Demand side resource mutual aid potential quantitative evaluation method considering space-time collaboration

Abstract

The invention provides a demand side resource mutual aid potential quantitative evaluation method considering space-time collaboration, relates to the technical field of power resource management, and aims to optimize a collaborative mutual aid potential quantitative evaluation method of demand side resources, comprising: determining a plurality of evaluation dimensions including space-time collaboration degree, peak clipping potential and economic applicability; obtaining a space-time collaboration degree; obtaining peak clipping potential; economic applicability is obtained; and obtaining demand side resource mutual aid potential quantitative evaluation by endowing different weights to different index parameters. The method has the advantages that the charging and discharging strategy is reasonably formulated, the adjustable potential of electric vehicle and air conditioner loads is stimulated, the load pressure of a power grid is relieved, and a scientific and accurate quantitative basis is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of power resource management, and more specifically, to a method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy. Background Art

[0002] With the rapid development of industry and commerce and the improvement of the electrification level of residents, the proportion of air-conditioning load in the total electricity load has increased significantly. In many regions, this proportion has exceeded 40%.

[0003] During the peak electricity consumption periods in winter and summer, extreme weather events occur frequently, the electricity consumption of air conditioners continues to climb, the peak load increases continuously, which brings great pressure to the stable operation of the power grid and the guarantee of power supply. At the same time, with the gradual improvement of the new energy vehicle industry policy, continuous technological progress and the optimization of supporting services, the penetration rate of electric vehicles has increased rapidly, the charging load has increased significantly, and the power grid's demand for efficient regulation of demand-side resources is more urgent. The traditional evaluation index system has a relatively limited consideration of the spatial scope of resource mutual assistance, usually focusing on the analysis of the regulation potential of electric vehicle charging load and air-conditioning load in local areas, and not fully considering the systematic synergy characteristics of electric vehicles and air-conditioning load in the spatial and temporal dimensions, resulting in certain deviations in the accuracy of the evaluation results.

[0004] Therefore, it is necessary to optimize the method for quantitatively evaluating the potential of mutual assistance of demand-side resources, promote the optimization of user electricity consumption and the stable development of the power grid, and realize the mutual assistance of air-conditioning and electric vehicle resources. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy, which can realize the mutual assistance of air-conditioning and electric vehicle resources.

[0006] The present invention is realized through the following technical solutions:

[0007] A method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy includes the following steps:

[0008] Determine multiple evaluation dimensions, where the evaluation dimensions include spatio-temporal synergy degree, peak shaving potential, and economic applicability, and each evaluation dimension includes multiple index parameters;

[0009] Obtain the spatio-temporal synergy degree, where the spatio-temporal synergy degree includes the interaction degree between the charging load and the air-conditioning load, the local resource availability rate, and the remote resource availability rate;

[0010] Obtain the peak shaving potential, where the peak shaving potential includes the peak shaving potential of local charging and discharging resources and the peak shaving potential of remote charging and discharging resources;

[0011] Obtain economic applicability, where the economic applicability includes the time cost of cross-regional scheduling and the economic evaluation of charging and discharging resources in different regions;

[0012] Obtain a quantitative evaluation of the potential for mutual assistance of demand-side resources by assigning different weights to different index parameters.

[0013] Preferably, the method for obtaining the degree of interaction between the charging load and the air-conditioning load is:

[0014]

[0015] where ρ A1 represents the degree of interaction, P ack and P EVk respectively represent the air-conditioning load and the charging load at the kth peak time point, P EVn represents the charging load at the nth time point, and respectively represent the average values of the air-conditioning load and the charging load at multiple time points in a day, N peak is the number of peak time points in a day.

[0016] Preferably, the method for obtaining the availability rate of local resources between the charging load and the air-conditioning load is:

[0017]

[0018] where ρ A2_local is the availability rate of local resources, N V2G_peak is the number of V2G vehicles during the peak period of the air-conditioning load, N V2G_max represents the maximum value of the number of V2G vehicles staying in the area within a day.

[0019] Preferably, the method for obtaining the availability rate of off-site resources between the charging load and the air-conditioning load is:

[0020] Construct a spatio-temporal prism, which describes the accessibility of charging and discharging resources in the spatio-temporal dimension;

[0021] Let the duration of the load peak at the target location be T, and judge the relationship between the moving duration T use of the charging and discharging resources and the duration of the load peak at the target location to determine whether the target location is within the spatio-temporal prism:

[0022]

[0023] where, A ij represents whether a user traveling from the ith region to the jth region can catch up with the target location within the duration of the load peak. A value of 1 represents yes, and a value of 0 represents no. (x, y) are spatial coordinates, and v represents the traveling speed;

[0024] Obtain the availability rate of the off-site resources:

[0025]

[0026] Among them, ρ A3_ither is the availability rate of the off-site resources, R represents the number of regions, M represents the number of users, and A ij,m represents whether the m-th user traveling from the i-th region to the j-th region can reach the destination within the peak load duration.

[0027] Preferably, the method for obtaining the peak shaving potential β of the local charging and discharging resources is: B1 as follows:

[0028] Obtain the peak shaving potential β of the local charging and discharging resources B1 :

[0029]

[0030] Among them, P V2G,k,local represents the discharging power of local V2G at the k-th peak time point, and P ac,k represents the destination air-conditioning load at the k-th peak time point, and N peak is the number of peak time points in a day.

[0031] Preferably, the method for obtaining the peak shaving potential β of the off-site charging and discharging resources is: B2 as follows:

[0032]

[0033] Among them, P V2G,k,other is the off-site V2G discharging power at the k-th peak time point, and P V2G,k,m is, and A ij,m is.

[0034] Preferably, the method for obtaining the economic applicability is:

[0035] Obtain the time cost E of the cross-regional scheduling C1 :

[0036] E C1 = S min T d ;

[0037] Among them, S min represents the lowest hourly wage in the region, and T d represents the time cost of resource invocation;

[0038] Obtain the economic evaluation E of the charging and discharging resources in different regions C2 :

[0039]

[0040] Among them, C max and C min are respectively the maximum and minimum values of the comprehensive cost among all regions and resource types;

[0041] Divide the economic level according to the value of E C2 , and the larger the value of E C2 , the better the economy of calling the charging and discharging resources in this region.

[0042] Preferably, the method of dividing the economic level according to the value of E C is as follows:

[0043] When E C2 ≥70%, it is a high economic level;

[0044] When 35% ≤ E C2 < 70%, it is a medium economic level;

[0045] When 0% ≤ E C2 < 35%, it is a low economic level.

[0046] Preferably, the method of obtaining the quantitative evaluation of the mutual assistance potential of demand-side resources by assigning different weights to different index parameters is as follows:

[0047] Construct an initial matrix X for evaluating the mutual assistance potential of demand-side resources:

[0048]

[0049] Among them, x ia represents the a-th index parameter of the i-th region, R is the total number of the regions, A is the total number of the index parameters, a = 1, 2,..., A, i = 1, 2,..., R;

[0050] Perform dimensionless processing on the initial matrix X to convert x ia into x i ′ a , and obtain a standard matrix X':

[0051]

[0052] Obtain the variability and conflict parameter of the index, and the variability and conflict parameter are used to comprehensively describe the variability and conflict of the index. max and min are respectively the functions of finding the maximum and finding the minimum;

[0053] Respectively obtain the weight w a of the a-th index parameter:

[0054]

[0055] Among them, K b is the variability and conflict parameter of the b-th index parameter.

[0056] Preferably, the method for obtaining the variability and conflict parameter of the index parameter is as follows:

[0057]

[0058] K a = S a R a ;

[0059] Among them, is the mean value of the a-th index parameter, S a is the standard deviation of the a-th index parameter, R a is the quantification value of the conflict between the a-th index and other indexes, r ta is the correlation coefficient between the t-th index and the a-th index, K a is the variability and conflict parameter of the a-th index parameter

[0060] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0061] The present invention considers the spatio-temporal coordination degree of air-conditioning and electric vehicle resources, fully considers the system coordination characteristics of electric vehicles and air-conditioning loads in the spatial and temporal dimensions, and improves the accuracy of the evaluation results;

[0062] From the two perspectives of the time cost of cross-regional scheduling and the economic evaluation of charging and discharging resources in different regions, the present invention realizes a more systematic analysis of the mutual aid potential of electric vehicles and air-conditioning loads;

[0063] The present invention realizes the coordinated mutual aid of air-conditioning and electric vehicle resources, which not only improves the utilization efficiency of electric vehicles as mobile energy storage resources, but also effectively alleviates the pressure on the power grid during the peak period of air-conditioning load, thus significantly enhancing the stability and flexibility of the power grid;

[0064] The present invention assigns different weights to different parameters, provides a scientific basis for the optimal scheduling of the power system and the rational allocation of resources, improves the operation efficiency and reliability of the power system, and promotes the efficient utilization and sustainable development of demand-side resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a schematic flow chart of a method for quantitatively evaluating the mutual aid potential of demand-side resources considering spatio-temporal coordination provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0067] Embodiment 1

[0068] This embodiment provides a method for quantitatively evaluating the potential for mutual assistance of demand-side resources considering spatio-temporal synergy. Refer to Figure 1 , and it includes the following steps:

[0069] Determine multiple evaluation dimensions, where the evaluation dimensions include spatio-temporal synergy degree, peak shaving potential, and economic applicability, and each evaluation dimension includes multiple index parameters;

[0070] Obtain the spatio-temporal synergy degree, where the spatio-temporal synergy degree includes the interaction degree between charging load and air-conditioning load, local resource availability rate, and remote resource availability rate;

[0071] Obtain the peak shaving potential, where the peak shaving potential includes local charging and discharging resource peak shaving potential and remote charging and discharging resource peak shaving potential;

[0072] Obtain the economic applicability, where the economic applicability includes the time cost of cross-regional scheduling and the economic evaluation of charging and discharging resources in different regions;

[0073] Obtain the quantitative evaluation of the potential for mutual assistance of demand-side resources by assigning different weights to different index parameters.

[0074] Generally speaking, the charging and discharging behavior of electric vehicles is greatly affected by user habits and shows discrete probability distribution characteristics, while the air-conditioning load is mainly driven by temperature changes. Therefore, their peak periods do not always completely overlap. Electric vehicles and air-conditioning load have significant spatio-temporal coupling characteristics under specific conditions, providing a new idea for the mutual assistance optimization of demand-side resources. From the time dimension, the air-conditioning load is not sensitive to electricity price changes, often leading to a continuous increase in the electricity load during peak periods; while the charging load of electric vehicles is highly sensitive to electricity price regulation and has strong peak-shifting regulation ability. The two show obvious "complementarity" in electricity usage time. From the space dimension, the charging of electric vehicles and the use of air-conditioning are usually concentrated in the same area, and their electricity usage and charging and discharging behaviors show significant "space overlap". With the excellent mobile energy storage potential of electric vehicles, by scientifically regulating their charging and discharging behaviors, the air-conditioning load can be effectively diverted, the peak load pressure on the power grid can be relieved, and the efficiency and stability of the power grid operation can be significantly improved.

[0075] Therefore, to more accurately evaluate the potential of electric vehicles and air-conditioning loads in the mutual assistance of demand-side resources, this embodiment starts from three dimensions: spatio-temporal coordination degree, peak shaving potential, and economic applicability. Based on multiple types of indicators, a quantitative evaluation method for the mutual assistance potential of demand-side resources considering spatio-temporal coordination is comprehensively proposed. That is, by deeply analyzing the spatio-temporal coordination characteristics, peak shaving potential, and economic applicability of demand-side resources such as electric vehicle charging loads and air-conditioning loads, calculating three types of indicators: the interaction degree between charging loads and air-conditioning loads, the availability rate of local resources, and the availability rate of off-site resources, the spatio-temporal coordination degree between electric vehicles and air conditioners can be evaluated; the peak shaving capabilities of electric vehicles and air conditioners can be evaluated from two dimensions: the peak shaving potential of local and off-site charging and discharging resources; finally, by combining two types of indicators: the time cost of cross-regional scheduling and the economy of charging and discharging resources in different regions, the economy is comprehensively evaluated. To ensure the accuracy and intuitiveness of the evaluation results, different weights are determined, and finally, the systematic quantitative evaluation of the mutual assistance potential of demand-side resources is realized, thereby providing a scientific basis for the optimal scheduling of the power system and the rational allocation of resources, improving the operation efficiency and reliability of the power system, and promoting the efficient utilization and sustainable development of demand-side resources.

[0076] The spatio-temporal coordination degree between electric vehicles and air conditioners refers to the coordination degree between the two under different time and space conditions, reflecting the interaction relationship between the charging and discharging loads of electric vehicles and the air-conditioning loads and their coordination effect on the power system. The acquisition methods of the three indicator parameters are as follows:

[0077] First, the interaction degree between the charging load and the air-conditioning load refers to accurately evaluating their interaction level in the time dimension by analyzing the air-conditioning and charging load data at multiple time points within a day and comprehensively considering the deviation of both relative to the mean value. The smaller the interaction degree between the charging load and the air-conditioning load, the smaller the charging demand during the peak period of the air-conditioning load, and the smaller the overlap between the two. The method for obtaining the interaction degree between the charging load and the air-conditioning load is as follows:

[0078]

[0079] Among them, ρ A1 represents the interaction degree, P ack and P EVk respectively represent the air-conditioning load and the charging load at the k-th peak time point, P EVn represents the charging load at the n-th time point, and respectively represent the average value of the air-conditioning load and the average value of the charging load at multiple time points in a day, and N peak is the number of peak time points in a day.

[0080] On the other hand, the local resource availability rate can quantify the availability of local charging and discharging resources by calculating the ratio of the number of V2G vehicles during the peak air-conditioning load period to the maximum number of V2G vehicles staying in the area throughout the day. The method for obtaining the local resource availability rate of the charging load and the air-conditioning load is as follows:

[0081]

[0082] Among them, ρ A2_local is the local resource availability rate, N V2G_peak is the number of V2G vehicles during the peak air-conditioning load period, and N V2G_max represents the maximum number of V2G vehicles staying in the area where the vehicle is located within a day.

[0083] The off-site resource availability rate is divided into two types of evaluation indicators: the spatio-temporal prism and the evaluation of callable resources based on the spatio-temporal prism. The spatio-temporal prism refers to constructing a model that describes the accessibility of charging and discharging resources in the spatio-temporal dimension based on the spatio-temporal prism theory. Taking the user's starting point as the center, spatio-temporal prisms are constructed considering different driving speeds, and by judging the relationship between the moving duration of the charging and discharging resources and the duration of the peak load at the target location, it is determined whether the target location is within the spatio-temporal prism. The method for obtaining the off-site resource availability rate of the charging load and the air-conditioning load is as follows:

[0084] Construct a spatio-temporal prism that describes the accessibility of charging and discharging resources in the spatio-temporal dimension;

[0085] Let the duration of the peak load at the target location be T, and judge the relationship between the moving duration T use of the charging and discharging resources and the duration of the peak load at the target location to determine whether the target location is within the spatio-temporal prism:

[0086]

[0087]

[0088] Among them, A ij represents whether a user traveling from the i-th area to the j-th area can catch up with the target location within the duration of the peak load. A value of 1 means yes, and a value of 0 means no. (x, y) are spatial coordinates, and v represents the driving speed;

[0089] Obtain the off-site resource availability rate:

[0090]

[0091] Among them, ρ A3_other is the off-site resource availability rate, R represents the number of regions, M represents the number of users, and A ij,mIt represents whether the m-th user traveling from the i-th area to the j-th area can reach the destination within the peak load duration.

[0092] Secondly, the peak shaving potential assessment is a method for evaluating the potential and effect of reducing or shifting the load during the peak electricity consumption period by analyzing the load characteristics of the power system and the electricity consumption behavior of users. The method for obtaining the peak shaving ability in this embodiment is as follows:

[0093] The peak shaving potential of local charging and discharging resources refers to the potential ability to reduce the peak load of the power grid by using resources such as electric vehicles and mobile energy storage devices in the area through flexible charging and discharging management. Specifically, it is evaluated by calculating the proportion of local charging and discharging resources in reducing the air-conditioning load during the peak period. Therefore, to obtain the peak shaving potential β of the local charging and discharging resources B1 :

[0094]

[0095] where, P V2G,k,local represents the discharging power of local V2G at the k-th peak time point, P ac,k represents the destination air-conditioning load at the k-th peak time point, N peak is the number of peak time points in a day;

[0096] The peak shaving potential of off-site charging and discharging resources refers to the ability to reduce the peak load of the local power grid by charging electric vehicles outside the area and discharging them within the area. This process usually depends on the connectivity of the inter-regional power grid, resource scheduling ability, and relevant policy support. Specifically, the peak shaving ability can be evaluated by calculating the proportion of off-site charging and discharging resources in reducing the air-conditioning load during the peak period. Therefore, to obtain the peak shaving potential β of the off-site charging and discharging resources B2 :

[0097]

[0098] where, P V2G,k,other is the off-site V2G discharging power at the k-th peak time point, P V2G,k,m is, A ij,m is.

[0099] Finally, the economic applicability assessment refers to comprehensively evaluating the economic benefit level between the input cost and the benefit when realizing the mutual assistance of load demands at different times and in different spaces by using resources with spatio-temporal mobility characteristics (such as electric vehicles and mobile energy storage devices) in the power system. This assessment needs to comprehensively consider the flexibility, schedulability, scheduling scope of the resources, as well as the benefits and costs brought by cross-regional cooperation. This embodiment focuses on the time cost of cross-regional scheduling and the economy of charging and discharging resources in different regions to achieve a more scientific and reasonable economic benefit assessment. Therefore, the method for obtaining the economic applicability in this embodiment is as follows:

[0100] The time cost of cross-regional scheduling, considering the time cost of nearby resources being called, is related to the call distance and the local minimum hourly wage. Obtain the time cost E of the cross-regional scheduling C1 :

[0101] E C1 = S min T d ;

[0102] Among them, S min represents the regional minimum hourly wage, and T d represents the time cost of resource call;

[0103] Obtain the economic assessment E of the charging and discharging resources in different regions C2 :

[0104]

[0105] Among them, C max and C min are respectively the maximum and minimum values of the comprehensive cost among all regions and resource types;

[0106] According to the value of E C2 , divide the economic level, and the larger the value of E C2 , the better the economy of calling the charging and discharging resources in this region.

[0107] As a preferred solution of this embodiment, the method for dividing the economic level according to the value of E C2 is as follows:

[0108] When E C2 ≥70%, it is a high economic level. At this time, the call cost is relatively low, and it has high competitiveness considering both cost and performance;

[0109] When 35% ≤ E C2 <70%, it is a medium economic level. This situation may be considered when the cost is not very sensitive or the power supply guarantee pressure is relatively high, etc., but it is necessary to weigh its cost and benefit;

[0110] When 0% ≤ E C2 <35%, it is in the low economic grade, which may bring higher costs. When there are other more economical flexibility resources available, it is generally not given priority.

[0111] After obtaining all the evaluations, in the process of evaluating the mutual assistance potential of demand-side resources, the weights of each index parameter reflect its important impact on the evaluation object. To ensure the objectivity and accuracy of the weights, this embodiment realizes the reasonable analysis and scientific determination of the weights of each parameter, and objectively measures the information volume of the index to determine the weights. This method comprehensively considers the dispersion degree and correlation of the indexes to ensure the scientificity and rationality of the weight allocation.

[0112] Therefore, in this embodiment, the method for obtaining the quantitative evaluation of the mutual assistance potential of demand-side resources by assigning different weights to different index parameters is as follows:

[0113] Construct an initial matrix X for evaluating the mutual assistance potential of demand-side resources:

[0114]

[0115] Among them, x ia represents the a-th index parameter of the i-th region, R is the total number of the regions, A is the total number of the index parameters, a = 1, 2,..., A, i = 1, 2,..., R;

[0116] Perform dimensionless processing on the initial matrix X to obtain a standard matrix X';

[0117] The general formula for dimensionless processing can be:

[0118]

[0119] Among them, x ′ and x are the processed and unprocessed values respectively, max represents the maximum value in the data, and min represents the minimum value in the data;

[0120] That is to say:

[0121]

[0122] Among them, max and min are the functions for finding the maximum and minimum respectively;

[0123] On this basis, the variability and conflict parameters of the indexes can be obtained:

[0124]

[0125] K a = S a R a;

[0126] Among them, is the mean value of the a-th index parameter, and S a is the standard deviation of the a-th index parameter, and R a is the quantification value of the conflict between the a-th index and other indexes, and r ta is the correlation coefficient between the t-th index and the a-th index, and K a is the variability and conflict parameter of the a-th index parameter;

[0127] Furthermore, the weight w of the a-th index parameter is obtained respectively a :

[0128]

[0129] Among them, K b is the variability and conflict parameter of the b-th index parameter.

[0130] In the above calculations, the variability and conflict of the index are calculated, and then the information content of the index is obtained. Variability reflects the distribution difference of the index in a certain layer, and usually the standard deviation or deviation is used to measure its dispersion degree. The greater the variability, the more significant the impact of the index on the system, and a higher weight should be assigned. An index with high variability can provide more unique information, which helps to avoid information redundancy in weight allocation and highlight important indexes with significant differences. Conflict is used to measure the relationship between different levels or indexes, especially the degree of opposition or complementarity. Through methods such as correlation or covariance, it can be analyzed whether there is conflict or consistency between indexes. If the conflict is high, the opposition between indexes is strong, and they may provide information in different dimensions, and reasonable weight allocation is needed to balance the perspective. If the conflict is low, the consistency between indexes is strong, and the information may overlap, and excessive repetition should be avoided when allocating weights. Through conflict analysis, the rationality of weight allocation can be optimized, and some indexes can be prevented from being over-emphasized or ignored due to high correlation. Evaluate the information content of the index, and determine the objective weight of the index according to the amount of information provided by the index. The comprehensive variability and conflict parameters obtained are important indexes to measure the overall contribution of the index to the system, which are used to reflect the degree of effective information contained in it. The greater it is, the stronger the explanatory power of the index to the system, and a higher weight should be assigned.

[0131] To sum up, this embodiment systematically evaluates the potential of electric vehicles and air conditioners in the mutual assistance of demand-side resources from three dimensions: spatio-temporal coordination degree, peak shaving potential, and economic applicability. This method provides a scientific and accurate quantitative basis for reasonably formulating charging and discharging strategies, stimulating the adjustable potential of electric vehicle and air conditioner loads, and alleviating the grid load pressure.

[0132] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy, characterized in that, Including the following steps: Determine multiple evaluation dimensions, which include spatio-temporal coordination degree, peak shaving potential, and economic applicability. Each evaluation dimension includes multiple index parameters; Obtain the spatio-temporal coordination degree, which includes the interaction degree between the charging load and the air-conditioning load, the local resource availability rate, and the off-site resource availability rate; Obtain the peak shaving potential, which includes the local charge-discharge resource peak shaving potential and the off-site charge-discharge resource peak shaving potential; Obtain the economic applicability, which includes the time cost of cross-regional scheduling and the economic evaluation of charge-discharge resources in different regions; Obtain the quantitative evaluation of the mutual assistance potential of demand-side resources by assigning different weights to different index parameters.

2. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 1, wherein The method for obtaining the interaction degree between the charging load and the air-conditioning load is: Among them, ρ A1 represents the degree of interaction, P ack and P EVk respectively represent the air-conditioning load and the charging load at the k-th peak time point, P EVn represents the charging load at the n-th time point, and respectively represent the average values of the air-conditioning load and the charging load at multiple time points in a day, N peak is the number of peak time points in a day.

3. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 1, characterized in that The method for obtaining the local resource availability rate of the charging load and the air-conditioning load is: Among them, ρ A2_local is the available rate of the local resources, N V2G_peak is the number of V2G vehicles during the peak period of air-conditioning load, and N V2G_max represents the maximum value of the number of V2G vehicles staying in the area within a day.

4. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 1, wherein The method for obtaining the off-site resource availability rate of the charging load and the air-conditioning load is: Construct a spatio-temporal prism, which describes the accessibility of charge-discharge resources in the spatio-temporal dimension; Set the duration of the peak load at the target location as T, and judge the moving duration T of the charging and discharging resources use The relationship with the duration of the peak load at the target location to determine whether the target location is within the spatio-temporal prism: Among them, A ij represents whether a user traveling from the i-th area to the j-th area can reach the destination within the peak load duration. The value of 1 means yes, and the value of 0 means no. (x, y) are spatial coordinates, and v represents the traveling speed; Obtain the off-site resource availability rate: Among them, ρ A3_other is the availability rate of the remote resources, R represents the number of regions, M represents the number of users, and A ij,m represents whether the m-th user traveling from the i-th region to the j-th region can reach the destination within the peak load duration.

5. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 1, characterized in that Method for obtaining the peak shaving potential β of the local charge and discharge resources B1 is as follows: Obtain the peak shaving potential β of the local charging and discharging resources B1 : Among them, P V2G,k,local represents the discharging power of local V2G at the k-th peak time point, and P ac,k represents the destination air-conditioning load at the k-th peak time point. N peak is the number of peak time points in a day.

6. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 5, characterized in that Method for obtaining peak shaving potential β of off-site charging and discharging resources B2 is as follows: Among them, P V2G,k,other is the off-site V2G discharge power at the k-th peak time point, P V2G,k,m is, A ij,m is.

7. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 1, characterized in that The method for obtaining the economic applicability is: Obtain the time cost E of the cross-region scheduling C1 : E C1 = S min T d ; Among them, S min represents the lowest hourly wage in the region, and T d represents the time cost of resource invocation; Obtain the economic evaluation E of the charging and discharging resources in the different regions C2 : Among them, C nax and C min are respectively the maximum and minimum values of the comprehensive cost among all regions and resource types; Divide the economic level according to the value of E C2 and the larger the value of E C2 , the better the economy of calling the charging and discharging resources in this area.

8. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 7, wherein The method of dividing the economic grade according to the value of E C is as follows: When E C2 ≥ 70%, it is a high economic grade; When 35% ≤ E C2 < 70%, it is of medium economic grade; When 0% ≤ E C2 < 35%, it is a low economic grade.

9. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 1, wherein The method for obtaining the quantitative evaluation of the mutual assistance potential of demand-side resources by assigning different weights to different index parameters is: Construct an initial matrix X for evaluating the mutual assistance potential of demand-side resources: where x ia represents the ath index parameter of the ith region, R is the total number of the regions, A is the total number of the index parameters, a = 1, 2, …, A, i = 1, 2, …, R; Perform dimensionless processing on the initial matrix X to convert x ia into x i ′ a , and obtain the standard matrix X': Obtain the variability and conflict parameter of the index. The variability and conflict parameter is used to comprehensively describe the variability and conflict of the index. max and min are functions for finding the maximum and minimum respectively; Obtain the weight w of the a-th index parameter respectively a : Among them, K b is the variability and conflict parameter of the b-th index parameter.

10. The method for quantitatively evaluating the potential of mutual assistance of demand-side resources considering spatio-temporal synergy according to claim 9, wherein The method for obtaining the variability and conflict parameter of the index parameter is: K a = S a R a ; Among them, is the mean of the a-th index parameter, S a is the standard deviation of the a-th index parameter, R a is the quantification value of the conflict between the a-th index and other indexes, r ta is the correlation coefficient between the t-th index and the a-th index, K a is the variability and conflict parameter of the a-th index parameter.

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