Electric vehicle elastic demand response and optimal scheduling method

Abstract

The invention discloses an electric vehicle elastic demand response and optimal scheduling method, and relates to the technical field of power system scheduling. And obtaining the corresponding generating capacity and scheduling time period of the target area in the set detection period, constructing a game model based on the generating capacity and the scheduling time period, enabling the game model to reach an equilibrium state and carrying out solving to obtain a solving result, and scheduling the electric vehicle based on the solving result to carry out charging optimization. According to the method, the economic benefits of three main bodies, namely a power grid operator, an electric vehicle aggregator and an electric vehicle can be balanced on the basis of realizing peak load shifting.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method for responding to electric vehicle elastic demand and optimizing dispatching. Background Art

[0002] With the adjustment of the national economic industrial structure and the vigorous development of new energy, the peak-to-valley difference of electricity consumption in the whole society has increased, and tapping the response potential on the demand side has become an urgent issue. In recent years, the scale of electric vehicles in the distribution network has been gradually increasing. Electric vehicles are both a flexible power load and a distributed energy storage resource. Therefore, they have great potential as demand response resources to help the power grid to reduce peaks and fill valleys. However, with the gradual popularization of electric vehicles, the problems of a large number of disorderly charging, such as the aggravated peak-to-valley difference of the power grid and insufficient capacity of distribution transformers, have gradually become prominent. In recent years, the growth of electric vehicles has far exceeded the growth rate of public charging piles, significantly lengthening the average waiting time for electric vehicle charging, further aggravating the spatial randomness and temporal randomness of electric vehicle charging, and thus a large number of electric vehicles have been uncoordinatedly charged or discharged, leading to power congestion, undervoltage, grid instability, power quality, relays, frequency and other grid problems. It is urgent to orderly control and manage the charging and discharging of electric vehicles connected to the grid on a large scale, so as to improve the carrying capacity and operation economy of the distribution network to cope with large-scale electric vehicle access.

[0003] Therefore, how to provide an electric vehicle flexible demand response and optimized scheduling method to solve the difficulties existing in the prior art is a problem that technical personnel in this field need to solve urgently. Summary of the invention

[0004] In view of this, the present invention provides an electric vehicle flexible demand response and optimization scheduling method, which can balance the economic benefits of the three entities of power grid operators, electric vehicle aggregators and electric vehicles on the basis of achieving peak shaving and valley filling.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] An electric vehicle elastic demand response and optimization scheduling method comprises the following steps:

[0007] S1. Obtain the power generation and scheduling period corresponding to the target area within the set detection cycle;

[0008] S2. Construct a game model based on power generation and dispatch period;

[0009] S3, making the game model reach an equilibrium state and solving it to obtain a solution;

[0010] S4. Dispatch electric vehicles for charging optimization based on the solution results.

[0011] In the above method, optionally, S1 also includes collecting battery information and access time information of electric vehicles connected to the charging station.

[0012] In the above method, optionally, constructing the game model in S2 includes constructing an upper-level pricing and scheduling demand response model and a lower-level unit power generation coordination model.

[0013] In the above method, optionally, the upper-level pricing and dispatching demand response model includes determining system constraints, an electric vehicle aggregator cost model, an electric vehicle charging and discharging price model, and an objective function;

[0014] Set the set of all electric vehicles in the area under the jurisdiction of the electric vehicle aggregator C = {1, 2, ..., n, ..., N}, the set of electric vehicle charging and discharging scheduling time periods in a day T = {1, 2, ..., t, ..., H}, and the charging and discharging power r of the nth electric vehicle in the scheduling period n =(e n,1 ,e n,2 ,…,e n,t ,…e n,H ).

[0015] Alternatively to the above method, the electric vehicle aggregator cost model can be expressed as:

[0016]

[0017] Among them, U EVA,tol is the total energy cost, U EVA,t is the cost of the electric vehicle aggregator during period t, L EV,t It is the sum of all electric vehicle loads during period t.

[0018] The above method, optionally, the electric vehicle charging and discharging price model λ n It can be expressed as:

[0019]

[0020] Among them, l is the profit index, e m,t is the charging and discharging power of the mth electric vehicle during the scheduling period t.

[0021] In the above method, optionally, the lower-level unit coordination model includes determining system constraints and setting an objective function based on minimizing electricity cost.

[0022] In the above method, optionally, the game in S3 is determined based on the logit protocol.

[0023] It can be seen from the above technical solution that compared with the prior art, the present invention provides an electric vehicle flexible demand response and optimization scheduling method, which has the following beneficial effects: 1) The present invention coordinates electric vehicles with thermal power units, wind power units and basic loads to optimize the load period of electric vehicles; 2) The present invention can effectively balance the economic benefits of the three entities of power grid operators, electric vehicle aggregators and electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0025] Figure 1 The present invention discloses a flow chart of an electric vehicle elastic demand response and optimization scheduling method. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Reference Figure 1 As shown, the present invention discloses an electric vehicle elastic demand response and optimization scheduling method, comprising the following steps:

[0028] S1. Obtain the power generation and scheduling period corresponding to the target area within the set detection cycle;

[0029] S2. Construct a game model based on power generation and dispatch period;

[0030] S3, making the game model reach an equilibrium state and solving it to obtain a solution;

[0031] S4. Dispatch electric vehicles for charging optimization based on the solution results.

[0032] Furthermore, S1 also includes collecting battery information and access time information of electric vehicles connected to the charging station.

[0033] Specifically, electric vehicle aggregators obtain electricity from the main grid, are responsible for meeting the charging needs of all electric vehicles (EVs) within their jurisdiction, and are responsible for market clearing and operation; electric vehicle users in the area register and authenticate their identities through smart mobile devices or smart vehicle-mounted devices, and communicate with electric vehicle aggregators through charging piles equipped with smart chips; electric vehicle users voluntarily sign an agreement with the electric vehicle aggregator, preset an expected departure time after connecting to the charging pile, and agree to be fined if they leave before the expected departure; electric vehicle aggregators will obtain scheduling control rights after the electric vehicles are connected to the charging piles, and all electric vehicles connected to the charging piles will be subject to unified scheduling. Electric vehicles will only participate in discharge scheduling during the valley filling demand response period, and the collected data information will be used as the basis for load forecasting in the electric vehicle aggregator area the next day, and electricity prices will be adjusted accordingly.

[0034] Furthermore, the communication between distribution network operators, electric vehicle aggregators and electric vehicles is achieved through high-speed communication means such as 5G networks and optical fiber communications, which meets the real-time requirements, and the electric vehicle charging piles have the ability to independently process information and calculate. Based on the parameters of the connected electric vehicles, travel behavior, battery information, grid time-of-use electricity prices, access to charging piles and other constraint information, the optimal charging and discharging scheduling strategy for electric vehicles can be automatically solved in real time with the lowest charging cost for EV users as the optimization goal.

[0035] Furthermore, constructing the game model in S2 includes constructing an upper-level pricing and scheduling demand response model and a lower-level unit power generation coordination model.

[0036] Furthermore, the upper-level pricing and dispatching demand response model includes determining system constraints, EV aggregator cost model, EV charging and discharging price model, and objective function;

[0037] Set the set of all electric vehicles in the area under the jurisdiction of the electric vehicle aggregator C = {1, 2, ..., n, ..., N}, the set of electric vehicle charging and discharging scheduling time periods in a day T = {1, 2, ..., t, ..., H}, and the charging and discharging power r of the nth electric vehicle in the scheduling period n =(e n,1 ,e n,2 ,…,e n,t ,…e n,H ), e n,t >1 means electric vehicle charging, e n,t <1 means the electric vehicle is discharged, e n,t =1 means free.

[0038] Furthermore, the set system constraints include: setting constraints for electric vehicle aggregators, expressed as:

[0039]

[0040] in, W max the maximum total charging power allowed for EV aggregators;

[0041] Set the charge and discharge power constraints, the expression is:

[0042] e min <e n,t <e max ,

[0043] Among them, e min 、e max are the minimum charging power and maximum charging power of electric vehicles respectively.

[0044] Set the battery capacity constraint. Based on the impact of overcharging and discharging on battery life, the battery capacity of electric vehicle batteries at any time must be kept within the specified range. The expression is:

[0045] S OCmin <S OC <S OCmax ,

[0046] Among them, S OCmin , S OCmax They are the minimum and maximum battery capacities of electric vehicles respectively;

[0047] The power demand constraint is set. When the charge and discharge scheduling ends, the charging demand of electric vehicle users should be met to ensure that the battery capacity of the electric vehicle reaches the expected battery capacity preset by the user when the electric vehicle leaves the charging pile. The expression is:

[0048] S OCneed ≤S OCed ,

[0049] Among them, S OCneed , S OCed To represent the expected battery capacity set by the electric vehicle owner and the actual battery capacity of the electric vehicle when it leaves the charging station;

[0050] Set the battery charge and discharge capacity constraint, the expression is:

[0051]

[0052] Among them, η ch , η dis is the charging efficiency and discharging efficiency of electric vehicles, V n is the rated capacity of the battery, and Δh is the span of the scheduling period.

[0053] Furthermore, a charging and discharging strategy set can be formed based on the system constraints, which represents a set of all measurements that can be used by the electric vehicle under the system constraints.

[0054] Furthermore, the feasible charging and discharging strategy set of each electric vehicle is affected by the strategies of other electric vehicles. Therefore, a price model is introduced to represent the energy cost of electric vehicle aggregators, and then the cost model of electric vehicle aggregators is obtained, which can be expressed as:

[0055]

[0056] Among them, U EVA,tol is the total energy cost, U EVA,t is the cost of the electric vehicle aggregator during period t, L EV,t It is the sum of all electric vehicle loads during period t.

[0057] Furthermore, the fee that EVn needs to pay to the electric vehicle aggregator at the end of charging depends on the charging and discharging volume and charging and discharging price during the scheduling period. The scheduling period should ensure that the electric vehicle aggregator is profitable or at least breaks even during the scheduling period, that is, l≥1. Therefore, the electric vehicle charging and discharging price model λ n It can be expressed as:

[0058]

[0059] Among them, l is the profit index, e m,t is the charging and discharging power of the mth electric vehicle during the scheduling period t.

[0060] Furthermore, setting the optimal charging and discharging strategy for electric vehicles is to choose the decision-making behavior that maximizes its own interests while meeting the charging needs and conditional constraints of electric vehicles, while minimizing the costs of electric vehicle aggregators, and then obtaining the objective function and constraints of the upper-level model.

[0061] Furthermore, the lower-level unit coordination model includes determining system constraints and setting an objective function based on minimizing electricity cost.

[0062] Furthermore, the lowest electricity cost in the objective function includes unit cost, user charging cost and wind curtailment cost, and the optimization goal is to minimize the total startup cost and expected operating cost of the power system under different scenarios.

[0063] The unit cost includes fuel cost, carbon emission cost and startup cost. The user charging cost is the financial cost of all electric vehicle users. The wind curtailment cost is the cost after considering the wind curtailment penalty. In different scenarios, the wind power generation scenario is based on Monte Carlo simulation rather than thermal units. It is generated independently according to different wind power generation distributions. The power supply and load demand are balanced according to the power system dispatch. The power of all units, electric vehicle discharge and wind farms must be equal to the base load and electric vehicle charging demand at any time.

[0064] Specifically, wind turbine scenarios include:

[0065] S1. Wind turbine output data collection and preprocessing: collect historical wind turbine output data of distributed wind turbine power generation system, and perform necessary preprocessing on the data, that is, remove the data generated by wind farm planning or accident shutdown, and use Min-max normalization method to map the wind turbine output data to the interval [0, 1];

[0066] S2. Draw a distribution histogram of the normalized data of each wind turbine output, select possible fitting functions based on the basic form of the histogram, try to use different functions to fit the wind turbine output data, and select the optimal fitting function and its fitting parameters by selecting better sum and variance, root mean square error and determination coefficient fitting indicators;

[0067] S3. Define and construct a hybrid Copula function;

[0068] S4, estimating the parameters of the mixed Copula function, wherein the unknown parameters of the mixed Copula function include the parameters of the Copula function itself and the proportional coefficient of the mixed Copula function, and the fitting method adopts distance estimation, maximum likelihood estimation and iteration method;

[0069] S5. According to the wind speed, the wind turbine output correlation analysis is divided into three scenarios, namely, strong wind scenario, light wind scenario and normal wind scenario;

[0070] S6. Calculate the wind turbine output correlation coefficients under different scenarios respectively, establish a wind power prediction model, and obtain a wind power prediction scenario.

[0071] Furthermore, the game in S3 is determined based on the logit protocol.

[0072] Specifically, evolutionary game is a theory that combines game theory analysis with dynamic evolution process analysis. It is based on "limited rationality" and "limited information". In the dynamic evolution process, it studies how participants constantly adjust their strategies to adapt to the environment or opponents. Due to the uncertainty of electric vehicle travel behavior, including the initial battery capacity when connecting to the charging pile, the expected battery capacity, and the time of connecting to and leaving the charging pile, as well as the uncertainty of the generator set, the individual differences of electric vehicle clusters and units participating in the power demand response will tend to different charging and discharging scheduling strategies. Therefore, each electric vehicle in the scheduling area can be abstracted as an electric vehicle population with the same charging demand, and each unit can be abstracted as a unit unit with the same power generation demand. In the game process, the electric vehicle population and the unit unit are randomly generated under constraints as a strategy set. Each electric vehicle and the unit in each case selects a strategy respectively. The selection results of all electric vehicles and units are used as the group state. All electric vehicles and units modify their own strategies. With the dynamic evolution of the population, the selection ratio of each strategy in the strategy set changes continuously, and the individual fitness function and conditional conversion probability are also updated accordingly. As the number of iterations increases, the probability of selecting only one strategy continues to increase, gradually approaches 1 and finally reaches stability. At this time, it is regarded as a game equilibrium and an evolutionary strategy is obtained.

[0073] Furthermore, in S4, the evolution strategy is used to obtain the charging and discharging power strategies for all electric vehicles in the area during each scheduling period.

[0074] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for flexible demand response and optimal scheduling of electric vehicles, characterized in that: The following steps are involved: S1. Obtain the power generation and scheduling period corresponding to the target area within the set detection cycle; S2. Construct a game model based on power generation and dispatch period; S3, making the game model reach an equilibrium state and solving it to obtain a solution; S4. Dispatch electric vehicles for charging optimization based on the solution results.

2. The electric vehicle elastic demand response and optimization scheduling method according to claim 1 is characterized in that: S1 also includes collecting battery information and access time information of electric vehicles connected to the charging station.

3. The electric vehicle elastic demand response and optimization scheduling method according to claim 1 is characterized in that: The game model constructed in S2 includes constructing an upper-level pricing and scheduling demand response model and a lower-level unit power generation coordination model.

4. The electric vehicle elastic demand response and optimization scheduling method according to claim 3 is characterized in that: The upper-level pricing dispatch demand response model includes determining system constraints, EV aggregator cost model, EV charging and discharging price model, and objective function; Set the set of all electric vehicles in the area under the jurisdiction of the electric vehicle aggregator C = {1, 2, ..., n, ..., N}, the set of electric vehicle charging and discharging scheduling time periods in a day T = {1, 2, ..., t, ..., H}, and the charging and discharging power r of the nth electric vehicle in the scheduling period n =(e n,1 ,e n,2 ,…,e n,t ,…e n,H ).

5. The electric vehicle elastic demand response and optimization scheduling method according to claim 4 is characterized in that: The cost model of electric vehicle aggregators is expressed as: Among them, U EVA,tol is the total energy cost, U EVA,t is the cost of the electric vehicle aggregator during period t, L EV,t It is the sum of all electric vehicle loads during period t.

6. The electric vehicle elastic demand response and optimization scheduling method according to claim 4 is characterized in that: Electric vehicle charging and discharging price model λ n It is expressed as: Among them, l is the profit index, e m,t is the charging and discharging power of the mth electric vehicle during the scheduling period t.

7. The electric vehicle elastic demand response and optimization scheduling method according to claim 3 is characterized in that: The lower-level unit coordination model includes determining system constraints and setting an objective function based on minimizing electricity cost.

8. The electric vehicle elastic demand response and optimization scheduling method according to claim 1 is characterized in that: The game in S3 is determined based on the logit protocol.

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