A method for participating in power distribution network dispatching based on V2G electric vehicles

Abstract

The application relates to a method for participating in power distribution network dispatching based on V2G electric vehicles, which comprises the following steps: predicting a daily load curve of the power distribution network, wind power and photovoltaic power, electric vehicle dispatchable power and battery power constraints, power grid peak-valley electricity price and electric vehicle charging and discharging efficiency according to historical data; adjusting electric vehicle peak-valley charging price according to the renewable energy power generation capacity prediction curve based on the given power grid peak-valley electricity price; taking the highest renewable energy self-utilization rate of the power distribution network and the minimum electric vehicle user cost as the target, establishing a double-layer dispatching model; using a second-order cone programming method to convex and linearize the power flow constraints of the power distribution network; using an ADMM distributed algorithm to solve the output of the distributed power supply, the electric energy exchange amount between the distributed power supply and the external network and the transaction electric quantity of the power distribution network and the electric vehicle aggregator; and the power distribution network dispatching center issuing the optimal dispatching plan to each electric vehicle aggregator in the region. The method is beneficial to improving renewable energy consumption.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and particularly relates to a method for V2G electric vehicles participating in distribution network dispatching based on aggregation. BACKGROUND

[0002] With the proposal of the two-step goal of "carbon peak" and "carbon neutralization", renewable energy such as wind power and photovoltaic power is increasingly valued due to its clean and low-carbon advantages. However, due to the volatility and randomness of renewable energy, part of the renewable energy cannot be consumed, resulting in resource waste. At the same time, electric vehicles have rapidly grown in scale due to their advantages of "zero pollution and zero emission". V2G electric vehicles use their distributed energy storage characteristics to participate in grid dispatching. When renewable energy is abundant in the distribution network, the electric vehicles are charged to store the electric energy. When the renewable energy output of the distribution network cannot meet the load at the load peak, the electric vehicle users can decide whether to feed the electric energy back to the grid according to their own needs and the grid dispatching plan to meet the grid demand and obtain benefits. Large-scale electric vehicles connected to the grid are a more economical and convenient way to improve the consumption of renewable energy in the grid. The distribution network can transfer the surplus renewable energy in the load valley period to the peak period through appropriate strategies to aggregate and dispatch the V2G electric vehicles, reduce the amount of abandoned wind and light in the valley period, and at the same time alleviate the tight supply of electricity in the peak period, thereby improving the consumption of renewable energy in the distribution network. SUMMARY

[0003] The purpose of the application is to provide a method for V2G electric vehicles participating in distribution network dispatching based on aggregation, which is beneficial to improving the consumption of renewable energy.

[0004] To achieve the above purpose, the technical solution adopted by the application is as follows: a method for V2G electric vehicles participating in distribution network dispatching based on aggregation, comprising the following steps:

[0005] Step S1: predicting the distribution network daily load curve, wind power and photovoltaic power available power, electric vehicle dispatchable power and battery power constraint, grid peak-valley electricity price and electric vehicle charging and discharging efficiency according to historical data;

[0006] Step S2: adjusting the electric vehicle peak-valley charging price based on the renewable energy available power prediction curve according to the given grid peak-valley electricity price to improve the consumption of renewable energy by user-side adjustable resources;

[0007] Step S3: establishing a double-layer dispatching model with the highest renewable energy self-utilization rate of the distribution network and the minimum cost of electric vehicle users as the target;

[0008] Step S4: convexifying and linearizing the distribution network power flow constraint by using the second-order cone programming method;

[0009] Step S5: solving the output of the day-ahead distributed power, the power exchange between the external network and the distribution network, and the transaction power between the distribution network and the electric vehicle aggregators by using the ADMM distributed algorithm;

[0010] Step S6: the distribution network dispatching center publishes the optimal dispatching plan to each electric vehicle aggregator in the region.

[0011] Further, the step S2 specifically comprises the following steps:

[0012] Step S21: assuming that the peak value of the predicted available power curve of the renewable energy source in the next day is P peak , and the valley value is P valley , then the peak-valley difference of the available power curve of the renewable energy source is ΔP = P peak -P valley ; dividing [P valley +0.8ΔP, P peak ] as the high peak period of the available power of the renewable energy source, [P valley , P valley +0.2ΔP] as the low valley period of the available power of the renewable energy source, and the rest as the normal period;

[0013] Step S22: setting the electric vehicle peak-valley charging price adjustment mechanism; the distribution network adjusts the charging price of the electric vehicle in the high peak and low valley periods of the available power of the renewable energy source, and the charging price in the normal period is unchanged:

[0014]

[0015] wherein, is the peak-valley price on the load side published by the large power grid; P max is the total available power of the renewable energy source predicted in the distribution network region; is the adjusted charging price of the electric vehicle.

[0016] Further, the step S3 specifically comprises the following steps:

[0017] Step S31: establishing an upper-layer distribution network dynamic optimization dispatching model;

[0018] The objective function is to maximize the self-utilization rate of the renewable energy source of the distribution network:

[0019]

[0020] wherein, N w , N s are the number of wind farms and photovoltaic power stations in the system respectively; are the available power of the wind farm and photovoltaic power station i at time t respectively. Pij(t) and Qij(t) are the active and reactive power of the wind farm and photovoltaic power station i at time period t, respectively; T is the number of time periods;

[0021] The constraints include distribution network security constraints, electric vehicle aggregator day-ahead reported power constraints, renewable energy constraints, electric vehicle charging and discharging state complementary constraints, distribution network and external network power exchange constraints, respectively:

[0022] (1) Node power flow equation constraints

[0023]

[0024]

[0025] wherein, r ij and x ij are the equivalent resistance and reactance of branch ij; P ij,t and Q ij,t are the active and reactive power flowing through branch ij; Pj and Qj are the sum of active and reactive power flowing out of node j, respectively; P j,t and Q j,t are the active and reactive power injected into node j; I ij,t is the current of branch ij; B is the set of distribution network nodes;

[0026] (2) Node voltage constraints

[0027]

[0028]

[0029] wherein, V j,t is the voltage amplitude of node j; V max and V min are the upper and lower limits of the voltage amplitude of node j; E is the set of distribution network branches;

[0030] (3) Branch current constraints

[0031]

[0032]

[0033] wherein, I ij,max and I ij,min are the upper and lower limits of the current of branch ij;

[0034] (4) Electric vehicle aggregator day-ahead reported power constraints

[0035]

[0036] wherein, respectively are the upper and lower limits of the total charging power of the electric vehicles in the area of the electric vehicle aggregator i in time period t; respectively are the upper and lower limits of the total discharging power of the electric vehicles in the area of the electric vehicle aggregator i in time period t; respectively are the charging and discharging plans of the electric vehicle aggregator i scheduled by the distribution network dispatching;

[0037] (5) Renewable energy constraint

[0038]

[0039] (6) Complementary charging and discharging state constraint of electric vehicles

[0040] The charging and discharging states of the V2G electric vehicles participating in the dispatching of the distribution network must be complementary, i.e. the total discharging power states of the electric vehicles in the area of the electric vehicle aggregator are complementary;

[0041]

[0042] (7) Energy exchange constraint between the distribution network and the external network

[0043] The energy exchange between the distribution network and the external network, i.e. the two states of purchasing and selling electricity, are complementary, and satisfy:

[0044] P sell,t ·P buy,t = 0 (12)

[0045]

[0046] wherein P sell,t and P buy,t are respectively the selling and purchasing power of the distribution network to the external network at time t;

[0047] Step S32: lower-level electric vehicle aggregator optimization dispatching model;

[0048] The objective function is to minimize the operating cost of the electric vehicle users, i.e. to minimize the cost of each aggregator;

[0049]

[0050] wherein B i is the cost of the electric vehicle aggregator i, i = 1, 2, …, N e ; are respectively the peak and valley electricity prices of the electric vehicles in time period t; N is the total number of electric vehicles; N e is the number of electric vehicle aggregators; are respectively the total charging and discharging power of the electric vehicles in the area of the electric vehicle aggregator i at time t; are respectively the charging and discharging power of the electric vehicle j in time period t;

[0051] The constraints include electric vehicle battery power constraints and safety constraints, power constraints, travel power constraints, daily charging and discharging power constraints, and electric vehicle charging and discharging state constraints, respectively:

[0052] Assuming that each electric vehicle is standardized due to production standards, the charging and discharging rated power is the same, and the battery capacity is the same, then the electric vehicle participating in the power distribution network optimization scheduling needs to meet the following conditions;

[0053] (1) Electric vehicle aggregator battery power constraints

[0054]

[0055]

[0056] Where, S ei,t is the battery power of electric vehicle aggregator i at the beginning of the time period; S ei,t+Δt is the battery power of electric vehicle aggregator i after the end of the Δt time period; N i.t is the number of schedulable electric vehicles of electric vehicle aggregator i in period t; η c , η D are the charging and discharging efficiencies of the electric vehicle battery, respectively, and η D = η c , and all electric vehicles have the same efficiency;

[0057] (2) Electric vehicle battery safety constraints

[0058]

[0059]

[0060]

[0061] Where, are the upper and lower limits of the total battery power of the electric vehicles under the jurisdiction of electric vehicle aggregator i in period t, and

[0062] (3) Electric vehicle power constraints

[0063]

[0064]

[0065] (4) Electric vehicle travel power constraints

[0066]

[0067]

[0068] wherein, the minimum battery capacity of the electric vehicle in the time period t to meet daily travel needs in the area of the electric vehicle aggregator i the sum, and take a margin of 0.05 to deal with sudden situations;

[0069] (5) Daily charging and discharging power constraints

[0070] After the electric vehicle ends a day of charging and discharging, the battery capacity should meet certain conditions:

[0071]

[0072] (6) Electric vehicle charging and discharging state constraints

[0073]

[0074] Further, the step S4 specifically comprises the following steps:

[0075] Step S41: Relax the voltage and current, define:

[0076]

[0077] Step S42: Perform second-order cone transformation on to obtain:

[0078]

[0079] Step S43: The (3)-(8) power flow constraints can be converted to:

[0080]

[0081] Further, the step S5 specifically comprises the following steps:

[0082] Step S51: Use the alternating direction multiplier method ADMM to solve the bi-level scheduling model, assuming that the distribution network has i electric vehicle aggregators, introduce the Lagrange multiplier λi representing the electric vehicle aggregator i i and the penalty factor ρ i , to obtain the augmented Lagrangian function of the bi-level model:

[0083]

[0084]

[0085] wherein α is the weight coefficient, i.e. the order of magnitude of balancing the upper and lower target functions, as a lever for the power grid side and the user side in the ADMM convergence process of the bi-level model Game of Dominance;

[0086] Step S52: set the maximum iteration number k max , the convergence precision δ = 1 x 10 -4 , and the penalty factor ρ i ; then, initialize the iteration number k = 0, the dispatching plan of the lower-level electric vehicle aggregator i , and the Lagrange multiplier λ i = 0;

[0087] Step S53: the dispatching plan is accepted from the electric vehicle aggregator side by the power distribution network Solve the upper-level model (29) by using CPLEX to obtain the upper-level power grid dispatching center expected electric vehicle aggregator dispatching plan

[0088] Step S54: the electric vehicle expected dispatching plan is received from the power grid dispatching center by the electric vehicle aggregator Solve the lower-level model (30) by using CPLEX to obtain the electric vehicle aggregator expected dispatching plan

[0089] Step S55: update the Lagrange multiplier:

[0090]

[0091] Step S56: update the iteration number k = k + 1;

[0092] Step S57: judge whether the calculation result satisfies the iteration termination condition:

[0093]

[0094] If the condition is satisfied, the iteration is terminated; otherwise, return to Step S53 to continue calculation until the convergence condition is satisfied.

[0095] Compared with the prior art, the present application has the following beneficial effects: the present application provides a method for improving renewable energy consumption based on V2G electric vehicles participating in power distribution network scheduling, first, the power distribution network adjusts the peak-valley charging price of electric vehicles according to the predicted renewable energy available power curve; second, the power distribution network operation and the demand of electric vehicle users are considered, and the conditions of meeting the safe operation of the power distribution network and the operation constraints of electric vehicles are considered, a double-layer optimization model of V2G electric vehicle aggregation participating in the "energy scheduling" of the power distribution network is established, and the highest self-use rate of renewable energy of the distribution network and the minimum cost of electric vehicle users are realized. The upper and lower layer models respectively take the highest self-use rate of renewable energy of the power distribution network and the minimum cost of electric vehicle users as the target, and optimize the charging and discharging power of electric vehicles. The upper layer constraint conditions include power distribution network power flow constraint, renewable energy available power constraint, and electric vehicle adjustable power upper and lower limit constraint; the lower layer constraint conditions mainly consider the charging and discharging power constraint of electric vehicles, the state of charge constraint and the use demand of electric vehicle users. The present application takes into account the self-use rate of renewable energy of the power distribution network and the economic benefit of electric vehicle users, establishes a double-layer optimization scheduling model of V2G electric vehicle aggregation participating in the power distribution network, considers privacy protection, realizes real-time exchange of information between the two parties by using ADMM, and finally obtains the optimal scheduling plan that meets the wishes of both parties, and promotes the clean and low-carbon operation of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0096] Figure 1 The method implementation flowchart of the embodiment of the present application.

[0097] Figure 2 The improved IEEE33 node power distribution network in the embodiment of the present application.

[0098] Figure 3 The renewable energy available power curve of the double-layer optimization scheduling model in the embodiment of the present application.

[0099] Figure 4 The scheduling power that can be provided by the electric vehicle aggregators 1 and 2 in the embodiment of the present application.

[0100] Figure 5 The total electric quantity of the electric vehicle battery safety constraint under the jurisdiction of the electric vehicle aggregator in the embodiment of the present application.

[0101] Figure 6 The sum of the minimum electric quantity of the electric vehicle battery in the embodiment of the present application.

[0102] Figure 7 The adjusted electric vehicle peak-valley charging price curve in the embodiment of the present application. DETAILED DESCRIPTION

[0103] The present application will be further described below in combination with the drawings and embodiments.

[0104] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0105] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0106] As shown in FIG. 1, the embodiment provides a method for V2G electric vehicles to participate in power distribution network dispatching based on V2G, which comprises the following steps: Figure 1

[0107] Step S1: According to historical data, predict the daily load curve of the power distribution network, the available power of wind power and photovoltaic power, the dispatchable power of electric vehicles, the battery capacity constraint, the peak-valley electricity price of the power grid, and the charging and discharging efficiency of electric vehicles, etc.

[0108] Step S2: Based on the given peak-valley electricity price of the power grid, adjust the peak-valley charging price of electric vehicles according to the available power prediction curve of renewable energy sources, so as to improve the consumption of renewable energy by user-side adjustable resources.

[0109] In the embodiment, the step S2 specifically comprises the following steps:

[0110] Step S21: Assuming that the peak value of the available power curve of renewable energy sources in the next day is P peak , and the valley value is P valley , then the peak-valley difference of the available power curve of renewable energy sources is ΔP=P peak -P valley ; divide [P valley +0.8ΔP, P peak ] as the peak period of the available power of renewable energy sources, [P valley , P valley +0.2ΔP] as the valley period of the available power of renewable energy sources, and the rest as the normal period;

[0111] Step S22: Set the electric vehicle peak-valley charging price adjustment mechanism; the power distribution network adjusts the charging price of electric vehicles in the peak and valley periods of the available power of renewable energy sources, and the charging price in the normal period remains unchanged:

[0112]

[0113] wherein,​ The peak-valley electricity price published by the large power grid on the load side; P max This represents the predicted total renewable energy generation capacity within the distribution network area; The adjusted electricity price for electric vehicle charging.

[0114] Step S3: Establish a two-layer scheduling model with the goal of maximizing the self-utilization rate of renewable energy in the distribution network and minimizing the cost for electric vehicle users.

[0115] In this embodiment, step S3 specifically includes the following steps:

[0116] Step S31: Establish a dynamic optimization scheduling model for the upper-level distribution network;

[0117] The objective function is to maximize the self-utilization rate of renewable energy in the distribution network.

[0118]

[0119] Where, N w N s These refer to the number of wind farms and photovoltaic power stations within the system, respectively. These represent the power generation capacity of wind farm i and photovoltaic power station i at time t (i.e., the upper limit of renewable energy output within time period t); , i represents the power generation of wind farm and photovoltaic power station i during time period t, respectively; T is the number of time periods, taken as 96.

[0120] The constraints include distribution network security constraints (branch power flow model), day-ahead power reporting constraints from electric vehicle aggregators, renewable energy constraints, electric vehicle charge / discharge state complementarity constraints, and power exchange constraints between the distribution network and the external grid, which are as follows:

[0121] (1) Nodal power flow equation constraints

[0122]

[0123]

[0124] Where, r ij x ij P represents the equivalent resistance and reactance of branch ij; ij,t Q ij,t Let i be the active and reactive power flowing through branch ij (outflow from node i); These represent the sum of active and reactive power flowing out of node j (excluding all branches connected to node j from node j); P j,t Q j,t These represent the active and reactive power injected into node j (excluding branch ij, and all branches connected to node j); I ij,tis the current of branch ij; B is the set of distribution network nodes.

[0125] (2) Node voltage constraint

[0126]

[0127]

[0128] wherein V j,t is the voltage amplitude of node j; V max , V min are the upper and lower limits of the voltage amplitude of node j, respectively; E is the set of distribution network branches.

[0129] (3) Branch current constraint

[0130]

[0131]

[0132] wherein I ij,max , I ij,min are the upper and lower limits of the current of branch ij, respectively.

[0133] (4) Day-ahead reported power constraint of electric vehicle aggregator

[0134]

[0135] wherein P are the upper and lower limits of the total charging power of electric vehicles in the jurisdiction of electric vehicle aggregator i in time period t, respectively; are the upper and lower limits of the total discharging power of electric vehicles in the jurisdiction of electric vehicle aggregator i in time period t, respectively; are the charging and discharging plans of electric vehicle aggregator i scheduled by the distribution network.

[0136] (5) Renewable energy constraint

[0137]

[0138] (6) Complementary constraint of charging and discharging states of electric vehicles

[0139] The charging and discharging states of V2G electric vehicles participating in distribution network scheduling must be complementary, i.e., the total discharging power states of electric vehicles in their jurisdiction are complementary.

[0140]

[0141] (7) Energy exchange constraint between distribution network and external network

[0142] The energy exchange between the distribution network and the external network, i.e., the two states of purchasing and selling electricity, are complementary, satisfying:

[0143] P sell,t ·P buy,t = 0 (12)

[0144]

[0145] where, P sell,t , P buy,t are the power of the distribution network at time t to the external network, the power of the distribution network to the external network.

[0146] Step S32: the lower layer electric vehicle aggregator optimizes the scheduling model.

[0147] The objective function is to minimize the operating cost of the electric vehicle user, that is, the cost of each aggregator is minimized;

[0148]

[0149] where, B i is the cost of the electric vehicle aggregator i, i = 1, 2, …, N e ; are the peak and valley prices of the electric vehicle charging and discharging in the t period; N is the total number of electric vehicles; N e is the number of electric vehicle aggregators; are the total charging and discharging power of the electric vehicles in the jurisdiction of the electric vehicle aggregator i at time t; are the charging and discharging power of the electric vehicle j in the period t.

[0150] The constraint conditions include the electric vehicle battery capacity constraint and the safety constraint, power constraint, travel capacity constraint, daily charging and discharging capacity constraint, and electric vehicle charging and discharging state constraint, which are respectively:

[0151] Assuming that each electric vehicle is standardized due to production standards, the rated power of charging and discharging is the same, and the battery capacity is the same, then the electric vehicle participating in the distribution network optimization scheduling needs to meet the following conditions.

[0152] (1) Electric vehicle aggregator battery capacity constraint

[0153]

[0154]

[0155] where, S ei,t is the battery capacity of the electric vehicle aggregator i at the beginning of the time period; S ei,t+Δt is the battery capacity of the electric vehicle aggregator i after the Δt time period; N i.t is the dispatchable number of electric vehicles of the electric vehicle aggregator i in the period t; η c , η DThe efficiency of the battery charging and discharging of the electric vehicle, respectively, is denoted by η D c , and all electric vehicles have the same efficiency.

[0156] (2) Safety constraints of the electric vehicle battery

[0157]

[0158]

[0159]

[0160] wherein, The upper and lower limits of the total battery capacity of the electric vehicle under the jurisdiction of the electric vehicle aggregator i in the time period t are denoted by

[0161] (3) Power constraints of the electric vehicle

[0162]

[0163]

[0164] (4) Electric vehicle travel power constraints

[0165]

[0166]

[0167] wherein, The minimum battery capacity of the electric vehicle in the time period t in the jurisdiction of the electric vehicle aggregator i to meet the daily travel demand is denoted by The sum is taken with a margin of 0.05 to adapt to unexpected situations.

[0168] (5) Daily charging and discharging power constraints

[0169] After the electric vehicle finishes charging and discharging for a day, the battery capacity should meet certain conditions:

[0170]

[0171] (6) Charging and discharging state constraints of the electric vehicle

[0172]

[0173] Step S4: convexity and linearization of the power flow constraints of the power distribution network by using a second-order cone programming method.

[0174] In this embodiment, the step S4 specifically includes the following steps:

[0175] ​Step S41: Relax the voltage and current, define:

[0176]

[0177] Step S42: Perform second-order cone transformation on to obtain:

[0178]

[0179] Step S43: The power flow constraints (3)-(8) can be transformed into:

[0180]

[0181] Step S5: Use the ADMM distributed algorithm to solve the day-ahead hourly distributed power output, power exchange with the external network, and the transaction power of the distribution network and the electric vehicle aggregator.

[0182] In this embodiment, the step S5 specifically comprises the following steps:

[0183] Step S51: Use the alternating direction multiplier method (ADMM) to solve the bi-level scheduling model. Assuming that the distribution network has i electric vehicle aggregators, introduce the Lagrange multiplier λ i representing the electric vehicle aggregator i i and the penalty factor ρ i , to obtain the augmented Lagrangian function of the bi-level model:

[0184]

[0185]

[0186] where α is the weight coefficient, i.e., balancing the order of magnitude of the upper and lower target functions, as a lever for the dominant power game between the two parties (grid side and user side) in the ADMM convergence process of the bi-level model.

[0187] Step S52: Set the maximum iteration number k max , the convergence precision δ = 1 × 10 -4 and the penalty factor ρ i ; then, initialize the iteration number k = 0, the scheduling plan of the lower electric vehicle aggregator i Lagrange multiplier λ i = 0.

[0188] Step S53: The distribution network receives the scheduling plan from the electric vehicle aggregator side Solve the upper model (29) using CPLEX to obtain the upper grid scheduling center expected electric vehicle aggregator scheduling plan

[0189] Step S54: The electric vehicle aggregator receives the electric vehicle expected dispatching plan from the power grid dispatching center The lower model (30) is solved by CPLEX to obtain the electric vehicle aggregator expected dispatching plan

[0190] Step S55: The Lagrange multiplier is updated:

[0191]

[0192] Step S56: The iteration number k is updated k=k+1.

[0193] Step S57: It is judged whether the calculation result satisfies the iteration termination condition:

[0194]

[0195] If the condition is satisfied, the iteration is terminated, otherwise the step S53 is returned to continue the calculation until the convergence condition is satisfied.

[0196] Step S6: The power grid dispatching center issues the optimal dispatching plan to each electric vehicle aggregator in the region.

[0197] This embodiment is based on an improved IEEE33 node distribution network, which includes 3 wind farms connected to nodes 3, 6, and 15, respectively, 2 photovoltaic power stations connected to nodes 5 and 16, respectively, two electric vehicle aggregators, aggregator 1 governing the charging piles of node 14, and aggregator 2 governing the charging piles of nodes 28 and 33, which have the same charging pile capacity; node 33 is connected to the external grid, as shown in Figure 2 The total power that the 3 wind farms and 2 photovoltaic power stations in the distribution network can generate is as shown in Figure 3 , and the time scale period T=96. The peak-valley transaction electricity price between the electric vehicle and the power grid is shown in Table 1. It is assumed that the number of electric vehicles under the jurisdiction of electric vehicle aggregators 1 and 2 is 160 and 200, respectively, and all the electric vehicles except those in the driving state, fault, and daily maintenance can be dispatched. The maximum charging and discharging power of a single electric vehicle is 5 kW, the rated capacity is 25 kWh, the battery power safety constraint is 2.5-22.5 kWh, and the charging and discharging efficiency of the electric vehicle is 0.85. According to the historical data prediction, the dispatching power that the electric vehicle aggregators 1 and 2 can provide is as shown in Figure 4 ; the total battery safety constraint of the electric vehicles under the jurisdiction of the electric vehicle aggregators at each time is as shown in Figure 5 ; and the total minimum battery power of the electric vehicles at each period is as shown in Figure 6 to ensure the daily travel needs of the electric vehicle users.

[0198] Table 1 Electric vehicle peak-valley electricity price (before adjustment)

[0199] Period On-grid electricity price / (Yuan / MWh) Charging electricity price / (Yuan / MWh) Low valley (1-31) 130 170 High peak (32-45) 650 830 Ordinary time (46-68) 380 490 High peak (69-83) 650 830 Ordinary time (84-96) 380 490

[0200] In order to compare the renewable energy consumption of the power distribution network with the double-layer optimal scheduling model (mode one) of the application, a single-layer scheduling scenario is constructed as a reference (mode two).

[0201] Mode two: the charging and discharging power of the electric vehicle is optimized and scheduled based on the maximum self-interest of the electric vehicle, and the electric vehicle load curve is obtained; then, the power distribution network is optimized and scheduled with the maximum renewable energy consumption as the target and the total load of the distribution network as the premise.

[0202] In this embodiment, the comparison results of the V2G electric vehicle aggregation participating in the optimal scheduling of the power distribution network are shown in Table 2. Compared with the reference mode, the scheduling method of the application is used, the peak-valley charging price of the electric vehicle is adjusted according to the renewable energy predicted available power curve, and the charging and discharging behavior of the electric vehicle is guided by the scheduling instruction of the upper power distribution network scheduling center. Figure 7 The electric vehicle user makes certain economic sacrifices to meet the scheduling needs of the distribution network, so that the self-use rate of renewable energy of the distribution network is increased by 1%. The charging and discharging behavior of the electric vehicle user further considers the load and renewable energy output characteristics of the distribution network in the double-layer scheduling mode, effectively realizes the consumption of surplus renewable energy in the valley period, and shifts it to the load peak period as a power supply, so that the renewable energy consumption of the distribution network is improved.

[0203] Table 2 comparison of results of two modes

[0204] Renewable energy self-utilization rate of distribution network Electric vehicle cost / Yuan Mode one (the present application) 82.8％ -182.0 Mode two 81.8％ -202.78

[0205] Note: When the cost of the electric vehicle is "-", the electric vehicle user makes a profit

[0206] Preferably, this embodiment not only considers the economic benefits of the electric vehicle user, but also optimizes the self-use rate of renewable energy of the distribution network and the charging and discharging benefits of the electric vehicle under the conditions of meeting the safety constraints of the distribution network, the purchase and sale of electricity constraints and the like, and uses the ADMM distributed algorithm to realize efficient information exchange between the distribution network and the V2G aggregator. The application can effectively improve the renewable energy consumption of the distribution network, improve the participation of the electric vehicle user, promote the clean and low-carbon operation of the distribution network, and has high application value.

[0207] The above merely describes preferred embodiments of the present application, but is not intended to limit the present application to other forms, and any person skilled in the art can make changes or modifications to the above disclosed technical contents into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application and according to the technical essence of the present application still belongs to the protection scope of the technical solution of the present application.

Claims

1. A method for V2G electric vehicle aggregation to participate in distribution network dispatch, characterized in that, Includes the following steps: Step S1: Based on historical data, predict the daily load curve of the distribution network, the available power generation of wind and solar power, the dispatchable power of electric vehicles and battery power constraints, the peak and valley electricity prices of the grid, and the charging and discharging efficiency of electric vehicles; Step S2: Based on the given peak-valley electricity price of the power grid, the distribution network adjusts the peak-valley charging price of electric vehicles according to the renewable energy generation forecast curve, so as to improve the absorption of renewable energy by the user-side adjustable resources; Step S3: Establish a two-layer scheduling model with the goal of maximizing the self-utilization rate of renewable energy in the distribution network and minimizing the cost for electric vehicle users; Step S4: Use the second-order cone programming method to convex and linearize the power flow constraints of the distribution network; Step S5: Use the ADMM distributed algorithm to solve for the hourly output of distributed power sources, the amount of power exchanged with the external grid, and the amount of electricity traded between the distribution network and electric vehicle aggregators. Step S6: The power distribution network dispatch center issues the optimal dispatch plan to each electric vehicle aggregator in the region; Step S2 specifically includes the following steps: Step S21: Let the peak value of the renewable energy generation curve predicted for the next day be... Valley value Then the peak-to-valley difference of the renewable energy generation curve is ; division This is the peak period for renewable energy generation. This is the period of low renewable energy generation capacity; the rest is the normal period. Step S22: Set up a peak-valley charging price adjustment mechanism for electric vehicles; the distribution network adjusts the charging price for electric vehicles during peak and off-peak periods of renewable energy generation, while the charging price remains unchanged during normal periods. in, Peak-valley electricity prices on the load side of the large power grid; This represents the predicted total renewable energy generation capacity within the distribution network area; The adjusted electricity price for electric vehicle charging; Step S3 includes: Step S31: Establish a dynamic optimization scheduling model for the upper-level distribution network; The objective function is to maximize the self-utilization rate of renewable energy in the distribution network. in, , These refer to the number of wind farms and photovoltaic power stations within the system, respectively. , Let i represent the power generation capacity of the wind farm and the photovoltaic power station i at time t, respectively. , , i represents the power generation of wind farm i and photovoltaic power station i during time period t, respectively; T represents the number of time periods; The constraints include distribution network security constraints, day-ahead power reporting constraints from electric vehicle aggregators, renewable energy constraints, electric vehicle charge / discharge state complementarity constraints, and power exchange constraints between the distribution network and the external grid, which are as follows: (A1) Nodal power flow equation constraints in, , Let be the equivalent resistance and reactance of branch ij; , Let represent the active and reactive power flowing through branch ij; , These are the sum of active and reactive power flowing out of node j, respectively. , These represent the active and reactive power injected into node j, respectively. Let be the current in branch ij; B is the set of distribution network nodes; (A2) Node voltage constraints in, Let be the voltage amplitude at node j; , These represent the upper and lower limits of the voltage amplitude at node j, respectively; E is the set of distribution network branches. (A3) Branch current constraint in, , These are the upper and lower limits of the current in branch ij, respectively; (A4) Electric vehicle aggregators recently reported power constraints. in, , These represent the upper and lower limits of the total charging power of electric vehicles within the jurisdiction of electric vehicle aggregator i during time period t; , These represent the upper and lower limits of the total discharge power of electric vehicles within the jurisdiction of electric vehicle aggregator i during time period t; , These are the charging and discharging plans of electric vehicle aggregator i in the power distribution network dispatching system; The number of electric vehicle aggregators; (A5) Renewable Energy Constraints (A6) Complementary constraints on the charging and discharging states of electric vehicles The charging and discharging states of V2G electric vehicles participating in distribution network dispatch must be complementary, that is, the total discharge power of electric vehicles in their respective jurisdictions must be complementary. (A7) Constraints on power exchange between distribution network and external grid Energy exchange between the distribution network and the external network, with the power purchase and sales states complementing each other, satisfies: in, , These represent the power sold and purchased by the distribution network to the external network at time t, respectively.

2. The method for V2G electric vehicle aggregation and participation in distribution network dispatch according to claim 1, characterized in that, After completing step S31, continue with step S32. Step S32: Optimize the scheduling model for lower-level electric vehicle aggregators; The objective function is to minimize the operating cost for electric vehicle users, which means minimizing the cost for each aggregator. in, Let i be the cost of electric vehicle aggregator i, i=1,2,… ; , t represents the peak and off-peak electricity prices for electric vehicle charging and discharging during time period t; N represents the total number of electric vehicles. , These represent the total charging and discharging power of electric vehicles within the jurisdiction of electric vehicle aggregator i at time t; The constraints include electric vehicle battery capacity constraints, as well as safety constraints, power constraints, travel capacity constraints, daily charge / discharge capacity constraints, and electric vehicle charge / discharge state constraints, which are as follows: Assuming that each electric vehicle has the same rated charging and discharging power and the same battery capacity due to standardized production, the following conditions must be met for electric vehicles to participate in the optimized scheduling of the power distribution network. (B1) Battery capacity constraints for electric vehicle aggregators in, The battery level of electric vehicle aggregator i at the start of the time period; i, an electric vehicle aggregator Battery level at the end of the time period; Let be the number of electric vehicles that electric vehicle aggregator i can schedule within time period t; , The charging and discharging efficiencies of electric vehicle batteries are respectively taken as... And all electric vehicles have the same efficiency; , These represent the charging and discharging power of electric vehicle j during time period t; (B2) Safety constraints for electric vehicle batteries in, , These represent the upper and lower limits of the total battery capacity of electric vehicles under the jurisdiction of electric vehicle aggregator i within time period t, to ensure battery safety. , ; (B3) Electric Vehicle Power Constraints (B4) Electricity Constraints for Electric Vehicle Trips in, The minimum total battery capacity of electric vehicles within the jurisdiction of electric vehicle aggregator i during time period t is required to meet daily travel needs, with a margin of 0.05 to cope with emergencies. (B5) Daily charge and discharge capacity constraints After a day of charging and discharging, the battery capacity of an electric vehicle should meet certain conditions: (B6) Electric vehicle charge / discharge state constraints 。 3. A method for V2G electric vehicle aggregation to participate in distribution network dispatch according to claim 2, characterized in that, Step S4 specifically includes the following steps: Step S41: Relax the voltage and current, defined as follows: Step S42: For Performing a second-order cone transformation, we obtain: Step S43: (3)-(8) Power flow constraints can be transformed into: 。 4. A method for V2G electric vehicle aggregation to participate in distribution network dispatch according to claim 3, characterized in that, Step S5 specifically includes the following steps: Step S51: Solve the two-level scheduling model using the Alternating Direction Multiplier Method (ADMM). Assume there are i electric vehicle aggregators in the distribution network, and introduce a Lagrange multiplier representing electric vehicle aggregator i. and penalty factor The augmented Lagrangian function of the two-layer model is obtained as follows: in, The weight coefficients are the order of magnitude of the objective functions of the upper and lower layers, serving as leverage in the power struggle between the grid side and the user side during the ADMM convergence process of the two-layer model. Step S52: Set the maximum number of iterations Convergence accuracy and punishment factors Next, initialize the iteration count k=0, and set the scheduling plan for the lower-level electric vehicle aggregator i. Lagrange multipliers ; Step S53: The distribution network receives the dispatch plan from the electric vehicle aggregator. By using CPLEX to solve the upper-level model, the desired electric vehicle aggregator scheduling plan of the upper-level power grid dispatch center is obtained. ; Step S54: The electric vehicle aggregator receives the desired electric vehicle dispatch plan from the power grid dispatch center. By using CPLEX to solve the lower-level model, the desired scheduling plan of the electric vehicle aggregator can be obtained. ; Step S55: Update the Lagrange multipliers: Step S56: Update the iteration count k = k + 1; Step S57: Determine whether the calculation result meets the iteration termination condition: If the condition is met, the iteration terminates; otherwise, return to step S53 to continue calculation until the convergence condition is met.

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