A Modeling Method for Electric Vehicles to Participate in Grid Flexibility Support under Severe Cold Weather

Abstract

The present invention discloses a modeling method for electric vehicles to participate in power grid flexibility support under severe cold weather, belonging to the technical field of flexibility resource aggregation and regulation. The method includes the following steps: characterization of charging and discharging characteristics of electric vehicles under severe cold weather; energy and power model of a single SPEV; probability model of the network connection duration of a single SPEV; feasible region of SPEV population aggregation based on single probability. Compared with the existing methods, the proposed regulation model of a single electric vehicle gives the energy and power regulation ranges of electric vehicles considering the environmental temperature; the proposed probability model of the network connection duration of a single electric vehicle considers the influence of environmental temperature and incentive policies on the probability density of the network connection duration of electric vehicles; the proposed aggregation model of a group of electric vehicles considers the randomness of the network connection duration of electric vehicles when aggregating, and gives a relatively robust calculation method for the adjustable energy and adjustable power adjustable ranges.

Description

Technical Field

[0001] The present invention relates to the technical field of flexibility resource aggregation and regulation, and in particular to a modeling method for electric vehicles to participate in power grid flexibility support under severe cold weather. Background Art

[0002] When severe cold weather occurs in a certain area, the source and load characteristics of the system change significantly. On the load side, residents' heating needs increase sharply, and the load in the system increases significantly. On the source side, wind turbine blades are difficult to generate electricity due to ice, and photovoltaic power generation is reduced due to factors such as lowered temperature and photovoltaic panels covered with ice and snow. The power that the upper power grid can provide is limited. The changes in source and load lead to a power supply gap in the system. Therefore, it is necessary to tap the flexible resources on the load side of the system and ensure the safety and continuous power supply of the power system by adjusting the energy consumption characteristics of flexible resources.

[0003] The flexibility resources of the system will change with the application scenarios. In severe cold weather, due to residents' demand for heating, flexible resources such as air conditioners and water heaters in daily situations are transformed into rigid loads. While ensuring the safety of residents' lives, non-people's livelihood loads such as heavy industrial loads and general industrial and commercial loads have also become relatively reducible resources. In recent years, the scale of electric vehicles has been gradually developing. Taking Jilin, a cold region in my country, as an example, it is predicted that the number of electric vehicles will reach 211,000 by 2025, which provides a base support for electric vehicles to participate in severe cold weather dispatch.

[0004] At present, there have been a lot of studies on the participation of electric vehicles (SPEV) in the flexibility regulation of power systems, including models for regulation in the form of fixed energy storage and flexibility regulation models that consider the coupling relationship between the power grid and the road network. However, there are few studies on the participation of electric vehicles in the regulation of power systems in severe cold weather, and no research has considered the impact of ambient temperature on the charging and discharging model of electric vehicles when calculating the regulation capacity of electric vehicles on the power grid. With the increase in the number of electric vehicles and the reduction in the flexibility resources that can be called upon in severe cold weather, it is necessary to study the regulation capacity of electric vehicles on the power system in severe cold weather.

[0005] At present, the existing technology still has the following shortcomings:

[0006] (1) In the existing modeling of the flexible adjustment capability of electric vehicles, the impact of temperature on the charging and discharging power, adjustable power domain, and adjustable energy domain of individual electric vehicles in severe cold weather is not considered, which makes it difficult to apply the existing flexible adjustment capability model of electric vehicles to the flexible adjustment scenario of the power system in severe cold weather.

[0007] (2) When modeling the probability density of the connected time of a single electric vehicle, the existing methods do not consider the influence of weather temperature and the demand response incentive policies introduced by the power grid on the connected time of electric vehicles, resulting in inaccurate probability density models of the connected time of single electric vehicles.

[0008] (3) When aggregating the adjustable capabilities of an electric vehicle cluster, the existing methods take the connected time of electric vehicles as known variables and simply superimpose the adjustable energy domains and adjustable power domains of all electric vehicles, resulting in the proposed method not conforming to the actual situation and being difficult to apply. Summary of the Invention

[0009] The object of the present invention is to provide a modeling method for electric vehicles to participate in power grid flexibility support in severe cold weather, which can accurately describe the flexibility support ability of electric vehicles for the power system in severe cold weather, and finally obtain the adjustable energy and power ranges of all electric vehicles in a certain charging area for the power grid.

[0010] To achieve the above object, the present invention provides a modeling method for electric vehicles to participate in power grid flexibility support in severe cold weather, including the following steps:

[0011] S1. Characterize the charging and discharging characteristics of electric vehicles in severe cold weather, analyze the charging and discharging characteristics of electric vehicles in severe cold weather, consider the influence of weather temperature on the charging and discharging of electric vehicles, and abstract the electric energy transfer path when the electric vehicle is connected to the network according to the self-heating method of discharging.

[0012] S2. Energy and power models of single SPEV, construct a single SPEV model, divide the single SPEV into two categories: non-adjustable charging demand and adjustable charging demand according to its connected time, and respectively construct single SPEV energy and power models, including single SPEV with non-adjustable charging demand and single SPEV with adjustable charging demand.

[0013] S3. Probability model of the connected time of single SPEV, consider the influence of weather temperature and the policies introduced by the power grid on the connected time of electric vehicles, and construct a probability model of the connected time of single SPEV.

[0014] S4. Feasible region boundary aggregation of SPEV population based on single probability, consider the connected time of single electric vehicles, and obtain the adjustable energy domain and adjustable power domain of electric vehicles for power grid flexibility support by aggregating the adjustable energy and adjustable power of all electric vehicles.

[0015] Preferably, in step S1, during charging, the charging pile charges the electric vehicle at a power The electric vehicle battery converts electric energy into the battery interior with an efficiency of η c and provides a heating power of P BTM to the BTMS. The relationship between variables during charging is:

[0016]

[0017] Among them, P c is the rated charging power of the battery; P ch is the power for the battery to store electrical energy. During discharging, the battery discharges to the charging pile and the BTMS at the rated discharging power P d . The power flowing to the charging pile is and the power flowing to the BTMS is P BTM . The relationship between the variables during discharging is:

[0018]

[0019] The BTMS is used to keep the electric vehicle battery operating within the normal operating temperature range. The BTMS power function is:

[0020]

[0021] P BTM 's heating time and the stop heating time can be expressed as:

[0022]

[0023] In the formula, m is the mass of the battery; c is the specific heat capacity of the battery material; η BTM is the electro-thermal conversion efficiency of the battery; k is the temperature dissipation coefficient; T out is the ambient temperature in severe cold weather;

[0024] P BTM is a variable related to the battery temperature. Under the condition of the change of P BTM , the state of charge of the electric vehicle battery and the fastest full charge time are approximately expressed by the average power of the BTMS within one working temperature cycle to represent the power consumption of the BTMS:

[0025]

[0026] Preferably, in step S2, the power and energy boundaries of the single SPEV depend on four parameters, which are respectively: and represents the fastest path for storing electrical energy from the power grid to the SPEV, represents the slowest path for storing electrical energy from the power grid to the SPEV, represents the upper power boundary of the interaction between the SPEV and the power grid, represents the lower power boundary of the interaction between the SPEV and the power grid, and The set constitutes the boundary of the feasible set for the charge and discharge behavior of the single SPEV.

[0027] Preferably, in step S2, before constructing the charge and discharge model of the single SPEV under severe cold weather conditions, the relationship between the initial and final states during grid connection and the parameters of the feasible set boundary is established.

[0028] Assume that the initial state of charge of the i-th SPEV connected to the grid is The state of charge that can meet the user's travel demand when leaving the grid is The battery capacity of the SPEV is C spev,i , at this time, the maximum amount of electricity obtained by the SPEV battery from the grid The amount of electricity obtained when it can meet the user's demand And the minimum amount of electricity obtained from the grid when the battery can meet the discharge requirement Are respectively:

[0029]

[0030] In the formula, Is the minimum state of charge of the SPEV, according to The shortest time Required for the SPEV to reach the state of charge Can be calculated as:

[0031]

[0032] Denote the expected grid connection time of the i-th SPEV as According to And The interaction between the SPEV and the grid is divided into two cases: irreconcilable charging demand and adjustable charging demand, and the SPEV is modeled separately.

[0033] Preferably, in step S2, for the single SPEV with irreconcilable charging demand, when , the SPEV continuously charges at the maximum charging power within ;

[0034] During , the upper and lower power boundaries of the interaction between the SPEV and the grid coincide, which is the maximum power for storing electrical energy, and the upper and lower energy boundaries coincide. At this time, the model of the SPEV is:

[0035]

[0036] In the formula, p spev,i (τ) is the energy storage power of the battery at time τ; e spev,i (τ) is the cumulative electrical energy obtained by the battery up to time τ.

[0037] Preferably, in step S2, when the charging demand adjustable single SPEV during the networking period, the state of charge reaches During the charging and discharging process of the SPEV, the BTMS is always in the state of heating the ambient temperature. The upper and lower boundary models of the SPEV charging and discharging power are shown in formulas (14)-(15):

[0038]

[0039] The upper boundary of the energy state of the interaction between the SPEV battery and the power grid depends on the energy storage rate and The lower boundary depends on the discharge rate, and the energy that the SPEV needs to reach when it is off the grid The upper and lower boundary models of energy are shown in formulas (16)-(17):

[0040]

[0041] In the formula, is the SPEV discharge power; is the SPEV discharge efficiency.

[0042] Preferably, in step S3, the specific construction steps of the single SPEV networking duration probability model are as follows:

[0043] When the single SPEV with non-adjustable charging demand and the single SPEV with adjustable charging demand participate in the V2G process, the power model and the energy model are aggregated. The power and energy aggregation models of the SPEV with non-adjustable charging demand are shown in formulas (18) and (19) respectively:

[0044]

[0045] The power and energy aggregation models of the SPEV with adjustable charging demand are shown in formulas (20) and (21) respectively:

[0046]

[0047] e ∈ {SPEV}

[0048] Construct the power and energy adjustable range probability model of the SPEV group in severe cold weather. First, model the single SPEV networking time model, and use the Gaussian distribution to describe the probability density of the single SPEV networking time:

[0049]

[0050] In the formula, μ and σ are the mean and variance of the probability density of the SPEV networking time respectively; It represents the shortest time for the i-th SPEV to be fully charged from the start of connecting to the grid; N is the number of SPEVs counted, and the average time for a single SPEV to be fully charged at the fastest speed is obtained by calculating the average value of the shortest charging times of N SPEVs. It is the average time for a single SPEV to be charged to the power level that can meet the travel demand at the fastest speed; γ is the policy factor.

[0051] When the SPEV is connected to the grid, the probability model of its adjustable charging demand is denoted as:

[0052]

[0053] In the formula, a ∈ [0, 1], and the value of a mainly depends on the policy factor γ and the temperature influence factor.

[0054] Preferably, in step S4, the specific process of the boundary of the SPEV population aggregation feasible region based on the single probability is as follows:

[0055] The SPEVs are divided into those that have been connected to the grid and those that are about to be connected to the grid. The upper and lower bounds of their power and energy are calculated respectively. At time t 0 , according to formulas (18)-(19), the power and energy of the SPEVs with non-adjustable charging demand that have been connected to the grid are calculated and denoted as {E in,unct (t), P in,unct (t)|t = t 0 , t 1 , …, t m}; According to formulas (20)-(21), the power and energy boundaries of the SPEVs with adjustable charging demand that have been connected to the grid are calculated and denoted as

[0056] At time t 1 , the number of newly connected SPEVs to the grid is . The power and energy exchanged between these SPEVs and the grid are multiplied by the probability density of the connection time and integrated to calculate the power and energy demands of the group of newly connected SPEVs with non-adjustable charging demand, as shown in formulas (27)-(28); the upper and lower boundaries of the power and energy of the group of newly connected SPEVs with adjustable charging demand, as shown in formulas (29)-(30):

[0057]

[0058] t k = t 1 , t 2 , …, t m

[0059] In the formula, is the number of SPEVs with non - adjustable charging demand; is the number of SPEVs with adjustable charging demand; P out,unct (t k ) and E out,unct (t k ) are the power and energy of the group of SPEVs with non - adjustable charging demand newly connected to the grid respectively; and are the upper and lower boundaries of the power and energy of the group of SPEVs with adjustable charging demand newly connected to the grid respectively; is the energy boundary obtained by accumulating the energy boundaries of SPEVs with adjustable charging demand, compared with the boundary considering power ramp - up limit, taking the minimum value, and calculating the final upper and lower boundaries of the energy of SPEVs with adjustable charging demand, as shown in Equation (31);

[0060] At time t 1 , add the power and energy boundaries of the two types of connected SPEVs at time t to the power and energy boundaries of the two types of newly connected SPEVs to the grid, to obtain the total power and energy boundaries of the two types of SPEVs at time t 1 :

[0061] P SPEVUN (t 1 ) = P in,unct (t 1 ) + P out,unct (t 1 ) (34)

[0062] E SPEVUN (t 1 ) = E in,unct (t 1 ) + E out,unct (t 1 ) (35)

[0063]

[0064] Therefore, the modeling method for electric vehicles to participate in grid flexibility support in severe cold weather using the above - mentioned structure in the present invention has the following beneficial effects:

[0065] Compared with the existing methods, the single - electric - vehicle adjustment model proposed in the present invention gives the energy and power adjustment ranges of electric vehicles considering the ambient temperature; the probability model of the connected duration of a single electric vehicle proposed takes into account the influence of ambient temperature and incentive policies on the probability density of the connected duration of electric vehicles; the aggregated model of a group of electric vehicles proposed considers the randomness of the connected duration of electric vehicles during aggregation and gives a relatively robust calculation method for the adjustable energy and adjustable power adjustment ranges.

[0066] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0067] Figure 1 It is a schematic flow chart of a modeling method for an electric vehicle to participate in grid flexibility support in severe cold weather according to the present invention;

[0068] Figure 2 It is a schematic diagram of electric vehicle charging in a modeling method for an electric vehicle to participate in grid flexibility support in severe cold weather according to the present invention;

[0069] Figure 3 It is a schematic diagram of electric vehicle discharging in a modeling method for an electric vehicle to participate in grid flexibility support in severe cold weather according to the present invention. Detailed Embodiments

[0070] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0071] Unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meaning as understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0072] Embodiment

[0073] As Figure 1 shown, the present invention provides a modeling method for an electric vehicle to participate in grid flexibility support in severe cold weather, including the following steps:

[0074] S1. Characterize the charging and discharging characteristics of electric vehicles in severe cold weather, analyze the charging and discharging characteristics of electric vehicles in severe cold weather, consider the influence of weather temperature on the charging and discharging of electric vehicles, and abstract the electric energy transfer path when the electric vehicle is connected to the network according to the self-heating method of discharging.

[0075] In extremely cold weather, the charging speed of SPEV slows down, the charging time increases, and the discharge efficiency decreases. This is mainly due to the battery's requirement for operating temperature. The battery thermal management system (BTMS) needs to consume electrical energy to heat the battery to ensure it operates within a reasonable temperature range.

[0076] There are mainly two ways to heat electric vehicle batteries: external heating and self-heating. The external heating method has a complex structure, low heating efficiency, and uneven heating, so it is rarely used at present. Battery self-heating mainly includes two types: charging self-heating and discharging self-heating. Charging self-heating heats the battery through the heat generated by internal chemical reactions in the battery. This method has a risk of lithium plating and requires strict control of the charging current amplitude. Discharging self-heating is widely used because during the discharging process, the potential of the battery's negative electrode is relatively high, and there is almost no risk of lithium plating.

[0077] According to the discharging self-heating method, the power transfer path when SPEV is connected to the network is abstracted into a schematic diagram as shown in Figure 2 、 3 When charging, the charging pile charges the electric vehicle at a power of The electric vehicle battery converts electrical energy into the battery with an efficiency of η c and provides a heating power of P BTM to the BTMS. The relationships between variables during charging are:

[0078]

[0079] Among them, P c is the rated charging power of the battery; P ch is the power for the battery to store electrical energy. When discharging, the battery discharges to the charging pile and the BTMS at the rated discharging power P d , the power flowing to the charging pile is and the power flowing to the BTMS is P BTM . The relationships between variables during discharging are:

[0080]

[0081] The BTMS is used to keep the electric vehicle battery operating within the normal operating temperature range. In extremely cold weather, when the battery operating temperature T B is lower than the lowest normal operating temperature , it needs to be heated to the highest temperature at which it can operate normally by the BTMS with a power of ε and then stop heating. The power function of the BTMS is:

[0082]

[0083] P BTM Heating time and stop heating time can be expressed as:

[0084]

[0085] In the formula, m is the mass of the battery; c is the specific heat capacity of the battery material; η BTM is the electro-thermal conversion efficiency of the battery; k is the temperature dissipation coefficient; T out is the ambient temperature in severe cold weather.

[0086] It can be seen from Equation (4) that during the period when the electric vehicle is connected to the network, P BTM is a variable related to the battery temperature. When P BTM changes, it is difficult to evaluate the state of charge of the electric vehicle battery and the time required to fully charge it fastest. Therefore, the average power of the BTMS within a working temperature cycle is used to approximately represent the power consumption of the BTMS:

[0087]

[0088] S2. Single electric vehicle SPEV energy and power model. A single SPEV model is constructed. According to the network connection duration of the SPEV, it is divided into two categories: non-adjustable charging demand and adjustable charging demand. The single SPEV energy and power models are constructed respectively, including the single SPEV with non-adjustable charging demand and the single SPEV with adjustable charging demand.

[0089] The power and energy boundaries of the single SPEV depend on four parameters, namely: and Among them, represents the upper energy boundary for the grid to charge the SPEV, that is, the fastest path for storing electrical energy from the grid to the SPEV; represents the lower energy boundary for the grid to charge the SPEV, that is, the slowest path for storing electrical energy from the grid to the SPEV; represents the upper power boundary for the interaction between the SPEV and the grid; represents the lower power boundary for the interaction between the SPEV and the grid. The set of the above four variables constitutes the feasible set boundary of the charging and discharging behavior of the single SPEV.

[0090] Before constructing the charging and discharging model of the single SPEV in severe cold weather, it is necessary to clarify the relationship between the initial and final states during its connection to the grid and the parameters of the feasible set boundary. Assume that the initial state of charge of the i-th SPEV connected to the grid is The state of charge when leaving the grid that can meet the travel needs of users is The SPEV battery capacity is C spev,i, at this time, the maximum power obtained by the SPEV battery from the power grid The power obtained when it can meet the user's needs And the minimum power obtained from the power grid when the battery can meet the discharge requirements Are respectively:

[0091]

[0092] In the formula, Is the minimum state of charge of the SPEV. According to It can be calculated that the SPEV reaches the state of charge The shortest time used Is:

[0093]

[0094] Record the expected connection time of the i-th SPEV as According to And The size relationship of, the interaction between the SPEV and the power grid is divided into two cases: irreconcilable charging demand and adjustable charging demand, and the SPEV is modeled respectively.

[0095] a) Irreconcilable charging demand

[0096] When , the SPEV needs to continuously charge at the maximum charging power within . In this case, the SPEV has no adjustment flexibility and can only be regarded as an electrical load. During , the upper and lower power boundaries of the interaction between the SPEV and the power grid coincide, which is the maximum power for storing electrical energy. Correspondingly, the upper and lower energy boundaries also coincide. At this time, the model of the SPEV is:

[0097]

[0098] In the formula, p spev,i (τ) is the energy storage power of the battery at time τ; e spev,i (τ) is the electrical energy accumulated by the battery up to time τ.

[0099] b) Adjustable charging demand

[0100] When , it can not only ensure that the state of charge can reach During the connection period, but also has a certain flexibility in charge and discharge adjustment.

[0101] During the charging and discharging process of the SPEV, due to the influence of severe cold weather conditions, the BTMS has been in a state of heating the ambient temperature. The upper and lower boundary models of the SPEV charging and discharging power are shown in Equations (14)-(15). The upper boundary of the energy state of the interaction between the SPEV battery and the power grid depends on the energy storage rate and The lower boundary depends on the discharge rate, and the energy to be achieved when the SPEV is off-grid The upper and lower boundary models of energy are shown in Equations (16)-(17).

[0102]

[0103]

[0104] In the formula, is the SPEV discharge power; is the SPEV discharge efficiency.

[0105] S3. Probability model of the connection duration of a single SPEV. Considering the influence of weather temperature and the policies introduced by the power grid on the connection time of electric vehicles, a probability model of the connection duration of a single SPEV is constructed.

[0106] When non-adjustable charging-demand single SPEVs, adjustable charging-demand single SPEVs, and single LPEVs participate in the V2G process, the power model and energy model need to be aggregated first. The power and energy aggregation models of non-adjustable charging-demand SPEVs are shown in Equations (18) and (19) respectively. The power and energy aggregation models of adjustable charging-demand SPEVs are shown in Equations (20) and (21) respectively.

[0107]

[0108] e ∈ {SPEV}

[0109] To construct the probability model of the adjustable range of power and energy of the SPEV group under severe cold weather, the connection time model of a single SPEV needs to be modeled first. In this paper, the Gaussian distribution is used to describe the probability density of the connection time of a single SPEV:

[0110]

[0111] In the formula, μ and σ are the mean and variance of the probability density of the connection time of the SPEV respectively; represents the shortest time for the i-th SPEV to be fully charged from the start of connection; N is the number of SPEVs counted. By calculating the mean of the shortest charging times of N SPEVs, the average time for a single SPEV to be fully charged at the fastest speed is obtained is the average time for a single SPEV to be charged to the power level that can meet the travel demand at the fastest speed; γ is the policy factor, a constant coefficient, used to describe the impact of the vehicle-to-grid (V2G) policy introduced by the grid operator on the network connection time of SPEVs. Each incentive policy corresponds to a policy factor. The higher the incentive, the larger the value of the policy factor; is the temperature influence factor, used to describe the impact of temperature changes on the actual distribution of network connections. At the reference temperature T 0 below, takes the value of 1. In Equation (23), considering that the main purpose of SPEV owners to connect to the network is to fully charge the electric vehicle, can be used to estimate the mean μ of the probability density of the network connection time of a single SPEV. When the operator issues the V2G policy, the purpose of SPEV network connection includes both charging and responding to the V2G policy. At this time, the mean μ will also be affected by the policy factor γ. Equation (25) considers the impact of temperature T on the probability density of SPEV network connection time. Assuming γ = 1 and when, the probability density f spev (t) is a deterministic function. Taking as the dividing line, the probabilities of adjustable and non-adjustable charging demands are definite probabilities. Therefore, when an SPEV connects to the grid, the probability model of its adjustable charging demand can be recorded as:

[0112]

[0113] In the formula, a ∈ [0, 1]. The value of a mainly depends on the policy factor γ and the temperature influence factor

[0114] S4. Based on the boundary of the feasible region of SPEV population aggregation with single-body probability, considering the network connection duration of single-body electric vehicles, by aggregating the adjustable energy and adjustable power of all electric vehicles, the adjustable energy domain and adjustable power domain for the flexibility support of electric vehicles to the grid are obtained.

[0115] Divide SPEVs into those that have already connected to the network and those that are about to connect to the network, and calculate the upper and lower bounds of their power and energy respectively. The grid operator can know the number of network connections of SPEVs at each moment based on experience and by using load forecasting methods. At time t 0 , the power and energy of non-adjustable charging demand SPEVs that have already connected to the network can be calculated according to Equations (18)-(19), denoted as {E in,unct (t), P in,unct (t)|t = t 0 , t 1 , …, t m}; The power and energy boundaries of adjustable charging demand SPEVs that have already connected to the network can be calculated according to Equations (20)-(21), denoted as At time t 1 , the number of newly connected SPEVs to the power grid is . By multiplying the power, energy, and the probability density of the connection time exchanged between these SPEVs and the power grid and integrating, the power and energy demands of the group of non-adjustable charging-demand SPEVs newly connected to the power grid can be calculated, as shown in Eqs. (27)-(28); and the upper and lower bounds of the power and energy of the group of adjustable charging-demand SPEVs newly connected to the power grid, as shown in Eqs. (29)-(30):

[0116]

[0117] t k =t 1 , t 2 , …, t m

[0118] In the equations, is the number of non-adjustable charging-demand SPEVs; is the number of adjustable charging-demand SPEVs; P out,unct (t k ) and E out,unct (t k ) are the power and energy of the group of non-adjustable charging-demand SPEVs newly connected to the power grid, respectively; and are the upper and lower bounds of the power and energy of the group of adjustable charging-demand SPEVs newly connected to the power grid, respectively; is the energy boundary obtained by accumulating the energy boundaries of the adjustable charging-demand SPEVs, which needs to be compared with the boundary considering the power ramp limitation and take the minimum value to calculate the final upper and lower bounds of the energy of the adjustable charging-demand SPEVs, as shown in Eq. (31).

[0119] Therefore, by adding the power and energy boundaries of the two types of connected SPEVs at time t 1 to the power and energy boundaries of the two types of newly connected SPEVs to the power grid, the total power and energy boundaries of the two types of SPEVs at time t 1 can be obtained:

[0120] P SPEVUN (t 1 ) = P in,unct (t 1 ) + P out,unct (t 1 ) (34)

[0121] E SPEVUN (t 1 ) = E in,unct (t 1 ) + E out,unct (t 1)(35)

[0122]

[0123] In the modeling of the participation of existing electric vehicles in the flexibility regulation of the power system, the influence of environmental temperature on the regulation ability of electric vehicles in extremely cold weather is not considered; in the connection duration model, the influence of environmental temperature and incentive policies on the connection duration of electric vehicles is not considered; in the aggregation of the adjustable energy domain and power domain of electric vehicles, the existing technology is simply a simple superposition, taking the connection duration as a known quantity, and the uncertainty of the connection duration is not considered. The present invention can accurately describe the flexibility support ability of electric vehicles to the power system in severe cold weather, and finally obtain the adjustable energy and power ranges of all electric vehicles in a certain charging area to the power grid. First, by analyzing the charging and discharging modes of electric vehicles in extremely cold weather, a charging and discharging model of electric vehicles considering temperature influence is constructed; secondly, the influence of the connection duration on the energy and power regulation of electric vehicles is analyzed, and a power and energy regulation model of a single electric vehicle is constructed; then, a random probability model of the connection duration of a single electric vehicle is constructed, considering the influence of incentive policies and weather temperature on the probability distribution of the connection duration of electric vehicles; finally, based on the flexible regulation ability of a single electric vehicle, the feasible regions of energy and power of the electric vehicle group in a charging area are calculated to characterize the adjustable ability of the electric vehicle group to the power system.

[0124] Therefore, compared with the existing method, the modeling method for the participation of electric vehicles in the flexibility support of the power grid in severe cold weather adopted by the present invention, the proposed single electric vehicle regulation model gives the energy and power regulation ranges of electric vehicles considering environmental temperature; the proposed random probability model of the connection duration of a single electric vehicle considers the influence of environmental temperature and incentive policies on the probability density of the connection duration of electric vehicles; the proposed aggregation model of the electric vehicle group considers the randomness of the connection duration of electric vehicles during aggregation, and gives a relatively robust calculation method for the adjustable energy and adjustable power ranges.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A modeling method for electric vehicles participating in grid flexibility support in severe cold weather, characterized in that: The following steps are involved: S1. Characterization of charging and discharging characteristics of electric vehicles in severe cold weather. Analyze the charging and discharging characteristics of electric vehicles in severe cold weather, consider the impact of weather temperature on the charging and discharging of electric vehicles, and abstract the power transmission path of electric vehicles when connected to the grid according to the discharge self-heating method; The path abstraction is as follows: when the electric vehicle is charging, the electric energy flows from the charging pile to the electric vehicle battery, and the electric vehicle battery supplies power to the BTMS; when the electric vehicle is discharging, the electric energy flows from the electric vehicle battery to the charging pile and BTMS respectively; S2. Construct a single electric vehicle SPEV energy and power model. According to the connection time of SPEV, it is divided into two categories: charging demand is not adjustable and charging demand is adjustable. Construct a single SPEV energy and power model respectively, including single SPEV with non-adjustable charging demand and single SPEV with adjustable charging demand; S3. Construct a probability model for the connection time of a single SPEV, taking into account the impact of weather temperature and grid policies on the connection time of electric vehicles, and construct a probability model for the connection time of a single SPEV; S4. Based on the probability of a single cell, the feasible domain boundary is obtained by aggregating the SPEV group. Considering the connection time of a single electric vehicle, the adjustable energy domain and adjustable power domain of electric vehicles supporting the flexibility of the power grid are obtained by aggregating the adjustable energy and adjustable power of all electric vehicles.

2. The modeling method for electric vehicles participating in grid flexibility support in severe cold weather according to claim 1 is characterized by: In step S1, during charging, the charging pile uses power Charging electric vehicles, electric vehicle batteries with efficiency Convert electrical energy into the battery and provide it to BTMS The heating power of the battery is 100W, and the relationship between the variables during charging is: (1) (2) in, is the rated charging power of the battery; The power of storing electrical energy in the battery. When discharging, the battery discharges at the rated power. Discharge to the charging pile and BTMS, the power flowing to the charging pile is , the power flowing to BTMS is , the relationship between the variables during discharge is: (3) BTMS is used to maintain the battery of electric vehicles within the normal operating temperature range. In severe cold weather, when the battery operating temperature Lower than the minimum normal operating temperature When the power is Heat to the highest temperature for normal operation , then stop heating, the BTMS power function is: (4) Heating time and stop heating time It is expressed as: (5) (6) In the formula, The quality of the battery; is the specific heat capacity of the battery material; is the electric-thermal conversion efficiency of the battery; is the temperature dissipation coefficient; Ambient temperature for severe cold weather; is a variable related to battery temperature. Under the condition of changes, the state of charge of the electric vehicle battery and the fastest time to fully charge are approximated by the average power of BTMS in an operating temperature cycle: (7)。 3. The modeling method for electric vehicles participating in grid flexibility support in severe cold weather according to claim 2 is characterized by: In step S2, the power and energy boundaries of a single SPEV depend on four parameters, namely: , , and , represents the fastest path from the grid to the SPEV for storing electrical energy, represents the slowest path from the grid to the SPEV for storing electrical energy, represents the upper power limit of SPEV interaction with the grid, represents the lower power boundary of SPEV interaction with the grid, , , and The collection of constitutes the feasible set boundary of the charging and discharging behavior of single-cell SPEV.

4. The modeling method for electric vehicles participating in grid flexibility support in severe cold weather according to claim 3 is characterized by: In step S2, it is assumed that The initial charge state of a SPEV connected to the grid is , the state of charge that can meet the user's travel needs when off-grid is , SPEV battery capacity is At this time, the maximum amount of electricity that the SPEV battery can obtain from the grid is , the amount of electricity obtained to meet user needs The minimum amount of electricity that can be obtained from the grid when the battery can meet the discharge requirements They are: (8) (9) (10) In the formula, is the minimum state of charge of SPEV, according to Calculate the SPEV to reach the state of charge Minimum time used for: (11) In the formula, , and Respectively SPEV ,and , , remember The expected connection time for a SPEV is ,according to and The interaction between SPEV and the power grid is divided into two cases: irreversible charging demand and adjustable charging demand, and SPEV is modeled separately.

5. The modeling method for electric vehicles participating in grid flexibility support in severe cold weather according to claim 4 is characterized by: In step S2, a single SPEV with an unadjustable charging demand When the SPEV is charged at the maximum power Continuous charging inside; exist During this period, the upper and lower boundaries of the power of SPEV interacting with the power grid coincide with each other, which is the maximum power of stored electric energy. The upper and lower boundaries of energy coincide with each other. At this time, the model of SPEV is: (12) (13) In the formula, for The energy storage capacity of the battery at all times; To The amount of electrical energy accumulated by the battery at all times.

6. A modeling method for electric vehicles participating in grid flexibility support in severe cold weather according to claim 5, characterized in that: In step S2, the charging demand adjustable single SPEV When the state of charge reaches During the charge and discharge process of SPEV, BTMS is always in a state of heating the ambient temperature. The upper and lower boundary models of SPEV charge and discharge power are shown in equations (14)-(15): (14) (15) The upper boundary of the energy state of the SPEV battery's interaction with the grid depends on the energy storage rate and , the lower boundary depends on the discharge rate, and the energy required when SPEV is off-grid , the energy upper and lower boundary models are shown in equations (16)-(17): (16) (17) In the formula, is the SPEV discharge power; is the SPEV discharge efficiency.

7. The modeling method for electric vehicles participating in grid flexibility support in severe cold weather according to claim 4 is characterized by: In step S3, the specific steps of constructing the probability model of the single SPEV connection time are as follows: When a single SPEV with non-adjustable charging demand and a single SPEV with adjustable charging demand participate in the V2G process, the power model and energy model are aggregated. The power aggregation model of the SPEV with non-adjustable charging demand , Energy Aggregation Model As shown in formula (18) and formula (19) respectively: (18) (19) In the formula, the power and energy aggregation models of SPEV with adjustable charging demand are shown in equations (20) and (21), respectively: (20) (21) To construct a probability model of the adjustable range of power and energy of a SPEV group in severe cold weather, we first modeled the single SPEV connection time model and used Gaussian distribution to describe the probability density of the single SPEV connection time: (22) (23) (24) (25) In the formula, and are the mean and variance of the probability density of SPEV connection time; The meaning is consistent with that of formula (8). , , The meaning is consistent with that of formula (11); Indicates The shortest time it takes for a SPEV to be fully charged from being connected to the internet; To calculate the number of SPEVs, The average of the shortest charging time of the SPEVs is used to obtain the average time for a single SPEV to be fully charged at the fastest speed. ; The average time it takes to charge a single SPEV at the fastest speed to a level sufficient to meet travel needs; It is a policy factor; When a SPEV is connected to the grid, the probability model of its adjustable charging demand is recorded as: (26) In the formula, , The size of the and temperature influencing factors .

8. The modeling method for electric vehicles participating in grid flexibility support in severe cold weather according to claim 7 is characterized by: In step S4, the specific process of obtaining the feasible region boundary through SPEV group aggregation based on the monomer probability is as follows: SPEVs are divided into those that are already connected to the network and those that are about to be connected to the network, and their upper and lower bounds of power and energy are calculated respectively. At time , the SPEV power and energy of the connected charging demand that cannot be adjusted are calculated according to equations (18)-(19), which are recorded as , represents the serial number of the time point; according to equations (20) and (21), the power and energy boundaries of the SPEV with adjustable charging demand connected to the network are calculated and recorded as ; exist At this moment, the number of newly connected SPEVs is The power and energy exchanged between these SPEVs and the grid are multiplied by the probability density of the connection time and then integrated to calculate the power and energy requirements of the newly connected SPEV group with non-adjustable charging demand, as shown in equations (27)-(28); the upper and lower boundaries of the power and energy of the newly connected SPEV group with adjustable charging demand are shown in equations (29)-(30): (27) (28) (29) (30) (31) (32) (33) In the formula, The number of SPEVs that cannot be adjusted for charging needs; The number of SPEVs that can be adjusted for charging needs; and They are the power and energy of the SPEV group with unadjustable charging demand newly connected to the grid; and They are the upper and lower bounds of power and energy of the newly connected grid-connected SPEV population with adjustable charging demand; To obtain the energy boundary by accumulating the energy boundary of the SPEV with adjustable charging demand, compare it with the boundary considering the power ramp limit, take the minimum value, and calculate the final upper and lower boundaries of the SPEV energy with adjustable charging demand, as shown in formula (31); Will The power and energy boundaries of the two types of SPEVs connected to the grid at the moment are added to the power and energy boundaries of the two types of SPEVs newly connected to the grid, respectively, to obtain The total power and energy boundaries of the two types of SPEVs at this moment: (34) (35) (36) (37)。

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