Electric taxi demand response scheduling method based on fuzzy control

Abstract

The invention relates to the technical field of electric power systems, solves the technical problem that demand response subsidy and an electric taxi charging plan can be calculated only by continuously gaming when an electric taxi participates in demand response at present, and particularly relates to an electric taxi demand response scheduling method based on fuzzy control. The method comprises the following steps that parameters of an electric vehicle being charged and parameters of a charging pile in a lower-layer main body are obtained from an upper-layer main body, the upper-layer main body is a power grid or an electric vehicle load aggregator, and the lower-layer main body is an electric taxi; and making an original charging plan of the electric taxi according to the electric vehicle parameters and the charging pile parameters, wherein the original charging plan comprises the residual electric quantity and the charging power after the electric taxi is charged. The method can assist the electric taxi in making the charging plan, can quickly calculate the expected demand response subsidy without a bidirectional game mode, and further supports the electric vehicle load aggregator to participate in the demand response.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to a demand response dispatching method for electric taxis based on fuzzy control. Background Art

[0002] The main problem of electric taxis participating in demand response is how to interact with the power grid and load aggregators. The content of the interaction is to negotiate demand response subsidies and electric taxi charging plans. The interactive method is a two-layer game, with the upper layer being the power grid or electric vehicle load aggregators and the lower layer being electric taxis. The upper layer of the power grid or electric vehicle load aggregators publishes load reduction tasks and subsidies to the lower layer, and the lower layer of electric taxis feed back charging plans to the upper layer. This model requires interaction between the upper and lower layers to iteratively calculate demand response subsidies and electric taxi charging plans.

[0003] However, electric taxis interact with power grids and load aggregators during the demand response process, and constant bargaining is required to calculate demand response subsidies and electric taxi charging plans. In actual practice, the technical conditions of the electric taxi infrastructure are immature, making it difficult to support electric taxi bargaining. The bargaining cost is high, and the bargaining efficiency is low, which will affect the user experience of participating in demand response. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an electric taxi demand response scheduling method based on fuzzy control, which solves the current technical problem that electric taxis participating in demand response need to constantly bargain in order to calculate demand response subsidies and electric taxi charging plans.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for demand response scheduling of electric taxis based on fuzzy control, the method comprising the following steps:

[0006] S1. Obtaining the parameters of the electric vehicle being charged and the parameters of the charging pile in the lower-level subject from the upper-level subject, the upper-level subject is the power grid or the electric vehicle load aggregator, and the lower-level subject is the electric taxi;

[0007] S2. Formulate an original charging plan for the electric taxi based on the parameters of the electric vehicle and the charging pile. The original charging plan includes the remaining power of the electric taxi after charging. and charging power

[0008] S3, extracting the electric taxi k that is being charged during the demand response period from the electric taxi set K;

[0009] S4. Calculate the expected subsidy for electric taxis being charged during demand response using fuzzy control method

[0010] S5. Based on subsidies Analyze the total cost of each electric taxi participating in demand response and the charging plan when participating in demand response;

[0011] S6. The electric vehicle load aggregator determines the charging plan for electric taxis when they participate in demand response.

[0012] Furthermore, in view of the technical problem that electric taxis need to constantly bargain to calculate demand response subsidies and electric taxi charging plans when participating in demand response, the present invention also provides another technical solution, including the following steps:

[0013] Step S1, extracting the parameters of the electric vehicle being charged, the parameters of the charging pile, and the demand response control parameters. The electric vehicle parameters include K, k, Charging pile parameters include P FAST , P SLOW ; Demand response control parameters include t, T, T DR ,

[0014] Step S2: extracting the parameters for electric vehicle load aggregators to participate in demand response, including e0; where is a constant, which represents the load in time period t after the electric vehicle charging station reduces the load; e0 is a constant, which represents the subsidy issued by the power grid to the electric vehicle load aggregator;

[0015] Step S3: Initialize control parameters

[0016] Step S4: the electric taxi formulates a load charging plan, that is, an original charging plan for the electric taxi;

[0017] Step S5: extract the set of electric taxis charged during the demand response period, and determine whether each electric taxi k in the electric taxi set K is still charging. The determination condition is:

[0018]

[0019] If the condition is met, it means that the vehicle is still charging. The electric taxi users who are charging during the demand response period are grouped into a set K DR express;

[0020] Step S6: For electric taxi users who are charging during the demand response period, fuzzy control is used to support their quotations and calculate the subsidies that electric taxis expect to receive when participating in demand response. The inputs considered by fuzzy control include the expected income x of each electric taxi user k when driving and operating on that day. k, comfort level of participating in demand response k , the output is the subsidy given by the electric vehicle load aggregator participating in the demand response k ;

[0021] Step S7: Analyze the total cost of each electric taxi participating in demand response and the charging plan when participating in demand response under a given subsidy, that is, And report the adjusted charging status;

[0022] Step S8: The electric vehicle load aggregator determines the electric taxi users who participate in the demand response and their charging plans when participating in the demand response. Specifically, the electric vehicle load aggregator cost model is used as the optimization objective function, the electric taxi demand response constraint model is used as the constraint, and the calculation is performed. like is equal to 1, and the output taxi k participates in the charging plan of demand response.

[0023] By means of the above technical solution, the present invention provides an electric taxi demand response dispatching method based on fuzzy control, which has at least the following beneficial effects:

[0024] 1. The electric taxi demand response scheduling method proposed in the present invention can assist electric taxis in formulating charging plans, and can quickly calculate their expected demand response subsidies without the need for two-way game, thereby supporting electric vehicle load aggregators to participate in demand response.

[0025] 2. The present invention adopts a fuzzy control method to support electric taxi users to participate in the quotation of demand response, and then supports load aggregators to formulate electric taxi charging strategies, which can assist electric vehicle load aggregators to participate in demand response. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0027] Figure 1 This is a flow chart of the electric taxi demand response scheduling method in Embodiment 1 of the present invention;

[0028] Figure 2 This is a flow chart for realizing rapid deployment and configuration of the electric taxi demand response scheduling method in the form of APP software development in the second embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0030] Under the new power system, a large number of electric vehicles are connected to the power grid for charging in an unordered manner, causing peak electricity consumption in local areas, which brings certain challenges to the stable operation of the power system. Electric taxis participate in demand response and orderly charge to reduce charging during peak hours of the power grid and increase charging during off-peak hours, which helps to reduce peaks and fill valleys and improve the stability of the power grid. Secondly, as distributed energy storage units, electric vehicles can achieve two-way interaction of charging and discharging through V2G technology, providing auxiliary services such as frequency regulation and peak regulation for the power grid. At present, the main electric vehicles participating in demand response are electric taxis. The number of electric taxis is large and they are organized to a certain extent, making them easy to aggregate.

[0031] The main problem of electric taxis participating in demand response is how to interact with the power grid and load aggregators. The content of the interaction is to negotiate demand response subsidies and electric taxi charging plans. The interactive method is a two-layer game, with the upper layer being the power grid or electric vehicle load aggregators and the lower layer being electric taxis. The upper layer of the power grid or electric vehicle load aggregators publishes load reduction tasks and subsidies to the lower layer, and the lower layer of electric taxis feed back charging plans to the upper layer. This model requires interaction between the upper and lower layers to iteratively calculate demand response subsidies and electric taxi charging plans.

[0032] However, electric taxis interact with power grids and load aggregators during the demand response process, and constant bargaining is required to calculate demand response subsidies and electric taxi charging plans. In actual practice, the technical conditions of the electric taxi infrastructure are immature, making it difficult to support electric taxi bargaining. The bargaining cost is high, and the bargaining efficiency is low, which will affect the user experience of participating in demand response.

[0033] Embodiment 1

[0034] At present, electric taxis participating in demand response need to constantly bargain in order to calculate demand response subsidies and electric taxi charging plans. In order to solve the technical defects of this method, the high cost of bargaining and low efficiency of bargaining will affect the user's experience of participating in demand response. Please refer to Figure 1 This example proposes a fuzzy control-based electric taxi demand response scheduling method to assist electric taxis in making charging plans. It can quickly calculate its expected demand response subsidy without the need for two-way game, thereby supporting electric vehicle load aggregators to participate in demand response. The method includes the following steps:

[0035] S1. Obtaining the parameters of the electric vehicle being charged and the parameters of the charging pile in the lower-level subject from the upper-level subject, the upper-level subject is the power grid or the electric vehicle load aggregator, and the lower-level subject is the electric taxi;

[0036] The electric vehicle parameters include the electric taxi set K, where k represents the kth electric taxi, k∈K; the time when the kth electric taxi arrives at and leaves the electric vehicle charging station The power of the kth electric taxi before connecting to the charging pile and when leaving the charging pile

[0037] Charging pile parameters include fast charging and slow charging power P FAST , P SLOW ;

[0038] S2. Formulate an original charging plan for the electric taxi based on the parameters of the electric vehicle and the charging pile. The original charging plan includes the remaining power of the electric taxi after charging. and charging power In step S2, the specific process includes the following steps:

[0039] S21, construct an electric taxi to minimize the charging cost min after arriving at the charging station The planned charging target is:

[0040]

[0041] In the above formula, is a variable, which represents the cost of charging electric taxi k; is a parameter, which represents the charge for the fast charging mode set by the electric vehicle load aggregator in time period t; is a parameter, which represents the charge for the slow charging mode set by the electric vehicle load aggregator in time period t; is a 0-1 variable, indicating whether electric taxi k uses fast charging in time period t, if yes, it is 1, otherwise it is 0; is a 0-1 variable, indicating whether the electric taxi k uses slow charging in time period t, if yes, it is 1, otherwise it is 0; Δt is the time interval;

[0042] S22. Establish the load constraints of electric taxis during charging, namely:

[0043]

[0044]

[0045] In the above formula, is a parameter, which represents the minimum battery capacity of electric taxi k; is a parameter, which represents the maximum battery capacity of electric taxi k; is a variable, which represents the remaining power of electric taxi k after charging in time period t; and They represent the electric taxi k in the time period and The remaining power; It means that electric taxis cannot be fast-charged and slow-charged at the same time;

[0046] S23. Calculate the remaining power of the electric taxi after charging based on the planned charging target and load constraints and charging power Right now:

[0047]

[0048] In the above formula, η CH is a parameter, which indicates the efficiency of charging electric taxis; represents the remaining power of electric taxi k in the previous period t.

[0049] S3. Extracting an electric taxi k that is being charged during the demand response period from the electric taxi set K; specifically, extracting the electric taxi k by judging whether each electric taxi k in the electric taxi set K is still being charged, and the judging condition is:

[0050]

[0051] In the above formula, T DR is a set, which represents the total set of demand response time periods; represents an empty set; if the judgment condition is met, it means that the user is still charging. The electric taxi k users who are charging during the demand response period are grouped as K DR express.

[0052] S4. Calculate the expected subsidy for electric taxis being charged during demand response using fuzzy control method In step S4, the specific process includes the following steps:

[0053] S41. Define the fuzzy distribution of the input and output of the fuzzy control method. The input includes the expected income x of each electric taxi user k driving and operating on that day. k , and comfort level of participating in demand response k ; Output is the subsidy given by the electric vehicle load aggregator participating in demand response e k ;

[0054] The fuzzy distribution of the input is:

[0055] Expected return x kAs input, the fuzzy subset includes {HR,LR}, where LR means that the electric taxi has a small passenger flow on that day and the overall revenue is low; HR means that the electric taxi has a large passenger flow on that day and the overall revenue is high. The membership functions of LR and HR are:

[0056]

[0057] Comfort level of participating in demand response k As input, its fuzzy subset includes {SD, BD}, where SD means that the comfort level of electric taxis participating in demand response is high; BD means that the comfort level of electric taxis participating in demand response is poor, then the membership functions of SD and BD are:

[0058]

[0059]

[0060] In the above formula, is a constant, representing the average revenue of electric taxi k from driving on the day; is a constant, indicating the average comfort level of electric taxi k participating in demand response, ranging from 0 to 1;

[0061] The output fuzzy distribution is:

[0062] Subsidy k As output, the fuzzy subsets include {CL - ,CL,CH,CH +}, where CL - means very low, CL means low, CH means high, CH + If it is very high, then CL - ,CL,CH,CH + The membership function of is:

[0063]

[0064] In the above formula, and are all constants, representing the coefficient of electric taxi k regarding demand response subsidies, where e0 is a constant, which represents the subsidy issued by the power grid to electric vehicle load aggregators;

[0065] S42, establish fuzzy rules of the fuzzy control method, the fuzzy rules include rule 1, rule 2, rule 3 and rule 4, namely:

[0066] Rule 1: The membership function is LR(x k ) and SD(y k ) is used in the case of CL -, rule 1 is expressed by relation R1, and A1=LR∧SD is defined, where ∧ is the operator that takes the minimum value of membership functions LR and SD;

[0067] Rule 2: The membership function is LR(x k ) and BD(y k ) is used, CL is used, rule 2 is expressed by relation R2, and A2 = LR∧BD is defined;

[0068] Rule 3: The membership function is HR(x k ) and SD(y k ) is adopted, and rule 3 is expressed by relation R3, and A3=HR∧SD is defined;

[0069] Rule 4: The membership function is HR(x k ) and BD(y k ) in the case of CH + , Rule 4 is expressed as relation R4, and definition A4=HR∧BD;

[0070] S43, calculate the output function U(e1) under fuzzy rules, using Mamdani theory, given the known comfort level y k and expected return x k In this case, the comfort level k and expected return x k Substitute the membership function of the input variable corresponding to each relationship from R1 to R4, take the minimum value of the input variable membership function, and then multiply it with R1 to R4 and A1 to A4 respectively to obtain the output function U(e k ). The above method is expressed as follows:

[0071]

[0072] In the above formula, R s represents the sth rule and also represents the sth relationship; Indicates the symbol for product of numbers; A s represents the sth function, s=1, then A s Indicates A1.

[0073] S44, calculate the output function U(e k ) Maximum subsidy e k Then, the minimum value is taken from the multiple values ​​to get the subsidy that the electric taxi expects to receive. It is expressed as:

[0074]

[0075] Specifically, by analyzing the output function U(e k )’s maximum membership, calculate U(ek ) Maximum subsidy e k The possible values ​​of , and then take the minimum value and assign it to

[0076] S5. Based on subsidies Analyze the total cost of each electric taxi participating in demand response and the charging plan when participating in demand response; in step S5, the specific process includes the following steps:

[0077] S51. The optimization goal of electric taxis is established with the goal of electric taxis participating in demand response as minimizing costs, namely:

[0078]

[0079] In the above formula, is a variable, which represents the total cost of electric taxi k after participating in demand response; is a variable, which represents the benefits of electric taxi k participating in demand response;

[0080] S52. Construct a profit model for electric taxi k to participate in demand response and analyze the benefits of electric taxi k under subsidies. The demand response benefits under , the benefit model includes 4 cases, namely:

[0081] Case 1: The charging period of electric taxis is during the demand response period. The profit model of participating in demand response is expressed as:

[0082]

[0083] In the above formula, is the variable, the benefit of electric taxi k participating in demand response; is a 0-1 variable, indicating whether electric taxi k uses fast charging in time period t, if yes, it is 1, otherwise it is 0; is a 0-1 variable, indicating whether the electric taxi k uses slow charging in time period t, if yes, it is 1, otherwise it is 0; Δt is the time interval; is the load of electric taxi k after adjusting the charging plan in time period t, The calculation formula is as follows:

[0084]

[0085] In the above formula, is a 0-1 variable, indicating the fast charging status of electric taxi k in time period t when it participates in demand response. Fast charging is 1, otherwise it is 0; Variable, which represents the slow charging status of electric taxi k in time period t when it participates in demand response, 1 for slow charging, otherwise 0; represents the reverse state of electric taxi k in time period t when participating in demand response, slow charging is 1, otherwise it is 0; P V2G The power of reverse discharge of electric taxi.

[0086] Case 2: The charging start time is earlier than the demand response start time, and the leaving time is later than the demand response end time. In this case, the profit model of participating in demand response is expressed as:

[0087]

[0088] Case 3: The charging start time is earlier than the demand response start time, and the departure time is earlier than the demand response end time. In this case, the profit model of participating in demand response is expressed as:

[0089]

[0090] Case 4: The charging start time is later than the demand response start time, and the departure time is earlier than the demand response end time. In this case, the profit model of participating in demand response is expressed as:

[0091]

[0092] In the above formula, T ET 、T ST They are demand response end time and demand response cut-off time; is the baseline load of electric taxi k in time period t calculated by the power grid.

[0093] S53. Establish operating constraints for electric taxis, namely:

[0094]

[0095] In the above formula, is a variable, representing the operating constraints of electric taxi k in period t; η V2G is a constant, indicating the efficiency of V2G reverse discharge of electric taxis; η CH is a parameter, which indicates the efficiency of charging electric taxis; It is a 0-1 variable, indicating whether the electric taxi has V2G reverse discharge. If V2G reverse discharge is 1, otherwise it is 0; is the load of electric taxi k after adjusting the charging plan in time period t; represents the operating constraints of electric taxi k in time period t-1;

[0096] S54. Solve the revenue model and operation constraints with the optimization goal of electric taxis as the goal, and obtain the charging plan for electric taxis participating in demand response. The charging plan includes Specifically, the goal is to minimize the cost of electric taxis participating in demand response, and the revenue model is solved under the four conditions and the operating constraints of electric taxis. After further optimization, the charging plan for participating in demand response is obtained, that is, And report the adjusted charging status.

[0097] S6. The electric vehicle load aggregator determines the charging plan for electric taxis when they participate in demand response.

[0098] In step S6, the specific process includes the following process:

[0099] Construct an electric vehicle load aggregator cost model. The electric vehicle load aggregator analyzes the available demand response benefits and the benefits brought by electric taxi charging based on the original charging plan reported by the electric taxi and the charging plan participating in the demand response. The electric vehicle load aggregator cost model is:

[0100]

[0101] In the above formula, C AGG is a variable representing the cost for electric vehicle load aggregators to organize electric taxi users to participate in demand response; is a 0-1 variable, indicating whether electric taxi k participates in demand response. If yes, it is 1, otherwise it is 0. is a variable, which represents the benefits of electric taxi k participating in demand response;

[0102] Construct an electric taxi demand response constraint model to ensure that electric taxis cooperate with electric vehicle load aggregators to complete the load reduction in each period specified by demand response. The total load connected to the electric charging pile during time period t should be less than the load reduction That is, the following constraints are satisfied:

[0103]

[0104] In the above formula, is the charging power of the original charging plan of electric taxi k in time period t, is the load of electric taxi k after adjusting the charging plan in time period t; is the charging status of the original charging plan of the electric taxi, which is a 0-1 variable,

[0105] Output the charging plan of electric taxis participating in demand response, with the electric vehicle load aggregator cost model as the optimization objective function and the electric taxi demand response constraint model as the constraint calculation like If it is equal to 1, the charging plan for taxi k participating in demand response is output.

[0106] The electric taxi demand response scheduling method proposed in this embodiment can assist electric taxis in formulating charging plans and quickly calculate their expected demand response subsidies without using a two-way game, thereby supporting electric vehicle load aggregators to participate in demand response.

[0107] Embodiment 2

[0108] Embodiment 1 adopts fuzzy control method to support electric taxi users to participate in the quotation of demand response, and then supports load aggregators to formulate electric taxi charging strategies, which can assist electric vehicle load aggregators to participate in demand response. Based on embodiment 1, this embodiment adapts to the personalized needs of electric taxis to participate in demand response market transactions, and can be quickly deployed and configured through APP software development, such as Figure 2 As shown, the following steps are specifically used to achieve rapid deployment and configuration.

[0109] Step S1: Extracting parameters of the electric vehicle being charged, parameters of the charging pile, and demand response control parameters. Specifically, the electric vehicle parameters include K, k, Charging pile parameters include P FAST , P SLOW The demand response control parameters include t, T, T DR ,

[0110] Where K is a set, which represents the set of electric taxis. k is a variable, which represents the kth electric taxi, k∈K. is a constant, indicating the time when the kth electric taxi arrives at the electric vehicle charging station. is a constant, indicating the time when the kth electric taxi leaves the electric vehicle charging station. is a constant, indicating the power of the kth electric taxi before it is connected to the charging pile. is a constant, indicating the power of the kth electric taxi when it leaves the electric vehicle charging station. is a constant, representing the average revenue of electric taxi k operating on that day. is a constant, indicating the average comfort level of electric taxi k participating in demand response, which is between 0 and 1. and is a constant, representing the coefficient of electric taxi k regarding demand response subsidy, where P FAST Is a constant, indicating the fast charging power of the charging pile. SLOW is a constant, indicating the slow charging power of the charging pile. T is a set, indicating the total set of one day. DRis a set, which represents the total set of demand response time periods. t is a variable, which represents the current time period. is a variable, which represents the subsidy that electric taxi k expects to receive when participating in demand response.

[0111] Step S2: extracting the parameters for electric vehicle load aggregators to participate in demand response, including e0. Among them, is a constant, which represents the load in time period t after the electric vehicle charging station reduces the load. e0 is a constant, which represents the subsidy issued by the power grid to the electric vehicle load aggregator.

[0112] Step S3: Initialize control parameters

[0113] Step S4: The electric taxi formulates a load charging plan, that is, an original charging plan for the electric taxi.

[0114] Step S5: Extract the set of electric taxis that are charged during the demand response period. Determine whether each electric taxi k in the electric taxi set K is still charging, and the judgment condition is:

[0115]

[0116] If the condition is met, it means that the vehicle is still charging. The electric taxi users who are charging during the demand response period are grouped as K DR express.

[0117] Step S6: For electric taxi users who are charging during the demand response period, fuzzy control is used to support their quotations and calculate the subsidies that electric taxis are expected to receive when participating in demand response. The inputs considered by fuzzy control include the expected income x of each electric taxi user k when driving and operating on that day. k , comfort level of participating in demand response k , the output is the subsidy given by the electric vehicle load aggregator participating in the demand response k .

[0118] Step S7: Analyze the total cost of each electric taxi participating in demand response and the charging plan when participating in demand response under a given subsidy, that is, And report the adjusted charging status.

[0119] Step S8: The electric vehicle load aggregator determines the electric taxi users who participate in the demand response and their charging plans when participating in the demand response. Specifically, the electric vehicle load aggregator cost model is used as the optimization objective function, the electric taxi demand response constraint model is used as the constraint, and the calculation is performed. like is equal to 1, and the output taxi k participates in the charging plan of demand response.

[0120] This embodiment also provides a computer device, which includes a processor, a memory, a network interface, an input device and a display screen connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory includes a storage medium and an internal memory. The storage medium can be a non-volatile storage medium or a volatile storage medium. The storage medium stores an operating system and can also store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can implement the electric taxi demand response scheduling method. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the storage medium. The internal memory can also store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the electric taxi demand response scheduling method. The network interface of the computer device is used to communicate with an external server through a network connection. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0121] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can 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 codes.

[0122] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0123] The above implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for demand response dispatching of electric taxis based on fuzzy control, characterized in that: The method comprises the following steps: S1. Obtaining the parameters of the electric vehicle being charged and the parameters of the charging pile in the lower-level subject from the upper-level subject, the upper-level subject is the power grid or the electric vehicle load aggregator, and the lower-level subject is the electric taxi; S2. Formulate an original charging plan for the electric taxi based on the electric vehicle parameters and charging pile parameters. The original charging plan includes the remaining power of the electric taxi after charging. and charging power S3, extracting the electric taxi k that is being charged during the demand response period from the electric taxi set K; S4. Calculate the expected subsidy for electric taxis being charged during demand response using fuzzy control method S5. Based on subsidies Analyze the total cost of each electric taxi participating in demand response and the charging plan when participating in demand response; S6. The electric vehicle load aggregator determines the charging plan for electric taxis when they participate in demand response.

2. The electric taxi demand response dispatching method according to claim 1, characterized in that: In step S1, the electric vehicle parameters include the electric taxi set K, where k represents the kth electric taxi, k∈K; the time when the kth electric taxi arrives at and leaves the electric vehicle charging station The power of the kth electric taxi before connecting to the charging pile and when leaving the charging pile Charging pile parameters include fast charging and slow charging power P FAST , P SLOW .

3. The electric taxi demand response dispatching method according to claim 1, characterized in that: In step S2, the specific process includes the following steps: S21, construct an electric taxi to minimize the charging cost min after arriving at the charging station The planned charging target is: In the above formula, is a variable, which represents the cost of charging electric taxi k; is a parameter, which represents the charge for the fast charging mode set by the electric vehicle load aggregator in time period t; is a parameter, which represents the charge for the slow charging mode set by the electric vehicle load aggregator in time period t; is a 0-1 variable, indicating whether electric taxi k uses fast charging in time period t, if yes, it is 1, otherwise it is 0; is a 0-1 variable, indicating whether the electric taxi k uses slow charging in time period t, if yes, it is 1, otherwise it is 0; Δt is the time interval; S22. Establish the load constraints of electric taxis during charging, namely: In the above formula, is a parameter, which represents the minimum battery capacity of electric taxi k; is a parameter, which represents the maximum battery capacity of electric taxi k; is a variable, which represents the remaining power of electric taxi k after charging in time period t; and They represent the electric taxi k in the time period and The remaining power; It means that electric taxis cannot be fast-charged and slow-charged at the same time; S23. Calculate the remaining power of the electric taxi after charging based on the planned charging target and load constraints and charging power Right now: In the above formula, η CH is a parameter, which indicates the efficiency of charging electric taxis; represents the remaining power of electric taxi k in the previous period t.

4. The electric taxi demand response dispatching method according to claim 1, characterized in that: In step S3, the electric taxis k in the electric taxi set K are extracted by judging whether they are still being charged. The judging condition is: In the above formula, T DR is a set, which represents the total set of demand response time periods; represents an empty set; if the judgment condition is met, it means that the user is still charging. The k users of electric taxis that are charging during the demand response period are grouped as K DR express.

5. The electric taxi demand response dispatching method according to claim 1, characterized in that: In step S4, the specific process includes the following steps: S41. Define the fuzzy distribution of the input and output of the fuzzy control method. The input includes the expected income x of each electric taxi user k driving and operating on that day. k , and comfort level of participating in demand response k ; Output is the subsidy given by the electric vehicle load aggregator participating in demand response e k ; S42, establish fuzzy rules of the fuzzy control method, the fuzzy rules include rule 1, rule 2, rule 3 and rule 4, namely: Rule 1: The membership function is LR(x k ) and SD(y k ) is used in the case of CL - , rule 1 is expressed by relation R1, and A1=LR∧SD is defined, where ∧ is the operator that takes the minimum value of membership functions LR and SD; Rule 2: The membership function is LR(x k ) and BD(y k ) is used, CL is used, rule 2 is expressed by relation R2, and A2 = LR∧BD is defined; Rule 3: The membership function is HR(x k ) and SD(y k ) is adopted, and rule 3 is expressed by relation R3, and A3=HR∧SD is defined; Rule 4: The membership function is HR(x k ) and BD(y k ) in the case of CH + , Rule 4 is expressed as relation R4, and definition A4=HR∧BD; S43, calculate the output function U(e) under fuzzy rules k ),Right now: In the above formula, R s represents the sth rule; Indicates the symbol for product of numbers; A s represents the sth function, s=1, then A s Indicates A1; S44, calculate the output function U(e k ) Maximum subsidy e k Then, the minimum value is taken from the multiple values ​​to get the subsidy that the electric taxi expects to receive. It is expressed as: In the above formula, U(e k ) is the output function, k∈[1,4].

6. The electric taxi demand response dispatching method according to claim 5, characterized in that: In step S41, the input fuzzy distribution is: Expected return x k As input, the fuzzy subset includes {HR,LR}, where LR means that the electric taxi has a small passenger flow on that day and the overall revenue is low; HR means that the electric taxi has a large passenger flow on that day and the overall revenue is high. The membership functions of LR and HR are: Comfort level of participating in demand response k As input, its fuzzy subset includes {SD, BD}, where SD means that the comfort level of electric taxis participating in demand response is high; BD means that the comfort level of electric taxis participating in demand response is poor, then the membership functions of SD and BD are: In the above formula, is a constant, representing the average revenue of electric taxi k from driving on the day; is a constant, indicating the average comfort level of electric taxi k participating in demand response, ranging from 0 to 1; The output fuzzy distribution is: Subsidy k As output, the fuzzy subsets include {CL - ,CL,CH,CH + }, where CL - means very low, CL means low, CH means high, CH + If it is very high, then CL - ,CL,CH,CH + The membership function of is: In the above formula, and are all constants, representing the coefficient of electric taxi k regarding demand response subsidies, where e0 is a constant, representing the subsidy issued by the power grid to electric vehicle load aggregators.

7. The electric taxi demand response dispatching method according to claim 1, characterized in that: In step S5, the specific process The following steps are involved: S51. The optimization goal of electric taxis is established with the goal of electric taxis participating in demand response as minimizing the cost, namely: In the above formula, is a variable, which represents the total cost of electric taxi k after participating in demand response; is a variable, which represents the benefits of electric taxi k participating in demand response; S52. Construct a profit model for electric taxi k to participate in demand response and analyze the benefits of electric taxi k under subsidy. Demand response benefits under S53. Establish operating constraints for electric taxis, namely: In the above formula, is a variable, representing the operating constraints of electric taxi k in period t; η V2G is a constant, indicating the efficiency of V2G reverse discharge of electric taxis; η CH is a parameter, which indicates the efficiency of charging electric taxis; It is a 0-1 variable, indicating whether the electric taxi has V2G reverse discharge. If V2G reverse discharge is 1, otherwise it is 0; is the load of electric taxi k after adjusting the charging plan in time period t; represents the operating constraints of electric taxi k in time period t-1; S54. Solve the revenue model and operation constraints with the optimization goal of electric taxis as the goal, and obtain the charging plan for electric taxis participating in demand response. The charging plan includes 8. The electric taxi demand response dispatching method according to claim 7, characterized in that: In step S52, the revenue model includes four cases, namely: Case 1: The charging period of electric taxis is during the demand response period. The profit model of participating in demand response is expressed as: In the above formula, is the variable, the benefit of electric taxi k participating in demand response; is a 0-1 variable, indicating whether electric taxi k uses fast charging in time period t, if yes, it is 1, otherwise it is 0; is a 0-1 variable, indicating whether the electric taxi k uses slow charging in time period t, if yes, it is 1, otherwise it is 0; Δt is the time interval; is the load of electric taxi k after adjusting the charging plan in time period t, The calculation formula is as follows: In the above formula, is a 0-1 variable, indicating the fast charging status of electric taxi k in time period t when it participates in demand response. Fast charging is 1, otherwise it is 0; Variable, which represents the slow charging status of electric taxi k in time period t when it participates in demand response, 1 for slow charging, otherwise 0; represents the reverse state of electric taxi k in time period t when participating in demand response, slow charging is 1, otherwise it is 0; P V2G The power of reverse discharge for electric taxis; Case 2: The charging start time is earlier than the demand response start time, and the leaving time is later than the demand response end time. In this case, the profit model of participating in demand response is expressed as: Case 3: The charging start time is earlier than the demand response start time, and the departure time is earlier than the demand response end time. In this case, the profit model of participating in demand response is expressed as: Case 4: The charging start time is later than the demand response start time, and the departure time is earlier than the demand response end time. In this case, the profit model of participating in demand response is expressed as: In the above formula, T ET 、T ST are the demand response end time and the demand response cut-off time respectively.

9. The electric taxi demand response dispatching method according to claim 1, characterized in that: In step S6, the specific process includes the following process: Construct an electric vehicle load aggregator cost model. The electric vehicle load aggregator analyzes the available demand response benefits and the benefits brought by electric taxi charging based on the original charging plan reported by the electric taxi and the charging plan participating in the demand response. The electric vehicle load aggregator cost model is: In the above formula, C AGG is a variable representing the cost for electric vehicle load aggregators to organize electric taxi users to participate in demand response; is a 0-1 variable, indicating whether electric taxi k participates in demand response. If yes, it is 1, otherwise it is 0. is a variable, which represents the benefits of electric taxi k participating in demand response; Construct an electric taxi demand response constraint model that satisfies the following constraints: In the above formula, is the charging power of the original charging plan of electric taxi k in time period t, is the load of electric taxi k after adjusting the charging plan in time period t; is the charging status of the original charging plan of the electric taxi, which is a 0-1 variable, Output the charging plan of electric taxis participating in demand response, with the electric vehicle load aggregator cost model as the optimization objective function and the electric taxi demand response constraint model as the constraint calculation like If it is equal to 1, the charging plan for taxi k participating in demand response is output.

10. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the electric taxi demand response scheduling method described in any one of claims 1 to 9.

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