V2G aggregator optimization scheduling method considering electric vehicle demand response

Abstract

The invention discloses a V2G aggregator optimization scheduling method considering electric vehicle demand response, and relates to the field of electric vehicle optimization scheduling. Optimization scheduling is carried out by establishing a day-ahead optimization scheduling model and an intra-day optimization scheduling model, and a default emergency solution is added; an electric vehicle response comprehensive evaluation index considering the V2G aggregator economy and the EV reliability is established, scheduling is carried out with the optimal index value as the target, an award mechanism is set for the V2G aggregator in the future, awards are given to the EV users who are on the conciliation to stimulate the relevant users to be on the conciliation, and on the basis that the demand response is met, the demand response of the V2G aggregator is met. The execution condition of the V2G aggregator scheduling instruction is improved, the default risk is reduced, and the benefits of the two parties are improved.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle optimization scheduling, and in particular to a V2G aggregator optimization scheduling method considering electric vehicle demand response. Background Art

[0002] With the increase in sales and utilization of electric vehicles (EVs), EVs, as a new type of load, can participate in grid demand response as a flexible demand-side resource. However, since traditional grid demand response usually uses power plants to increase their own spare capacity and build energy storage to achieve demand response, such methods are too costly and the cost of participating in peak-shaving and frequency regulation is too high, it is feasible to use electric vehicles as demand-side resources to participate in grid peak-shaving and frequency regulation demand response regulation and control, and to participate in grid demand response in the form of vehicle-to-grid (V2G) aggregators.

[0003] However, there are many uncertainties in using V2G aggregators to participate in grid demand response. When V2G participates in demand response, the charging time and location of electric vehicles are affected by the travel habits and needs of the owners, and the EV owners' willingness to participate in V2G services and the battery status of the electric vehicles will affect the EV's potential to participate in demand response to a certain extent. These uncertainties make it impossible for EVs to participate in demand response in real time and accurately, which not only affects the quality of grid operation, but also cannot guarantee the interests of V2G aggregators and users. Therefore, an effective demand response optimization scheduling strategy is needed to regulate EVs.

[0004] However, usually the strategies for EV to participate in demand response are mostly carried out on a single time scale. For example, relevant strategies are used to formulate methods for EV to participate in day-ahead demand response. However, due to the strong randomness of EV and too many influencing factors, it is difficult to achieve the target effect by relying solely on day-ahead demand response, and it is difficult to achieve real-time and accurate demand response. Therefore, it is necessary to study the optimization scheduling strategy of V2G aggregators considering electric vehicle demand response at multiple time scales, and provide solutions for the uncertainty factors of EV users, while ensuring that EV demand response can achieve peak shaving and valley filling of the power grid, and at the same time increase the benefits of V2G aggregators and EVs participating in demand response. Summary of the invention

[0005] The purpose of the present invention is: in response to the above-mentioned problems, the present invention provides a V2G aggregator optimization scheduling method considering electric vehicle demand response, optimizes scheduling by establishing a day-ahead optimization scheduling model and an intraday optimization scheduling model, adds a breach of contract emergency solution, and establishes an electric vehicle response comprehensive evaluation index considering the economy of the V2G aggregator and the reliability of the EV, and performs scheduling with the optimal value of the index as the goal. In the future, a reward mechanism is set up for the V2G aggregator to issue rewards to EV users who abide by the contract, so as to motivate the relevant users to abide by the contract. On the basis of meeting the demand response, the execution of the V2G aggregator's scheduling instructions is improved, the risk of breach of contract is reduced, and the benefits of both parties are increased.

[0006] The technical solution adopted by the present invention is as follows:

[0007] The V2G aggregator optimization dispatching method considering electric vehicle demand response is implemented in the following steps:

[0008] Step 1: EV users send demand responses to the grid through V2G aggregators, and the grid issues a winning curve based on the demand responses;

[0009] Step 2: The V2G aggregator obtains the winning bid curve in step 1 and establishes a day-ahead optimization scheduling model based on the credit-power comprehensive evaluation index;

[0010] Step 3, establish an intraday optimal dispatch model through the comprehensive evaluation index of electric vehicle response of V2G aggregator economy and EV reliability;

[0011] Step 4, solving the day-ahead optimization scheduling model and the intraday optimization scheduling model established in steps 2 and 3;

[0012] Step 5: Establish a demand response results evaluation and compliance reward mechanism for the future.

[0013] Furthermore, the credit-power comprehensive evaluation index in step 2 includes the user's historical credit index and the power evaluation index;

[0014] The user's historical credit index is as follows:

[0015]

[0016] In the formula, R ij,its is the initial credit, R ijrec is the recommended credit, k is the number of direct transactions between electric vehicle i and V2G aggregator j, and m is the threshold of the number; T e is the total number of periods in which electric vehicle i participates in the demand credit of V2G aggregator j in a month; and are the bid-winning charging and discharging power of electric vehicle i under V2G aggregator j in period t within one month, and They are the actual response charging and discharging power of electric vehicle i under V2G aggregator j in period t within one month;

[0017] Combined with the above formula, the power evaluation index is the ratio of the EV declared power to the average value of the EV declared total power as shown in the following formula:

[0018]

[0019] In the formula, and are the dispatchable charging and discharging powers reported by electric vehicle i in V2G aggregator j on the previous day; is the total number of EVs that submitted demand response on the day before, The dispatchable charging and discharging power reported on the V2G aggregation on that date, i.e., the total power reported by EVs;

[0020] In summary, the credit-power comprehensive evaluation index is as follows:

[0021]

[0022] In the formula, w 1 、w 2 is the day-ahead weight coefficient.

[0023] Furthermore, in step 2, a day-ahead optimization scheduling model is established based on the credit-power comprehensive evaluation index, and the objective function is to maximize the comprehensive evaluation index value, that is, maxf day ;

[0024] The constraints in the day-ahead optimization scheduling model include charging and discharging power constraints, charging and discharging equation constraints, battery safety constraints, travel constraints, and time constraints;

[0025] The specific charging and discharging power constraint is that the bid power issued by the V2G aggregator shall not be higher than the dispatchable power declared by the EV, as shown in the following formula:

[0026]

[0027] The charge and discharge equation constraints are as follows:

[0028]

[0029] In the formula, S t is the battery status of EV at time t, η x,t With η d,t The charging efficiency and discharging efficiency of EV;

[0030] The battery safety constraint is as follows:

[0031]

[0032] In the formula, They are the maximum safe charge and minimum safe charge of EV batteries respectively;

[0033] The travel constraints are as follows:

[0034]

[0035] Where: D t represents the travel demand of EV at time t;

[0036] The specific time constraint is: let the charging and discharging time period of EV be [T start , T end ], the time constraint is expressed as:

[0037] T start ≤t≤T end (10)

[0038] Where T start With T end They represent the start time and end time of the EV charging and discharging time period respectively.

[0039] Furthermore, the comprehensive evaluation index of electric vehicle response of EV reliability in step 3 is as follows:

[0040] EV User Response Credibility Index As follows:

[0041]

[0042] In the formula, is the user compliance rate, as shown in the following formula:

[0043]

[0044] In the formula, and are the dispatchable charging and discharging powers fed back by electric vehicle i in V2G aggregator j within the day;

[0045] The power evaluation index of the intraday optimization scheduling model is as follows:

[0046]

[0047] In summary, the comprehensive evaluation index of electric vehicle response for EV reliability is as follows:

[0048]

[0049] In the formula, w 1,in 、w 2,inand w 3,in is the weight coefficient of the comprehensive evaluation index of EV cluster response; It is the number of EVs reported after the emergency response mechanism was activated; It is the flag bit indicating whether electric vehicle i in V2G aggregator j is an emergency response vehicle in period t. If so, Take 1, otherwise take 0; is the time priority of EV, as follows:

[0050]

[0051] Where t ij is the estimated arrival time of the electric vehicle i at the charging station, max(t ij ) and min(t ij ) are respectively the maximum and minimum values ​​of the estimated arrival time reported by all EVs participating in the intraday optimization scheduling.

[0052] Furthermore, the establishment of the intraday optimization scheduling model in step 3 is as follows:

[0053] The objective function is defined as the maximum comprehensive evaluation index value of the electric vehicle response of EV reliability, that is, maxf in ;

[0054] The intraday optimization scheduling model specifically includes emergency response constraints, supply and demand constraints, and related constraints in the day-ahead scheduling model;

[0055] The emergency response constraint is to judge the default of EV within a day: within a certain period of time t, the power grid allocates a certain demand response amount to the V2G aggregator, which needs to be the same as the dispatchable power fed back by EV within the day. If they are not the same, it is judged as a default and the default emergency response plan is initiated;

[0056] The supply and demand constraints are as follows:

[0057]

[0058] In the formula, are the bid amounts of charging and discharging power issued by V2G aggregator j to electric vehicle i within a day;

[0059] The charge and discharge equation constraints are as follows:

[0060]

[0061] The battery safety constraints and time constraints are the same as those in the day-ahead optimization scheduling model;

[0062] The travel constraints are as follows:

[0063]

[0064] Furthermore, the specific emergency response constraint plan is as follows:

[0065] The emergency response constraints are as follows:

[0066]

[0067]

[0068] In the formula, are all the invited time periods t of electric vehicle i in V2G aggregator j after time period t. sur The total bid charging and discharging power; They are the charging and discharging powers of the demand response of the electric vehicle i that won the bid among the V2G aggregators recently; are the charging and discharging powers reported by EV i in V2G aggregator j within a day after the emergency mechanism is activated; is the battery capacity of electric vehicle i in the V2G aggregator; are the upper and lower limits of the battery safety constraints of electric vehicle i in V2G aggregator j at time t, is the normalization coefficient.

[0069] Furthermore, in the emergency response constraint scheme, if the shortfall in demand response cannot be met, the shortfall in demand response can be made up by issuing emergency demand response invitations to other idle electric vehicles within the area where the V2G aggregator is located.

[0070] Furthermore, step 4 is specifically to use the CPLEX solver to solve the day-ahead optimization scheduling model to obtain the day-ahead scheduling strategy; use deep reinforcement learning to solve the intraday optimization scheduling model. Affected by the emergency response constraints, it is necessary to update the optimization scheduling strategy in real time according to the feedback during the intraday solution.

[0071] Furthermore, the future demand response result evaluation and compliance reward mechanism in step 5 specifically include the benefit evaluation of electric vehicles participating in demand response and the benefit evaluation of V2G aggregators;

[0072] Benefit assessment of electric vehicles participating in demand response:

[0073] The benefits of electric vehicles participating in demand response are as follows:

[0074]

[0075] In the formula, is the revenue of electric vehicle i participating in demand response under V2G aggregator j within one day; T d is the number of time periods throughout the day; are the charging and discharging power revenue, battery loss cost, credit reward, and default penalty of electric vehicle i in V2G aggregator j participating in demand response in period t;

[0076] The charge and discharge benefits are defined as follows:

[0077]

[0078] In the formula, r ij G is the revenue sharing ratio agreed between electric vehicle i and V2G aggregator j; d,sub , G c,sub are the charging and discharging unit prices of EVs participating in demand response; are the actual charging and discharging power of electric vehicle i participating in demand response under V2G aggregator j;

[0079] The battery wear cost is defined as follows:

[0080]

[0081] In the formula, c bat is the battery cost, C max is the battery capacity, D t The discharge depth of the electric vehicle in the period t;

[0082] V2G aggregator's revenue assessment: V2G aggregator's revenue includes the revenue share of EVs participating in demand response and the default cost;

[0083] The revenue sharing of EVs participating in demand response can be expressed as follows:

[0084]

[0085] The default cost of the V2G aggregator is defined as the default fee that the V2G aggregator j needs to pay to the grid during period t as follows:

[0086]

[0087] In the formula, ρ V2G is the default settlement unit price between the power grid and the V2G aggregator, and Δt is the time when the V2G aggregator defaults.

[0088] Furthermore, the contract compliance reward mechanism is as follows:

[0089] The remaining fee after deducting the default fee paid to the grid from the default fee charged by the V2G aggregator is used as the electric vehicle credit reward, which is issued according to the actual demand response amount participated by the EV, as shown in the following formula:

[0090]

[0091] Where: Indicates that the result is rounded down; A flag indicating that the demand response quantity is qualified; is the default fee that electric vehicle i needs to pay to the V2G aggregator during time period t; is the default fee that V2G aggregator j needs to pay to the grid during time period t; is the total number of EVs with defaults, in is the number of EVs whose compliance rate is less than 1, It is the number of EVs that defaulted during the day and won the bid in the day's optimized scheduling.

[0092] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0093] The V2G aggregator optimization scheduling method considering the demand response of electric vehicles of the present invention divides the scheduling of electric vehicles into two processes: day-ahead scheduling and intraday scheduling. In the day-ahead scheduling, the V2G aggregator schedules the EVs day-ahead based on the number of winning bids in the power grid auxiliary services by defining a credit-power comprehensive evaluation index, with the goal of optimizing the comprehensive evaluation index value of each EV. Further, intraday scheduling is performed. In order to avoid the default phenomenon of too many demand responses, a default emergency solution is set for the V2G aggregator. When the EV participating in the demand response in the day-ahead scheduling cannot participate in real time, the idle EVs in the surrounding area that have participated in the demand response are invited to participate in the demand response. A comprehensive evaluation index of electric vehicle response considering the economy of the V2G aggregator and the reliability of the EV is established, and the scheduling is performed with the optimal value of the index as the goal. In the future, a reward mechanism is set for the V2G aggregator to issue rewards to EV users who abide by the contract, so as to encourage the relevant users to abide by the contract. On the basis of meeting the demand response, the execution of the V2G aggregator's scheduling instructions is improved, the default risk is reduced, and the benefits of both parties are increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 is a flow chart of a V2G aggregator optimization scheduling method considering electric vehicle demand response according to the present invention;

[0095] Figure 2 This is a result diagram of the day-ahead optimization scheduling in Example 3 of the V2G aggregator optimization scheduling method considering electric vehicle demand response of the present invention. DETAILED DESCRIPTION

[0096] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0097] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0098] Example 1

[0099] V2G aggregator optimization dispatch method considering electric vehicle demand response, such as Figure 1 As shown, the specific implementation steps are as follows:

[0100] Step 1: After the power grid initiates the demand response invitation, EV users make an appointment to participate in the demand response through the relevant platform, and reserve the time and power for EV users to participate in the demand response in advance. The V2G aggregator submits the capacity and price information for participating in the demand response to the power grid; on this basis, the power grid dispatching center issues the winning curve to each responding entity according to the demand response application;

[0101] Step 2: The V2G aggregator obtains the winning bid curve in step 1 and establishes a day-ahead optimization scheduling model based on the credit-power comprehensive evaluation index;

[0102] Step 3, establish an intraday optimal dispatch model through the comprehensive evaluation index of electric vehicle response of V2G aggregator economy and EV reliability;

[0103] Step 4, solving the day-ahead optimization scheduling model and the intraday optimization scheduling model established in steps 2 and 3;

[0104] Step 5: Establish a demand response results evaluation and compliance reward mechanism for the future.

[0105] Example 2

[0106] This embodiment provides a specific establishment process of a day-ahead optimization scheduling model, an intraday optimization scheduling model, and subsequent solutions, which are as follows:

[0107] In step 2, in the day-ahead optimization dispatch, after receiving the winning bid curve issued by the power grid, the V2G aggregator optimizes the dispatch of EV users in its jurisdiction who apply to participate in demand response and whose credit meets the requirements according to the credit-power comprehensive evaluation index;

[0108] The definition of the credit-power comprehensive evaluation index is divided into two aspects. One aspect is the user's historical credit index, as shown in the following formula:

[0109]

[0110] In the formula, R ij,its is the initial credit, R ijrecis the recommended credit, k is the number of direct transactions between electric vehicle i and V2G aggregator j, and m is the threshold of the number, which is set to 20 in this embodiment; T e is the total number of periods in which electric vehicle i participates in the demand credit of V2G aggregator j in a month; and are the bid-winning charging and discharging power of electric vehicle i under V2G aggregator j in period t within one month, and They are the actual response charging and discharging power of electric vehicle i under V2G aggregator j in period t within one month;

[0111] Combined with the above formula, on the other hand, the power evaluation index is defined as the ratio of the EV reported power to the average value of the EV reported total power as shown in the following formula:

[0112]

[0113] In the formula, and are the dispatchable charging and discharging powers reported by electric vehicle i in V2G aggregator j on the previous day; is the total number of EVs that submitted demand response on the day before, The dispatchable charging and discharging power reported on the V2G aggregation on that date, i.e., the total power reported by EVs;

[0114] In summary, the credit-power comprehensive evaluation index is obtained as follows:

[0115]

[0116] In the formula, w 1 、w 2 is the day-ahead weight coefficient;

[0117] In summary, this embodiment establishes a day-ahead optimization scheduling model based on the credit-power comprehensive evaluation index, and the objective function is to maximize the comprehensive evaluation index value, that is, maxf day ;

[0118] The constraints in the day-ahead optimization scheduling model include charging and discharging power constraints, charging and discharging equation constraints, battery safety constraints, travel constraints, and time constraints;

[0119] The specific charging and discharging power constraint is that the bid power issued by the V2G aggregator shall not be higher than the dispatchable power declared by the EV, as shown in the following formula:

[0120]

[0121] The charge and discharge equation constraint means that there is a certain relationship between the charge and discharge power of the EV and the change in the battery state, as shown in the following equation:

[0122]

[0123] In the formula, S t is the battery status of EV at time t, η c,t With η d,t The charging efficiency and discharging efficiency of EV;

[0124] Battery safety constraints mean that the battery state of the EV must be within a certain range to ensure the safe operation of the battery, as shown in the following formula:

[0125]

[0126] In the formula, They are the maximum safe charge and minimum safe charge of EV batteries respectively;

[0127] Travel constraints mean that the charging and discharging behavior of EVs must meet their travel needs, as shown in the following formula:

[0128]

[0129] Where: D t It represents the travel demand of EV at time t, which can be charging demand or discharging demand;

[0130] Time constraint means that the charging and discharging behavior of EV must be completed within a specific time period. Let the charging and discharging time period of EV be [T start , T end ], the time constraint is expressed as:

[0131] T start ≤t≤T end (10)

[0132] Where T start With T end They represent the start time and end time of the EV charging and discharging time period respectively.

[0133] In this embodiment, the electric vehicle response comprehensive evaluation index of EV reliability in step 3 is specifically as follows:

[0134] In the user's historical credit index R ij Based on the user compliance rate Combined to get the EV user response credibility index As follows:

[0135]

[0136] In the formula, The user compliance rate is used to guide EVs to provide intraday feedback according to the day-ahead bid amount as much as possible. It is defined as the ratio of the dispatchable power fed back by EVs to the day-ahead bid power, as shown in the following formula:

[0137]

[0138] In the formula, and are the dispatchable charging and discharging powers fed back by electric vehicle i in V2G aggregator j within the day;

[0139] Referring to the power evaluation index defined in the day-ahead scheduling model, the power evaluation index of the intraday optimization scheduling model is as follows:

[0140]

[0141] In summary, the comprehensive evaluation index of electric vehicle response for EV reliability is as follows:

[0142]

[0143] In the formula, w 1,in 、w 2,in and w 3,in is the weight coefficient of the comprehensive evaluation index of EV cluster response; It is the number of EVs reported after the emergency response mechanism was activated; It is the flag bit indicating whether electric vehicle i in V2G aggregator j is an emergency response vehicle in period t. If so, Take 1, otherwise take 0; is the time priority of EV, as follows:

[0144]

[0145] Where t ij is the estimated arrival time of the electric vehicle i at the charging station, max(t ij ) and min(t ij ) are the maximum and minimum values ​​of the estimated time to pile reported by all EVs participating in the intraday optimization scheduling;

[0146] Based on the above content, the establishment of the intraday optimization scheduling model is as follows:

[0147] The objective function is defined as the maximum comprehensive evaluation index value of the electric vehicle response of EV reliability, that is, maxf in ;

[0148] The intraday optimization scheduling model specifically includes emergency response constraints, supply and demand constraints, and related constraints in the day-ahead scheduling model;

[0149] The emergency response constraint is to judge the EV default phenomenon within the day. The judgment process is as follows: within a certain period of time t, the power grid allocates a certain demand response amount to the V2G aggregator, which needs to be the same as the dispatchable power fed back by the EV within the day. If they are not the same, it is judged as a default and the default emergency response plan is initiated;

[0150] The shortfall of demand response is supplemented by electric vehicles that have accepted invitations in the past few days. However, the participating EVs need to charge and discharge on the premise of meeting their own pre-dispatch instructions for subsequent time periods. When V2G aggregators dispatch EVs, they need to consider whether there is a time period after this period for participating in demand response, and consider battery safety constraints to avoid safety problems. The specific emergency response constraint plan is as follows:

[0151] The emergency response constraints are as follows:

[0152]

[0153] In the formula, are all the invited time periods t of electric vehicle i in V2G aggregator j after time period t. sur The total bid charging and discharging power; They are the charging and discharging powers of the demand response of the electric vehicle i that won the bid among the V2G aggregators recently; are the charging and discharging powers reported by EV i in V2G aggregator j within a day after the emergency mechanism is activated; is the battery capacity of electric vehicle i in the V2G aggregator; are the upper and lower limits of the battery safety constraints of electric vehicle i in V2G aggregator j at time t, is the normalization coefficient.

[0154] If the above scheme cannot meet the shortfall in demand response, an emergency demand response invitation will be issued to other idle electric vehicles within a certain range of the area where the V2G aggregator is located to make up the shortfall in demand response;

[0155] The supply and demand constraints are as follows:

[0156]

[0157] In the formula, are the bid amounts of charging and discharging power issued by V2G aggregator j to electric vehicle i within a day;

[0158] The charge and discharge equation constraints are as follows:

[0159]

[0160] The battery safety constraints and time constraints are the same as those in the day-ahead optimization scheduling model;

[0161] The travel constraints are as follows:

[0162]

[0163] In this embodiment, step 4 is specifically to use the CPLEX solver to solve the day-ahead optimization scheduling model to obtain the day-ahead scheduling strategy; use deep reinforcement learning to solve the intraday optimization scheduling model. Affected by the emergency response constraints, it is necessary to update the optimization scheduling strategy in real time according to the feedback during the intraday solution;

[0164] The process of solving the intraday optimization scheduling model using deep reinforcement learning is as follows:

[0165] Establish a deep convolutional neural network (DCN) model, select deep Q learning (DQN) as the reinforcement learning algorithm, initialize the parameters of the deep convolutional neural network and the reinforcement learning algorithm, train the model, iterate the strategy, optimize the model structure according to the feedback, and update the optimization strategy in real time according to the newly collected information;

[0166] The future demand response result evaluation and compliance reward mechanism in step 5 of this embodiment specifically includes the benefit evaluation of electric vehicles participating in demand response and the benefit evaluation of V2G aggregators;

[0167] Benefit assessment of electric vehicles participating in demand response:

[0168] The benefit of electric vehicles participating in demand response is the economic benefit obtained from the transaction between them and the V2G aggregator minus their default costs, where the economic benefit is shown as follows:

[0169]

[0170] In the formula, is the revenue of electric vehicle i participating in demand response under V2G aggregator j within one day; T d is the number of time periods throughout the day; are the charging and discharging power revenue, battery loss cost, credit reward, and default penalty of electric vehicle i in V2G aggregator j participating in demand response in period t;

[0171] The charge and discharge benefits are defined as follows:

[0172]

[0173] In the formula, r ij G is the revenue sharing ratio agreed between electric vehicle i and V2G aggregator j; d,sub , G c,sub are the charging and discharging unit prices of EVs participating in demand response; are the actual charging and discharging power of electric vehicle i participating in demand response under V2G aggregator j;

[0174] The battery wear cost is defined as follows:

[0175]

[0176] In the formula, c bat is the battery cost, C max is the battery capacity, D t The discharge depth of the electric vehicle in the period t;

[0177] V2G aggregator's revenue assessment: V2G aggregator's revenue includes the revenue share of EVs participating in demand response and the default cost;

[0178] The revenue sharing of EVs participating in demand response can be expressed as follows:

[0179]

[0180] The default cost of the V2G aggregator is defined as the default fee that the V2G aggregator j needs to pay to the grid during period t as follows:

[0181]

[0182] In the formula, ρ V2G is the default settlement unit price between the power grid and the V2G aggregator, and Δt is the time when the V2G aggregator defaults.

[0183] In order to encourage electric vehicle users to abide by the contract and participate in demand response, the designed electric contract reward mechanism is as follows:

[0184] The remaining fee after deducting the default fee paid to the grid from the default fee charged by the V2G aggregator is used as the electric vehicle credit reward, which is issued according to the actual demand response amount participated by the EV, as shown in the following formula:

[0185]

[0186] Where: Indicates that the result is rounded down. When it is 1, the user compliance rate of electric vehicle i is qualified; The flag bit for the qualified demand response quantity, 1 means that the demand response quantity of electric vehicle i in time period t is qualified, and 0 means it is unqualified; is the default fee that electric vehicle i needs to pay to the V2G aggregator during time period t; is the default fee that V2G aggregator j needs to pay to the grid during time period t; is the total number of EVs with defaults, in is the number of EVs whose compliance rate is less than 1, It is the number of EVs that defaulted during the day and won the bid in the day's optimized scheduling.

[0187] Example 3

[0188] Specific case analysis: This embodiment designs two scenarios to verify the effectiveness of the proposed optimization scheduling strategy. Since the proposed strategy designs an emergency response mechanism, the effects of whether this mechanism is enabled are compared:

[0189] Scenario 1: The emergency response mechanism is not considered, that is, the V2G aggregator does not perform emergency response scheduling for the demand response shortfall caused by the default of its subordinate EVs;

[0190] Scenario 2: Considering the emergency response mechanism, the proposed method is used for scheduling. When a default occurs, the emergency response mechanism is used to deal with the demand response shortage and rewards are given.

[0191] Table 1 Demand response benefits and default costs for EV users and V2G aggregators

[0192]

[0193] The optimization scheduling results within the previous day are as follows: Figure 2 As shown in the figure, only the scheduling status of some EV users is shown, which shows the role of the proposed emergency response mechanism. For example, EV users 2, 12, and 13 did not submit their demand response plans on the previous day, but in the intraday scheduling, due to the default of users 8 and 15, their intraday feedback value is 0, resulting in a demand response shortage. Therefore, the V2G aggregator sends a certain amount of demand response power to the idle EV users 2, 12, and 13 to enable them to participate in the demand response and meet the shortage.

[0194] Therefore, the present invention considers the V2G aggregator optimization scheduling strategy of electric vehicle demand response, and effectively solves the problem of demand response shortage by setting up an emergency response mechanism, and can significantly reduce the risk of default, increase the benefits of V2G aggregators and EV users, and stimulate healthy competition among EV users.

[0195] The day-ahead optimization dispatch model established based on the comprehensive evaluation index of EV user credit-power and the intraday optimization dispatch model established based on the comprehensive evaluation index of electric vehicle response of V2G aggregator economy and EV reliability can select EVs with high user credit index and large demand response volume for pre-dispatch; on the basis of meeting the demand response during the day, it can improve the execution of V2G aggregator dispatch instructions and reduce the risk of default.

[0196] The principles and implementation methods of the present invention are described in this article using specific embodiments. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A V2G aggregator optimization scheduling method considering electric vehicle demand response, characterized in that: Please follow the steps below to implement: Step 1: EV users send demand responses to the grid through V2G aggregators, and the grid issues a winning curve based on the demand responses; Step 2: The V2G aggregator obtains the winning bid curve in step 1 and establishes a day-ahead optimization scheduling model based on the credit-power comprehensive evaluation index; Step 3, establish an intraday optimal dispatch model through the comprehensive evaluation index of electric vehicle response of V2G aggregator economy and EV reliability; Step 4, solving the day-ahead optimization scheduling model and the intraday optimization scheduling model established in steps 2 and 3; Step 5: Establish a demand response results evaluation and compliance reward mechanism for the future.

2. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 1 is characterized in that: The credit-power comprehensive evaluation index in step 2 includes the user's historical credit index and power evaluation index; The user's historical credit index is as follows: In the formula, R ij,its is the initial credit, R ijrec is the recommended credit, k is the number of direct transactions between electric vehicle i and V2G aggregator j, and m is the threshold of the number; T e is the total number of periods in which electric vehicle i participates in the demand credit of V2G aggregator j in a month; and are the bid-winning charging and discharging power of electric vehicle i under V2G aggregator j in period t within one month, and They are the actual response charging and discharging power of electric vehicle i under V2G aggregator j in period t within one month; Combined with the above formula, the power evaluation index is the ratio of the EV declared power to the average value of the EV declared total power as shown in the following formula: In the formula, and are the dispatchable charging and discharging powers reported by electric vehicle i in V2G aggregator j on the previous day; is the total number of EVs that submitted demand response on the day before, The dispatchable charging and discharging power reported on the V2G aggregation on that date, i.e., the total power reported by EVs; In summary, the credit-power comprehensive evaluation index is as follows: Where w1 and w2 are the day-ahead weight coefficients.

3. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 2 is characterized in that: In step 2, a day-ahead optimization scheduling model is established based on the credit-power comprehensive evaluation index, and the objective function is to maximize the comprehensive evaluation index value, i.e., maxf day ; The constraints in the day-ahead optimization scheduling model include charging and discharging power constraints, charging and discharging equation constraints, battery safety constraints, travel constraints, and time constraints; The specific charging and discharging power constraint is that the bid power issued by the V2G aggregator shall not be higher than the dispatchable power declared by the EV, as shown in the following formula: The charge and discharge equation constraints are as follows: In the formula, S t is the battery status of EV at time t, η c,t With η d,t The charging efficiency and discharging efficiency of EV; The battery safety constraints are as follows: In the formula, They are the maximum safe charge and minimum safe charge of EV batteries respectively; The travel constraints are as follows: Where: D t represents the travel demand of EV at time t; The specific time constraint is: let the charging and discharging time period of EV be [T start , T end ], the time constraint is expressed as: T start ≤t≤T end (10) Where T start With T end They represent the start time and end time of the EV charging and discharging time period respectively.

4. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 2 is characterized in that: The comprehensive evaluation index of electric vehicle response of EV reliability in step 3 is as follows: EV User Response Credibility Index As follows: In the formula, is the user compliance rate, as shown in the following formula: In the formula, and are the dispatchable charging and discharging powers fed back by electric vehicle i in V2G aggregator j within the day; The power evaluation index of the intraday optimization scheduling model is as follows: In summary, the comprehensive evaluation index of electric vehicle response for EV reliability is as follows: In the formula, w 1,in 、w 2,in and w 3,in is the weight coefficient of the comprehensive evaluation index of EV cluster response; It is the number of EVs reported after the emergency response mechanism was activated; It is the flag bit indicating whether electric vehicle i in V2G aggregator j is an emergency response vehicle in period t. If so, Take 1, otherwise take 0; is the time priority of EV, as follows: Where t ij is the estimated arrival time of the electric vehicle i at the charging station, max(t ij ) and min(t ij ) are respectively the maximum and minimum values ​​of the estimated arrival time reported by all EVs participating in the intraday optimization scheduling.

5. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 4 is characterized in that: The establishment of the intraday optimization scheduling model in step 3 is as follows: The objective function is defined as the maximum comprehensive evaluation index value of the electric vehicle response of EV reliability, that is, maxf in ; The intraday optimization scheduling model specifically includes emergency response constraints, supply and demand constraints, and related constraints in the day-ahead scheduling model; The emergency response constraint is to judge the default of EV within a day: within a certain period of time t, the power grid allocates a certain demand response amount to the V2G aggregator, which needs to be the same as the dispatchable power fed back by EV within the day. If they are not the same, it is judged as a default and the default emergency response plan is initiated; The supply and demand constraints are as follows: In the formula, are the bid amounts of charging and discharging power issued by V2G aggregator j to electric vehicle i within a day; The charge and discharge equation constraints are as follows: The battery safety constraints and time constraints are the same as those in the day-ahead optimization scheduling model; The travel constraints are as follows:

6. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 5 is characterized in that: The specific emergency response constraint plan is as follows: The emergency response constraints are as follows: In the formula, are all the invited time periods t of electric vehicle i in V2G aggregator j after time period t. sur The total bid charging and discharging power; They are the charging and discharging powers of the demand response of the electric vehicle i that won the bid among the V2G aggregators recently; are the charging and discharging powers reported by EV i in V2G aggregator j within a day after the emergency mechanism is activated; is the battery capacity of electric vehicle i in the V2G aggregator; are the upper and lower limits of the battery safety constraints of electric vehicle i in V2G aggregator j at time t, is the normalization coefficient.

7. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 6 is characterized in that: In the emergency response constraint scheme, if the shortfall in demand response cannot be met, the shortfall in demand response can be made up by issuing emergency demand response invitations to other idle electric vehicles within the area where the V2G aggregator is located.

8. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 1 is characterized in that: Specifically, step 4 is to use the CPLEX solver to solve the day-ahead optimization scheduling model to obtain the day-ahead scheduling strategy; and to use deep reinforcement learning to solve the intraday optimization scheduling model. Affected by the emergency response constraints, it is necessary to update the optimization scheduling strategy in real time according to the feedback during the intraday solution.

9. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 1 is characterized in that: The future demand response result evaluation and compliance reward mechanism in step 5 specifically include the benefit evaluation of electric vehicles participating in demand response and the benefit evaluation of V2G aggregators; Benefit assessment of electric vehicles participating in demand response: The benefits of electric vehicles participating in demand response are as follows: In the formula, is the revenue of electric vehicle i participating in demand response under V2G aggregator j within one day; T d is the number of time periods throughout the day; are the charging and discharging power revenue, battery loss cost, credit reward, and default penalty of electric vehicle i in V2G aggregator j participating in demand response in period t; The charge and discharge benefits are defined as follows: In the formula, r ij G is the revenue sharing ratio agreed between electric vehicle i and V2G aggregator j; d,sub , G c,sub are the charging and discharging unit prices of EVs participating in demand response; are the actual charging and discharging power of electric vehicle i participating in demand response under V2G aggregator j; The battery wear cost is defined as follows: In the formula, c bat is the battery cost, C max is the battery capacity, D t The discharge depth of the electric vehicle in the period t; V2G aggregator's revenue assessment: V2G aggregator's revenue includes the revenue share of EVs participating in demand response and the default cost; The revenue sharing of EVs participating in demand response can be expressed as follows: The default cost of the V2G aggregator is defined as the default fee that the V2G aggregator j needs to pay to the power grid during period t as follows: In the formula, ρ V2G is the default settlement unit price between the power grid and the V2G aggregator, and Δt is the time when the V2G aggregator defaults.

10. The V2G aggregator optimization scheduling method considering electric vehicle demand response according to claim 9 is characterized in that: The specific details of the contract compliance reward mechanism are as follows: The remaining fee after deducting the default fee paid to the grid from the default fee charged by the V2G aggregator is used as the electric vehicle credit reward, which is issued according to the actual demand response amount participated by the EV, as shown in the following formula: Where: Indicates that the result is rounded down; A flag indicating that the demand response quantity is qualified; is the default fee that electric vehicle i needs to pay to the V2G aggregator during time period t; is the default fee that V2G aggregator j needs to pay to the grid during time period t; is the total number of EVs with defaults, in is the number of EVs whose compliance rate is less than 1, It is the number of EVs that defaulted during the day and won the bid in the day's optimized scheduling.

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