Demand side resource mutual aid potential evaluation method and system considering time-space coupling characteristics

Abstract

The invention relates to the technical field of mutual aid potential evaluation of electric vehicle charging and discharging resources and air conditioner loads, and relates to a demand side resource mutual aid potential evaluation method and system considering time-space coupling characteristics, and the method comprises the steps: obtaining the operation data of an electric vehicle and an air conditioner load; constructing an economy evaluation index model and a feasibility evaluation index model, analyzing the electric vehicle and air conditioner load operation data based on the economy evaluation index model and the feasibility evaluation index model, and solving a subjective weight and an objective weight of an analysis result; a combined weight matrix is calculated through a combined weighting method, the combined weight matrix is multiplied by the normalized analysis result, and demand side resource mutual aid potential evaluation of the electric vehicle and the air conditioner load is completed. The utilization efficiency of the electric vehicle as mobile energy storage can be improved, the pressure of the air conditioner load on the power grid in the peak period can be effectively relieved, and therefore the stability and flexibility of the power grid are enhanced.

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 time-space coupling characteristics. Background Art

[0002] During the winter and summer, due to the frequent occurrence of extreme weather events, the power consumption of air conditioners continues to increase, and the peak load continues to climb, which brings great pressure to the power grid supply. At the same time, with the continuous improvement of new energy vehicle industry policies, technologies and supporting services, the penetration rate of electric vehicles continues to rise, the charging load continues to grow, and disordered charging will cause peaks on peaks. The power grid relies more and more urgently on demand-side resources. At present, the evaluation method of the mutual assistance potential of electric vehicle charging and discharging resources and air conditioning loads still has certain limitations, and fails to fully consider the potential of mutual assistance between the two resources under the conditions of spatiotemporal coupling. Existing studies usually explore the adjustable potential of air conditioners and electric vehicles separately, and fail to effectively combine the two, resulting in certain limitations in the systematicity and accuracy of the evaluation results. Although existing studies have widely explored the evaluation methods of the adjustable potential of electric vehicles and air conditioning loads, they usually analyze the two independently, and lack a comprehensive study of their mutual assistance potential as a systematic project under the conditions of spatiotemporal coupling. Therefore, we provide a demand-side resource mutual assistance potential evaluation method and system that takes into account the spatiotemporal coupling characteristics. 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 time-space 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 mutual assistance potential of demand-side resources taking into account the characteristics of temporal and spatial coupling, 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 is constructed, and a comprehensive assessment of the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads is completed;

[0007] Among them, 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] 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;

[0010] Solve the subjective and objective weights of economic evaluation indicators and feasibility evaluation indicators;

[0011] Combining the subjective weights with the objective weights, the combined weight matrix is ​​calculated through the combined weighting method, and 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.

[0012] Optionally, the economic evaluation index model includes: user benefits, grid benefits and environmental benefits.

[0013] Optionally, the user benefits include: user air conditioning electricity cost reduction rate, user charging cost reduction rate and electric vehicle user comprehensive cost reduction rate;

[0014] Among them, the user's air conditioning electricity cost reduction rate The calculation formula is:

[0015]

[0016] 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 the i-th period of the baseline day, f i is the electricity price in the i-th period, k i is the subsidy price in the i-th period. If there is no subsidy, the coefficient is 0;

[0017] The user charging cost reduction rate The calculation formula is:

[0018]

[0019] in, is the charging power of the electric vehicle in the i-th period of the response day, is the electric vehicle charging power in the i-th period of the baseline day;

[0020] The comprehensive cost reduction rate C of electric vehicle users 3 , and its calculation formula is:

[0021]

[0022] Among them, λ dis the proportion of electric vehicle users in area d, d∈[residential area, office area, commercial building, …].

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

[0024] The air conditioning load reduction rate C during the peak period of the power grid 4 , and its calculation formula is:

[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 C during the peak period of the power grid 5 , and its calculation formula is:

[0028]

[0029] in, is the charging load reduction rate during the peak period of the power grid in area 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 comprehensive load reduction rate C of electric vehicle users 6 , and its calculation formula is:

[0031]

[0032] The cross-temporal and spatial correlation coefficient C between electric vehicles and air conditioning loads 7 , and its calculation formula is:

[0033] C 7 =α xy ×ρ xy (k,Δx,Δy)+β d ×ρ d (k,Δd 1 ,Δd 2 ,Δd 3 )

[0034]

[0035] Among them, ρxy (k,Δx d ,Δy d ) is the time coupling characteristic of electric vehicles and air conditioning loads in each region, ρ d (k,Δd 1 ,Δd 2 ,Δd 3 ) is 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, 1 Indicates residential area, d 2 Indicates office area, d 3 represents commercial buildings, σd 1 ,σd 2 ,σd 3 They represent the standard deviation of electric vehicle and air conditioning load in residential area, office area and commercial building respectively, xy is the time coupling characteristic weight of the two types of demand-side resources, air-conditioning load and electric vehicles, β d is the weight of spatial coupling characteristics of each region for the same type of load;

[0036] The dispatchable capacity C of charging and discharging resources during the peak period of the power grid 8 , and its calculation formula is:

[0037] C 8 =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 power grid can adjust the air conditioning capacity C during peak hours 9 , and its calculation formula is:

[0040] C 9 =S ac ×γ ac

[0041] Among them, S ac is the scale of air conditioner users; 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 air conditioning energy saving and emission reduction improvement rate 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 of the baseline day, is the carbon emission factor of the air conditioning per kilowatt-hour in the i-th period;

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

[0053]

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

[0055] Optionally, the feasibility evaluation index model includes: standard specification index C 14 、Key equipment indicators C 15 、Technical solution indicators C 16 、Market Mechanism Index 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 =(γ 1 ,γ 2 ,...,γj )

[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 combination weight matrix.

[0060] Optionally, the normalized economic evaluation index and feasibility evaluation index are calculated as follows:

[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 the electric vehicle and the air-conditioning load is calculated as follows:

[0064]

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

[0066] The demand-side resource mutual assistance potential assessment system taking into account the characteristics of time and space coupling 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 economic evaluation indexes and feasibility evaluation indexes;

[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 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.

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

[0071] The present invention considers the spatiotemporal coupling characteristics of electric vehicles and air-conditioning loads, combines the analytic hierarchy process, systematically analyzes their mutual assistance potential from the two dimensions of economy and feasibility, and constructs a resource mutual assistance potential evaluation model for 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 formulate a more scientific incentive mechanism for the mutual assistance of air-conditioning 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 A schematic flow chart of a method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics provided by the present invention. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, what is described is only a part of the present invention, not all of it. Generally, the components of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0074] The charging and discharging behavior of electric vehicles and the peak hours of air conditioning loads do not always completely overlap, and the degree of temporal and spatial overlap has a significant impact on the demand-side resource mutual assistance potential. In order to more accurately evaluate the demand-side resource mutual assistance potential of electric vehicles and air conditioning loads, the present invention proposes an embodiment from the two dimensions of economy and feasibility: a demand-side resource mutual assistance potential evaluation method taking into account the 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 performed 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] The present invention takes the demand-side resource mutual assistance potential of the spatiotemporal coupling characteristics of electric vehicles and air-conditioning loads as the target layer, and selects the two dimensions of economy and feasibility as the standard layer. Among them, economy includes 3 judgment layer indicators and 13 calculation indicators; feasibility includes 5 judgment layer indicators and 5 calculation indicators.

[0081] Among them, economic efficiency is divided into three judgment levels: user benefit, grid benefit and environmental benefit. Among them, user benefit refers to the fact that electric vehicle and air conditioning load users can reduce electricity costs by adjusting loads, and electric vehicle owners can obtain rewards or subsidies from grids and operators through discharge, thereby reducing the overall cost of use for users. The user benefit judgment level is mainly divided into the user air conditioning electricity cost reduction rate (C 1 ), user charging cost reduction rate (C 2 ), comprehensive cost reduction rate of electric vehicle users (C 3 ) three calculation indicators, as follows:

[0082] Reduction rate of electricity cost of air conditioning for users

[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 the i-th period of the baseline day; f i : is the electricity price in the i-th period, 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 electric vehicles in the i-th period of the response day; : is the charging power of the electric vehicle in the i-th period of the baseline day; f i : is the electricity price in the i-th period, 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 a single owner, 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 of electric vehicle users (C 3 )

[0089]

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

[0091] Grid benefit means that during the peak period of air conditioning demand, electric vehicles as mobile energy storage devices can effectively divert the power load of the air conditioning system, reduce the peak load of the power grid, avoid overload of the power grid, and thus maintain the stability of the power grid. Since electric vehicles discharge during the peak period of air conditioning load, the superposition of charging load and charging load is avoided, further reducing the peak load of the power grid.

[0092] Air conditioning load reduction rate during power grid peak hours (C 4 )

[0093]

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

[0095] Charging load reduction rate during peak hours of the power grid (C 5 )

[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 daily power grid; : Maximum electric vehicle charging load during the peak hours of the power grid on the baseline day.

[0098] Electric vehicle users' comprehensive load reduction rate (C 6 )

[0099]

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

[0101] The cross-temporal and spatial correlation coefficient between electric vehicles and air conditioning load (C 7 )

[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 area d; σy: Standard deviation of electric vehicle charging load in area d.

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

[0106]

[0107] Where, d 1 Indicates residential area, d 2 Indicates office area, d 3 represents commercial buildings, σd 1 ,σd 2 ,σd 3 They represent the standard deviation of electric vehicle and air conditioning loads in residential areas, office areas, and commercial buildings, respectively.

[0108] C 7 =α xy ×ρ xy (k,Δx,Δy)+β d ×ρ d (k,Δd 1 ,Δd 2 ,Δd 3 )

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

[0110] The dispatchable capacity of charging and discharging resources during peak hours of the power grid (C 8 )

[0111] C 8 =S ev ×γ ev

[0112] Where: S ev : Electric vehicle charging and discharging resource scale; γ ev : The proportion of electric vehicle users willing to participate;

[0113] Adjustable air conditioning capacity during power grid peak hours (C 9 )

[0114] C 9 =S ac ×γ ac

[0115] In the formula, S ac : Air conditioning user scale; γ ac : Proportion of air-conditioning users’ willingness 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 of 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 and better meet load demands, thereby improving 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 fact that electric vehicles are charged by renewable energy and fed back to the grid through vehicle-grid interaction technology to provide power for the air conditioning system. This process not only effectively promotes the consumption of green electricity, but also reduces dependence on fossil fuel power generation, thereby reducing greenhouse gas emissions and promoting environmental sustainable development.

[0120] Increase in the proportion of renewable energy consumption

[0121]

[0122] 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; S i : The proportion of green electricity in the i-th period.

[0123] Air conditioning energy saving and emission reduction improvement rate

[0124]

[0125] In the formula, 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 the i-th period, 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 the i-th period, unit: kgCO2 / kWh.

[0129] The feasibility standard layer includes five judgment layers, namely standard specifications, key equipment, technical solutions, market mechanisms and information security. Among them, the standard specification judgment layer mainly refers to the evaluation of the effectiveness, feasibility and compliance of relevant standards and specifications when evaluating the demand-side resource mutual assistance potential between electric vehicles and air-conditioning loads. The core goal of this judgment layer is to determine whether the coordinated scheduling of electric vehicles and air-conditioning loads can be carried out in compliance with regulations in various scenarios through clear standards and indicator systems.

[0130] Standard specification index (C 14 ): According to 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, general, 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 level is to ensure that all relevant key equipment (such as electric vehicles, charging piles, substations, inverters, battery management systems, etc.) can efficiently, stably and safely transmit power and achieve system coordination. Key equipment indicators (C 15 ): 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, general, poor, and poor, and correspond to corresponding values ​​(5 to 1), which are used to measure the specific values ​​of the 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 level is to ensure that the selected technical solution can meet the system requirements. Technical Solution Index (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, general, 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 use of resources and enhance the potential for resource mutual assistance on the demand side on the basis of fully considering market competition, resource scheduling, price signals and policy orientation. Market mechanism indicators (C 17 ): According to 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 refers to the level 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 process of energy dispatch, 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 and security 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 evaluating the mutual assistance potential of demand-side resources, the weight of the indicator parameters reflects the important impact of each indicator on the evaluation object. In order to comprehensively consider the qualitative analysis of experts and objective data, the present invention adopts a comprehensive weighting method to reasonably analyze and determine the weight 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, according to the 1-9 scale correspondence table shown in Table 2, combined with expert opinions, the indicators are compared pairwise to evaluate the importance of each indicator to the upper-level indicator. Through comparative analysis, the importance ratio between each pair of indicators is obtained, and a complete judgment matrix is ​​established based on this.

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

[0139] 2. According to 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 can pass the consistency test.

[0140]

[0141] In the formula, λ is the maximum eigenvalue of the judgment matrix; n is the unique 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] The present invention adopts the entropy weight method to calculate the objective weight. The entropy weight method determines the weight according to 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, the indicator matrix X is dimensionless processed 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] In the formula, 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 weights

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

[0156]

[0157] In the formula, C j The larger the value, the greater the impact of the indicator on the target layer, and more weight should be assigned to it. That is, 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 =(γ 1 ,γ 2 ,…,γ j ).

[0162] Considering that the calculation intervals of each indicator vary greatly, 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, and the normalized indicator eigenvalue matrix is ​​as follows:

[0163]

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

[0165]

[0166] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for evaluating the mutual assistance potential of demand-side resources taking into account the characteristics of temporal and spatial coupling, characterized in that: 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 is constructed, and a comprehensive assessment of the demand-side resource mutual assistance potential of electric vehicles and air-conditioning loads is completed; Among them, 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; Solve the subjective and objective weights of economic evaluation indicators and feasibility evaluation indicators; Combining the subjective weights with the objective weights, the combined weight matrix is ​​calculated through the combined weighting method, and 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.

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 economic evaluation index model includes: user benefit, grid benefit and environmental benefit.

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: The user benefits include: the reduction rate of user air-conditioning electricity costs, the reduction rate of user charging costs and the comprehensive cost reduction rate of electric vehicle users; Among them, the user's air conditioning electricity cost reduction rate The 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 the i-th period of the baseline day, f i is the electricity price in the i-th period, k i is the subsidy price in the i-th period. If there is no subsidy, the coefficient is 0; The user charging cost reduction rate The 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 of the baseline day; The electric vehicle user comprehensive cost reduction rate C3 is calculated as follows: Among them, λ d is the proportion of electric vehicle users in area d, d∈[residential area, office area, commercial building, …].

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 grid benefits include: air conditioning load reduction rate during grid peak hours, charging load reduction rate during grid peak hours, comprehensive load reduction rate of electric vehicle users, cross-temporal and spatial correlation coefficient between electric vehicle and air conditioning load, 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; The air conditioning load reduction rate C4 during the peak period of the power grid is calculated as follows: 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 C5 during the peak period of the power grid is calculated as follows: in, is the charging load reduction rate during the peak period of the power grid in area 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 electric vehicle user comprehensive load reduction rate C6 is calculated as follows: The cross-temporal and spatial correlation coefficient C7 between the electric vehicle and the air conditioning load is calculated as follows: C7=a xy ×r xy (k,Δx,Δy)+β d ×r d (k,Δd1,Δd2,Δd3) 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 types of demand-side resources, air-conditioning load and electric vehicles, β d is the weight of spatial coupling characteristics of each region for the same type of load; The calculation formula of the dispatchable capacity C8 of charging and discharging resources during the peak period of the power grid is: C8=S ev ×γ ev 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; The air conditioning capacity C9 can be adjusted during the peak period of the power grid, and its calculation formula is: C9=S ac ×γ ac Among them, S ac is the scale of air conditioner users; ac The proportion of air-conditioning users willing to participate; The spatiotemporal matching degree C 10 , and its calculation formula is: 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.

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 environmental benefits include: the increase in the proportion of renewable energy consumption, the increase in the energy conservation and emission reduction rate of air conditioners and electric vehicles; The increase in the proportion of renewable energy consumption The calculation formula is: 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; The air conditioning energy saving and emission reduction improvement rate The calculation formula is: 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, is the carbon emission factor of the air conditioning per kilowatt-hour in the i-th period; The calculation formula for the energy saving and emission reduction improvement rate of electric vehicles is: in, is the carbon emission factor per kilowatt-hour of electric vehicles in the i-th period.

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 feasibility evaluation index model includes: standard specification index C 14 、Key equipment indicators C 15 、Technical solution indicators C 16 、Market Mechanism Index C 17 Information security judgment index C 18 .

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 combined weight matrix is ​​calculated by the combined weighting method, and its calculation formula is: c T =(γ1,γ2,...,γ j ) 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 combination weight matrix.

8. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 7 is characterized in that: The normalized economic evaluation index and feasibility evaluation index are calculated as follows: Among them, R is the normalized indicator eigenvalue matrix, C j ′ is the normalized eigenvalue of the jth indicator.

9. The method for evaluating the demand-side resource mutual assistance potential taking into account the temporal and spatial coupling characteristics according to claim 8 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 between electric vehicles and air-conditioning loads.

10. A demand-side resource mutual assistance potential assessment system taking into account the characteristics of time and space coupling, characterized in that: 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 economic evaluation indexes and feasibility evaluation indexes; Indicator layer, solving the subjective weight and objective weight of economic evaluation indicators and feasibility evaluation indicators; 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 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.

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