CN111652405A - Double-layer optimization method for electric vehicle charging and discharging strategy and power grid side-sharing electricity price - Google Patents
Double-layer optimization method for electric vehicle charging and discharging strategy and power grid side-sharing electricity price Download PDFInfo
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Abstract
The invention provides a double-layer optimization method for a charge and discharge strategy of an electric vehicle and a power grid side-time power price, wherein a decision maker of an upper layer model is a user of the electric vehicle, takes the minimum charge and discharge cost as a target, considers the driving requirement of the user, relieves the charge and discharge frequency constraint of battery loss and the like, and optimizes the charge and discharge strategy at each time interval; and a decision maker of the lower-layer model is a power grid, and optimizes the time-of-use electricity price in three periods of peak, valley and average by taking the social welfare maximization as a target. According to the method, a double-layer model is established and solved in the business optimization GAMS, and meanwhile, the optimal decision of the charging and discharging strategy and the time-of-use electricity price is obtained, so that the load peak-valley difference of the system can be effectively reduced, the user benefit and the power generation side cost of the electric automobile can be considered, and the interaction between the time-of-use electricity price and the charging and discharging behaviors of the electric automobile is reflected.
Description
Technical Field
The invention relates to the technical field of electric automobiles, in particular to a double-layer optimization method for a charge-discharge strategy and a time-of-use electricity price at the side of a power grid of an electric automobile.
Background
The electric automobile has outstanding environmental protection advantages compared with the traditional fuel oil vehicle as a representative of zero pollution and low energy consumption. However, because the charging behavior of the electric vehicle has a great randomness, the disordered charging behavior of a large number of electric vehicles after being connected to the power grid can cause great influence on the safe and stable operation of the power system, for example, when the charging load of the electric vehicle is overlapped with the peak time period of the original load of the system, the peak-to-valley difference of the load of the power grid is further increased, and the peak regulation is difficult. With the development of the electric Vehicle V2G (Vehicle-to-grid) technology, the electric Vehicle and the power grid can realize bidirectional interaction of energy and information, and the power grid can guide the charging and discharging behaviors of the electric Vehicle through time-of-use electricity price signals, so that a Vehicle owner of the electric Vehicle can voluntarily change the charging and discharging time period, and a battery of the electric Vehicle is used as an energy storage source, thereby achieving the effect of peak load shifting.
At present, most researches are concentrated on an electric vehicle charging and discharging scheduling method based on fixed time-of-use electricity price and a time-of-use electricity price optimization method based on demand price elasticity, however, dynamic interaction relations between two factors of user side electric vehicle charging and discharging behaviors and power grid side time-of-use electricity price are not considered in the methods, and the decision of two main bodies of a user side and a power grid side cannot be optimized in actual operation.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
Therefore, the technical problem to be solved by the invention is to overcome the defect that the time-of-use electricity price of charging and discharging the electric vehicle cannot be optimally scheduled in the prior art, so that a double-layer optimization method for the charging and discharging strategy of the electric vehicle and the time-of-use electricity price on the power grid side is provided.
In order to solve the technical problems, the invention provides the following technical scheme: a double-layer optimization method for electric vehicle charging and discharging strategy and electric network side time sharing electricity price comprises,
collecting basic data of a user side and unit data of a power grid side;
time-of-use electricity price time interval division is carried out on the basis of the fuzzy membership function;
sampling the trip probability density of the electric vehicle user;
establishing a double-layer optimization model of the charging and discharging power of the electric automobile at the user side and the time-sharing electricity price of the power grid side, and importing the data into the double-layer optimization model;
and solving a user-side electric vehicle charge and discharge strategy and a power grid-side time-sharing electricity price which aim at minimizing charge and discharge fees and maximizing social welfare.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: the user side basic parameters comprise the outgoing data of the electric automobile user, the battery capacity of the electric automobile, the maximum charging and discharging power, the daily load curve of residents and the fixed electricity price; and the power grid side unit data comprises the installed capacity, the upper and lower output limits and the climbing/landslide limit of the generator set.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: time-of-use electricity price period division is carried out on 24 periods of the whole day based on the fuzzy membership function, the time-of-use electricity price period division comprises the steps of carrying out peak-valley-level three-period division on a resident daily load curve,
respectively taking the highest point and the lowest point of a load curve;
calculating peak membership and valley membership for the load value in a certain time period based on the larger semi-trapezoid membership function and the smaller semi-trapezoid membership function;
and dividing the peak-valley time periods according to the result of the peak membership degree and the valley membership degree, and taking the rest time periods as the ordinary time periods.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: the method comprises the steps of sampling the trip characteristic probability density of an electric vehicle user on a typical working day, carrying out probability density function fitting on trip time, end time and single-day trip mileage in the trip characteristic of the electric vehicle user, and calculating the trip time period of the electric vehicle user.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: in the establishment of a double-layer optimization model of the charging and discharging power of the electric automobile at the user side and the time-sharing electricity price of the power grid side, the upper layer is the electric automobile user, and the optimization target is that the charging and discharging charge under the time-sharing electricity price mechanism is minimum:
wherein N is the number of the electric automobiles in the region; t isf、TgAnd TpRespectively representing three load periods of peak, valley and flat; lambda [ alpha ]f、λg、λpRespectively representing the electricity prices of the peak time interval, the valley time interval and the flat time interval;
the charging power of the electric automobile i in the t period is represented and is a positive value;
and the discharge power of the electric automobile i in the t period is represented and is a negative value.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: the upper layer constraint conditions comprise available capacity constraint, charge and discharge electric quantity balance constraint, charge and discharge state constraint, charge and discharge power constraint, non-scheduling time period constraint, driving requirement constraint and charge and discharge frequency limitation.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: the lower model is a power grid side, and the goal is that the social welfare is maximized:
maxCinc-Cgen
wherein, CincRepresenting overall user utility, CgenRepresents the cost of electricity generation:
wherein Lt is the original load of the t period; k is the total number of the generator sets;
for the output power of the generator set k in the period t,
representing the power generation cost function of the generator set k in the period t,
ak、bkand ckConstant term, primary term and secondary term coefficients of the power generation cost function; lambda [ alpha ]cIs the original fixed electricity price.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: the lower layer constraint conditions comprise upper and lower limit constraints of the generator set, climbing/sliding constraints of the generator set, peak-valley flat electricity price characteristic constraints, peak-valley electricity price proportion constraints and user total electricity cost constraints.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: modeling and optimization solving of a double-layer optimization model are realized based on a GAMS framework, the established double-layer model of the charging and discharging power of the electric automobile at the user side and the time-sharing electrovalence of the power grid side is represented by a compact model under the GAMS framework, wherein,
the charging and discharging strategy optimization model of the electric vehicle user side at the upper layer is expressed as follows:
in the formula (f)1(x,y)Is an objective function of a model with minimum charge and discharge cost under a time-of-use electricity price mechanism, x is a decision variable of an upper layer model, and g(x,y)Constraint conditions for each upper layer;
the charging and discharging strategy optimization model of the electric vehicle user side at the upper layer is expressed as follows:
in the formula (f)2(x,y)The lower layer power grid side model takes the social welfare maximization as an objective function of a target, y is a decision variable of the lower layer model, h(x,y)For each underlying constraint.
The invention relates to a preferable scheme of a double-layer optimization method for a charge-discharge strategy and a power grid side-sharing electricity price of an electric vehicle, wherein the method comprises the following steps: in the process of solving the double-layer optimization model, converting the double-layer model into a single-layer model by adopting a KKT optimization condition, introducing a Lagrange multiplier gamma, linking the constraint condition of the lower layer model with an objective function, and converting the double-layer model into:
wherein l is the number of constraint conditions in the lower model; gamma rayjA Lagrange multiplier corresponding to the jth constraint condition; the last row represents the lagrangian function L (x, y, γ) of the underlying model objective function to partially derive the decision variable y, where lagrangian function L (x, y, γ) can be written as:
and then, calling a GAMS/CPLEX solver to obtain the optimal solution of the double-layer model.
The invention has the beneficial effects that: according to the double-layer optimization method for the electric vehicle charge and discharge strategy and the power grid side time-sharing electricity price, a decision maker of an upper layer model is an electric vehicle user, the charge and discharge cost is the minimum, the driving requirement of the user and the charge and discharge frequency constraint for relieving the battery loss are considered, and the charge and discharge strategy of each time period is optimized; and a decision maker of the lower-layer model is a power grid, and optimizes the time-of-use electricity price in three periods of peak, valley and average by taking the social welfare maximization as a target. According to the method, a double-layer model is established and solved in the business optimization GAMS, and meanwhile, the optimal decision of the charging and discharging strategy and the time-of-use electricity price is obtained, so that the load peak-valley difference of the system can be effectively reduced, the user benefit and the power generation side cost of the electric automobile can be taken into consideration, and the interaction between the time-of-use electricity price and the charging and discharging behaviors of the electric automobile is reflected.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor. Wherein:
FIG. 1 is a general framework diagram of a double-layer optimization method for considering a user-side electric vehicle charging and discharging strategy and a power grid-side time-sharing electricity price;
FIG. 2 is a diagram illustrating exemplary work sunrise features of a user;
FIG. 3 is a general flow chart of a double-layer optimization method for the charging and discharging strategy and the power grid side-sharing electricity price of the electric vehicle provided by the invention;
FIG. 4 is a load graph during peak-to-valley flat rate time division;
FIG. 5 is a graph of peak-to-valley membership during time division of peak-to-valley horizontal tariffs;
FIG. 6 is a schematic diagram illustrating a daily mileage sampling situation of an electric vehicle user;
FIG. 7 is a schematic diagram illustrating a first trip time sampling condition of an electric vehicle;
FIG. 8 is a schematic diagram illustrating a second trip time sampling condition of an electric vehicle user;
FIG. 9 is a time-of-use electricity price optimization result diagram;
FIG. 10 is a schematic view of a load curve before and after time-of-use electricity price optimization;
fig. 11 is a schematic diagram of the result of optimizing the charging and discharging power of the electric vehicle.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Example 1
In the embodiment, the double-layer optimization model is divided into an upper layer and a lower layer, wherein a decision maker of the upper layer model is an electric vehicle user, the optimization target is the minimum charge and discharge cost, the decision variables are the charge power, the discharge power, the charge state, the discharge state, the charge starting state, the charge ending state and the charge state of each time interval on a running day, and meanwhile, the available capacity constraint, the charge and discharge electric quantity balance constraint, the charge and discharge state constraint, the charge and discharge power constraint, the non-dispatchable time interval constraint, the driving requirement constraint and the charge and discharge frequency limit constraint of the electric vehicle are considered; a decision maker of the lower-layer model is a power grid and aims at maximizing social welfare, namely maximizing overall user benefit and minimizing power generation cost, decision variables of the lower-layer model are time-of-use electricity prices in three periods of peak, valley and average and power of a generator set, and peak-valley-average electricity price characteristic constraint, peak-valley electricity price proportion constraint, user total electricity cost constraint, generator set upper and lower limit constraint and generator set climbing constraint are considered at the same time. Before constructing the double-layer optimization model of the user-side electric vehicle charging and discharging strategy and the grid-side time-sharing electricity price, the trip probability characteristic density of the electric vehicle user needs to be sampled, and the time period of peak-valley leveling is carried out on the current-day predicted load curve. The overall framework is shown in fig. 2, and the detailed description of each step is as follows:
(1) target parameter import
The method specifically comprises basic parameters such as electric automobile user outgoing data, electric automobile battery capacity, maximum charging and discharging power and the like, daily load curves and fixed electricity prices of residents, and unit data such as installed capacity, upper and lower output limits, climbing/landslide limits and the like of a generator set.
(2) Peak-valley level valence time interval division based on fuzzy membership function
Time-of-use electricity price time interval division is carried out on 24 time intervals of the whole day based on a fuzzy membership function, specifically, the time-of-use electricity price time interval division comprises the steps of dividing a load curve of a resident day into three time intervals of peak-valley-average, and the highest point and the lowest point of the load curve are respectively marked as DmaxAnd DminAnd based on a partial large semi-trapezoidal membership function F(x)And a small semi-trapezoidal membership function G(x)Load value D for t periodtCalculating peak membership and valley membership:
the peak membership degree and the valley membership degree are the possibility that the load value is in the peak time period and the valley time period, the peak time period and the valley time period can be divided according to the value of the load value, and the rest time period is the flat time period.
(2) Electric vehicle user trip probability density sampling
The method specifically includes performing probability density function fitting on travel time, end time and single-day travel mileage in travel characteristics of an electric vehicle user, and assuming that travel distances of the electric vehicle to and from a house and a working place are equal, the travel characteristics of the user on a typical working day can be described as shown in fig. 3.
Approximately representing the starting time of the first trip and the ending time of the last trip of the user as normal distribution functions in one day, and taking the normal distribution functions as the ending time and the starting time of the first and last charging and discharging periods, wherein the probability density function of the first trip time of the user is as follows:
wherein, tsIs the trip time period of the user; mu.ssIs a desired value; sigmasIs the standard deviation.
The probability density function for the last trip completion period of the user is:
wherein, teA return period for the user; mu.seIs a desired value; sigmaeIs the standard deviation. Probability density function of mileage driven by user on single day:
wherein d is the single-day mileage of the user; mu.sdIs a desired value; sigmadIs the standard deviation. And sampling according to the probability density function of the trip time interval, the return time interval and the single-day driving mileage, and calculating the time interval when the electric vehicle user arrives at the working place and the time interval when the electric vehicle user departs from the working place to home.
(3) Double-layer optimization model establishment for user-side electric automobile charge and discharge power and grid-side time-sharing electricity price
In the double-layer optimization model, the upper layer is an electric vehicle user, and the optimization target is that the charge and discharge cost under a time-of-use electricity price mechanism is minimum:
wherein N is the number of the electric automobiles in the region; t isf、TgAnd TpRespectively representing three load periods of peak, valley and flat; lambda [ alpha ]f、λg、λpRespectively representing the electricity prices of the peak time interval, the valley time interval and the flat time interval;
the charging power of the electric automobile i in the t period is represented and is a positive value;
and the discharge power of the electric automobile i in the t period is represented and is a negative value. The constraint conditions are as follows:
available capacity constraint
The available capacity of the electric automobile is represented by a state of charge (SOC), is the percentage of the residual electric quantity to the total available electric quantity, and is constrained by the upper limit and the lower limit of the SOC of the storage battery:
wherein S isi,tThe SOC of the electric vehicle i in the t period;
and
respectively representing the upper and lower limits of the SOC of the storage battery of the electric automobile i.
② charge-discharge electric quantity balance constraint
The SOC of the electric vehicle is related to the charge and discharge power and the travel power consumption in the previous period:
wherein,
and
charge/discharge efficiency of electric vehicle i, respectively;
for the electric automobile i to run in unit power consumption of hundred kilometers in t time periodThe unit of the amount is kW/100 km; eiThe battery capacity of the electric automobile i; Δ t represents the duration of each period in units of h.
Third, charge and discharge state constraint
The charging/discharging state of the electric vehicle in a certain period of time is unique, i.e., it cannot be charged and discharged simultaneously.
Wherein,
and
the variable 0-1 of the charging/discharging state of the electric vehicle i in the period t respectively, and the value of 1 represents that the electric vehicle is in the charging/discharging state.
Charge and discharge power constraint
The charge and discharge power of the electric automobile is limited by the maximum charge and discharge power:
wherein,
and
respectively, the maximum charge/discharge power of the electric vehicle i.
Constraint of non-scheduling period
When electric automobile is in the period of traveling, electric automobile is in the state of can not dispatching:
wherein, ti,s1And ti,s3Respectively for the electric automobile i leaving home and workingThe time of the place; t is ti,s2And ti,s4The arrival times of the electric vehicle i at home and at work, respectively.
Driving demand constraint
Before the electric automobile starts to run in a running period, the SOC meeting the driving requirement of an automobile owner is required to be achieved, the SOC upper limit is assumed to be reached before the automobile owner leaves home every morning, and the SOC enough to run home is required to be reached when the automobile owner returns home from a working place every afternoon.
Seventh, limit of charge and discharge frequency
In order to delay the charge and discharge loss of the battery, the charge and discharge times of each electric automobile are limited:
wherein,
and
respectively representing the charging and discharging times of the electric automobile i in a period t;
and
representing the maximum number of times per day that the electric vehicle i was charged and discharged, respectively.
The lower model is a power grid side, and the goal is that the social welfare is maximized:
max Cinc-Cgen(15)
wherein, CincTo representOverall user utility, CgenRepresents the cost of electricity generation:
wherein Lt is the original load of the t period; k is the total number of the generator sets;
for the output power of the generator set k in the period t,
representing the power generation cost function of the generator set k in the period t,
ak、bkand ckConstant term, primary term and secondary term coefficients of the power generation cost function; lambda [ alpha ]cIs the original fixed electricity price. The constraint conditions are as follows:
upper and lower limit constraint of generator set
Wherein,
and
respectively an upper limit and a lower limit of the output power of the generator set k.
② climbing/landslide restriction of generator set
Wherein,
and
the ramp power limit and the landslide power limit of the generator set k are respectively.
Third, peak-to-valley flat electrovalence characteristic constraint
In order to ensure that the load peak-valley characteristics do not change after the time-of-use electricity price is implemented and avoid the peak-valley dislocation of the load, the electricity prices in three periods of peak, valley and average should meet the following constraints:
λf＞λp＞λg(20)
peak-to-valley electricity price ratio constraint
αminλg≤λf≤αmaxλg(21)
Wherein, αmaxAnd αminα are generally taken as the maximum and minimum values set for defining peak and valley time electrovalence ratios, respectivelymin=2，αmax＝5。
Constraint of total electricity consumption of user
After the time-of-use electricity price is implemented, the original electricity utilization mode is changed in the response process of a user, and certain electricity utilization comfort level is sacrificed. In order to make the user actively participate in the time-of-use electricity price response, it needs to be ensured that the total electricity fee of the user is not increased after the time-of-use electricity price is implemented:
wherein, the left side of the unequal number represents the total electric charge that the user needs to pay after implementing the time-of-use electric price, and the right side represents the total electric charge that the user needs to pay at the fixed electric price.
(4) Double-layer optimization model solving method based on GAMS framework
The modeling and optimization solution of the double-layer optimization model are realized based on the GAMS framework, the relationship of the mutual variables between the upper layer and the lower layer can be well reflected, and finally the equilibrium solution is realized. Under the GAMS framework, the established double-layer model of the charging and discharging power of the electric automobile at the user side and the time-sharing electrovalence of the power grid side can be expressed as follows:
wherein, formula (23) represents an upper-layer electric vehicle user side charge-discharge strategy optimization model: f. of1(x,y)For the objective function expressed by equation (6), x is a decision variable of the upper model, i.e.
g(x,y)A constraint represented by formula (7) to formula (14); equation (24) represents the lower grid side time-sharing electricity price optimization model, f2(x,y)For the objective function represented by equation (15), y is the decision variable of the underlying model, i.e., [ lambda ]f,λp,λg,
]，h(x,y)The constraint conditions are expressed by the following equations (18) to (22). In the double-layer model, when the user side of the electric automobile makes a decision, the time-of-use electricity price is given after the decision is made by the power grid side; when the decision is made on the power grid side, the charge and discharge power of the electric automobile is given after the decision is made on the user side, the double-layer model is solved at the same time, and the objective functions of the upper layer model and the lower layer model are mutually influenced and are difficult to solve. In the embodiment, a KKT (Karush-Kuhn-Tucker, KKT) optimality condition is adopted to convert a double-layer model into a single-layer model for solving, and the main idea is to introduce a new parameter gamma (namely, Lagrange's multiplier), link the constraint condition of a lower-layer model with an objective function, obtain the solution of each variable under the original objective function extremum, and convert the lower-layer model into the constraint of an upper-layer model. The two-layer model as equations (23) and (24) can be converted into:
wherein l is the number of constraint conditions in the lower model; gamma rayjA Lagrange multiplier corresponding to the jth constraint condition; the last row represents the lagrangian function L (x, y, γ) of the underlying model objective function to partially derive the decision variable y, where lagrangian function L (x, y, γ) can be written as:
at this time, the GAMS/CPLEX solver may be called to obtain the optimal solution of the two-layer model, and the overall flow of the calculation method is shown in FIG. 3.
In the embodiment, based on the GAMS framework, the double-layer optimization problem of the user-side electric vehicle charging and discharging strategy and the grid-side time-sharing electricity price is solved by adopting the optimality KKT condition, and the complex mixed integer nonlinear problem is processed, so that the upper layer and the lower layer in the double-layer model can obtain the optimal solution through the optimality condition.
Example 2
In this embodiment, taking actual data of a certain area of 9, 3 and 9 months in 2019 as an example, a specific implementation of a double-layer optimization method for a charging and discharging strategy of an electric vehicle and a power grid side time-sharing electricity price is further described.
(1) Basic data import
TABLE 1 partial genset parameters
TABLE 2 electric vehicle parameters
| Battery capacity (kWh) | 58.5 |
| Maximum charge and discharge power (kW) | 9.6、3.6 |
| Charging efficiency (%) | 90 |
| Discharge efficiency (%) | 85 |
| Hundred kilometers of power consumption (kWh/100km) | 28.9 |
(2) Peak-valley level valence time interval division based on fuzzy membership function
The load curve is shown in fig. 4, and the peak and valley membership degrees calculated based on the fuzzy membership function are shown in fig. 5.
As can be seen from fig. 5, the load value of 10:00 has the highest peak membership degree, the load value of 6:00 has the highest valley membership degree, and the load value of the day is divided by using 0.5 as a reference value, and finally, the result of dividing the load of the day by the peak-valley flat electricity price period is shown in table 3, in the division result, the peak period is 10 hours, the normal period is 6 hours, and the valley period is 8 hours. Comparing the load curve with the peak-valley-level electricity price time interval division result, it can be seen that the peak value and the valley value of the load are respectively in the divided peak electricity price time interval and the divided valley electricity price time interval on the same day, which shows that the peak-valley-level time interval division method based on the fuzzy membership function can effectively divide the peak-valley time interval.
TABLE 3 Peak-to-valley plateau partitioning results
| Peak electricity price time period (h) | 9、10、11、12、19、20、21、22、23、24 |
| Flat time period (h) | 13、14、15、16、17、18 |
| Valley electricity price period (h) | 1、2、3、4、5、6、7、8 |
(3) Electric vehicle user trip probability density sampling
The user travel characteristics of the electric vehicle are sampled according to the probability density function, and the results are shown in fig. 6, fig. 7 and the figure, and it can be seen that the daily mileage and the user travel time obtained by sampling obey the corresponding probability density distribution function.
(4) And solving a result by a double-layer optimization method of the charging and discharging power of the electric automobile at the user side and the time-sharing electricity price of the power grid side.
The time-of-use electricity price and the single fixed electricity price obtained by the double-layer optimization are shown in fig. 9, and as can be seen from fig. 9, compared with the single fixed electricity price, price signals of the time-of-use electricity price in three load periods of peak-valley level are more obvious, and the electricity utilization behavior of the user can be guided.
The load curves before and after the time-of-use electricity price optimization are shown in fig. 10, and it can be seen that the load peak-valley difference is reduced from 783.21MW to 410.51MW, and the overall reduction is 48.35%, which shows that the method provided by the invention can effectively reduce the load peak-valley difference, thereby reducing the investment of a peak-load unit and standby equipment to a certain extent.
After the method provided by the invention is adopted, the charging and discharging power of the electric automobile is shown in fig. 11, and it can be seen that the charging power of the electric automobile is higher due to lower electricity price in the load valley period. During the peak load periods of 10:00-12:00 and 19:00-24:00, the electric automobile users choose to discharge to gain income, and during the peak load period of 13:00-18:00, the total charging power is lower, because part of electric automobiles select to charge due to lower electric quantity, and the rest electric automobile users are limited by the discharging times, select not to charge and not discharge.
As can be seen from fig. 11, the time-of-use electricity price has a significant effect on the charging and discharging power of the electric vehicle user, and the user can be guided to charge at the load valley and discharge at the load peak, so that the electricity charge of the user is reduced, and the effect of peak clipping and valley filling is achieved. The charge and discharge cost ratio of the user before and after the time-of-use electricity price is shown in table 4, and it can be seen that the charge and discharge cost of the user is reduced by 69.67% under the double-layer optimization method provided by the invention.
TABLE 4 subscriber Charge and discharge cost comparison
| Charge and discharge expense (Yuan) | |
| Before optimization | 155843.06 |
| After optimization | 47276.16 |
From the above analysis results, the method of the present invention can reduce the load peak-valley difference, achieve the purpose of peak shaving and valley filling, and balance the benefits of the user side and the power grid side, thereby also enabling the measures to be smoothly executed.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A double-layer optimization method for electric vehicle charging and discharging strategies and grid side-sharing time-sharing electricity prices is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting basic data of a user side and unit data of a power grid side;
time-of-use electricity price time interval division is carried out on the basis of the fuzzy membership function;
sampling the trip probability density of the electric vehicle user;
establishing a double-layer optimization model of the charging and discharging power of the electric automobile at the user side and the time-sharing electricity price of the power grid side, and importing the data into the double-layer optimization model;
and solving a user-side electric vehicle charge and discharge strategy and a power grid-side time-sharing electricity price which aim at minimizing charge and discharge fees and maximizing social welfare.
2. The double-layer optimization method for the electric vehicle charge and discharge strategy and the grid side time-sharing electricity price according to claim 1 is characterized in that: the user side basic parameters comprise electric vehicle user outgoing data, electric vehicle battery capacity, maximum charging and discharging power, resident daily load curves and fixed electricity prices; and the power grid side unit data comprises the installed capacity, the upper and lower output limits and the climbing/landslide limit of the generator set.
3. The double-layer optimization method for the electric vehicle charge and discharge strategy and the grid side time-sharing electricity price according to claim 1 is characterized in that: time-of-use electricity price time interval division is carried out on 24 time intervals of the whole day based on the fuzzy membership function, the time-of-use electricity price time interval division comprises the steps of dividing a daily load curve of residents into three time intervals of peak-valley-level,
respectively taking the highest point and the lowest point of a load curve;
calculating peak membership and valley membership for the load value in a certain time period based on the partial large half-trapezoid membership function and the partial small half-trapezoid membership function;
and dividing the peak-valley time periods according to the result of the peak membership degree and the valley membership degree, and taking the rest time periods as flat time periods.
4. The double-layer optimization method for the electric vehicle charge and discharge strategy and the grid side time-sharing electricity price according to claim 1 is characterized in that: the method comprises the steps of sampling the trip characteristic probability density of an electric vehicle user on a typical working day, carrying out probability density function fitting on trip time, end time and single-day trip mileage in the trip characteristic of the electric vehicle user, and calculating the trip time period of the electric vehicle user.
5. The double-layer optimization method for the electric vehicle charge and discharge strategy and the grid side time-sharing electricity price according to claim 1 is characterized in that: in the establishment of a double-layer optimization model of the charging and discharging power of the electric automobile at the user side and the time-sharing electricity price of the power grid side, the upper layer is the electric automobile user, and the optimization target is that the charging and discharging cost under the time-sharing electricity price mechanism is minimum:
wherein N is the number of the electric automobiles in the region; t isf、TgAnd TpRespectively representing three load periods of peak, valley and average; lambda [ alpha ]f、λg、λpRespectively representing the electricity prices of the peak time interval, the valley time interval and the flat time interval;
the charging power of the electric automobile i in the t period is represented and is a positive value;
and the discharge power of the electric automobile i in the t period is represented and is a negative value.
6. The double-layer optimization method for the electric vehicle charge and discharge strategy and the grid side time-sharing electricity price according to claim 5, is characterized in that: the upper layer constraint conditions comprise available capacity constraint, charge and discharge electric quantity balance constraint, charge and discharge state constraint, charge and discharge power constraint, non-scheduling time period constraint, driving requirement constraint and charge and discharge frequency limitation.
7. The double-layer optimization method for the electric vehicle charge and discharge strategy and the grid side time-sharing electricity price according to claim 1 is characterized in that: the lower model is a power grid side, and the goal is that the social welfare is maximized:
max Cinc-Cgen
wherein, CincRepresenting overall user utility, CgenRepresents the cost of electricity generation:
wherein Lt is the original load of the t period; k is the total number of the generator sets;
for the output power of the genset k during the time period t,
representing the power generation cost function of the generator set k in the period t,
ak、bkand ckConstant term, primary term and secondary term coefficients of the power generation cost function; lambda [ alpha ]cIs the original fixed electricity price.
8. The electric vehicle charging and discharging strategy and power grid side time sharing electricity price double-layer optimization method according to claim 7, characterized in that: the lower layer constraint conditions comprise upper and lower limit constraints of the generator set, climbing/sliding constraints of the generator set, peak-valley flat electricity price characteristic constraints, peak-valley electricity price proportion constraints and user total electricity cost constraints.
9. The double-layer optimization method for the electric vehicle charge and discharge strategy and the grid-side time-sharing electricity price according to any one of claims 1 to 8, is characterized in that: modeling and optimization solving of a double-layer optimization model are realized based on a GAMS framework, the established double-layer model of the charging and discharging power of the electric automobile at the user side and the time-sharing electrovalence of the power grid side is represented by a compact model under the GAMS framework, wherein,
the charging and discharging strategy optimization model of the electric vehicle user side at the upper layer is expressed as follows:
in the formula (f)1(x,y)Is an objective function of a model with minimum charge and discharge cost under a time-of-use electricity price mechanism, x is a decision variable of an upper layer model, and g(x,y)Constraint conditions for each upper layer;
the charging and discharging strategy optimization model of the electric vehicle user side at the upper layer is expressed as follows:
in the formula (f)2(x,y)The lower layer power grid side model takes the social welfare maximization as an objective function of a target, y is a decision variable of the lower layer model, h(x,y)For each underlying constraint.
10. The electric vehicle charging and discharging strategy and power grid side time sharing electricity price double-layer optimization method according to claim 9, characterized in that: in the process of solving the double-layer optimization model, converting the double-layer model into a single-layer model by adopting a KKT optimization condition, introducing a Lagrange multiplier gamma, linking the constraint condition of the lower layer model with an objective function, and converting the double-layer model into:
wherein l is the number of constraint conditions in the lower model; gamma rayjA Lagrange multiplier corresponding to the jth constraint condition; the last row represents the lagrangian function L (x, y, γ) of the underlying model objective function to partially derive the decision variable y, where lagrangian function L (x, y, γ) can be written as:
and then, calling a GAMS/CPLEX solver to obtain the optimal solution of the double-layer model.
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