Stable V2G capability evaluation method and device based on incentive willingness decision framework

Abstract

A stable V2G capability evaluation method and device based on an incentive-willingness decision framework, the method obtains the current status information and historical V2G participation data of EV users staying at each destination; constructs the maximum discharge feasible domain of each EV with known current status information by setting objective constraints such as battery performance, arrival time, and departure time; constructs a unit discharge triangle model to determine the stable discharge period of each idle EV within the maximum discharge feasible domain; obtains the maximum stable discharge potential of all idle EVs in a specified area through statistics; determines the V2G decision of dischargeable EV users according to the incentive-willingness-decision framework; determines the V2G participation status of dischargeable EV users; and counts the V2G participation status of dischargeable EV users to evaluate and obtain the actual V2G capability of dischargeable EV users. The present invention combines the maximum discharge potential evaluation and user decision-making under multi-attribute influencing factors to evaluate the maximum stable V2G capability.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grids, and in particular relates to a stable V2G capability evaluation method and device based on an incentive willingness decision framework. Background Art

[0002] The popularity of electric vehicles (EVs) brings huge vehicle-to-grid (V2G) potential. Compared with a small number of easily coordinated public service EVs, private and corporate EVs are considered to have huge energy storage and discharge potential. However, the V2G capabilities of such EVs are more susceptible to user decisions. Therefore, evaluating and improving their V2G capabilities has become a focus and challenge for related research and applications.

[0003] The V2G capability of each location can be uniformly described by the number of local EVs that are able and willing to participate in V2G in the corresponding period. Therefore, the V2G capability assessment mainly includes two stages: EV discharge potential assessment and user decision assessment.

[0004] The purpose of evaluating EV discharge potential is to determine the number of EVs that can be discharged in a specific area within the required period, that is, the dischargeable state of each EV staying in the area during the corresponding period. The dischargeable state of EVs is usually limited by conditions such as time, location and state of charge (SOC), as well as performance parameters such as charging / discharging power and safety limits. The traditional SOC threshold method directly assumes that the electric vehicle can be discharged when the SOC is above a certain threshold. This method is too simplistic and cannot ensure the reliability of the results. Although different formula constraints can be set to reflect the user's discharge restrictions, it is still a huge challenge to comprehensively and concisely summarize the interactions between multiple factors. To this end, many studies have introduced more intuitive and practical EV discharge feasible domains to assist in the evaluation. However, compared with the purchase of equipment that can be directly controlled, the randomness of EV users' travel and charging arrangements amplifies the uncertainty of V2G capacity evaluation and scheduling. However, the existing methods cannot determine the continuous and stable EV discharge potential within the required unit discharge interval, thereby failing to ensure the quality and continuity of power supply and the stable implementation of scheduling strategies.

[0005] User decision evaluation essentially requires determining the V2G participation status of each dischargeable EV under current conditions and incentives. Some studies only evaluate user satisfaction after participating in V2G, which deviates from the user logic of pre-behavior prediction and makes it difficult to ensure the actual V2G capability before implementing relevant strategies. Although, many studies have begun to proactively consider the impact of user participation decisions on V2G capability in advance. However, most existing evaluations believe that meeting travel SOC requirements is a necessary and sufficient condition for users to participate in V2G. They directly constrain users' travel SOC requirements after discharge or charge-discharge interaction to screen EVs. However, user V2G decisions are also affected by multiple factors including travel demand, stay time, electricity price compensation, battery degradation, uncertainty, and personal preferences. Evaluating discharge potential or V2G capability based solely on user travel demand may not only misjudge the capabilities of EV users whose travel needs have been met, but also completely ignore the travel and decision flexibility of EV users whose needs have not been met and the corresponding incentivized discharge potential, ultimately leading to a waste of precious EV energy storage resources. Even though some studies have considered the impact of other factors besides travel demand on users' V2G decisions, none of them have been able to evaluate V2G decisions after integrating all key factors. In particular, existing methods have failed to consider the different unit attributes of multiple factors and the cumulative impact as the decision-making stage progresses.

[0006] Therefore, how to design and develop a stable V2G capability evaluation method based on the incentive willingness decision-making framework has become an urgent problem to be solved. Summary of the invention

[0007] To this end, the present invention provides a stable V2G capability evaluation method and device based on an incentive willingness decision framework, which can evaluate the maximum stable V2G capability by combining the maximum discharge potential evaluation and user decision under multi-attribute influencing factors.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a stable V2G capability evaluation method based on an incentive willingness decision framework, comprising:

[0009] By collecting information data of EV users staying at each destination in the assessment area, the current status information and historical V2G participation data of EV users staying at each destination are obtained;

[0010] According to the current status information of EV users staying at each destination, the maximum discharge feasible domain of each EV with known current status information is constructed by setting objective constraints such as battery performance, arrival time, and departure time.

[0011] A unit discharge triangle model is constructed, and a stable discharge period of each idle EV within the maximum discharge feasible domain is determined by the unit discharge triangle model; the stable discharge period of all idle EVs in a specified area is counted to obtain the maximum stable discharge potential of all idle EVs in the specified area;

[0012] The V2G decision of the dischargeable EV user is determined according to the incentive-willingness-decision framework; the V2G participation status of the dischargeable EV user is determined through the V2G decision of the dischargeable EV user; the V2G participation status of the dischargeable EV user in each time period in a set area is counted, and the actual V2G capability of the dischargeable EV user in the set area is obtained by evaluation.

[0013] As a preferred solution of the stable V2G capability evaluation method based on the incentive willingness decision framework, the current status information of the EV users staying at each destination includes: the time of arrival and departure at each destination, the SOC when arriving at the destination and the expected SOC when leaving;

[0014] The historical V2G participation data of EV users staying at each destination includes: the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the largest; and the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the smallest.

[0015] As a preferred solution of the stable V2G capability evaluation method based on the incentive willingness decision framework, the step of determining the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user includes:

[0016] Based on the current status information of the dischargeable EV user, construct and calculate incentive indicators for travel demand, stay time, electricity price compensation and direct factors of battery degradation;

[0017] Evaluate the current willingness value of the dischargeable EV user according to the incentive value and willingness value when the historical V2G participation comprehensive willingness of the dischargeable EV user was the largest;

[0018] The minimum value of the historical V2G participation comprehensive willingness of the dischargeable EV user is selected as a judgment threshold, and the V2G participation status of the dischargeable EV user is determined according to the judgment threshold.

[0019] As a preferred solution of the stable V2G capability evaluation method based on the incentive willingness decision framework, in the step of determining the V2G participation status of the dischargeable EV user, the incentive indicators of travel demand, stay time, electricity price compensation and direct factors of battery attenuation are constructed and calculated as follows:

[0020]

[0021] In the formula, and They represent the current incentive indicators corresponding to travel demand, stay time, electricity price compensation and battery degradation factors respectively; Indicates the expected SOC upon departure; represents the SOC of the EV when it departs after participating in V2G at the destination ds during period t; and They represent the actual stay time and the expected stay time when not participating in V2G; c s,t represents the compensation price; Indicates the total number of charging cycles after participating in V2G; P FC Indicates fast charging power; P SC Indicates slow charging power; B indicates battery capacity;

[0022] Multiply the current incentive index of each direct influencing factor to get the current comprehensive incentive index:

[0023]

[0024] In the formula, Indicates the current comprehensive incentive index;

[0025] According to the incentive value and willingness value when the dischargeable EV user's historical V2G participation comprehensive willingness is the largest, the incentive value of each direct influencing factor is normalized to obtain the current uncertain comprehensive willingness value of the dischargeable EV user; the expression of the current uncertain comprehensive willingness value of the dischargeable EV user is:

[0026]

[0027] In the formula, UP′ s，t Indicates a comprehensive willingness with uncertainty; UP max It represents the historical maximum comprehensive willingness value of V2G participation of dischargeable EV users; It represents the comprehensive incentive index of the users of dischargeable EVs at the historical maximum V2G participation willingness value; Indicates uncertainty;

[0028] Determine the V2G participation status of the dischargeable EV user by using the judgment threshold:

[0029]

[0030] In the formula, φ s，t Indicates V2G participation status; UP min Indicates the minimum value of the comprehensive willingness of dischargeable EV users to participate in V2G in history; 1 or 0 indicates participation or non-participation in V2G, respectively.

[0031] As a preferred solution of the stable V2G capability evaluation method based on the incentive willingness decision framework, the actual V2G capability expression of the dischargeable EV user in the set area is:

[0032]

[0033] In the formula, LC s，t It is represented as the actual V2G capacity of all dischargeable EVs at the destination ds during time period t; Represents the set of EVs that can be discharged.

[0034] The present invention also provides a stable V2G capability evaluation device based on an incentive willingness decision framework, which adopts the above-mentioned stable V2G capability evaluation method based on an incentive willingness decision framework, including:

[0035] An EV user information data acquisition module is used to collect information data of EV users staying at each destination in the assessment area, and obtain current status information and historical V2G participation data of EV users staying at each destination;

[0036] The maximum discharge feasible domain construction module is used to construct the maximum discharge feasible domain of each EV with known current status information by setting objective constraints such as battery performance, arrival time, and departure time based on the current status information of EV users staying at each destination;

[0037] A maximum stable discharge potential acquisition module is used to construct a unit discharge triangle model, and determine the stable discharge period of each idle EV within the maximum discharge feasible domain through the unit discharge triangle model; the stable discharge period of all idle EVs in the specified area is counted to obtain the maximum stable discharge potential of all idle EVs in the specified area;

[0038] The actual V2G capability evaluation and acquisition module is used to determine the V2G decision of the dischargeable EV user according to the incentive-willingness-decision framework; determine the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user; count the V2G participation status of the dischargeable EV user in each time period in a set area, and evaluate and obtain the actual V2G capability of the dischargeable EV user in the set area.

[0039] As a preferred solution of a stable V2G capability evaluation device based on an incentive willingness decision framework, in the EV user information data acquisition module, the current status information of the EV users staying at each destination includes: the time of arrival and departure at each destination, the SOC when arriving at the destination and the expected SOC when leaving;

[0040] The historical V2G participation data of EV users staying at each destination includes: the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the largest; and the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the smallest.

[0041] As a preferred solution of a stable V2G capability evaluation device based on an incentive willingness decision framework, in the actual V2G capability evaluation acquisition module, the step of determining the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user includes:

[0042] Based on the current status information of the dischargeable EV user, construct and calculate incentive indicators for travel demand, stay time, electricity price compensation and direct factors of battery degradation;

[0043] Evaluate the current willingness value of the dischargeable EV user according to the incentive value and willingness value when the historical V2G participation comprehensive willingness of the dischargeable EV user was the largest;

[0044] The minimum value of the historical V2G participation comprehensive willingness of the dischargeable EV user is selected as a judgment threshold, and the V2G participation status of the dischargeable EV user is determined according to the judgment threshold.

[0045] As a preferred solution of a stable V2G capability evaluation device based on an incentive willingness decision framework, in the actual V2G capability evaluation acquisition module, in the step of determining the V2G participation status of the dischargeable EV user, incentive indicators of travel demand, stay time, electricity price compensation and direct factors of battery attenuation are constructed and calculated as follows:

[0046]

[0047] In the formula, and They represent the current incentive indicators corresponding to travel demand, stay time, electricity price compensation and battery degradation factors respectively; Indicates the expected SOC upon departure; represents the SOC of the EV when it departs after participating in V2G at the destination ds during period t; and They represent the actual stay time and the expected stay time when not participating in V2G; c s,t represents the compensation price; Indicates the total number of charging cycles after participating in V2G; P FC Indicates fast charging power; P SC Indicates slow charging power; B indicates battery capacity;

[0048] Multiply the current incentive index of each direct influencing factor to obtain the current comprehensive incentive index:

[0049]

[0050] In the formula, Indicates the current comprehensive incentive index;

[0051] According to the incentive value and willingness value when the dischargeable EV user's historical V2G participation comprehensive willingness is the largest, the incentive value of each direct influencing factor is normalized to obtain the current uncertain comprehensive willingness value of the dischargeable EV user; the expression of the current uncertain comprehensive willingness value of the dischargeable EV user is:

[0052]

[0053] In the formula, UP′ s，t Indicates a comprehensive willingness with uncertainty; UP max It represents the historical maximum comprehensive willingness value of V2G participation of dischargeable EV users; It represents the comprehensive incentive index of the users of dischargeable EVs at the historical maximum V2G participation willingness value; Indicates uncertainty;

[0054] Determine the V2G participation status of the dischargeable EV user by using the judgment threshold:

[0055]

[0056] In the formula, φ s，t Indicates V2G participation status; UP min Indicates the minimum value of the comprehensive willingness of dischargeable EV users to participate in V2G in history; 1 or 0 indicates participation or non-participation in V2G, respectively.

[0057] As a preferred solution of a stable V2G capability evaluation device based on an incentive willingness decision framework, in the actual V2G capability evaluation acquisition module, the actual V2G capability expression of the dischargeable EV user in the set area is:

[0058]

[0059] In the formula, LC s，t It is represented as the actual V2G capacity of all dischargeable EVs at the destination ds during time period t; Represents the set of EVs that can be discharged.

[0060] The present invention has the following advantages: by collecting information data of EV users staying at each destination in the evaluation area, the current state information and historical V2G participation data of EV users staying at each destination are obtained; according to the current state information of EV users staying at each destination, the maximum discharge feasible domain of each EV with known current state information is constructed by setting objective constraints such as battery performance, arrival time, and departure time; a unit discharge triangle model is constructed, and the stable discharge time period of each idle EV staying in the maximum discharge feasible domain is determined through the unit discharge triangle model; the stable discharge time period of all idle EVs staying in the specified area is counted to obtain the maximum stable discharge potential of all idle EVs staying in the specified area; the V2G decision of the dischargeable EV user is determined according to the incentive-willingness-decision framework; the V2G participation status of the dischargeable EV user is determined through the V2G decision of the dischargeable EV user; the V2G participation status of the dischargeable EV user in each time period in the set area is counted, and the actual V2G capability of the dischargeable EV user in the set area is obtained by evaluation. The present invention combines the maximum discharge potential evaluation and the user decision under multi-attribute influencing factors to evaluate the maximum stable V2G capability. First, a unit discharge triangle (UDT) model within the EV maximum discharge feasible region (MDFR) is proposed, which can cover the stable discharge state of all dischargeable EV objects, thereby realizing the maximum stable discharge potential assessment. Subsequently, an incentive-willingness-decision (IWD) framework is proposed, which combines the user's entire decision-making process with the accumulated multi-attribute influencing factors to determine the V2G participation willingness and decision of users with stable discharge, thereby conducting a practical V2G capability assessment. Specifically, travel demand, stay time, electricity price compensation, and battery degradation are introduced as direct incentives into the entire decision-making process. Personal preferences and uncertainties are taken into account in the willingness and decision-making stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0062] Figure 1 This is a flow chart of a stable V2G capability assessment method based on an incentive willingness decision framework provided in Example 1 of the present invention;

[0063] Figure 2 It is a schematic diagram of the maximum discharge feasible region MDFR I+II of each EV in the stable V2G capability evaluation method based on the incentive willingness decision framework provided in Example 1 of the present invention;

[0064] Figure 3 A schematic diagram of a unit discharge triangle within a maximum discharge feasible region in a stable V2G capability assessment method based on an incentive willingness decision framework provided in Example 1 of the present invention;

[0065] Figure 4 This is an architecture diagram of a stable V2G capability assessment device based on an incentive willingness decision framework provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0066] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Example 1

[0068] See also Figure 1 Embodiment 1 of the present invention provides a stable V2G capability evaluation method based on an incentive willingness decision framework, comprising the following steps:

[0069] S1. By collecting information data of EV users staying at each destination in the assessment area, current status information and historical V2G participation data of EV users staying at each destination are obtained;

[0070] S2. Based on the current status information of EV users staying at each destination, the maximum discharge feasible domain of each EV with known current status information is constructed by setting objective constraints such as battery performance, arrival time, and departure time;

[0071] S3, constructing a unit discharge triangle model, and determining the stable discharge period of each idle EV within the maximum discharge feasible domain through the unit discharge triangle model; and performing statistics on the stable discharge period of all idle EVs in the specified area to obtain the maximum stable discharge potential of all idle EVs in the specified area;

[0072] S4. Determine the V2G decision of the dischargeable EV user according to the incentive-willingness-decision framework; determine the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user; count the V2G participation status of the dischargeable EV user in each time period in a set area, and evaluate and obtain the actual V2G capability of the dischargeable EV user in the set area.

[0073] In this embodiment, in step S1, the current status information of the EV user staying at each destination includes: the time of arrival and departure at each destination, the SOC when arriving at the destination and the expected SOC when leaving;

[0074] The historical V2G participation data of EV users staying at each destination includes: the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the largest; and the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the smallest.

[0075] In this embodiment, in step S2, covering all dischargeable EVs in each time period means determining all dischargeable time periods for each EV. To this end, for each EV with a known state, the maximum discharge feasible region MDFR I+II is constructed based only on objective constraints such as battery performance, arrival and departure time, as follows: Figure 2 As shown in the figure, and represents the time of arrival and departure at the destination ds; and represents the SOC when arriving at the destination ds and the expected SOC when leaving; S max and S min Indicates the upper and lower limits of SOC to prevent overcharging and overdischarging; β c Indicates the charging slope; β d Indicates the discharge slope.

[0076] β c =P SC / B or P FC / B

[0077] β d =-P D / B

[0078] Where P FC , P SC , P D A and B represent fast charging power, slow charging power, discharge power and battery capacity respectively.

[0079] If the SOC of the EV when it arrives is (point a'), which is lower than the subsequent travel demand, then the EV usually needs to be charged to point a first to ensure travel. For the EV at point a, if it is directly charged to the highest SOC and then transitions to the idle state, the upper boundary abc of the feasible domain is defined; conversely, if it is directly discharged to the lowest SOC and then stops, the lower boundary age of the feasible domain is defined. The EV stops responding after leaving the location, and its departure time defines the right boundary (ce).

[0080] It is worth noting that for an evaluation based entirely on meeting user travel needs, it is necessary to ensure that the EV can at least run to point d when it sets off, and df represents the corresponding mandatory charging boundary, thus forming a feasible domain I. However, region I assumes that meeting travel needs is a prerequisite for participating in V2G, ignoring the role of incentive mechanisms and the flexibility of user travel, thereby directly losing the relevant dischargeable time period. In addition, if post-discharge charging is not considered, the lower boundary gfe will rise to the same height as d, further narrowing the scope of region I. Therefore, only the constructed MDFR I+II can thoroughly capture all the dischargeable time periods of each EV.

[0081] In this embodiment, in step S3, a unit discharge triangle model is constructed, and a stable discharge period of each idle EV within the maximum discharge feasible region is determined through the unit discharge triangle model.

[0082] Specifically, for each EV, it is necessary to exclude the unit time period that cannot be discharged continuously and stably during the evaluation process to ensure that the evaluated discharge state is consistent with the required unit discharge timing. Figure 3 The UDT model is constructed as shown in the figure, where the parallel side length represents the unit discharge interval Δt, and the vertical side length represents the unit discharge SOC consumption ΔSd. The relationship between Δt, ΔSd and βd is expressed as:

[0083] |β d |=△S d / △t

[0084] In the feasible domain, each EV operating point can continuously generate multiple triangles along the time axis. However, only when a triangle is precisely aligned and completely contained in the feasible domain within a unit discharge time interval, the triangle is considered feasible (light gray), which indicates that the EV has a stable discharge potential in the corresponding time period. The judgment expression is:

[0085]

[0086] In the formula, e is the serial number of EV; Indicates the stable discharge state of the EV at the destination ds during period t; 1 and 0 indicate that stable discharge is possible or not.

[0087] Counting the stable discharge time periods of all idle EVs in the designated area to obtain the maximum stable discharge potential of all idle EVs in the designated area;

[0088] Specifically, after determining the first feasible triangle, the positions of subsequent feasible triangles can be determined in sequence until an infeasible triangle (dark gray) appears. For each EV operating point, all feasible triangles are connected along its time axis to obtain all subsequent time periods with discharge potential. By adding up the number of EVs that can be discharged in each time period in the specified area, the total discharge potential can be evaluated. The specific formula is as follows:

[0089]

[0090]

[0091] Among them, LP s,t is the stable discharge potential at the corresponding position ds during period t. and denote the set of all parked and dischargeable EVs respectively.

[0092] Therefore, the stable discharge state and potential obtained by the proposed UDT model can be accurately and efficiently consistent with the unit discharge timing.

[0093] In this embodiment, in step S4, the step of determining the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user includes:

[0094] Based on the current status information of the dischargeable EV user, construct and calculate incentive indicators for travel demand, stay time, electricity price compensation and direct factors of battery degradation;

[0095] Specifically, factors such as travel demand, dwell time, electricity price compensation, and battery degradation directly motivate users to participate in V2G by affecting the corresponding variables of concern to users. In essence, the size of each variable of concern reflects the degree of motivation of the corresponding direct factor.

[0096] Specifically, electricity price compensation incentivizes users through price. Users' concerns about travel demand mainly involve the difference between the SOC at departure and the SOC of travel demand. As for the stay time, users are usually concerned about the delay time that exceeds expectations. In addition, if V2G is involved, the charging frequency of EV may increase to replenish additional energy, which will increase users' concerns about battery degradation. The incentive indicators for travel demand, stay time, electricity price compensation and direct factors of battery degradation are constructed and calculated as follows:

[0097]

[0098]

[0099]

[0100]

[0101] In the formula, and They represent the current incentive indicators corresponding to travel demand, stay time, electricity price compensation and battery degradation factors respectively; Indicates the expected SOC upon departure; represents the SOC of the EV when it departs after participating in V2G at the destination ds during period t; and They represent the actual stay time and the expected stay time when not participating in V2G; c s,t represents the compensation price; Indicates the total number of charging cycles after participating in V2G; P FC Indicates fast charging power; P SC Indicates slow charging power; B indicates battery capacity;

[0102] Obviously, and Higher values ​​of indicate higher travel satisfaction, lower dwell delays, higher compensation, and lower battery anxiety.

[0103] Multiply the current incentive index of each direct influencing factor to obtain the current comprehensive incentive index:

[0104]

[0105] In the formula, Represents the current comprehensive incentive index.

[0106] Evaluate the current willingness value of the dischargeable EV user according to the incentive value and willingness value when the historical V2G participation comprehensive willingness of the dischargeable EV user was the largest;

[0107] Specifically, participation willingness refers to the possibility of EV users using V2G after receiving incentives, expressed as a probability value between [0,1]. Due to different habits and preferences, different users may even have different willingness under the same incentives. In addition, incentive indicators of different direct factors often have different unit attributes. Therefore, in order to integrate all direct factors and personal preferences, the current incentive value of each factor is normalized according to the incentive value and willingness value at the time of each user's historical maximum participation willingness to determine the current willingness value.

[0108]

[0109]

[0110]

[0111]

[0112] Where, TS s,t ,DD s,t ,TI s,t and CI s,t Represents the current willingness value of the user corresponding to each direct factor. The value with the suffix "max" represents the corresponding value of the maximum historical participation willingness of each user.

[0113] Adopting a multiplication structure, comprehensive willingness UP s,t It can also be concisely expressed as the corresponding comprehensive incentive The normalized form of is:

[0114]

[0115] In addition, it can introduce random willingness fluctuations based on consumer psychology to describe the comprehensive willingness UP′ with uncertainty. s,t :

[0116]

[0117]

[0118]

[0119]

[0120] In the formula, Represents uncertainty; is the inflection point of the excitation; k1 and k2 are proportional coefficients, is a defined logical indicator; yes The logical negation of ξ; n is a random variable with a truncated standard normal distribution.

[0121] The minimum value of the historical V2G participation comprehensive willingness of the dischargeable EV user is selected as a judgment threshold, and the V2G participation status of the dischargeable EV user is determined according to the judgment threshold.

[0122] Specifically, after determining the current willingness to participate, the user will decide whether to participate in V2G based on historical experience. However, personal preferences still affect the user's judgment criteria. To this end, select the UP with the minimum historical comprehensive willingness of each EV user min As the judgment threshold, to determine the final V2G participation state φ of each EV user s,t :

[0123]

[0124] Among them, 1 or 0 represents participation or non-participation in V2G, respectively.

[0125] Counting the V2G participation status of the dischargeable EV users in each time period in a set area, and evaluating and obtaining the actual V2G capabilities of the dischargeable EV users in the set area;

[0126] The actual V2G capability expression of the dischargeable EV users in the set area is:

[0127]

[0128] In the formula, LC s，t It is represented as the actual V2G capacity of all dischargeable EVs at the destination ds during time period t; Represents the set of EVs that can be discharged.

[0129] The IWD framework provides an efficient method for model calibration and updating based on the accumulated V2G status and participation data. In addition, the continuous updating of the model can more accurately capture the changes in individual decision preferences over time.

[0130] In summary, the present invention collects information data of EV users staying at each destination in the evaluation area to obtain the current status information and historical V2G participation data of EV users staying at each destination; according to the current status information of EV users staying at each destination, by setting objective constraints of battery performance, arrival time, and departure time, the maximum discharge feasible domain of each EV with known current status information is constructed; a unit discharge triangle model is constructed, and the stable discharge time period of each idle EV staying in the maximum discharge feasible domain is determined through the unit discharge triangle model; the stable discharge time period of all idle EVs staying in the specified area is counted to obtain the maximum stable discharge potential of all idle EVs staying in the specified area; the V2G decision of the dischargeable EV user is determined according to the incentive-willingness-decision framework; the V2G participation status of the dischargeable EV user is determined through the V2G decision of the dischargeable EV user; the V2G participation status of the dischargeable EV user in each time period in the set area is counted, and the actual V2G capability of the dischargeable EV user in the set area is obtained by evaluation. The present invention combines the maximum discharge potential evaluation and the user decision under multi-attribute influencing factors to evaluate the maximum stable V2G capability. First, a unit discharge triangle (UDT) model within the EV maximum discharge feasible region (MDFR) is proposed, which can cover the stable discharge state of all dischargeable EV objects, thereby realizing the maximum stable discharge potential assessment. Subsequently, an incentive-willingness-decision (IWD) framework is proposed, which combines the user's entire decision-making process with the accumulated multi-attribute influencing factors to determine the V2G participation willingness and decision of users with stable discharge, thereby conducting a practical V2G capability assessment. Specifically, travel demand, stay time, electricity price compensation, and battery degradation are introduced as direct incentives into the entire decision-making process. Personal preferences and uncertainties are taken into account in the willingness and decision-making stages.

[0131] It should be noted that the method of the embodiment of the present disclosure can be performed by a single device, such as a computer or a server. The method of the present embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present disclosure, and the multiple devices will interact with each other to complete the described method.

[0132] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] Example 2

[0134] See also Figure 4 Embodiment 2 of the present invention further provides a stable V2G capability evaluation device based on an incentive willingness decision framework, which adopts the stable V2G capability evaluation method based on an incentive willingness decision framework of the above embodiment 1, including:

[0135] EV user information data acquisition module 001, used to collect information data of EV users staying at each destination in the assessment area, and obtain current status information and historical V2G participation data of EV users staying at each destination;

[0136] The maximum discharge feasible domain construction module 002 is used to construct the maximum discharge feasible domain of each EV with known current state information by setting objective constraints such as battery performance, arrival time, and departure time according to the current state information of EV users staying at each destination;

[0137] The maximum stable discharge potential acquisition module 003 is used to construct a unit discharge triangle model, and determine the stable discharge period of each idle EV in the maximum discharge feasible domain through the unit discharge triangle model; the stable discharge period of all idle EVs in the specified area is counted to obtain the maximum stable discharge potential of all idle EVs in the specified area;

[0138] The actual V2G capability evaluation and acquisition module 004 is used to determine the V2G decision of the dischargeable EV user according to the incentive-willingness-decision framework; determine the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user; and count the V2G participation status of the dischargeable EV user in each time period in a set area to evaluate and obtain the actual V2G capability of the dischargeable EV user in the set area.

[0139] In this embodiment, in the EV user information data acquisition module 001, the current status information of the EV user staying at each destination includes: the time of arrival and departure at each destination, the SOC when arriving at the destination and the expected SOC when leaving;

[0140] The historical V2G participation data of EV users staying at each destination includes: the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the largest; and the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the smallest.

[0141] In this embodiment, in the actual V2G capability evaluation acquisition module 004, the step of determining the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user includes:

[0142] Based on the current status information of the dischargeable EV user, construct and calculate incentive indicators for travel demand, stay time, electricity price compensation and direct factors of battery degradation;

[0143] Evaluate the current willingness value of the dischargeable EV user according to the incentive value and willingness value when the historical V2G participation comprehensive willingness of the dischargeable EV user was the largest;

[0144] The minimum value of the historical V2G participation comprehensive willingness of the dischargeable EV user is selected as a judgment threshold, and the V2G participation status of the dischargeable EV user is determined according to the judgment threshold.

[0145] In this embodiment, in the actual V2G capability evaluation acquisition module 004, in the step of determining the V2G participation status of the dischargeable EV user, incentive indicators of travel demand, stay time, electricity price compensation and direct factors of battery attenuation are constructed and calculated as follows:

[0146]

[0147]

[0148]

[0149]

[0150] In the formula, and They represent the current incentive indicators corresponding to travel demand, stay time, electricity price compensation and battery degradation factors respectively; Indicates the expected SOC upon departure; represents the SOC of the EV when it departs after participating in V2G at the destination ds during period t; and They represent the actual stay time and the expected stay time when not participating in V2G; c s,t represents the compensation price; Indicates the total number of charging cycles after participating in V2G; P FC Indicates fast charging power; P SC Indicates slow charging power; B indicates battery capacity;

[0151] Multiply the current incentive index of each direct influencing factor to get the current comprehensive incentive index:

[0152]

[0153] In the formula, Indicates the current comprehensive incentive index;

[0154] According to the incentive value and willingness value when the dischargeable EV user's historical V2G participation comprehensive willingness is the largest, the incentive value of each direct influencing factor is normalized to obtain the current uncertain comprehensive willingness value of the dischargeable EV user; the expression of the current uncertain comprehensive willingness value of the dischargeable EV user is:

[0155]

[0156] In the formula, UP′ s，t Indicates a comprehensive willingness with uncertainty; UP max It represents the historical maximum comprehensive willingness value of V2G participation of dischargeable EV users; It represents the comprehensive incentive index of the users of dischargeable EVs at the historical maximum V2G participation willingness value; Indicates uncertainty;

[0157] Determine the V2G participation status of the dischargeable EV user by using the judgment threshold:

[0158]

[0159] In the formula, φ s，t Indicates V2G participation status; UP min Indicates the minimum value of the comprehensive willingness of dischargeable EV users to participate in V2G in history; 1 or 0 indicates participation or non-participation in V2G, respectively.

[0160] In this embodiment, in the actual V2G capability evaluation acquisition module 004, the actual V2G capability expression of the dischargeable EV user in the set area is:

[0161]

[0162] In the formula, LC s，t It is represented as the actual V2G capacity of all dischargeable EVs at the destination ds during time period t; Represents the set of EVs that can be discharged.

[0163] It should be noted that the information interaction, execution process and other contents between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown in the previous part of the present application, and will not be repeated here.

[0164] Example 3

[0165] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code of a stable V2G capability assessment method based on an incentive willingness decision framework is stored, and the program code includes instructions for executing a stable V2G capability assessment method based on an incentive willingness decision framework of embodiment 1 or any possible implementation thereof.

[0166] The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0167] Example 4

[0168] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0169] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a stable V2G capability assessment method based on an incentive willingness decision framework of embodiment 1 or any possible implementation thereof.

[0170] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor implemented by reading software codes stored in a memory. The memory can be integrated in the processor or can be located outside the processor and exist independently.

[0171] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0172] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0173] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A stable V2G capability evaluation method based on an incentive willingness decision framework, characterized in that: include: By collecting information data of EV users staying at each destination in the assessment area, the current status information and historical V2G participation data of EV users staying at each destination are obtained; According to the current status information of EV users staying at each destination, the maximum discharge feasible domain of each EV with known current status information is constructed by setting objective constraints such as battery performance, arrival time, and departure time. A unit discharge triangle model is constructed, and a stable discharge period of each idle EV within the maximum discharge feasible domain is determined by the unit discharge triangle model; the stable discharge period of all idle EVs in a specified area is counted to obtain the maximum stable discharge potential of all idle EVs in the specified area; Determine the V2G decision of the dischargeable EV user according to the incentive-willingness-decision framework; determine the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user; count the V2G participation status of the dischargeable EV user in each time period in a set area, and evaluate and obtain the actual V2G capability of the dischargeable EV user in the set area; The parallel side length of the unit discharge triangle model represents the unit discharge interval △t, and the vertical side length represents the unit discharge SOC consumption △S d ; △t, △S d and β d The relationship expression is: |b d |=△S d / △t In the feasible domain, each EV operating point continuously generates multiple triangles along the time axis. When a triangle is aligned within a unit discharge time interval and is completely contained in the feasible domain, the triangle is considered feasible, indicating that the EV has a stable discharge potential in the corresponding time period. The judgment expression is: In the formula, e is the serial number of EV; Indicates the stable discharge state of the EV at the destination ds during period t; 1 and 0 indicate that stable discharge is possible or not; After determining the first feasible triangle, the positions of subsequent feasible triangles are determined in sequence until an infeasible triangle appears; for each EV operating point, all feasible triangles are connected along its time axis to obtain all subsequent time periods with discharge potential. The number of EVs that can be discharged in each time period in the specified area is added up to evaluate the total discharge potential. The specific formula is as follows: Where LP s,t is the stable discharge potential at the corresponding position ds during period t; and denote the set of all parked and dischargeable EVs respectively; In the step of determining the V2G participation status of the dischargeable EV user, the incentive indicators of travel demand, stay time, electricity price compensation and direct factors of battery attenuation are constructed and calculated as follows: In the formula, and They represent the current incentive indicators corresponding to travel demand, stay time, electricity price compensation and battery degradation factors respectively; Indicates the expected SOC upon departure; represents the SOC of the EV when it departs after participating in V2G at the destination ds during period t; and They represent the actual stay time and the expected stay time when not participating in V2G; c s,t represents the compensation price; Indicates the total number of charging cycles after participating in V2G; P FC Indicates fast charging power; P SC Indicates slow charging power; B indicates battery capacity; Multiply the current incentive index of each direct influencing factor to get the current comprehensive incentive index: In the formula, Indicates the current comprehensive incentive index; According to the incentive value and willingness value when the dischargeable EV user's historical V2G participation comprehensive willingness is the largest, the incentive value of each direct influencing factor is normalized to obtain the current uncertain comprehensive willingness value of the dischargeable EV user; the expression of the current uncertain comprehensive willingness value of the dischargeable EV user is: In the formula, UP′ s,t Indicates a comprehensive willingness with uncertainty; UP max It represents the historical maximum comprehensive willingness value of V2G participation of dischargeable EV users; It represents the comprehensive incentive index of the users of dischargeable EVs at the historical maximum V2G participation willingness value; Indicates uncertainty; The V2G participation status of the dischargeable EV user is determined by judging the threshold: In the formula, φ s,t Indicates V2G participation status; UP min Indicates the minimum value of the comprehensive willingness of dischargeable EV users to participate in V2G in history; 1 or 0 indicates participation or non-participation in V2G, respectively.

2. The stable V2G capability evaluation method based on the incentive willingness decision framework according to claim 1 is characterized in that: The current status information of the EV users staying at each destination includes: the time of arrival and departure at each destination, the SOC when arriving at the destination and the expected SOC when leaving; The historical V2G participation data of EV users staying at each destination includes: the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the largest; and the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the smallest.

3. The stable V2G capability evaluation method based on the incentive willingness decision framework according to claim 2 is characterized in that: The step of determining the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user comprises: Based on the current status information of the dischargeable EV user, construct and calculate incentive indicators for travel demand, stay time, electricity price compensation and direct factors of battery degradation; Evaluate the current willingness value of the dischargeable EV user according to the incentive value and willingness value when the historical V2G participation comprehensive willingness of the dischargeable EV user was the largest; The minimum value of the historical V2G participation comprehensive willingness of the dischargeable EV user is selected as a judgment threshold, and the V2G participation status of the dischargeable EV user is determined according to the judgment threshold.

4. The stable V2G capability evaluation method based on the incentive willingness decision framework according to claim 3 is characterized in that: The actual V2G capability expression of the dischargeable EV users in the set area is: In the formula, LC s,t It is represented as the actual V2G capacity of all dischargeable EVs at the destination ds during time period t; Represents the set of EVs that can be discharged.

5. A stable V2G capability evaluation device based on an incentive willingness decision framework, adopting a stable V2G capability evaluation method based on an incentive willingness decision framework as claimed in any one of claims 1 to 4, characterized in that: include: An EV user information data acquisition module is used to collect information data of EV users staying at each destination in the assessment area, and obtain current status information and historical V2G participation data of EV users staying at each destination; The maximum discharge feasible domain construction module is used to construct the maximum discharge feasible domain of each EV with known current status information by setting objective constraints such as battery performance, arrival time, and departure time based on the current status information of EV users staying at each destination; A maximum stable discharge potential acquisition module is used to construct a unit discharge triangle model, and determine the stable discharge period of each idle EV within the maximum discharge feasible domain through the unit discharge triangle model; the stable discharge period of all idle EVs in the specified area is counted to obtain the maximum stable discharge potential of all idle EVs in the specified area; An actual V2G capability evaluation and acquisition module is used to determine the V2G decision of a dischargeable EV user according to an incentive-willingness-decision framework; determine the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user; count the V2G participation status of the dischargeable EV user in each time period in a set area, and evaluate and obtain the actual V2G capability of the dischargeable EV user in the set area; The parallel side length of the unit discharge triangle model represents the unit discharge interval △t, and the vertical side length represents the unit discharge SOC consumption △S d ; △t, △S d and β d The relationship expression is: |b d |=△S d / △t In the feasible domain, each EV operating point continuously generates multiple triangles along the time axis. When a triangle is aligned within a unit discharge time interval and is completely contained in the feasible domain, the triangle is considered feasible, indicating that the EV has a stable discharge potential in the corresponding time period. The judgment expression is: In the formula, e is the serial number of EV; Indicates the stable discharge state of the EV at the destination ds during period t; 1 and 0 indicate that stable discharge is possible or not; After determining the first feasible triangle, the positions of subsequent feasible triangles are determined in sequence until an infeasible triangle appears; for each EV operating point, all feasible triangles are connected along its time axis to obtain all subsequent time periods with discharge potential. The number of EVs that can be discharged in each time period in the specified area is added up to evaluate the total discharge potential. The specific formula is as follows: Where LP s,t is the stable discharge potential at the corresponding position ds during period t; and denote the set of all parked and dischargeable EVs respectively; In the actual V2G capability evaluation acquisition module, in the step of determining the V2G participation status of the dischargeable EV user, incentive indicators of travel demand, stay time, electricity price compensation and direct factors of battery attenuation are constructed and calculated as follows: In the formula, and They represent the current incentive indicators corresponding to travel demand, stay time, electricity price compensation and battery degradation factors respectively; Indicates the expected SOC upon departure; represents the SOC of the EV when it departs after participating in V2G at the destination ds during period t; and They represent the actual stay time and the expected stay time when not participating in V2G; c s,t represents the compensation price; Indicates the total number of charging cycles after participating in V2G; P FC Indicates fast charging power; P SC Indicates slow charging power; B indicates battery capacity; Multiply the current incentive index of each direct influencing factor to get the current comprehensive incentive index: In the formula, Indicates the current comprehensive incentive index; According to the incentive value and willingness value when the dischargeable EV user's historical V2G participation comprehensive willingness is the largest, the incentive value of each direct influencing factor is normalized to obtain the current uncertain comprehensive willingness value of the dischargeable EV user; the expression of the current uncertain comprehensive willingness value of the dischargeable EV user is: In the formula, UP′ s,t Indicates a comprehensive willingness with uncertainty; UP max It represents the historical maximum comprehensive willingness value of V2G participation of dischargeable EV users; It represents the comprehensive incentive index of the users of dischargeable EVs at the historical maximum V2G participation willingness value; Indicates uncertainty; Determine the V2G participation status of the dischargeable EV user by using the judgment threshold: In the formula, φ s,t Indicates V2G participation status; UP min Indicates the minimum value of the comprehensive willingness of dischargeable EV users to participate in V2G in history; 1 or 0 indicates participation or non-participation in V2G, respectively.

6. The stable V2G capability evaluation device based on the incentive willingness decision framework according to claim 5 is characterized in that: In the EV user information data acquisition module, the current status information of the EV users staying at each destination includes: the time of arrival and departure at each destination, the SOC when arriving at the destination and the expected SOC when leaving; The historical V2G participation data of EV users staying at each destination includes: the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the largest; and the incentive value and the willingness value when the historical V2G participation comprehensive willingness is the smallest.

7. The stable V2G capability evaluation device based on the incentive willingness decision framework according to claim 6 is characterized in that: In the actual V2G capability evaluation acquisition module, the step of determining the V2G participation status of the dischargeable EV user through the V2G decision of the dischargeable EV user includes: Based on the current status information of the dischargeable EV user, construct and calculate incentive indicators for travel demand, stay time, electricity price compensation and direct factors of battery degradation; Evaluate the current willingness value of the dischargeable EV user according to the incentive value and willingness value when the historical V2G participation comprehensive willingness of the dischargeable EV user was the largest; The minimum value of the historical V2G participation comprehensive willingness of the dischargeable EV user is selected as a judgment threshold, and the V2G participation status of the dischargeable EV user is determined according to the judgment threshold.

8. The stable V2G capability evaluation device based on the incentive willingness decision framework according to claim 7 is characterized in that: In the actual V2G capability evaluation acquisition module, the actual V2G capability expression of the dischargeable EV user in the set area is: In the formula, LC s,t It is represented as the actual V2G capacity of all dischargeable EVs at the destination ds during time period t; Represents the set of EVs that can be discharged.

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