Method and system for evaluating demand side resource mutual aid potential considering spatiotemporal coupling characteristics

Abstract

The present application relates to the electric vehicle charging and discharging resource and air conditioner load mutual benefit potential evaluation technical field, relates to the demand side resource mutual benefit potential evaluation method and system considering the space-time coupling characteristics, including: obtaining the operation data of electric vehicle and air conditioner load;Build economic evaluation index model and feasibility evaluation index model, based on economic evaluation index model and feasibility evaluation index model, analyze the electric vehicle and air conditioner load operation data, and solve the subjective weight and objective weight of the analysis result;The combined weight matrix is obtained by combining weighting method, the combined weight matrix is multiplied by the normalized analysis result, and the demand side resource mutual benefit potential evaluation of electric vehicle and air conditioner load is completed.The present application can not only improve the utilization efficiency of electric vehicle as mobile energy storage, but also effectively alleviate the pressure of air conditioner load on power grid during peak period, so as to enhance the stability and flexibility of power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of mutual assistance potential assessment of electric vehicle charging and discharging resources and air conditioning loads, and in particular to a demand-side resource mutual assistance potential assessment method and system taking into account spatiotemporal coupling characteristics. Background Art

[0002] During winter and summer, frequent extreme weather events increase air conditioning electricity consumption and peak loads, placing significant pressure on power grid supply. Furthermore, with the continuous improvement of new energy vehicle industry policies, technologies, and supporting services, electric vehicle penetration continues to rise, leading to a growing charging load. Disorderly charging will compound peaks, making the grid's reliance on demand-side resources increasingly urgent. Currently, methods for assessing the potential for mutual assistance between electric vehicle charging and discharging resources and air conditioning loads still have limitations, failing to fully consider the potential for mutual assistance under spatiotemporal coupling. Existing research typically explores the adjustable potential of air conditioning and electric vehicles separately, failing to effectively integrate the two, resulting in limited systematicity and accuracy in the assessment results. Although existing research has extensively explored methods for assessing the adjustable potential of electric vehicles and air conditioning loads, they typically analyze them independently, lacking a comprehensive study of their mutual assistance potential as a systematic project under spatiotemporal coupling. Therefore, we propose a method and system for assessing the potential for mutual assistance between demand-side resources that takes into account spatiotemporal coupling. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics. By systematically analyzing the economy and feasibility of electric vehicles and air-conditioning loads as mutual assistance objects in actual applications, it can accurately evaluate the demand-side resource mutual assistance potential, and provide a theoretical basis for power grid companies to accurately evaluate the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads.

[0004] The present invention is achieved through the following technical solutions:

[0005] A method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics, the method comprising the following steps:

[0006] Based on the spatiotemporal coupling characteristics of electric vehicles and air conditioning loads, a demand-side resource mutual assistance potential assessment model was constructed, and a comprehensive assessment of the demand-side resource mutual assistance potential of electric vehicles and air conditioning loads was completed;

[0007] The construction process of the demand-side resource mutual assistance potential assessment model is as follows:

[0008] Obtain operating data of electric vehicles and air conditioning loads;

[0009] constructing an economic evaluation index model and a feasibility evaluation index model, analyzing the operation data of the electric vehicle and the air conditioner load based on the economic evaluation index model, and analyzing the operation data of the electric vehicle and the air conditioner load based on the feasibility evaluation index model, and outputting the economic evaluation index and the feasibility evaluation index;

[0010] solving the subjective weight and the objective weight of the economic evaluation index and the feasibility evaluation index;

[0011] combining the subjective weight and the objective weight, and calculating a combination weight matrix through a combination weighting method, multiplying the combination weight matrix and the normalized economic evaluation index and the normalized feasibility evaluation index, and taking the multiplication result as the demand side resource mutual aid potential of the electric vehicle and the air conditioner load.

[0012] Optionally, the economic evaluation index model comprises user benefit, power grid benefit and environmental benefit.

[0013] Optionally, the user benefit comprises user air conditioner electricity cost reduction rate, user charging cost reduction rate and electric vehicle user comprehensive cost reduction rate.

[0014] The user air conditioner electricity cost reduction rate is calculated according to the following formula: The user air conditioner electricity cost reduction rate is calculated according to the following formula:

[0015]

[0016] The user air conditioner electricity cost reduction rate is calculated according to the following formula: is the air conditioner consumption electric quantity in the i th time period of the response day, is the air conditioner consumption electric quantity in the i th time period of the baseline day, i is the electricity price in the i th time period, i is the subsidy price in the i th time period, and the coefficient is 0 if there is no subsidy;

[0017] The user charging cost reduction rate is calculated according to the following formula: The user charging cost reduction rate is calculated according to the following formula:

[0018]

[0019] The user charging cost reduction rate is calculated according to the following formula: is the electric vehicle charging electric quantity in the i th time period of the response day, is the electric vehicle charging electric quantity in the i th time period of the baseline day;

[0020] The electric vehicle user comprehensive cost reduction rate C3 is calculated according to the following formula:

[0021]

[0022] The electric vehicle user comprehensive cost reduction rate C3 is calculated according to the following formula: dis the proportion of electric vehicle users in area d, d∈[residential area, office area, commercial building, …].

[0023] Optionally, the grid benefits include: air conditioning load reduction rate during grid peak hours, charging load reduction rate during grid peak hours, comprehensive load reduction rate for electric vehicle users, cross-temporal and spatial correlation coefficient between electric vehicle and air conditioning loads, dispatchable capacity of charging and discharging resources during grid peak hours, and adjustable air conditioning capacity and temporal and spatial matching degree during grid peak hours;

[0024] The air conditioning load reduction rate C4 during the peak period of the power grid is calculated as follows:

[0025]

[0026] in, In response to the maximum air conditioning load during the daily power grid peak period, is the maximum air conditioning load during the peak period of the power grid on the baseline day;

[0027] The charging load reduction rate C5 during the peak period of the power grid is calculated as follows:

[0028]

[0029] in, is the charging load reduction rate during the peak period of the power grid in region d, In response to the maximum electric vehicle charging load during the daily power grid peak period, is the maximum EV charging load during the peak hours of the power grid on the baseline day;

[0030] The electric vehicle user comprehensive load reduction rate C6 is calculated as follows:

[0031]

[0032] The cross-temporal and spatial correlation coefficient C7 between the electric vehicle and the air conditioning load is calculated as follows:

[0033] C7=α xy ×ρ xy (k,Δx,Δy)+β d ×ρ d (k,Δd1,Δd2,Δd3)

[0034]

[0035] Among them, ρ xy (k,Δx d ,Δy d ) is the time coupling characteristic of electric vehicles and air conditioning loads in each region, ρ d(k, Δd1, Δd2, Δd3) are the spatial coupling characteristics of electric vehicles and air conditioning loads in each region, x is the air conditioning load in region d, y is the charging load in region d, σX d is the standard deviation of air conditioning load in area d, σy is the standard deviation of electric vehicle charging load in area d, d1 represents residential area, d2 represents office area, d3 represents commercial building, σd1, σd2, σd3 represent the standard deviation of electric vehicle and air conditioning load in residential area, office area and commercial building respectively, α xy is the time coupling characteristic weight of the two demand-side resources, air conditioning load and electric vehicles, β d is the spatial coupling characteristic weight of each region for the same type of load;

[0036] The calculation formula for the dispatchable capacity C8 of charging and discharging resources during the peak period of the power grid is:

[0037] C8=S ev ×γ ev

[0038] Among them, S ev is the scale of electric vehicle charging and discharging resources, γ ev The proportion of electric vehicle users willing to participate;

[0039] The air conditioning capacity C9 can be adjusted during the peak period of the power grid, and its calculation formula is:

[0040] C9=S ac ×γ ac

[0041] Among them, S ac is the scale of air-conditioning users; γ ac The proportion of air-conditioning users willing to participate;

[0042] The spatiotemporal matching degree C 10 , and its calculation formula is:

[0043]

[0044] Among them, P ev (t,x,y) represents the charging and discharging power of the electric vehicle, P ac (t,x,y) represents the air conditioning power.

[0045] Optionally, the environmental benefits include: an increase in the proportion of renewable energy consumption, an increase in the energy conservation and emission reduction rate of air conditioners, and an increase in the energy conservation and emission reduction rate of electric vehicles;

[0046] The increase in the proportion of renewable energy consumption The calculation formula is:

[0047]

[0048] Among them, E c,i E is the amount of electricity in the i-th period of the response day, j,i is the electricity consumption in the i-th period of the baseline day, S i is the proportion of green electricity in the i-th period;

[0049] The improvement rate of energy conservation and emission reduction of air conditioners The calculation formula is:

[0050]

[0051] Among them, E c,i E is the amount of electricity in the i-th period of the response day, j,i is the electricity consumption in the i-th period on the baseline day, is the carbon emission factor of air conditioning per kilowatt-hour in period i;

[0052] The calculation formula for the energy conservation and emission reduction improvement rate of electric vehicles is:

[0053]

[0054] in, is the carbon emission factor per kilowatt-hour of electric vehicles in period i.

[0055] Optionally, the feasibility evaluation index model includes: standard specification index C 14 、Key equipment indicators C 15 、Technical solution indicator C 16 、Market mechanism indicator C 17 Information security judgment index C 18 .

[0056] Optionally, the combined weight matrix is ​​calculated by the combined weighting method, and its calculation formula is:

[0057]

[0058] γ T =(γ1,γ2,...,γ j )

[0059] Among them, γ j is the comprehensive weight of the jth indicator, α j is the subjective weight, β j is the objective weight, γ T is the combined weight matrix.

[0060] Optionally, the normalized economic evaluation index and feasibility evaluation index are calculated using the following formula:

[0061]

[0062] Among them, R is the normalized indicator eigenvalue matrix, C j ′ is the normalized eigenvalue of the jth indicator.

[0063] Optionally, the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads is calculated as follows:

[0064]

[0065] Among them, W is the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads.

[0066] The demand-side resource mutual assistance potential assessment system taking into account the temporal and spatial coupling characteristics includes:

[0067] The judgment layer obtains the operating data of electric vehicles and air-conditioning loads, constructs an economic evaluation index model and a feasibility evaluation index model, analyzes the operating data of electric vehicles and air-conditioning loads based on the economic evaluation index model, and analyzes the operating data of electric vehicles and air-conditioning loads based on the feasibility evaluation index model, and outputs the economic evaluation index and feasibility evaluation index;

[0068] Indicator layer, solving the subjective weight and objective weight of economic evaluation indicators and feasibility evaluation indicators;

[0069] At the target layer, the subjective weights and objective weights are combined, and the combined weight matrix is ​​calculated through the combined weighting method. The combined weight matrix is ​​multiplied with the normalized economic evaluation index and feasibility evaluation index. The multiplication result represents the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads.

[0070] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0071] This paper considers the spatiotemporal coupling characteristics of electric vehicles and air conditioning loads, combines the analytic hierarchy process, and systematically analyzes their mutual assistance potential from the perspectives of economic efficiency and feasibility. It also constructs an evaluation model for the resource mutual assistance potential of electric vehicles and air conditioning loads. This model can systematically evaluate the resource mutual assistance potential of electric vehicles and air conditioning loads under spatiotemporal constraints. Based on this evaluation model, power grid companies can develop more scientific incentive mechanisms for resource mutual assistance between air conditioners and electric vehicles, which can not only improve the utilization efficiency of electric vehicles as mobile energy storage, but also effectively alleviate the pressure of air conditioning loads on the power grid during peak periods, thereby enhancing the stability and flexibility of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a flow chart of the demand-side resource mutual assistance potential assessment method provided by the present invention that takes into account the temporal and spatial coupling characteristics. DETAILED DESCRIPTION

[0073] To make the objectives, technical solutions, and advantages of the present invention more apparent, the following will provide a clear and complete description of the technical solutions of the present invention in conjunction with the accompanying drawings. It should be understood that the description is only a portion of the present invention, not all of it. The components of the present invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations.

[0074] The charging and discharging behavior of electric vehicles and the peak hours of air conditioning loads do not always completely overlap. The degree of temporal and spatial overlap significantly impacts the potential for demand-side resource synergy. To more accurately assess the demand-side resource synergy potential between electric vehicles and air conditioning loads, this paper proposes an embodiment from the two dimensions of economic efficiency and feasibility: a demand-side resource synergy potential assessment method that accounts for temporal and spatial coupling characteristics.

[0075] The calculation process of this evaluation method is shown in the attached Figure 1 As shown in Table 1, the model consists of three levels: target level (demand-side resource mutual assistance potential assessment), indicator level, and judgment level (see Table 1 for details).

[0076]

[0077] First, based on the two standard layers of economy and feasibility, analyze and identify the main influencing factors in the judgment layer, and clarify the calculation indicators of the main influencing factors.

[0078] Then, the calculation index judgment matrix is ​​constructed and consistency test is carried out to determine the subjective weight of each index. In order to improve the objectivity and accuracy of the weight coefficient,

[0079] Finally, the entropy weight method is used to calculate the objective weight of each indicator, and the comprehensive weight is determined by the combined weighting method, ultimately achieving a comprehensive assessment of the demand-side resource mutual assistance potential.

[0080] This paper uses the demand-side resource synergy potential of the spatiotemporal coupling characteristics of electric vehicles and air conditioning loads as the target layer, and selects economic efficiency and feasibility as the standard layer. Among them, economic efficiency includes three judgment-level indicators and 13 calculation indicators, while feasibility includes five judgment-level indicators and five calculation indicators.

[0081] Among them, economic benefits are divided into three judgment levels: user benefits, grid benefits, and environmental benefits. Among them, user benefits refer to the fact that electric vehicle and air conditioning load users reduce electricity costs by adjusting their loads, and electric vehicle owners receive rewards or subsidies from the grid and operators through discharge, thereby reducing the user's overall usage costs. The user benefit judgment level is mainly divided into three calculation indicators: user air conditioning electricity cost reduction rate (C1), user charging cost reduction rate (C2), and electric vehicle user comprehensive cost reduction rate (C3), as shown below:

[0082] Reduction rate of users' air conditioning electricity costs

[0083]

[0084] Where: : is the power consumption of air conditioner in the i-th period of the response day; : is the power consumption of air conditioner in period i on baseline day; f i : is the electricity price in period i, k i is the subsidy price in the i-th period. If there is no subsidy, the coefficient is 0.

[0085] Reduction rate of charging costs for electric vehicle users

[0086]

[0087] Where: : is the charging power of the electric vehicle in the i-th period of the response day; : is the charging power of the electric vehicle in the i-th period on the baseline day; f i : is the electricity price in period i, k i is the subsidy price in the i-th period. If there is no subsidy, the coefficient is 0.

[0088] Considering the mobility characteristics of electric vehicle users, for individual car owners, their charging behavior is related to the electricity price level in different scenarios. Therefore, it is necessary to comprehensively consider the cost reduction of all scenarios and propose the comprehensive cost reduction rate (C3) for electric vehicle users.

[0089]

[0090] Where: d : The proportion of electric vehicle users in area d, d∈[residential area, office area, commercial building, …].

[0091] Grid benefits refer to the fact that during peak air conditioning demand, electric vehicles, acting as mobile energy storage devices, can effectively divert the power load from the air conditioning system, alleviating peak grid loads and preventing overloads, thereby maintaining grid stability. Since electric vehicles discharge during peak air conditioning demand, the overlap of charging and discharging loads is avoided, further reducing peak grid loads.

[0092] Air conditioning load reduction rate during power grid peak hours (C4)

[0093]

[0094] Where: : The maximum air conditioning load during the peak period of the power grid on the response day; : Maximum air conditioning load during the peak period of the power grid on the baseline day.

[0095] Charging load reduction rate during grid peak hours (C5)

[0096]

[0097] Where: : Charging load reduction rate during peak hours of the power grid in region d; : The maximum electric vehicle charging load during the peak period of the power grid on the response day; : Maximum electric vehicle charging load during the peak hours of the power grid on the baseline day.

[0098] Comprehensive load reduction rate of electric vehicle users (C6)

[0099]

[0100] Where: d : The proportion of electric vehicle users in area d, d∈[residential area, office area, commercial building, …].

[0101] Cross-temporal and spatial correlation coefficient between electric vehicles and air conditioning load (C7)

[0102] (1) Temporal coupling characteristics of electric vehicles and air conditioning loads in each region

[0103]

[0104] Where, x: air conditioning load in area d; y: charging load in area d; σX d : Standard deviation of air conditioning load in region d; σy: Standard deviation of electric vehicle charging load in region d.

[0105] (2) Spatial coupling characteristics of electric vehicles and air conditioning loads in each region

[0106]

[0107] Where d1 represents residential area, d2 represents office area, d3 represents commercial building, and σd1, σd2, and σd3 represent the standard deviations of electric vehicle and air conditioning loads in residential area, office area, and commercial building scenarios, respectively.

[0108] C7=α xy ×ρ xy (k,Δx,Δy)+β d ×ρ d (k,Δd1,Δd2,Δd3)

[0109] Where, α xy : The time coupling characteristic weights of the two demand-side resources, air conditioning load and electric vehicles, β d The weights of spatial coupling characteristics of different regions for the same type of load.

[0110] Dispatchable capacity of charging and discharging resources during peak hours of the power grid (C8)

[0111] C8=S ev ×γ ev

[0112] Where: S ev : Electric vehicle charging and discharging resource scale; γ ev : Proportion of electric vehicle users’ willingness to participate;

[0113] Adjustable air conditioning capacity during power grid peak hours (C9)

[0114] C9=S ac ×γ ac

[0115] Where S ac : Air conditioning user scale; γ ac : The proportion of air-conditioning users willing to participate.

[0116] This invention proposes the concept of time-space matching for the first time. Time-space matching (TSM) refers to the degree of coordination between the charging and discharging behavior of electric vehicles and the load demand of the air-conditioning system in time and space. Time-space matching is a key indicator of the mutual assistance between electric vehicles and air-conditioning load resources. It characterizes whether electric vehicles can provide electricity to the air-conditioning system at the right time and place, thereby directly affecting the effect of resource mutual assistance on the demand side. A higher degree of time-space matching means that resources can be used more efficiently, better meet load demand, and thus improve the overall operating efficiency and economy of the power system. Time-space matching (TSM) 10 ) satisfies the following relationship:

[0117]

[0118] Where, P ev (t,x,y) represents the charging and discharging power of the electric vehicle, P ac (t,x,y) represents the air conditioning power.

[0119] Preferably, environmental benefits refer to the ability of electric vehicles to charge using renewable energy and then feed that energy back into the grid through vehicle-grid interaction to power the air conditioning system. This process not only effectively promotes the consumption of green electricity but also reduces reliance on fossil fuel power generation, thereby lowering greenhouse gas emissions and promoting environmental sustainability.

[0120] Increase in the proportion of renewable energy consumption

[0121]

[0122] Where: Ec,i : the amount of electricity in the i-th period of the response day; E j, i : is the electricity consumption in the i-th period of the baseline day; S i : The proportion of green electricity in the i-th period.

[0123] Improvement rate of energy conservation and emission reduction of air conditioners

[0124]

[0125] Where, E c,i : the amount of electricity in the i-th period of the response day; E j,i : is the electricity consumption in the i-th period of the baseline day; : Carbon emission factor of air conditioning per kilowatt-hour in period i, unit: kgCO2 / kWh.

[0126] Improvement rate of energy conservation and emission reduction of electric vehicles

[0127]

[0128] Where: E c,i : the amount of electricity in the i-th period of the response day; E j,i : is the electricity consumption in the i-th period of the baseline day; : Carbon emission factor per kilowatt-hour of electric vehicles in period i, unit: kgCO2 / kWh.

[0129] The feasibility criteria layer includes five judgment layers: standards and specifications, key equipment, technical solutions, market mechanisms, and information security. The standards and specifications judgment layer primarily assesses the effectiveness, feasibility, and compliance of the demand-side resource synergy potential between EVs and air conditioning loads based on relevant standards and specifications. The core objective of this judgment layer is to determine, through a clear system of standards and indicators, whether coordinated scheduling of EVs and air conditioning loads can be carried out in compliance with regulations in various scenarios.

[0130] Standard specification index (C 14 ): Based on parameters such as vehicle charging and discharging protocols, vehicle grid interface standards, emergency response mechanisms and relevant legal compliance, the standard specification indicators are divided into five levels (5-1), namely better, good, average, poor and poor, and correspond to corresponding values ​​(5 to 1), which are used to measure the specific values ​​of the standard specification indicators.

[0131] The key equipment judgment layer refers to the evaluation and standardized judgment of key equipment and facilities that realize energy exchange between electric vehicles and air conditioning systems. The core goal of this layer is to ensure that all relevant key equipment (such as electric vehicles, charging piles, substations, inverters, battery management systems, etc.) can transmit power efficiently, stably and safely and achieve system coordination. Key equipment indicators (C15 ): According to the parameters of electric vehicles and their battery systems, charging piles and electric vehicle connection equipment, inverters and grid interface equipment, air-conditioning systems and load management equipment, the key equipment indicators are divided into five levels (5-1), namely better, good, average, poor and poor, and correspond to corresponding values ​​(5 to 1), which are used to measure the specific values ​​of key equipment indicators.

[0132] The technical solution judgment layer is to evaluate and judge the technical solution adopted for energy exchange between electric vehicles and air conditioning systems. The core goal of this layer is to ensure that the selected technical solution can meet the system requirements. Technical solution indicators (C 16 ): According to the charging and discharging technical solutions, intelligent scheduling and load management, energy conversion efficiency, economy and cost-effectiveness in the scenario, the technical solution indicators are divided into five levels (5-1), namely better, good, average, poor, and poor, and correspond to corresponding values ​​(5 to 1), which are used to measure the specific values ​​of the technical solution indicators.

[0133] The market mechanism judgment layer is designed to evaluate and judge the market mechanism of energy exchange between electric vehicles and air conditioning systems. Its core goal is to ensure that energy exchange complies with market rules and incentive mechanisms, and to promote efficient resource utilization and enhance the potential for resource mutual assistance on the demand side while fully considering market competition, resource scheduling, price signals and policy guidance. Market mechanism indicators (C 17 ): Based on factors such as electricity market mechanism, resource scheduling and coordination, market participant incentive mechanism, price signal and market transparency, as well as participant fairness and market competition, the market mechanism indicators are divided into five levels (5-1), namely better, good, average, poor, and poor, and correspond to corresponding values ​​(5 to 1), which are used to measure the specific values ​​of market mechanism indicators.

[0134] The information security judgment layer is the layer that evaluates and ensures the security of information flow and data interaction during the energy exchange process between electric vehicles and air-conditioning loads. Its core purpose is to ensure that in the energy dispatch process, the collection, transmission, processing and storage of information are not threatened by external factors, thereby maintaining the stability of the system and protecting the privacy of user data. Information security judgment index (C 18 ): According to the system's identity authentication and access control, network isolation and partitioning, privacy protection, system anomaly detection and alarm, data encryption and protection, etc., the information security indicators are divided into five levels (5-1), namely better, good, average, poor, and poor, and correspond to corresponding values ​​(5 to 1), which are used to measure the specific values ​​of the information security judgment indicators.

[0135] In the process of assessing demand-side resource mutual assistance potential, the weights of indicator parameters reflect the importance of each indicator on the assessment object. To comprehensively consider the qualitative analysis of experts and objective data, this paper adopts a comprehensive weighting method to rationally analyze and determine the weights of each parameter.

[0136] The calculation process of the subjective weight determination method of the present invention is as follows:

[0137] 1. When constructing the judgment matrix, based on the 1-9 scale correspondence table shown in Table 2 and expert opinions, perform pairwise comparisons of indicators to assess their importance to the upper-level indicators. Through comparative analysis, determine the importance ratio between each pair of indicators and use this to build a complete judgment matrix.

[0138] Value Importance 1 Equally important 3 Slightly important 5 Generally important 7 Very important 9 Extremely important 2，4，6，8 The median of the two adjacent judgments on the left

[0139] 2. Based on the judgment matrix obtained in step 1, calculate its maximum eigenvalue and unique non-zero eigenvalue to obtain the consistency ratio (CR). When the CR value is less than 0.1, the consistency of the judgment matrix is ​​within the allowable range and passes the consistency test.

[0140]

[0141] Where λ is the maximum eigenvalue of the judgment matrix; n is the only non-zero eigenvalue of the judgment matrix; CI is the consistency index; RI is the random consistency index.

[0142] 3. Solving for subjective weight

[0143] For the judgment matrix that passes the consistency test, solve its eigenvector and normalize the eigenvector to obtain the weight vector, that is, the subjective weight vector α i .

[0144] This paper uses the entropy weight method to calculate objective weights. The entropy weight method determines the weights based on the amount of information contained in the indicator data. The amount of information is mainly reflected by the correlation and standard deviation between indicators. The specific calculation steps are as follows.

[0145] 1. Dimensionless processing of indicators: First, perform dimensionless processing on the indicator matrix X to obtain the standardized matrix X1. The dimensionless processing formulas of the positive indicator and the reverse indicator are:

[0146]

[0147] Where i and j represent the i-th row and j-th column in the indicator matrix respectively.

[0148] 2. Determination of correlation coefficient and standard deviation

[0149] The calculation formulas for the standard deviation and correlation coefficient of each indicator in X1 in the standardized matrix are as follows

[0150]

[0151]

[0152] r ij =cov(X′ i ,X′ j ) / (S i ,S j )

[0153] Where S i ,S j are the standard deviations of the i-th and j-th indicators respectively; : the average value of the jth column after dimensionless processing; r ij is the correlation coefficient between the i-th indicator and the j-th indicator; X′ i ,X′ j denote the i-th row and j-th column of the normalized matrix respectively.

[0154] 3. Calculate objective weight

[0155] The importance of information contained in the jth indicator C j The calculation formula is:

[0156]

[0157] Where C j The larger the value, the greater the impact of the indicator on the target layer, and the more weight should be assigned to it. The weight calculation formula for the jth indicator is:

[0158]

[0159] In order to avoid the subjectivity and bias problems that may be caused by the single weighting method of subjective weight or objective weight, this study adopts a comprehensive weighting method, combining subjective and objective weights to finally determine the weight of each indicator.

[0160]

[0161] The combined weight matrix γ can be obtained T =(γ1,γ2,…,γ j ).

[0162] Considering the large differences in the calculation ranges of various indicators, in order to ensure the objectivity of the calculation results, the indicator parameters should be normalized. The normalization method is consistent with the subjective weight calculation. The normalized indicator eigenvalue matrix is ​​as follows:

[0163]

[0164] The demand-side resource potential of the spatiotemporal coupling characteristics of electric vehicles and air-conditioning loads can be determined based on the normalized index results and parameter weights. The calculation formula is as follows:

[0165]

[0166] The above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the potential for resource mutual assistance on the demand side taking into account the temporal and spatial coupling characteristics, characterized by: The steps of the method include: Based on the spatiotemporal coupling characteristics of electric vehicles and air conditioning loads, a demand-side resource mutual assistance potential assessment model was constructed, and a comprehensive assessment of the demand-side resource mutual assistance potential of electric vehicles and air conditioning loads was completed; The construction process of the demand-side resource mutual assistance potential assessment model is as follows: Obtain operating data of electric vehicles and air conditioning loads; Construct an economic evaluation index model and a feasibility evaluation index model, analyze the electric vehicle and air conditioning load operation data based on the economic evaluation index model, and analyze the electric vehicle and air conditioning load operation data based on the feasibility evaluation index model, and output the economic evaluation index and feasibility evaluation index; Determine the subjective and objective weights of economic evaluation indicators and feasibility evaluation indicators; Combining subjective and objective weights, and using the combined weighting method to calculate the combined weight matrix, the combined weight matrix is ​​multiplied by the normalized economic evaluation index and feasibility evaluation index. The multiplication result represents the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads. The economic evaluation index model includes: user benefits, grid benefits and environmental benefits; The user benefits include: the reduction rate of user air conditioning electricity costs , user charging cost reduction rate Comprehensive cost reduction rate for electric vehicle users ; The grid benefits include: air conditioning load reduction rate during grid peak hours , Charging load reduction rate during peak hours of the power grid , Comprehensive load reduction rate of electric vehicle users , cross-temporal and spatial correlation coefficients of electric vehicles and air conditioning loads , Dispatchable capacity of charging and discharging resources during peak hours of the power grid , Air conditioning capacity can be adjusted during peak hours of the power grid Matching degree with time and space ; The cross-temporal and spatial correlation coefficients of electric vehicles and air conditioning loads , and its calculation formula is: in, is the time coupling characteristic of electric vehicles and air conditioning loads in each region, is the spatial coupling characteristics of electric vehicles and air conditioning loads in each region, is the air conditioning load in area d, is the charging load of area d, is the standard deviation of air conditioning load in area d, is the standard deviation of electric vehicle charging load in area d, Indicates residential area, Indicates office area. Indicates commercial buildings, 、 、 Respectively represent the standard deviation of electric vehicle and air conditioning load in residential area, office area and commercial building scenarios, is the time coupling characteristic weight of the two demand-side resources, air conditioning load and electric vehicles, is the spatial coupling characteristic weight of each region for the same type of load; The spatiotemporal matching degree , and its calculation formula is: in, Represents the charging and discharging power of electric vehicles, Indicates the air conditioning power; The environmental benefits include: the increase in the proportion of renewable energy consumption , air conditioning energy conservation and emission reduction improvement rate and the improvement rate of energy conservation and emission reduction of electric vehicles ; The feasibility evaluation index model includes: standard specification index C 14 、Key equipment indicators C 15 、Technical solution indicator C 16 、Market Mechanism Index C 17 Information security judgment index C 18 .

2. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 1 is characterized in that: The user's air conditioning electricity cost reduction rate , and its calculation formula is: in, is the power consumption of air conditioner in the i-th period of the response day, is the power consumption of air conditioner in period i on baseline day, is the electricity price in period i, is the subsidy price in period i. If there is no subsidy, the coefficient is 0; The user charging cost reduction rate , and its calculation formula is: in, is the charging capacity of the electric vehicle in the i-th period of the response day, is the electric vehicle charging power in the i-th period on the baseline day; The comprehensive cost reduction rate for electric vehicle users , and its calculation formula is: in, is the proportion of electric vehicle users in area d, d∈[residential area, office area, commercial building, …].

3. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 2 is characterized in that: Air conditioning load reduction rate during the peak period of the power grid , and its calculation formula is: in, In response to the maximum air conditioning load during the daily power grid peak period, is the maximum air conditioning load during the peak period of the power grid on the baseline day; The charging load reduction rate during the peak period of the power grid , and its calculation formula is: in, is the charging load reduction rate during the peak period of the power grid in region d, In response to the maximum electric vehicle charging load during the daily power grid peak period, is the maximum EV charging load during the peak hours of the power grid on the baseline day; The comprehensive load reduction rate of electric vehicle users , and its calculation formula is: The dispatchable capacity of charging and discharging resources during the peak period of the power grid , and its calculation formula is: in, is the scale of electric vehicle charging and discharging resources, The proportion of electric vehicle users willing to participate; The air conditioning capacity can be adjusted during the peak period of the power grid , and its calculation formula is: in, The scale of air-conditioning users; The proportion of air-conditioning users willing to participate.

4. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 3 is characterized in that: The increase in the proportion of renewable energy consumption , and its calculation formula is: in, is the amount of electricity in the i-th period of the response day, is the electricity consumption in the i-th period on the baseline day, is the proportion of green electricity in the i-th period; The improvement rate of energy conservation and emission reduction of air conditioners , and its calculation formula is: in, is the amount of electricity in the i-th period of the response day, is the electricity consumption in the i-th period on the baseline day, is the carbon emission factor of air conditioning per kilowatt-hour in period i; The energy conservation and emission reduction improvement rate of electric vehicles , and its calculation formula is: in, is the carbon emission factor per kilowatt-hour of electric vehicles in period i.

5. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 4 is characterized in that: The combined weight matrix is ​​calculated by the combined weighting method, and its calculation formula is: in, is the comprehensive weight of the jth indicator, is the subjective weight, is the objective weight, is the combined weight matrix.

6. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 5 is characterized in that: The calculation formula for the normalized economic evaluation index and feasibility evaluation index is: in, is the normalized indicator eigenvalue matrix, is the normalized eigenvalue of the jth indicator.

7. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 6 is characterized in that: The demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads is calculated as follows: Among them, W is the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads.

8. The demand-side resource mutual assistance potential assessment system taking into account the characteristics of time and space coupling is characterized by: include: The judgment layer obtains the operating data of electric vehicles and air-conditioning loads, constructs an economic evaluation index model and a feasibility evaluation index model, analyzes the operating data of electric vehicles and air-conditioning loads based on the economic evaluation index model, and analyzes the operating data of electric vehicles and air-conditioning loads based on the feasibility evaluation index model, and outputs the economic evaluation index and feasibility evaluation index; Indicator layer, solving the subjective weight and objective weight of economic evaluation indicators and feasibility evaluation indicators; At the target level, subjective and objective weights are combined and a combined weight matrix is ​​calculated using the combined weighting method. The combined weight matrix is ​​multiplied by the normalized economic evaluation index and feasibility evaluation index. The multiplication result represents the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads. The economic evaluation index model includes: user benefits, grid benefits and environmental benefits; The user benefits include: the reduction rate of user air conditioning electricity costs , user charging cost reduction rate Comprehensive cost reduction rate for electric vehicle users ; The grid benefits include: air conditioning load reduction rate during grid peak hours , Charging load reduction rate during peak hours of the power grid , Comprehensive load reduction rate of electric vehicle users , cross-temporal and spatial correlation coefficients of electric vehicles and air conditioning loads , Dispatchable capacity of charging and discharging resources during peak hours of the power grid , Air conditioning capacity can be adjusted during peak hours of the power grid Matching degree with time and space ; The cross-temporal and spatial correlation coefficients of electric vehicles and air conditioning loads , and its calculation formula is: in, is the time coupling characteristic of electric vehicles and air conditioning loads in each region, is the spatial coupling characteristics of electric vehicles and air conditioning loads in each region, is the air conditioning load in area d, is the charging load of area d, is the standard deviation of air conditioning load in area d, is the standard deviation of electric vehicle charging load in area d, Indicates residential area, Indicates office area. Indicates commercial buildings, 、 、 Respectively represent the standard deviation of electric vehicle and air conditioning load in residential area, office area and commercial building scenarios, is the time coupling characteristic weight of the two demand-side resources, air conditioning load and electric vehicles, is the spatial coupling characteristic weight of each region for the same type of load; The spatiotemporal matching degree , and its calculation formula is: in, Represents the charging and discharging power of electric vehicles, Indicates the air conditioning power; The environmental benefits include: the increase in the proportion of renewable energy consumption , air conditioning energy conservation and emission reduction improvement rate and the improvement rate of energy conservation and emission reduction of electric vehicles ; The feasibility evaluation index model includes: standard specification index C 14 、Key equipment indicators C 15 、Technical solution indicator C 16 、Market Mechanism Index C 17 Information security judgment index C 18 .

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