CN106651002B - Large-scale electric vehicle charging and discharging multi-objective optimization method based on sine and cosine algorithm - Google Patents
Large-scale electric vehicle charging and discharging multi-objective optimization method based on sine and cosine algorithm Download PDFInfo
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Abstract
The application discloses a large-scale electric vehicle charging and discharging multi-objective optimization method based on a Sine Cosine Algorithm (SCA). according to the method, large-scale electric vehicles are classified in groups through a similar grouping method, and a group charging and discharging strategy of the electric vehicles is formulated by using the SCA algorithm, so that the goals of peak clipping and valley filling on a power grid side and minimum charging charge on a user side are achieved. In addition, the adopted sine and cosine Algorithm is a newer intelligent Algorithm, and compared with a classical Genetic Algorithm (GA), a Particle Swarm Algorithm (PSO) and the like, the Algorithm has the characteristics of higher convergence speed, stronger overall convergence and the like, so that the electric vehicle charging and discharging plan is more reasonable to make.
Description
Technical Field
The invention relates to the field of intelligent power distribution network optimization and electric vehicle charging and discharging strategy optimization, in particular to a large-scale electric vehicle charging and discharging multi-target optimization method based on a sine and cosine algorithm.
Background
With the progressive development of society and the increasing of economic level, user-side distributed equipment (mainly electric vehicles) will largely infiltrate into the power grid in the visible future. The electric automobile has the advantages of zero emission, low noise, no pollution, high efficiency and the like, is considered as one of effective measures for relieving the energy crisis, and is being promoted by countries in the world at an accelerated speed. However, if the charging behavior of the electric vehicle is not guided, the disordered charging of a large number of electric vehicles will greatly aggravate the peak-valley difference of the load curve of the power grid, increase the grid loss of the system, cause serious conditions such as voltage out-of-limit and the like, and cause serious threats to the safe and reliable operation of the power grid. Therefore, the power Grid needs To guide the charging behavior of the electric Vehicle, a reasonable charging and discharging plan is made for the accessed electric Vehicle, the V2G (Vehicle To Grid) technology can feed the residual energy in the battery back To the power Grid when the power Grid has a demand, the EV can participate in power Grid peak regulation, output fluctuation and frequency modulation of renewable energy sources are stabilized, power Grid peak clipping and valley filling are realized, the load curve of a power distribution network is improved, intermittence of the renewable energy sources is stabilized, the charging cost of a user can be reduced, and the win-win effect of the power Grid side and the user side is realized. Meanwhile, the electric automobile is used as an important component of an energy internet and an active power distribution network, and under the large background of electric power market reformation, research on the content of the component based on time-of-use electricity price is of great significance for promoting the construction of the energy internet and enhancing the response capability of a demand side.
At present, power grid companies increase the research on the operation problem of electric vehicles after being connected into a power grid, so that the production management mode of power enterprises is effectively improved, and the enterprise benefit is improved. However, there still exist some problems, for example, the current research on the optimal modeling of charging and discharging of electric vehicles mostly uses a single electric vehicle as an optimization unit, and the method faces the problems of low computational efficiency, poor computational accuracy and even dimension disaster when large-scale electric vehicles are accessed.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the existing large-scale electric vehicle charging and discharging optimization technology, the invention provides a multi-target charging and discharging optimization algorithm for large-scale electric vehicles, which can adapt to the large-scale electric vehicle charging and discharging optimization algorithm and the calculation amount of large-scale electric vehicle charging and discharging optimization, has higher calculation speed, better calculation speed and better calculation precision, and solves the problems of slow calculation and low precision when the traditional electric vehicle optimization model is accessed to the large-scale electric vehicles.
(II) technical scheme
Based on the method, a large-scale electric vehicle charging and discharging multi-objective optimization method based on a Sine and Cosine Algorithm (SCA) is provided, and through providing a mathematical model of characteristics and constraints of large-scale electric vehicles and electric vehicle groups, under a time-of-use electricity price mechanism, a power grid side load curve optimization and user side charging fee objective are provided. Therefore, the charging and discharging power of the electric automobile can be optimized, the accepting capacity of the power grid to the electric automobile is improved, and the win-win effect of the power grid side and the user side is generated.
A large-scale electric vehicle charging and discharging multi-objective optimization method based on a Sine and Cosine Algorithm (SCA) comprises the following steps: acquiring conventional load prediction information, time-of-use electricity price and electric vehicle information of accessing the power grid at each moment, wherein the information comprises access time, departure time, battery capacity, battery charge-discharge electrode limit and charging electric quantity;
dividing an electric vehicle group according to the access time and the leaving time of the electric vehicles accessed to the power grid;
taking the power grid load curve fluctuation in a reduced area from the moment to the optimization ending moment and the reduction of the user charging cost as optimization targets, and performing weighting processing on each target value to determine a fitness function;
determining a basic control model according to the charge and discharge electrode limit of the electric automobile group, the charge requirement and relevant constraint conditions;
according to the conventional load forecasting situation and the time-of-use electricity price situation in the region, a charging and discharging strategy of the electric automobile group in each period is formulated;
initializing a controllable variable according to the model parameters and the constraint conditions, wherein the initialized controllable variable is the charge and discharge power of each electric automobile group in each time period;
carrying out solution fitness evaluation by using an SCA algorithm and a fitness function;
determining an SCA algorithm searching direction according to a mathematical model of sine and cosine change, and updating the charge and discharge power of each electric vehicle group at each time interval;
and judging whether the maximum iteration times or convergence is reached, if so, finishing the calculation and outputting the charge and discharge power strategy of each electric vehicle group in each time period, and otherwise, returning to the step of evaluating the fitness by using the SCA algorithm and the fitness function.
In one embodiment, the electric vehicle is a residential household electric vehicle.
In one embodiment, the parameters of the electric vehicle include access time, departure time, battery capacity, charging demand, battery charge-discharge limit, and initial charge. All parameters are generated randomly.
In one embodiment, large-scale electric vehicles are grouped according to different access time and leaving time of the electric vehicles, and the electric vehicle group is used as an optimization unit, so that a charging and discharging strategy is formulated.
In one embodiment, the optimization targets are divided into a power grid side peak clipping and valley filling target and a user side economic target according to the large-scale electric vehicle charging and discharging strategy, and the weighted targets are used as fitness functions
(III) advantageous effects
Compared with the prior art, the invention provides a large-scale electric vehicle charging and discharging multi-objective optimization method based on a Sine and Cosine Algorithm (SCA), which has the following beneficial effects:
the large-scale electric vehicle charging and discharging multi-objective optimization model based on the sine and cosine algorithm solves the problem that a charging and discharging strategy is formulated after a large-scale electric vehicle is connected to a power grid. The large-scale electric automobiles are grouped according to the characteristics of the electric automobiles, the electric automobile groups are used as units for optimization, and the defect that a single electric automobile is used as an optimization unit is overcome. According to a conventional load curve and time-of-use electricity price of a power grid within one day, a charging and discharging strategy of the electric automobile is reasonably formulated by taking power grid side peak clipping and valley filling and user side economy as targets. In addition, the Sine Cosine Algorithm (SCA) adopted by the large-scale electric vehicle charging and discharging multi-target Optimization method based on the Sine Cosine Algorithm is a novel group search intelligent Algorithm, and compared with a classical Genetic Algorithm (GA), a Particle Swarm Algorithm (PSO) and the like, the method has the characteristics of higher convergence speed, stronger global convergence and the like, so that the electric vehicle charging and discharging plan is more reasonable to formulate.
Drawings
FIG. 1 is a flow chart of a method of a large-scale electric vehicle charging and discharging multi-objective optimization method based on a Sine and Cosine Algorithm (SCA) of the invention;
FIG. 2 is a graphical illustration of day ahead predicted data for a conventional load curve;
fig. 3 is a time-of-use electricity price.
Detailed Description
Referring to fig. 1, fig. 2 and fig. 3, an embodiment of the present invention provides a large-scale electric vehicle charging and discharging multi-objective optimization method based on a Sine and Cosine Algorithm (SCA). In the embodiment, a large-scale electric vehicle is taken as a research object, and in the calculation example, the peak clipping and valley filling of the power grid side and the weight coefficient in the economic objective function of the user side are performed
Are all set to 1, indicating that grid load shedding and economy are equally important for achieving both grid and consumer win. The types of the electric automobiles are all household automobiles, and the types of the electric automobiles can be selected according to actual conditions. The large-scale electric vehicle charging and discharging multi-objective optimization method based on the sine and cosine algorithm comprises the following steps of:
step S110, collecting power grid conventional load prediction information, time-of-use electricity price and electric vehicle information of each time of accessing the power grid, wherein the information comprises access time, leaving time, battery capacity, battery charge-discharge electrode limit and charging electric quantity. The parameters are required according to the requirements of a large-scale electric automobile charge-discharge multi-objective optimization model.
Considering the practical situation of urban office workers, a two-charge-per-day charging mode is adopted, wherein:
the time of entering the power grid between each vehicle day follows normal distribution of N (9, 0.6);
the daytime grid leaving time follows normal distribution of N (17, 0.4);
the night access to the power grid follows normal distribution of N (18, 0.8);
the time of leaving the power grid the next day follows normal distribution of N (7.5, 0.4);
the initial SOC of each vehicle connected to the power grid follows normal distribution of N (0.6, 0.1);
the battery type is Nissan-L eaf as an example, the battery capacity is 24kW.h, the charging power limit is 6kW, and the discharging power limit is-6 kW;
the expected electric quantity of the vehicle owner is 1, and the charging and discharging efficiency η is 0.95;
the conventional daily load curve in the area is given by the day-ahead forecast data, as shown in fig. 2.
The time-of-use electricity price information of the power grid is shown in fig. 3.
And step S120, dividing the electric vehicles with the same access time and the same leaving time into a group, and uniformly optimizing.
And step S130, taking reduction of peak clipping and valley filling on the power grid side from the moment to the optimization ending moment and minimum user charging cost as optimization targets, and determining a fitness function after weighting processing is carried out on each target value. The method comprises the following specific steps:
F＝ω1*F(1)+ω2*F (2)
Pcon,trepresents the power of the conventional load at time t; pi,tRepresenting the charge and discharge power of the electric automobile group i at the moment t; n is the number of clusters divided at time t；TstartAnd TendRespectively an optimized starting time interval and an optimized ending time interval;
and
the weight coefficients of the two targets are respectively.
And step S140, determining a basic control model according to the charge and discharge electrode limit of the electric automobile group, the charge requirement and related constraint conditions. The method comprises the following specific steps:
the charge-discharge model of a single electric vehicle can be described as
S＝(1-SOC)Cmax
Pdis.max≤P≤Pchar.max
Wherein S is the charging demand, SOC is the current battery state of charge, CmaxFor battery capacity, P is the charge and discharge power for each time period, Pdis.maxAnd Pchar.maxThe charging limit and the discharging limit of the battery are respectively, Q is reactive power during charging and discharging, and lambda is a power factor of the charging pile.
The electric vehicle population charge-discharge model can be described as follows:
wherein m is the population number, Ci max,mI is the total capacity of the group battery, n is the total number of the electric vehicles in the group, SiInto a groupThe total charge requirement.
And S150, dividing 24 hours a day into 96 time intervals, wherein each time interval is 15 minutes, and establishing a charge and discharge strategy by taking each time interval as a unit.
And step S160, initializing the controllable variable to be the charge and discharge power of each electric automobile group in each time period.
The constraints are specifically as follows:
the formula shows that the charging and discharging power of each time interval of the electric automobile group is equal to the group charging demand.
The expression indicates that the sum of the charging power of each period cannot exceed the total charging demand, i.e., the overcharge phenomenon cannot occur.
Wherein, Pm dis.maxAnd Pm char.maxThe electric vehicle is respectively the charge and discharge limit power which can be absorbed by the electric vehicle group in each time period. The charging and discharging power of each period cannot exceed the consumption range of the electric automobile group.
Step S170, randomly generating a population, wherein each population comprises N individuals, each individual is the charge-discharge power of each time period of the electric vehicle population, and the fitness of the individuals in the population is evaluated by a sine-cosine algorithm and a fitness function.
And step S180, determining the searching direction of each individual according to the mathematical model of sine and cosine change.
The method comprises the following specific steps:
wherein, Xk+1 iIs the solution at the i-th individual k +1 iterations, Xk iIs the solution of the ith individual at k iterations, Pk iFor the optimal solution in the population in k iterations, r3And r4All random quantities are random quantities within 0 to 1, and are random variables within the current positive and negative search step length.
Under the search mode, all individuals in the population are close to each other towards the direction of the optimal solution, new variables are generated, and finally the optimal solution is converged.
And step S190, judging whether the maximum iteration frequency is reached or whether the judgment result is converged, if so, outputting the charge and discharge strategy of each electric automobile group in each time period, and otherwise, returning to the step of evaluating the fitness by using a sine and cosine algorithm and a fitness function. After each calculation, determining whether the iteration number k is k +1, if so, ending the calculation, otherwise, returning to step S170. The charging and discharging power of the electric automobile group in each time period can be obtained through the steps, the load curve level of the power grid can be improved, the charging cost of a user can be reduced, and the win-win effect is achieved.
Compared with the prior art, the large-scale electric vehicle charging and discharging multi-objective optimization method based on the sine and cosine algorithm has the following advantages and effects:
(1) the multi-target charging and discharging optimization method for the large-scale electric vehicle based on the sine and cosine algorithm considers the situation of popularization of the large-scale future electric vehicles, groups the large-scale electric vehicles, and proposes a method using the electric vehicle groups as optimization units to make charging and discharging strategies, so that the calculation efficiency is improved, and the optimization result is improved.
(2) The multi-target charging and discharging optimization method for the large-scale electric automobile based on the sine and cosine algorithm comprehensively considers the targets of the power grid side and the user side, improves the load curve of the power grid, reduces the charging cost of the user, and realizes the win-win result of the power grid and the user.
(3) The sine and cosine algorithm adopted by the large-scale electric vehicle charging and discharging multi-target Optimization method based on the sine and cosine algorithm is a novel group search intelligent algorithm, and compared with a classical Genetic Algorithm (GA), a Particle Swarm algorithm (PSO) and the like, the method has the characteristics of higher convergence speed, stronger overall convergence and the like, so that the electric vehicle charging and discharging plan is made more reasonable.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (4)
1. A large-scale electric vehicle charging and discharging multi-objective optimization method based on a sine and cosine SCA algorithm comprises the following steps:
acquiring conventional load prediction information, time-of-use electricity price and electric vehicle information of accessing the power grid at each moment, wherein the information comprises access time, departure time, battery capacity, battery charge-discharge electrode limit and charging electric quantity;
dividing an electric vehicle group according to the access time and the leaving time of the electric vehicles accessed to the power grid;
and taking the minimum fluctuation of the power grid load curve and the minimum charging cost of the user in the reduced area from the moment to the optimization ending moment as optimization targets, and performing weighting processing on each target value to determine a fitness function, wherein the method specifically comprises the following steps:
wherein,
indicating the time of day
The power of the conventional load;
representing electric vehicle groups
At the moment of time
The charging and discharging power of (1);
is a time of day
The number of clusters divided;
and
respectively an optimized starting time interval and an optimized ending time interval;
and
the weight coefficients of the two targets are respectively;
determining a basic control model by using the charging and discharging electrode limit of an electric automobile group, the charging requirement and related constraint conditions, specifically as follows:
the charge and discharge model of a single electric vehicle is described as follows:
wherein,
in order to meet the demand for charging,SOCfor the current state of charge of the battery,C max as the capacity of the battery, there is,Pis the charge and discharge power of each time period,P dis.max andP char.max respectively the limit of the discharge electrode and the limit of the charge electrode of the battery,Qis the reactive power during the charge and the discharge,λis the power factor of the charging pile;
the electric vehicle population charge-discharge model is described as follows:
wherein m is the group number,
the total capacity of the group batteries is represented by i, the total number of the group, n, and Si, wherein n is the total number of electric vehicles contained in the group, and Si is the total charging requirement of the group;
according to the conventional load forecasting situation and the time-of-use electricity price situation in the region, a charging and discharging strategy of the electric automobile group in each period is formulated;
initializing a controllable variable according to the model parameters and the constraint conditions, wherein the initialized controllable variable is the charge and discharge power of each electric automobile group in each time period;
carrying out solution fitness evaluation by using an SCA algorithm and a fitness function;
determining an algorithm searching direction according to a mathematical model of sine and cosine change, and updating the charge and discharge power of each electric automobile group at each time period;
the method specifically comprises the following steps:
randomly generating a population, wherein each population comprises N individuals, each individual is the charge-discharge power of each time period of the electric vehicle population, and the fitness evaluation is carried out on the individuals in the population by using a sine and cosine algorithm and a fitness function;
each individual determines the search direction of the individual according to a mathematical model of sine and cosine change;
the method comprises the following specific steps:
wherein,
is as followsiIndividual onekThe solution at +1 iteration,
is as followsiIndividual onekThe solution at the time of the sub-iteration,
is composed ofkThe optimal solution within the population at the time of the second iteration,
and
all random quantities within 0 to 1Random variable in the current positive and negative search step length;
under the search mode, all individuals in the population approach towards the direction of the optimal solution to generate a new variable and finally converge to the optimal solution; and judging whether the maximum iteration times or convergence is reached, if so, finishing the calculation and outputting the charge and discharge power strategy of each electric vehicle group in each time period, and otherwise, returning to the step of evaluating the fitness by using the SCA algorithm and the fitness function.
2. The large-scale electric vehicle charging and discharging multi-objective optimization method based on the sine and cosine SCA algorithm as claimed in claim 1, wherein the large-scale electric vehicles are grouped according to different access time and leaving time of the electric vehicles, and the electric vehicle groups are used as optimization units to make charging and discharging strategies.
3. The large-scale electric vehicle charging and discharging multi-objective optimization method based on the sine and cosine SCA algorithm as claimed in claim 1, wherein the electric vehicle population parameters comprise: the method comprises the following steps of access time, leaving time, total capacity of batteries of the electric automobile group, total charging requirement of the batteries of the electric automobile group and total charging and discharging limit of the batteries of the electric automobile group.
4. The large-scale electric vehicle charging and discharging multi-objective optimization method based on the sine and cosine SCA algorithm as claimed in claim 1, wherein the fitness function is determined after weighting according to the large-scale electric vehicle charging and discharging optimization objective that the load fluctuation on the power grid side is minimum and the charging cost on the user side is minimum.
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| CN109167347B (en) * | 2018-08-06 | 2022-03-29 | 云南民族大学 | Cloud-adaptive-particle-swarm-based multi-target electric vehicle charge-discharge optimization scheduling method |
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