i-th public building and the minimum value Where: m = 3, is the maximum value of the jth index after normalization, is the minimum value of the jth index after standardization; z ij It is the standardized value of the jth indicator of the ith evaluation object; when j is 1, it represents the control ability indicator; when j is 1, it represents the adjustment ability indicator; when j is 1, it represents the adjustment willingness indicator.

7. A method for evaluating the potential of public building power demand response according to claim 6, characterized in that: Step S4 specifically includes: The calculation formula of the comprehensive evaluation index of public building demand response capability is as follows: Where: C i is the evaluation object of the i-th public building.

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