Electric vehicle demand response potential evaluation method considering user participation degree

Abstract

An electric vehicle demand response potential evaluation method considering user participation degree comprises the following steps: simulating charging characteristics of an electric vehicle by adopting a Monte Carlo method, and generating uncertain parameter information of departure time, arrival time, departure time, arrival charge level and the like of the electric vehicle; a fuzzy algorithm is adopted to construct a participation degree function of an electric vehicle user, and three influence factors of residual travel time, residual charge and price excitation are considered; according to the position of the single electric vehicle at the t moment, the single electric vehicle belongs to the corresponding electric vehicle cluster, and the delayed charging power of the cluster, the participating discharging response power and the discharging response capacity are calculated; and accumulating the participating discharge response capacities of all the electric vehicle clusters in the t-moment region to obtain the demand response potentials of all the electric vehicles in the t-moment region. According to the method, the ability of the electric vehicle participating in the power grid demand response under the uncertain condition is analyzed by using the fuzzy theory, and a scientific basis is provided for power system scheduling and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of power system distribution network, and in particular to a method for evaluating electric vehicle demand response potential taking user participation into consideration. Background Art

[0002] New energy vehicles represented by electric vehicles are connected to the power supply network through charging and swapping facilities, which can build a two-way interactive system of information flow and energy flow between new energy vehicles and the power supply network, thereby effectively exerting the flexible adjustment ability of power batteries as controllable loads or mobile energy storage, and providing important support for the efficient and economical operation of new power systems.

[0003] In addition, large-scale electric vehicle cluster loads can provide considerable adjustable capacity for the power grid system. Therefore, by analyzing and controlling the charging and discharging behavior of electric vehicles and utilizing electric vehicles to charge and discharge in an orderly manner, it is possible to adjust the peak and valley differences in the power grid load, reduce peak loads, optimize the operation of traditional power units, reduce network losses, maintain voltage operating levels, and improve the utilization rate of new energy.

[0004] Therefore, in order to enable large-scale electric vehicles to access the distribution network, it is essential to design an electric vehicle potential assessment method that takes into account the time-varying characteristics of the road network and user participation.

[0005] It should be understood that the above statements merely provide background technology related to the present invention and do not necessarily constitute prior art. Summary of the invention

[0006] The purpose of this invention is to provide an electric vehicle demand response potential assessment method taking into account user participation, using fuzzy theory to analyze the ability of electric vehicles to participate in grid demand response under uncertain conditions, and providing a scientific basis for power system scheduling and optimization.

[0007] In order to achieve the above-mentioned purpose, the present invention provides an electric vehicle demand response potential assessment method considering user participation, comprising the following steps: S1, using the Monte Carlo method to simulate the charging characteristics of electric vehicles; S2, using a fuzzy algorithm to construct an electric vehicle user participation function, considering three influencing factors: remaining travel time, remaining charge and price incentives; S3, according to the spatial position of a single electric vehicle at time t, it is assigned to the corresponding electric vehicle cluster, and the delayed charging power, participation discharge response power and discharge response capacity of the cluster are calculated; S4, the participation discharge response capacity of all electric vehicle clusters in the area at time t is accumulated, and the demand response potential of all electric vehicles in the area at time t is obtained.

[0008] Preferably, the calculation of the charging characteristics of the electric vehicle specifically comprises the following steps: S11, establishing a power grid model and a road network model according to the power grid information and the road network information respectively, and using the travel chain theory and the resident travel survey database to form an electric vehicle travel characteristics model, and using the Monte Carlo method to simulate the charging characteristics of the electric vehicle; S12, establishing an electric vehicle power consumption per kilometer calculation model based on manufacturer data and actual driving data, and establishing a single electric vehicle charging model based on vehicle information and charging facility information;

[0009] Preferably, in S2, the remaining charge and remaining travel time of the electric vehicle at time t are calculated by simulating a probability distribution model obtained by sampling.

[0010] Preferably, in S2, the objective participation ability calculation formula is:

[0011]

[0012] in, Indicates the amount of electricity of the electric vehicle at the current destination; Indicates the amount of electricity required for the next trip; ΔS indicates the remaining amount of electricity to reach the next destination; S lim Indicates the minimum safety value of the battery of electric vehicles; E m Indicates the rated capacity of electric vehicle power; represents the charging power; η represents the charging efficiency; Indicates the charging time required to meet the power required for the next trip; Indicates the dwell time at the current destination.

[0013] Preferably, in S2, the user's subjective participation degree ρ m The value range of (t) is:

[0014]

[0015] in, Indicates the value range of the user's subjective participation in delayed charging; Indicates the value range of the user's subjective participation in discharging.

[0016] Optimized user controllability subjective participation under the influence of three key factors: remaining travel time, remaining charge, and price incentives It is expressed as:

[0017]

[0018] Where f(·) is the controllability responsiveness function of the electric vehicle m at the time t where s resides; R is the set of users who delay charging and participate in discharging; αm (t),β m (t),γ m (t) are the remaining travel time participation, remaining charge participation, and price incentive participation, respectively.

[0019] Preferably, electric vehicles can participate in demand response only when they objectively have the demand response capability. Otherwise, even if electric vehicle users have a strong subjective desire to participate, they are objectively unable to participate.

[0020] Preferably, in S3, the calculation formula for the delayed charging power response potential and the participating discharge power response potential of the electric vehicle cluster is:

[0021]

[0022] Among them, RP del (i, t) is the delayed charging power response potential of the i-th cluster at time t; RP v2g (i, t) is the potential response of the V2G discharge power of the ith cluster at time t; EV num,i is the number of electric vehicles under the jurisdiction of cluster i; are the subjective participation of electric vehicle m in delayed charging and discharging, respectively; is the charging and discharging power level of the electric vehicle at the residence.

[0023] Preferably, the participating discharge response capacity C(i,t) of cluster i at time t is:

[0024]

[0025] Among them, S(m,t) is the charge of electric vehicle m at time t; is the subjective participation in discharge; E m is the rated capacity of electric vehicle m; m is the minimum battery charge level for vehicle m to participate in the response.

[0026] Preferably, in S4, the total capacity C of all electric vehicles in the region participating in the discharge response at time t is tot (t) is:

[0027]

[0028] Among them, N a is the number of regional clusters.

[0029] In summary, compared with the prior art, the present invention provides an electric vehicle potential assessment method that takes into account the time-varying characteristics of the road network and user participation. By constructing a road network-power grid mapping relationship and using fuzzy theory to analyze the ability of electric vehicles to participate in power grid demand response under uncertain conditions, it provides a scientific basis for power system scheduling and optimization, and has great practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of a method for evaluating electric vehicle demand response potential considering user participation of the present invention;

[0031] Figure 2 It is a schematic diagram of the electric vehicle travel chain in the present invention. DETAILED DESCRIPTION

[0032] The following is combined with Figures 1-2 , the present invention is further explained by describing a preferred specific embodiment in detail.

[0033] It should be noted that the drawings are in a very simplified form and use non-precise proportions. They are only used to conveniently and clearly assist in explaining the embodiments of the present invention, and are not used to limit the conditions for the implementation of the present invention. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention.

[0034] It should be noted that, in the present invention, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only the elements explicitly listed, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0035] like Figure 1 As shown, the present invention provides an electric vehicle potential assessment method considering the time-varying characteristics of the road network and the user participation, which specifically comprises the following steps:

[0036] S1. Using the Monte Carlo method to simulate the charging characteristics of electric vehicles, generate parameter information of the departure time, arrival time, departure time and charge level of the electric vehicles upon arrival;

[0037] S2, using fuzzy algorithm to construct the participation function of electric vehicle users, considering the three influencing factors of remaining travel time, remaining charge and price incentives;

[0038] S3, according to the spatial position of a single electric vehicle at time t, it is assigned to the corresponding electric vehicle cluster, and the delayed charging power of the cluster, as well as the participating discharge response power and discharge response capacity are calculated;

[0039] S4. Accumulate the participating discharge response capacity of all electric vehicle clusters in the region at time t to obtain the demand response potential of all electric vehicles in the region at time t.

[0040] Furthermore, in the S1, the calculation of the charging characteristics of the electric vehicle specifically includes the following steps: S11, establishing a power grid model and a road network model according to the power grid information and the road network information respectively, and using the travel chain theory and the resident travel survey database to form an electric vehicle travel characteristics model, and using the Monte Carlo method to simulate the charging characteristics of the electric vehicle; S12, establishing an electric vehicle power consumption per kilometer calculation model based on manufacturer data and actual driving data, and establishing a single electric vehicle charging model based on vehicle information and charging facility information.

[0041] It can be understood that the travel chain theory is described as follows: when an electric vehicle passes through a single or multiple destinations after traveling, there are often multiple travel paths between two consecutive travel destinations, and its mathematical expression is:

[0042] Q={q0(x0,y0),q1(x1,y1),L,q s (x s ,y s ),L},

[0043] Among them, Q is the set of stay points corresponding to the travel chain, s is the sequence number of the stay point, (x s ,y s ) is the coordinate corresponding to the stay point s, and q0 is the starting point of the travel chain;

[0044] If path is a path between two consecutive stay points, ψ(q s ,q s+1 ) is the path set between two consecutive stop points, path∈ψ(q s ,q s+1 ) is a path corresponding to a travel chain, Π is the path set corresponding to the travel chain, and the formula is:

[0045] Π={ψ(q0,q1),ψ(q1,q2),L,ψ(q s ,q s+1 )}.

[0046] In a preferred embodiment of the present invention, Figure 2 As shown in Figure 1, it describes the purpose of electric vehicle users' daily travel, the number of activities and the order in which they occur. In the figure, s represents the sequence number of the stay point, (x s ,y s ) is the coordinate corresponding to s, which is the starting point of the travel chain. It is assumed that electric vehicles all start their day trips at home. s ,q s+1 ) is the path set between two consecutive stay points, L s-1,s is the corresponding mileage, ΔT s-1,s For the corresponding travel time, represents the residence time at the activity destination s, and denote the arrival and departure times at s, respectively.

[0047] Furthermore, it should be noted that in S2, the remaining charge and remaining travel time of the electric vehicle at time t are obtained by sampling and deriving a probability distribution model, thereby obtaining simulation calculations.

[0048] Furthermore, in S2, it can be understood that when the electric vehicle m arrives at a certain travel destination s, it can participate in the demand response project only when the electric vehicle objectively has the demand response capability. Otherwise, even if the electric vehicle user has a strong subjective willingness to participate, he or she is objectively unable to participate. Therefore, the objective participation capability is introduced. Characterize the demand response participation ability of electric vehicle users at time t, and the formula is:

[0049]

[0050] in,

[0051] In the above formula, Indicates the amount of electricity of the electric vehicle at the current destination, represents the power required for the next trip, ΔS represents the remaining power to the next destination, S lim Indicates the minimum safety value of the battery of electric vehicles; E m Indicates the rated capacity of electric vehicle. represents the charging power, η represents the charging efficiency, then Indicates the charging time required to meet the power required for the next trip. Indicates the residence time at the current destination; A indicates that the current remaining power is sufficient, that is, the electric vehicle still has surplus power when meeting the needs of the next trip, and its objective participation capacity is considered to be 1; B indicates that although the current remaining charge of the electric vehicle is not enough to support the use of the next trip, because it stays at the current destination for a long time, there is enough time to replenish basic needs to ensure power safety. The vehicle has the objective conditions for demand response in this case, and its objective participation capacity is considered to be 1; C indicates that not only the remaining power of the electric vehicle is not enough to support the remaining power after the subsequent trip, but also the charging demand time required to replenish the power is greater than the residence time. It is considered that the objective participation capacity of the electric vehicle in this case is 0. In this case, the user will choose a public charging station for emergency charging on the way to the next trip destination or postpone the departure time of the next trip until the charged energy can meet the basic needs of the next trip.

[0052] Furthermore, in the aforementioned S2, it can be understood that the results of different users participating in the demand response based on information such as the current battery state of charge, electricity price signals, and remaining travel time will vary greatly. Even for the same electric vehicle user, the decision results may be different under the influence of random factors such as emotions at different time periods. Therefore, the subjective participation is defined to characterize the subjective participation of the user under the premise that the objective participation ability is 1, indicating the subjective willingness of the electric vehicle m to participate in the demand response call at time t. 0 indicates no subjective willingness, and 1 indicates full willingness. The value indicates the degree of subjective participation. Therefore, the user subjective participation ρ corresponding to the aforementioned situations A, B, and C m The value range of (t) is:

[0053]

[0054] in, It represents the value range of the user's subjective participation in adjusting the charging time (i.e., delayed charging), and the superscript del is used to represent delayed charging; It represents the value range of the user's subjective participation in discharging, and the superscript v2g indicates that the electric vehicle discharges to the grid.

[0055] It can be concluded that the user's subjective participation in controllability under the influence of the three key factors of remaining travel time, remaining charge, and price incentives is It is expressed as:

[0056]

[0057] Where f(·) is the controllability responsiveness function of the electric vehicle m at the time t where s resides; R is the set of del and v2g, α m (t),β m (t),γm (t) are the remaining travel time participation, remaining charge participation, and price incentive participation, respectively.

[0058] It should be noted that the controllable responsiveness function is an uncertainty function, which adopts fuzzy rules to calculate the user demand response participation. When carrying out a demand response pilot project, a user participation questionnaire survey including three factors, namely, remaining travel time, remaining charge and price compensation, can be designed, and then determined by statistical methods, or by examples and expert experience, to obtain the user demand response participation, that is, the membership function under fuzzy rules.

[0059] Furthermore, for the remaining travel time participation, the travel time participation of the mth electric vehicle parked at s and connected to the grid at time t is recorded as α m (t), which indicates the degree to which the user's participation is affected by the departure time of his next trip; where the current time t is the distance from the next departure time The remaining time ΔT(m,t) The more , the higher the user participation, that is, α m The larger the value of ΔT(m,t), the smaller the value of ΔT(m,t), the lower the user participation, that is, α m (t) is smaller, and when the remaining travel time is less than a certain limit T set (depending on the user's subjective will), the user will not participate in the controllability response, that is, ΔT≤T set When α m (t)=0.

[0060] Furthermore, for the remaining charge participation, it mainly reflects the degree to which the user participation is affected by the current remaining battery power, denoted as β m (t); the higher the current remaining power, the higher the user participation; the lower the current remaining power, the lower the user participation. It should be noted that, considering the battery safety needs, it is necessary to set the minimum charge state limit ξ for electric vehicles to participate in controllability response.

[0061] Furthermore, for price incentive participation, the implementation of demand response requires certain incentives for participating users, and the price incentive participation is denoted as γ m (t), which represents the user participation degree affected by the price compensation intensity. It should be noted that for different categories of users, their specific participation degree is related to price compensation, policy incentives and other factors.

[0062] It can be understood that if multiple functional plots are powered by a certain grid node, all vehicles within the power supply range of the node are called an electric vehicle cluster, and the response potential of the electric vehicle cluster is characterized by response power, capacity and time.

[0063] Furthermore, in S3, the calculation formula for the delayed charging power response potential and the participating discharge power response potential of the electric vehicle cluster is:

[0064]

[0065] Among them, RP del (i,t) is the delayed charging power response potential of the i-th cluster at time t, RP v2g (i, t) is the potential response of the V2G discharge power of the ith cluster at time t, EV num,i is the number of electric vehicles under the jurisdiction of cluster i, are the subjective participation of electric vehicle m in delayed charging and discharging, is the charging and discharging power level of the electric vehicle at the residence.

[0066] Therefore, the participating discharge response capacity C(i,t) of cluster i at time t is:

[0067]

[0068] In the above formula, S(m,t) is the charge of electric vehicle m at time t, is the subjective participation in discharge, E m is the rated capacity of electric vehicle m, ξ m is the minimum battery charge level for vehicle m to participate in the response, which can be the minimum battery safety value S lim .

[0069] Furthermore, in S4, the total capacity C of all electric vehicles in the region participating in the discharge response at time t is obtained. tot (t), that is, the demand response potential of all electric vehicles in the region at time t is:

[0070]

[0071] Among them, N a is the number of regional clusters.

[0072] In summary, the present invention provides an electric vehicle demand response potential assessment method taking user participation into account, by constructing a road network-grid mapping relationship and using fuzzy theory to analyze the ability of electric vehicles to participate in grid demand response under uncertain conditions, providing a scientific basis for power system scheduling and optimization, and having great practical significance.

[0073] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A method for evaluating the demand response potential of electric vehicles considering user participation, characterized in that: The following steps are involved: S1. Use the Monte Carlo method to simulate the charging characteristics of electric vehicles and generate parameter information of the departure time, arrival time, departure time, and charge level of the electric vehicle upon arrival; S2, using fuzzy algorithm to construct the participation function of electric vehicle users, considering the three influencing factors of remaining travel time, remaining charge and price incentives; S3, according to the spatial position of a single electric vehicle at time t, it is assigned to the corresponding electric vehicle cluster, and the delayed charging power of the cluster, as well as the participating discharge response power and discharge response capacity are calculated; S4. Accumulate the discharge response capacity of all electric vehicle clusters in the region at time t to obtain the demand response potential of all electric vehicles in the region at time t.

2. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 1, characterized in that: In the above S1, the calculation of the electric vehicle charging characteristics specifically includes the following steps: S11. Establish a power grid model and a road network model based on the power grid information and the road network information respectively, and use the travel chain theory and the resident travel survey database to form an electric vehicle travel characteristic model, and use the Monte Carlo method to simulate the charging characteristics of electric vehicles; S12. A calculation model for the electric vehicle's power consumption per kilometer is established based on the manufacturer's data and actual driving data, and a charging model for a single electric vehicle is established based on the vehicle information and charging facility information.

3. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 1, characterized in that: In S2, the remaining charge and remaining travel time of the electric vehicle at time t are calculated by simulating a probability distribution model obtained by sampling.

4. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 1, characterized in that: In S2, the objective participation ability calculation formula is: in, Indicates the amount of electricity of the electric vehicle at the current destination; Indicates the amount of electricity required for the next trip; ΔS indicates the remaining amount of electricity to reach the next destination; S lim Indicates the minimum safety value of the battery of electric vehicles; E m Indicates the rated capacity of electric vehicle power; represents the charging power; η represents the charging efficiency; Indicates the charging time required to meet the power required for the next trip; Indicates the dwell time at the current destination.

5. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 4, characterized in that: In S2, the user's subjective participation level ρ m The value range of (t) is: in, Indicates the value range of the user's subjective participation in delayed charging; Indicates the value range of the user's subjective participation in discharging.

6. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 5, characterized in that: User controllability subjective participation under the influence of three key factors: remaining travel time, remaining charge, and price incentives It is expressed as: Where f(·) is the controllability responsiveness function of the electric vehicle m at the time t where s resides; R is the set of users who delay charging and participate in discharging; α m (t),β m (t),γ m (t) are the remaining travel time participation, remaining charge participation, and price incentive participation, respectively.

7. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 6, characterized in that: Electric vehicles can only participate in demand response when they objectively have the ability to respond to demand. Otherwise, even if electric vehicle users have a strong subjective desire to participate, they are objectively unable to participate.

8. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 1, characterized in that: In S3, the calculation formula for the delayed charging power response potential and the participating discharge power response potential of the electric vehicle cluster is: Among them, RP del (i, t) is the delayed charging power response potential of the i-th cluster at time t; RP v2g (i, t) is the potential response of the V2G discharge power of the ith cluster at time t; EV num,i is the number of electric vehicles under the jurisdiction of cluster i; are the subjective participation of electric vehicle m in delayed charging and discharging, respectively; is the charging and discharging power level of the electric vehicle at the residence.

9. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 8, characterized in that: The participating discharge response capacity C(i,t) of cluster i at time t is: Among them, S(m,t) is the charge of electric vehicle m at time t; is the subjective participation in discharge; E m is the rated capacity of electric vehicle m; m is the minimum battery charge level for vehicle m to participate in the response.

10. The electric vehicle demand response potential assessment method considering user participation as claimed in claim 9, characterized in that: The total capacity C of all electric vehicles in the region participating in the discharge response at time t tot (t) is: Among them, N a is the number of regional clusters.

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