Method and system for evaluating adjustment capability of electric vehicle cluster

Abstract

The invention discloses an electric vehicle cluster adjustment capability assessment method and system, and the method comprises the steps: building an EV cluster response cost model based on time-of-use electricity price and excitation compensation electricity price, solving the model, and obtaining the number of EV users capable of participating in a response mode 1 and the number of EV users capable of participating in a response mode 1 and a response mode 2; according to the response mode of the EV user, obtaining the adjustment capability of the single EV in the response mode 1 and the response mode 2 in the evaluation time period; according to the EV user responsivity and excitation compensation electricity price relation equation, the responsivity of the EV cluster users participating in the response mode 1 and the response mode 2 is solved; and based on the solved EV user number, responsivity and adjustment capability, according to an EV cluster adjustment capability evaluation model, obtaining the adjustment capability of the EV cluster in an evaluation period. According to the method, the upper limit and the lower limit of the EV cluster adjustment capability can be directly displayed, and the EV cluster adjustment capability can be evaluated more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of interaction between electric vehicles and power grids, and in particular to a method and system for evaluating the regulation capability of an electric vehicle cluster taking user needs into consideration. Background Art

[0002] With the rapid development of social economy, energy consumption and carbon emissions are showing a rapid growth trend. Under the background of "double carbon", new energy vehicles, mainly electric vehicles (EV), have ushered in development opportunities, and the number of EVs has increased rapidly. The large-scale access of EVs to the power grid brings challenges to the power grid on the one hand, but also provides considerable regulation capabilities for the power grid on the other hand. It is of great significance to evaluate the regulation capabilities of EV clusters. Summary of the invention

[0003] The purpose of the present invention is to provide an electric vehicle cluster regulation capability evaluation method and system, which takes into account the impact of user diversified needs on EV cluster uncertainty, the battery loss cost of EV participating in V2G (Vehicle-to-grid), and the response mode and responsiveness of EV users under the influence of time-of-use electricity prices and incentive compensation electricity prices, establishes an EV cluster regulation capability evaluation model, and improves the accuracy of evaluating EV cluster regulation capabilities.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a method for evaluating the regulation capability of an electric vehicle cluster, comprising:

[0006] Based on the response mode of EV users participating in V2G under the influence of time-of-use electricity prices and incentive compensation electricity prices, an EV cluster response cost model is established, and the EV cluster response cost model is solved to obtain the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2; among which, response mode 1 is "charging-idle" and response mode 2 is "idle-discharging";

[0007] According to the response mode of EV users, the regulation capability of single EV in different scenarios is analyzed to obtain the regulation capability of single EV in response mode 1 and response mode 2 during the evaluation period;

[0008] According to the relationship equation between EV user responsiveness and incentive compensation electricity price, the responsiveness of EV cluster users participating in response mode 1 and response mode 2 is solved;

[0009] Based on the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2, the responsiveness of EV cluster users in response mode 1 and response mode 2, and the regulation capability of single EV in response mode 1 and response mode 2 during the evaluation period, the regulation capability of the EV cluster during the evaluation period is obtained according to the EV cluster regulation capability evaluation model.

[0010] Furthermore, the objective function of the EV cluster response cost model is:

[0011]

[0012] In the formula, represents the total charging cost of EV cluster users; represents the charging cost of the i-th EV; is the battery loss cost of the i-th EV during the discharge process under non-rated conditions; Represents the total number of EV users;

[0013] The constraints are:

[0014]

[0015]

[0016]

[0017]

[0018] In the formula, For the The output power of the EV; and Respectively Rated charging and discharging power of EV; and are the SOC values ​​of EV at time t and time t-1 respectively; is the time interval; It is the charging and discharging state coefficient of EV users; For the The capacity of the EV battery; and They are the charging and discharging efficiency of EV respectively; and are the minimum SOC value and maximum SOC value of the EV battery respectively; It is the SOC value when the EV user is off-grid; Expected SOC value for EV users.

[0019] Furthermore, EV users are divided into three categories: A, B, and C, where Category A EVs do not participate in V2G response, Category B EVs only participate in response mode 1, and Category C EVs participate in both response mode 1 and response mode 2;

[0020] For Class A EVs, the charging cost for each EV is as follows:

[0021]

[0022] In the formula, Charging costs for Class A EVs; is the time when the EV enters the network; Actual charging time for EV; is the real-time electricity price; For the The output power of a Class A EV; is the time interval; For the Rated charging power of EV;

[0023] For Class B EVs, the charging cost for each EV is as follows:

[0024]

[0025] In the formula, Charging costs for Class B EVs; is the off-grid time of the EV; Compensate electricity prices for incentives; For the The output power of a Class B EV;

[0026] For Class C EVs, the charging cost for each EV is as follows:

[0027]

[0028] In the formula, Charging costs for Class C EVs; For the The output power of a Class C EV; No. The rated discharge power of an EV.

[0029] Further, The battery loss cost of an EV during discharge under non-rated conditions is obtained according to the following steps:

[0030] The total effective discharge capacity of the battery under rated conditions is:

[0031]

[0032] In the formula, is the rated total effective discharge capacity of the battery; Rated cycle life for EV batteries; is the rated depth of discharge of the EV battery; is the rated discharge SOC value of the EV battery; is the rated capacity of the EV battery;

[0033] According to the following formula, the discharge capacity under non-rated conditions is equivalent to that under rated conditions:

[0034]

[0035] In the formula, For the The actual battery capacity during discharge under non-rated conditions; For the The discharge process under non-rated conditions is equivalent to the effective battery capacity under rated conditions; is the discharge rate influence coefficient; is the discharge depth influence coefficient; is the discharge interval influence coefficient;

[0036] Then the battery loss model of EV in response mode 2 is:

[0037]

[0038] In the formula, For the The battery loss cost of an EV during discharge under non-rated conditions; For EV battery prices.

[0039] Furthermore, according to the response mode of the EV user, the regulation capability of the single EV in different scenarios is analyzed to obtain the regulation capability of the single EV in response mode 1 and response mode 2 during the evaluation period, including:

[0040] For EV users participating in response mode 1:

[0041] When the EV is not connected to the grid for a period of time during the evaluation period and cannot respond to any mode, the EV will only have response power during the period of connection to the grid, increasing the grid's ability to adjust the rated charging power;

[0042] The EV has been connected to the grid before the evaluation period and has been in an idle state. However, in order to meet the user's travel energy requirements and reach the mandatory charging period during the idle period, the EV can only stop idling and switch to charging mode;

[0043] When an EV leaves the grid during the evaluation period, the EV only participates in the response during the period of access to the grid, increasing the grid's ability to adjust the rated charging power.

[0044] For EV users participating in Response Mode 2:

[0045] If the EV is not connected to the grid before the evaluation period, and is off-grid during this period and cannot respond to any mode, then the EV only has response power during the period of being connected to the grid, which can increase the grid's ability to adjust the rated discharge power;

[0046] The EV has been connected to the grid before the evaluation period and starts to respond to the discharge state during the evaluation period. However, during the discharge process, the battery SOC value reaches the minimum value allowed by the battery and meets the user's charging slack requirements. The EV can only operate in an idle state;

[0047] Before the evaluation period, the EV has been connected to the grid and is in a discharging state. However, during the discharging process, in order to meet the user's travel energy demand and reach the mandatory charging period, the EV can only stop discharging and switch to a charging state.

[0048] When an EV leaves the grid during the evaluation period, the EV can participate in the response only during the period when it is connected to the grid, thus increasing the grid's ability to regulate the rated discharge power.

[0049] Furthermore, the relationship equation between EV user responsiveness and incentive compensation electricity price is:

[0050]

[0051] In the formula, and are the responsiveness of EV cluster users participating in response mode 1 and response mode 2, respectively; , , and are the price compensation coefficients for EV user response mode 1 and response mode 2, respectively; To provide incentives to compensate electricity prices.

[0052] Furthermore, the upper and lower limits of the regulation capability of the EV cluster are:

[0053]

[0054] In the formula, , They are the up- and down-regulation capabilities of the EV cluster, respectively; is the number of Class B users in the EV cluster that can participate in response mode 1, It is the upward adjustment capability of Class B EV in response mode 1; and They are the up-regulation capabilities of Class C EV in response mode 1 and response mode 2, respectively; and are the number of Class C users in the EV cluster that can participate in response mode 1 and response mode 2, respectively;

[0055] According to the relationship equation between EV user responsiveness and incentive compensation electricity price and the upper and lower limit formulas of EV cluster regulation capacity, the EV cluster regulation capacity evaluation model is obtained:

[0056]

[0057] In the formula, Adjusting capacity for EV clusters; and They are the upper and lower deviation adjustment capabilities of the EV cluster, respectively; is the coefficient of deviation; , Respectively Rated charging and discharging power of EV; for and The difference.

[0058] In a second aspect, the present invention provides an electric vehicle cluster regulation capability evaluation system, comprising:

[0059] The response mode EV quantity solving module is used to establish an EV cluster response cost model based on the response mode of EV users participating in V2G under the influence of time-of-use electricity prices and incentive compensation electricity prices, solve the EV cluster response cost model, and obtain the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2; wherein response mode 1 is "charging-idle" and response mode 2 is "idle-discharging";

[0060] A single EV regulation capability acquisition module is used to analyze the regulation capability of a single EV in different scenarios according to the response mode of the EV user, and obtain the regulation capability of the single EV in response mode 1 and response mode 2 during the evaluation period;

[0061] A responsiveness calculation module is used to solve the responsiveness of EV cluster users participating in response mode 1 and response mode 2 according to the relationship equation between EV user responsiveness and incentive compensation electricity price;

[0062] The EV cluster regulation capability evaluation module is used to obtain the regulation capability of the EV cluster during the evaluation period according to the EV cluster regulation capability evaluation model based on the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2, the responsiveness of EV cluster users in response mode 1 and response mode 2, and the regulation capability of single EVs in response mode 1 and response mode 2 during the evaluation period.

[0063] In a third aspect, the present invention provides a computing device comprising a processor and a memory, wherein the processor is configured to implement the steps of the electric vehicle cluster regulation capability evaluation method described in any one of the first aspects when executing a computer program stored in the memory.

[0064] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the electric vehicle cluster regulation capability evaluation method described in any one of the first aspects are implemented.

[0065] Compared with the prior art, the beneficial technical effects of the present invention are:

[0066] 1. The present invention proposes an EV network access uncertainty model, evaluates the network access characteristics of a single EV, increases the accuracy of evaluating the regulation capability of an EV cluster, and at the same time, uncertainty protects user data from being leaked;

[0067] 2. The present invention proposes a refined battery loss model during the charging and discharging process under non-rated conditions, which mainly considers the influence of discharge depth and discharge range, and improves the accuracy of cost assessment of EV participating in the V2G process;

[0068] 3. The present invention considers the impact of different incentive compensation electricity prices on user response behavior, and can evaluate the EV cluster regulation capability under different time periods and different compensation electricity prices;

[0069] 4. The present invention takes into account the diverse needs of users, analyzes the impact of battery loss and incentive compensation on users, and can directly display the upper and lower limits of the EV cluster regulation capability compared to traditional algorithms, and displays the EV cluster regulation capability as a range area, thereby being able to more accurately evaluate the EV cluster regulation capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a flow chart of the method of the present invention;

[0071] Figure 2 These are the three different scenario diagrams under EV response mode 1;

[0072] Figure 3 These are the four different scene diagrams under EV response mode 2;

[0073] Figure 4 The impact diagram of incentive compensation electricity price on user response to the “charging-idle” mode;

[0074] Figure 5 The impact diagram of incentive compensation electricity price on user response "idle-discharge" mode;

[0075] Figure 6 Potential adjustment diagram for price response;

[0076] Figure 7 This is the potential adjustment diagram when the incentive compensation electricity price is 0.6 yuan / kWh;

[0077] Figure 8 This is the potential adjustment diagram of the incentive compensation electricity price of 0.6 yuan / kWh under the traditional algorithm. DETAILED DESCRIPTION

[0078] The present invention will be further described below in conjunction with specific examples. The following examples are only used to more clearly illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the present invention.

[0079] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the regulation capability of an electric vehicle cluster, comprising the following steps:

[0080] Step 1: Based on the response mode of EV users participating in V2G under the influence of time-of-use electricity prices and incentive compensation electricity prices, an EV cluster response cost model is established, and the EV cluster response cost model is solved to obtain the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2;

[0081] Step 101, based on the EV cluster uncertainty model, consider the impact of user demand on the regulation characteristics;

[0082] In order to accurately evaluate the cluster regulation capability, it is necessary to consider user needs when building the EV model. User needs are mainly reflected in different interaction preferences.

[0083] For EVs connected to the grid, the load regulation characteristics are related to the EV on-grid time and charging time. The EV on-grid time is defined as follows:

[0084]

[0085] In the formula, is the time EV spends online, and are the time when EV enters the grid and the time when it leaves the grid, respectively.

[0086] When EV is connected to the power grid, there are four response modes, including response mode 1: "Charge-Idle", response mode 2: "Idle-Discharge", response mode 3: "Discharge-Idle", and response mode 4: "Idle-Charge".

[0087] This paper takes response mode 1 and response mode 2 as research objects, considers the impact of EV users' interaction preferences on regulation characteristics and combines time-of-use electricity prices and compensation electricity prices, and divides EV users into the following three categories according to their interaction preferences:

[0088] 1) Class A EV

[0089] This type of user does not participate in V2G response and is not affected by time-of-use electricity prices and compensation electricity prices. This type of EV charges as soon as it enters the grid and becomes idle after charging is completed until it leaves the grid. This type of EV is an uncontrollable load;

[0090] 2) Class B EV

[0091] This type of user participates in V2G response. Affected by the time-of-use electricity price and the compensation electricity price, this type of EV user wants to reduce the charging cost but does not want to increase the battery loss cost. Therefore, this type of EV only responds to mode 1 and does not respond to mode 2. This type of EV is a controllable load.

[0092] 3) Class C EV

[0093] This type of user participates in V2G response. Affected by time-of-use electricity prices and compensation electricity prices, this type of EV user pursues the lowest charging cost; therefore, this type of EV responds to both mode 1 and mode 2, and this type of EV is a controllable load.

[0094] Step 102, analyzing the single EV access operation area;

[0095] Considering the diverse needs of users, the grid-connected operation area of ​​a single EV is probabilistically distributed. The user's off-grid time will be different due to the randomness of the user's grid-connected time and the user's expected SOC value. If the grid-connected time is later than the predicted time, the actual area will shift to the right, and vice versa. According to the charging needs of different users, different EVs are adjusted to change the charging power to relieve the pressure on the grid and achieve economic dispatch. Different behaviors and intentions of users will cause EVs to be randomly distributed in the area in the figure. This area is composed of the EV's grid-connected time, off-grid time, grid-connected SOC value, the maximum SOC value allowed by the EV user, the expected SOC value when the EV user is off the grid, and the minimum SOC value allowed by the EV user.

[0096] When a single EV is connected to the grid, the power meets the constraints shown in the following formula:

[0097]

[0098] In the formula, For the The output power of the EV; and Respectively The rated charging and discharging power of an EV.

[0099] According to the changes between various states of EV access to the grid, the battery SOC value changes as shown in the following formula:

[0100]

[0101] In the formula, and Respectively The SOC value of the battery of an EV at the current moment and the next moment; is the time interval; and They are the charging and discharging efficiency of EV respectively; For the The capacity of an EV battery.

[0102] Step 103, considering the impact of battery loss, establishing a refined battery loss model;

[0103] Analyze the impact of different response modes on the loss of EV batteries, especially the loss model involved in discharge under different discharge depths and different discharge intervals; the cycle life of the battery is related to the discharge depth and discharge interval. The cycle life of the battery decreases with the increase of discharge depth. The higher the discharge interval, the more the cycle life decays. The discharge loss is large when the power is high, and the discharge loss is small when the power is low. Therefore, the total effective discharge power of the battery under rated conditions is shown as follows:

[0104]

[0105] In the formula, is the rated total effective discharge capacity of the battery; Rated cycle life for EV batteries; is the rated depth of discharge of the EV battery; is the rated discharge SOC value of the EV battery; It is the rated capacity of EV battery.

[0106] In order to calculate the battery loss cost under non-rated conditions, it is necessary to equate the discharge capacity under non-rated conditions to that under rated conditions. The equivalence process is mainly affected by the discharge depth, discharge range and discharge rate. Considering the influence of various factors comprehensively, the equivalence method is obtained:

[0107]

[0108] In the formula, For the The actual battery capacity during discharge under non-rated conditions; For the The discharge process under non-rated conditions is equivalent to the effective battery capacity under rated conditions; is the discharge rate influence coefficient; is the discharge depth influence coefficient; is the discharge interval influence coefficient;

[0109] Among them, the discharge rate is the ratio of the EV discharge current. Since the voltage fluctuation when the EV is connected to the grid is very small during normal and stable operation, the voltage can be considered to be basically constant; therefore, the power ratio can be used instead of the current ratio. The discharge rate influence coefficient is shown in the following formula:

[0110]

[0111] In the formula, , They are the EV rated discharge current and rated discharge power respectively; For the The actual discharge current of the discharge process under non-rated conditions;

[0112] The discharge depth influence coefficient can be expressed as follows:

[0113]

[0114] In the formula, For the Actual cycle life of discharge process under non-rated conditions;

[0115] Taking lithium batteries as an example, the actual cycle life of the battery discharge process is calculated using the following formula:

[0116]

[0117] In the formula, , , is the fitting coefficient; For the The actual discharge depth of the discharge process under non-rated conditions;

[0118] The discharge interval influence coefficient can be expressed as:

[0119]

[0120] In the formula, is the Faraday constant; is the molar gas constant, determined by the battery type; is the number of electron transfers; is the charge state of the discharge cycle at time t;

[0121] Combining the above expressions, the battery loss cost during EV response to V2G can be expressed as:

[0122]

[0123] In the formula, For the The battery loss cost of an EV during discharge under non-rated conditions; For EV battery prices.

[0124] Step 104, establishing an EV user response cost model based on the response mode of EV users under the influence of electricity prices;

[0125] Due to the uncertainty of electricity prices, different degrees of user response and different response methods, users' interactive preferences are random; users' interactive preferences are as follows: 1. Not affected by electricity prices, charging occurs as soon as the network is connected; 2. Affected by electricity prices, reducing charging costs while considering battery loss; 3. Affected by electricity prices, pursuing the lowest charging cost.

[0126] EV users are divided into three categories according to their response patterns, and the response costs of different types of users are different;

[0127] (1) Class A EV

[0128] For Class A EV, which does not respond to any mode and is connected to the grid only to obtain electricity, the charging cost is shown in the following formula;

[0129]

[0130] In the formula, Charging costs for Class A EVs; Actual charging time for EV; is the real-time electricity price; For the The output power of a Class A EV; For the time interval.

[0131] (2) Class B EV

[0132] For Class B EVs, the discharge time can be shifted to reduce the charging cost without increasing the battery discharge loss. The charging cost is shown in the following formula:

[0133]

[0134] In the formula, Charging costs for Class B EVs; Compensate electricity prices for incentives; For the The output power of a Class B EV.

[0135] (3) Class C EV

[0136] For Class C EV, it can respond to V2G and pursue charging at the lowest charging cost. The charging cost is shown in the following formula:

[0137]

[0138] In the formula, Charging costs for Class C EVs; For the The output power of a Class C EV.

[0139] In order for EVA to dispatch sufficient response power and reduce the battery life loss of users participating in V2G, the scheduling is carried out according to the following strategy:

[0140] 1) Obtain the operating status of EVs during the evaluation period, and obtain the number of EVs that can participate in the “charge-idle” and “idle-charge” response modes based on the current SOC value and network time;

[0141] 2) According to the dispatch capacity of the current period, EVs that can participate in the "charge-idle" response mode are prioritized. When all EVs that respond to the "charge-idle" mode respond and still cannot meet the dispatch requirements, EVs that can participate in the "idle-charge" response mode are dispatched, arranged in ascending order according to the battery loss cost, and EVs with the least battery loss are prioritized to participate in the response until the dispatch requirements are met;

[0142] 3) Record the access status of participating EVs and obtain the current regulation capacity and incentive compensation electricity price.

[0143] From the perspective of EV users, they hope to charge at the lowest charging cost. The response cost includes user charging cost, battery loss cost and incentive compensation cost. Therefore, the objective function of the EV cluster response cost model is:

[0144]

[0145] In the formula, represents the total charging cost of EV cluster users; represents the charging cost of the i-th EV; is the battery loss cost of the i-th EV during the discharge process under non-rated conditions; Indicates the total number of EV users.

[0146] The EV cluster response cost model should satisfy the following constraints:

[0147] (1) Charge and discharge constraints

[0148] When EV is connected to the grid, it should meet the user's charging and discharging power range constraints, as shown in the following formula:

[0149]

[0150] (2) Battery SOC equation constraint

[0151] After EV is connected to the grid, affected by different response modes, the battery SOC value status is as follows:

[0152]

[0153] In the formula, and are the SOC values ​​of EV at time t and time t-1 respectively; It is the charging and discharging state coefficient of EV users;

[0154] (3) Battery SOC value constraint

[0155] When EVs are connected to the grid and participate in V2G response, they cannot operate beyond the maximum and minimum allowable range of battery power:

[0156]

[0157] In the formula, and are the minimum and maximum SOC values ​​of the EV battery, respectively;

[0158] (4) Constraints on user travel energy demand

[0159] EV users should meet travel energy requirements when leaving the grid, as shown in the following formula:

[0160]

[0161] In the formula, Indicates the SOC value when the EV user is off-grid; Expected SOC value for EV users.

[0162] Step S2, analyzing the regulation capabilities of the single EV in different scenarios according to the response mode of the EV user, and obtaining the regulation capabilities of the single EV in response mode 1 and response mode 2 during the evaluation period;

[0163] During the time when EV is connected to the grid, the SOC value of EV will change over time according to the response mode. Different response modes and different user needs will lead to different regulation capabilities of EV. Therefore, according to the user's response mode, the regulation capability of EV in different scenarios is analyzed, and then the regulation capability in multiple time periods is evaluated, including:

[0164] (1) “Charging-Idle” response mode

[0165] When the EV is in the charging state and meets the user's power slack, it can respond to mode 1. Power slack refers to the time margin for the user to participate in the grid interaction and finally meet the power demand from the current moment to the moment of leaving the grid. At this time, the EV stops charging and turns to the idle state, with the ability to adjust upward. When the EV has the following three typical scenarios, the EV's adjustment ability will change, such as Figure 2 As shown, there are three different scenarios in EV response mode 1:

[0166] Scenario 1: When the EV is not connected to the grid for a period of time during the evaluation period, that is, the EV is off-grid and cannot respond to any mode, then the EV only has response power during the period of being connected to the grid, which can increase the grid's ability to adjust the rated charging power;

[0167] Scenario 2: The EV has been connected to the grid before the evaluation period and has been in an idle state. However, in order to meet the user's travel energy requirements and reach the mandatory charging period during the idle period, the EV can only stop idling and switch to charging mode;

[0168] Scenario 3: When the EV leaves the grid during the evaluation period, that is, the EV is not connected to the grid during the evaluation period, the EV can only participate in the response during the period of grid connection, which can increase the regulation capacity of the rated charging power for the grid;

[0169] (2) “Idle-discharge” response mode

[0170] When the EV is in an idle state and meets the user's power consumption relaxation, it can respond to mode 2. At this time, the EV stops idling and turns to a discharging state, with the ability to adjust upward. When the EV has the following four typical scenarios, the EV's adjustment ability will change, such as Figure 3 As shown, there are four different scenarios in EV response mode 2;

[0171] Scenario 1: Similar to Scenario 1 of Response Mode 1, the EV is not connected to the grid before the evaluation period. During this period, the EV is off-grid and cannot respond to any mode. The EV only has response power during the period of grid connection, which can increase the grid's ability to adjust the rated discharge power.

[0172] Scenario 2: The EV has been connected to the grid before the evaluation period and responds to the discharge state at the beginning of the evaluation period. However, during the discharge process, the battery SOC value reaches the minimum value allowed by the battery and also meets the user's charging slack requirements. The EV can only operate in an idle state.

[0173] Scenario 3: Before the evaluation period, the EV has been connected to the grid and is in a discharging state. However, during the discharging process, in order to meet the user's travel energy demand and reach the mandatory charging period, the EV can only stop discharging and switch to a charging state;

[0174] Scenario 4: When the EV leaves the grid during the evaluation period, that is, the EV is not connected to the grid during the evaluation period, the EV can only participate in the response during the period when it is connected to the grid, which can increase the regulation capacity of the rated discharge power for the grid.

[0175] Step S3, solving the responsiveness of EV cluster users participating in response mode 1 and response mode 2 according to the relationship equation between EV user responsiveness and incentive compensation electricity price;

[0176] EV cluster regulation capability refers to the ability of electric vehicles to coordinate charging and discharging behavior within a specific time to respond to grid demand, balance loads, reduce peak loads, or provide backup power.

[0177] In order to attract more users to participate in V2G in the operation of the power grid, EVA often formulates an incentive mechanism to encourage users to participate in response mode 1 or even response mode 2. The greater the incentive, the more EVs will participate in response modes 1 and 2. Incentive compensation can encourage users to actively participate in V2G, and at the same time, it will also output more adjustable capabilities to the power grid, and have a positive impact on the reliability, flexibility and stability of power grid operation. Through statistical analysis of previous day-ahead electricity price data, we can find that the electricity price has a certain regularity. The responsiveness of EV users to participate in V2G is related to the incentive compensation price, as shown in the following formula:

[0178]

[0179] In the formula, and are the responsiveness of EV cluster users participating in response mode 1 and response mode 2, respectively; , , and are the price compensation coefficients for EV user response mode 1 and response mode 2, respectively; To provide incentives to compensate electricity prices.

[0180] Step S4, based on the number of Class B EV users in the EV cluster that can participate in response mode 1 and the number of Class C EV users that can participate in response mode 1 and response mode 2, the responsiveness of EV cluster users to response mode 1 and response mode 2, and the regulation capability of a single EV in response mode 1 and response mode 2 during the evaluation period, the regulation capability of the EV cluster during the evaluation period is obtained according to the EV cluster regulation capability evaluation model;

[0181] Affected by the diversified needs of EV users, the regulation capacity of EVs will change over time during the V2G process. The upper and lower limits of the regulation capacity of EV clusters are evaluated, and the upper and lower limits of the regulation capacity of EV clusters are:

[0182]

[0183] In the formula, , They are the up- and down-regulation capabilities of the EV cluster, respectively; is the number of Class B users in the EV cluster that can participate in response mode 1 “Charge-Idle”, It is the upward adjustment capability of Class B EV in response mode 1; and They are the up-regulation capabilities of Class C EV in response modes 1 and 2, respectively; and are the number of Class C users in the EV cluster that can participate in response modes 1 and 2, respectively.

[0184] According to the relationship between EV user responsiveness and incentive compensation electricity price, the EV cluster regulation capacity evaluation model is obtained:

[0185]

[0186] In the formula, Adjusting capacity for EV clusters; , Respectively Rated charging and discharging power of EV; and They are the upper and lower deviation adjustment capabilities of the EV cluster, respectively; is the coefficient of deviation; for and The difference.

[0187] Examples are given below:

[0188] Taking 10,000 EVs in the region as an example, the simulation verification is carried out. The time when EV is connected to the grid follows the probability distribution of N (17.47, 3.41), the time when it leaves the grid follows the probability distribution of N (8.92, 3.24), the initial SOC value of the grid connection follows the probability distribution of N (0.3, 0.05), the user's expected SOC value follows the probability distribution of N (0.8, 0.03), the minimum allowable SOC value of the battery is 0, and the maximum operating SOC value is 1. The battery capacity is 30kWh, the charging and discharging power is 6kW, the charging and discharging efficiency is probability distributed, the rated depth of discharge of the battery is 0.8, the rated initial discharge SOC value of the battery is 0.8, the rated cycle life of the battery is 10.66 million times, the battery cycle life coefficients a, b and c are 694, 1.98 and 0.016 respectively, the price of the battery is 60,000 yuan, and the grid time-of-use electricity price is shown in Table 1.

[0189] Table 1 Grid time-of-use electricity price

[0190] Since users have diverse needs, EVs have different response behaviors after being connected to the grid. The impact of incentive compensation electricity prices on user response behaviors is as follows: Figure 4 and 5 As shown in the figure, it can be seen that when the incentive compensation electricity price is lower than 0.5 yuan / kWh, due to the low incentive compensation electricity price, EV users only respond to the "charging-idle" mode, but not the "idle-charging" mode, and obtain the minimum charging cost by shifting the charging time; when the incentive compensation electricity price is higher than 0.5 yuan / kWh, due to the high incentive compensation electricity price, EV users tend to discharge to the grid, and the charging vehicles also start to discharge to the grid, thereby obtaining the minimum charging cost.

[0191] Depend on Figure 4 It can be seen that with the increase of incentive compensation electricity price, the proportion of EV users responding to "charging-idle" is increasing. The lower the electricity price, the faster the response rate increases. When the incentive compensation electricity price is 0.5 yuan / kWh, the response rate is the highest. Since the electricity price is flat during the period from 16:00 to 18:00, and most EVs have just started to be connected to the power grid, the charging margin is large. Therefore, the proportion of users responding to "charging-idle" when receiving compensation is relatively large. During the period from 2:00 to 12:00, due to the small number of EVs in charging state, the proportion of EVs that can participate in "charging-idle" is small.

[0192] Depend on Figure 5It can be seen that during the low electricity price period, due to the high incentive compensation price, when the incentive compensation price is 0.6 yuan / kWh, the proportion of users responding to "idle-discharge" is 100%; during the normal electricity price period, when the incentive compensation price is 0.7 yuan / kWh, the proportion of users responding to "idle-discharge" reaches 100%; during the peak electricity price period, the incentive compensation price needs to be increased to increase the proportion of responding to "idle-discharge". Therefore, the proportion of users responding to "idle-discharge" does not change much during the low electricity price period and the normal period, and the proportion of responsiveness increases during the peak electricity price period as the incentive compensation price increases.

[0193] Depend on Figure 5 and Figure 6 It can be seen that the EV user response is greatly affected by the incentive compensation electricity price and the time-of-use electricity price. As the incentive compensation electricity price increases, the EV user response increases accordingly. Even when the incentive compensation electricity price is higher than 0.6 yuan / kWh, during the electricity price valley period, the EV user response to "idle-charging" reaches 100%. Therefore, the uncertainty of user participation response can be controlled to some extent by the electricity price and the incentive compensation electricity price.

[0194] The regulation capacity of the EV cluster considering user responsiveness under time-of-use electricity prices is analyzed based on the regulation capacity evaluation model. The price response curve of the EV cluster considering user responsiveness under different incentive compensation electricity prices is as follows: Figure 6 The regulation capability of the EV cluster when the incentive compensation electricity price is 0.6 yuan / kWh is shown in Figure 7 As shown. Figure 6 , Figure 7 It can be seen that there is a confidence interval for the cluster regulation ability in each period, which is consistent with the existing cluster model ( Figure 8 ), the upper and lower deviation powers are used to evaluate the cluster regulation capability, which avoids the evaluation results being too conservative, thus making the evaluation results more accurate. By comparing and analyzing the regulation capability of EV clusters under different incentive compensation electricity prices at different time periods, it can be seen that the regulation capability of EV clusters has the characteristics of time and incentive compensation electricity price distribution.

[0195] Under the same compensation electricity price, when the incentive compensation electricity price is small, the number of EV load clusters connected to the grid during the valley period is large, and the regulation capacity is large. During the peak period, there are few users responding to mode 1, and the regulation capacity is small. When the incentive compensation electricity price increases, the regulation capacity during the peak and valley periods is significantly improved due to the increase in the number of users responding to mode 2.

[0196] In the same evaluation period, the regulation capacity increases with the increase of incentive compensation electricity price. In the 24:00 period, due to the low real-time electricity price, the mode 1 response method has reached the maximum response rate when the incentive compensation electricity price is 0.3 yuan / kWh. With the increase of incentive compensation electricity price, at 0.5 yuan / kWh, users respond to the "idle-discharge" response method, and the regulation capacity is improved; in the 8:00 and 16:00 periods, the incentive compensation electricity price can only reach the maximum regulation capacity when it is 0.7 yuan / kWh. Due to the difference in the number of EVs connected to the grid and the current SOC value, the regulation capacity at 8:00 is larger; in the 8:00 period, because the EV battery is currently fully charged and is already in an idle state, it cannot respond to mode 1.

[0197] The battery loss cost of a single EV is calculated based on the dispatching strategy and the SOC value at each moment, and the dispatching task is allocated according to the dispatching task of the load aggregator and the battery loss cost of the EV participating in the discharge. The access status of 1,000 EVs and the number of EVs that can participate in the response are shown in Table 2. Taking the 19:00-20:00 time period as an example, the number of EVs that can participate in the "charge-idle" mode in the current time period is 357, and the number of EVs that can participate in the "idle-discharge" mode is 585. The maximum regulation capacity of participating in the "charge-idle" response mode is 2.142MW. If the dispatching task of the load aggregator is less than or equal to 2.142MW, it is only necessary to dispatch EVs to respond to the "charge-idle" mode to complete the dispatching task; if the dispatching task of the load aggregator is greater than 2.142MW, it is necessary to dispatch EVs to respond to the "idle-discharge" mode to complete the dispatching task. Considering the battery loss, EVs with lower battery loss costs are given priority for discharge during the dispatching discharge process to achieve the minimum compensation cost for the load aggregator while taking into account the user cost.

[0198] Table 2 Distribution of EV status on the grid at each time

[0199] and Figure 8 Compared with the results of the traditional algorithm, it can be clearly seen that the present invention can directly display the range of the regulation ability at different time periods and can more accurately evaluate the regulation ability.

[0200] The present invention also provides an electric vehicle cluster regulation capability evaluation system, comprising:

[0201] The response mode EV quantity solving module is used to establish an EV cluster response cost model based on the response mode of EV users participating in V2G under the influence of time-of-use electricity prices and incentive compensation electricity prices, solve the EV cluster response cost model, and obtain the number of EV users in the EV cluster who can participate in response mode 1 and the number of EV users who can participate in response mode 1 and response mode 2; wherein response mode 1 is "charging-idle" and response mode 2 is "idle-discharging";

[0202] A single EV regulation capability acquisition module is used to analyze the regulation capability of a single EV in different scenarios according to the response mode of the EV user, and obtain the regulation capability of the single EV in response mode 1 and response mode 2 during the evaluation period;

[0203] A responsiveness calculation module is used to solve the responsiveness of EV cluster users participating in response mode 1 and response mode 2 according to the relationship equation between EV user responsiveness and incentive compensation electricity price;

[0204] The EV cluster regulation capability evaluation module is used to obtain the regulation capability of the EV cluster during the evaluation period according to the EV cluster regulation capability evaluation model based on the number of EV users in the EV cluster that can participate in response mode 1 and the number of EV users that can participate in response mode 1 and response mode 2, the responsiveness of EV cluster users in response mode 1 and response mode 2, and the regulation capability of single EVs in response mode 1 and response mode 2 during the evaluation period.

[0205] The present invention also provides a computing device, including a processor and a memory, wherein the processor is used to implement the steps of the aforementioned electric vehicle cluster regulation capability evaluation method when executing a computer program stored in the memory.

[0206] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the aforementioned electric vehicle cluster regulation capability evaluation method are implemented.

[0207] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0208] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0211] The present invention has been disclosed above with preferred embodiments, but they are not intended to limit the present invention. Any technical solutions obtained by adopting equivalent replacement or equivalent transformation solutions fall within the protection scope of the present invention.

Claims

1. A method for evaluating the regulation capability of an electric vehicle cluster, characterized in that: include: Based on the response mode of EV users participating in V2G under the influence of time-of-use electricity price and incentive compensation electricity price, an EV cluster response cost model is established, and the EV cluster response cost model is solved to obtain the number of Class B EV users who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2 in the EV cluster; among them, response mode 1 is "charging-idle" and response mode 2 is "idle-discharging"; According to the response mode of EV users, the regulation capability of single EV in different scenarios is analyzed to obtain the regulation capability of single EV in response mode 1 and response mode 2 during the evaluation period; According to the relationship equation between EV user responsiveness and incentive compensation electricity price, the responsiveness of EV cluster users participating in response mode 1 and response mode 2 is solved; Based on the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2, the responsiveness of EV cluster users in response mode 1 and response mode 2, and the regulation capability of single EV in response mode 1 and response mode 2 during the evaluation period, the regulation capability of the EV cluster during the evaluation period is obtained according to the EV cluster regulation capability evaluation model.

2. The electric vehicle cluster regulation capability evaluation method according to claim 1, characterized in that: The objective function of the EV cluster response cost model is: In the formula, represents the total charging cost of EV cluster users; represents the charging cost of the i-th EV; is the battery loss cost of the i-th EV during the discharge process under non-rated conditions; Represents the total number of EV users; The constraints are: In the formula, For the The output power of the EV; and Respectively Rated charging and discharging power of EV; and are the SOC values ​​of EV at time t and time t-1 respectively; is the time interval; It is the charging and discharging state coefficient of EV users; For the The capacity of the EV battery; and They are the charging and discharging efficiency of EV respectively; and are the minimum SOC value and maximum SOC value of the EV battery respectively; It is the SOC value when the EV user is off-grid; Expected SOC value for EV users.

3. The electric vehicle cluster regulation capability evaluation method according to claim 2 is characterized in that: EV users are divided into three categories: A, B, and C. Category A EVs do not participate in V2G response, Category B EVs only participate in response mode 1, and Category C EVs participate in both response mode 1 and response mode 2. For Class A EVs, the charging cost for each EV is as follows: In the formula, Charging costs for Class A EVs; is the time when the EV enters the network; Actual charging time for EV; is the real-time electricity price; For the The output power of a Class A EV; is the time interval; For the Rated charging power of EV; For Class B EVs, the charging cost for each EV is as follows: In the formula, Charging costs for Class B EVs; is the off-grid time of the EV; Compensate electricity prices for incentives; For the The output power of a Class B EV; For Class C EVs, the charging cost for each EV is as follows: In the formula, Charging costs for Class C EVs; For the The output power of a Class C EV; No. The rated discharge power of an EV.

4. The electric vehicle cluster regulation capability evaluation method according to claim 2 is characterized in that: No. The battery loss cost of an EV during discharge under non-rated conditions is obtained by the following steps: The total effective discharge capacity of the battery under rated conditions is: In the formula, is the rated total effective discharge capacity of the battery; Rated cycle life for EV batteries; is the rated depth of discharge of the EV battery; is the rated discharge SOC value of the EV battery; is the rated capacity of the EV battery; According to the following formula, the discharge capacity under non-rated conditions is equivalent to that under rated conditions: In the formula, For the The actual battery capacity during discharge under non-rated conditions; For the The discharge process under non-rated conditions is equivalent to the effective battery capacity under rated conditions; is the discharge rate influence coefficient; is the discharge depth influence coefficient; is the discharge interval influence coefficient; Then the battery loss model of EV in response mode 2 is: In the formula, For the The battery loss cost of an EV during discharge under non-rated conditions; For EV battery prices.

5. The electric vehicle cluster regulation capability evaluation method according to claim 1, characterized in that: According to the response mode of EV users, the regulation capability of single EV in different scenarios is analyzed to obtain the regulation capability of single EV in response mode 1 and response mode 2 during the evaluation period, including: For EV users participating in response mode 1: When the EV is not connected to the grid for a period of time during the evaluation period and cannot respond to any mode, the EV will only have response power during the period of connection to the grid, increasing the grid's ability to adjust the rated charging power; The EV has been connected to the grid before the evaluation period and has been in an idle state. However, in order to meet the user's travel energy requirements and reach the mandatory charging period during the idle period, the EV can only stop idling and switch to charging mode; When an EV leaves the grid during the evaluation period, the EV only participates in the response during the period of access to the grid, increasing the grid's ability to adjust the rated charging power. For EV users participating in response mode 2: If the EV is not connected to the grid before the evaluation period, and is off-grid during this period and cannot respond to any mode, then the EV only has response power during the period of being connected to the grid, which can increase the grid's ability to adjust the rated discharge power; The EV has been connected to the grid before the evaluation period and starts to respond to the discharge state during the evaluation period. However, during the discharge process, the battery SOC value reaches the minimum value allowed by the battery and meets the user's charging slack requirements. The EV can only operate in an idle state; Before the evaluation period, the EV has been connected to the grid and is in a discharging state. However, during the discharging process, in order to meet the user's travel energy demand and reach the mandatory charging period, the EV can only stop discharging and switch to a charging state. When an EV leaves the grid during the evaluation period, the EV can participate in the response only during the period when it is connected to the grid, thus increasing the grid's ability to regulate the rated discharge power.

6. The electric vehicle cluster regulation capability evaluation method according to claim 1 is characterized in that: The relationship equation between EV user responsiveness and incentive compensation electricity price is: In the formula, and are the responsiveness of EV cluster users participating in response mode 1 and response mode 2, respectively; , , and are the price compensation coefficients for EV user response mode 1 and response mode 2, respectively; To provide incentives to compensate electricity prices.

7. The electric vehicle cluster regulation capability evaluation method according to claim 6 is characterized in that: The upper and lower limits of the regulation capability of the EV cluster are: In the formula, , They are the up- and down-regulation capabilities of the EV cluster, respectively; is the number of Class B users in the EV cluster that can participate in response mode 1, It is the upward adjustment capability of Class B EV in response mode 1; and They are the up-regulation capabilities of Class C EV in response mode 1 and response mode 2, respectively; and are the number of Class C users in the EV cluster that can participate in response mode 1 and response mode 2, respectively; According to the relationship equation between EV user responsiveness and incentive compensation electricity price and the upper and lower limit formulas of EV cluster regulation capacity, the EV cluster regulation capacity evaluation model is obtained: In the formula, Adjusting capacity for EV clusters; and They are the upper and lower deviation adjustment capabilities of the EV cluster, respectively; is the coefficient of deviation; , Respectively Rated charging and discharging power of EV; for and The difference.

8. An electric vehicle cluster regulation capability evaluation system, characterized in that: include: The response mode EV quantity solving module is used to establish an EV cluster response cost model based on the response mode of EV users participating in V2G under the influence of time-of-use electricity prices and incentive compensation electricity prices, solve the EV cluster response cost model, and obtain the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2; among which, response mode 1 is "charging-idle" and response mode 2 is "idle-discharging"; A single EV regulation capability acquisition module is used to analyze the regulation capability of a single EV in different scenarios according to the response mode of the EV user, and obtain the regulation capability of the single EV in response mode 1 and response mode 2 during the evaluation period; A responsiveness calculation module is used to solve the responsiveness of EV cluster users participating in response mode 1 and response mode 2 according to the relationship equation between EV user responsiveness and incentive compensation electricity price; The EV cluster regulation capability evaluation module is used to obtain the regulation capability of the EV cluster during the evaluation period according to the EV cluster regulation capability evaluation model based on the number of Class B EV users in the EV cluster who can participate in response mode 1 and the number of Class C EV users who can participate in response mode 1 and response mode 2, the responsiveness of EV cluster users in response mode 1 and response mode 2, and the regulation capability of single EVs in response mode 1 and response mode 2 during the evaluation period.

9. A computing device, characterized in that The method comprises a processor and a memory, wherein the processor is used to implement the steps of the electric vehicle cluster regulation capability evaluation method as claimed in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the electric vehicle cluster regulation capability evaluation method according to any one of claims 1 to 7 are implemented.

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