# Firefly algorithm based method for determining position and capacity of electric automobile charging station

## A firefly algorithm, electric vehicle technology, applied in the field of determining the location and capacity of electric vehicle charging stations, can solve problems such as overloading of lines and transformers, and deterioration of power quality

Inactive Publication Date: 2016-03-02
QUANZHOU POWER SUPPLY COMPANY OF STATE GRID FUJIAN ELECTRIC POWER +1
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## AI-Extracted Technical Summary

### Problems solved by technology

As a part of the grid, the electric vehicle charging station will inevitably affect the planning of the distribution network, such as the incr...
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## Abstract

The invention discloses a firefly algorithm based method for determining the position and the capacity of an electric automobile charging station. The method is characterized by comprising the following steps: 1, setting a strategy combination determined by the position and the capacity of the charging station according to actual conditions in a planning area; 2, performing searching optimization on a target function by use of a firefly algorithm to find out a minimum value of the target function so as to determine an optimal position and capacity strategy in the strategy combination, wherein the target function is a power distribution network rack planning model associated with a rack construction investment cost and a power distribution network operation loss cost; and 3, verifying the optimal position and capacity strategy in a constraint condition, if the strategy satisfies the constraint condition, determining the position and the capacity of the charging station, if the strategy does not satisfy the constraint condition, deleting the optimal position and capacity strategy from the strategy combination, and carrying out step two again. The firefly algorithm can be used for solving a power distribution network plan of the electric automobile charging station, the algorithm employs fewer parameters, the operation is simple, the realization is easy, and the method has the advantage of good global searching optimization.

Application Domain

Forecasting

Technology Topic

Charging stationDistribution grid +5

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### Example Embodiment

 A method for determining the location and capacity of an electric vehicle charging station based on the Firefly algorithm includes the following steps:
 (1) Set the strategy combination determined by the location and capacity of the charging station according to the actual situation in the planning area, and the location is expressed as the k-th node of the i-th line.
 (2) Use the firefly algorithm to optimize the objective function, find the minimum value of the objective function, and determine the optimal location and capacity strategy in the strategy combination. The objective function is related to the grid construction investment cost and power distribution Distribution network planning model related to network operation loss costs;
 (3) Verify the optimal location and capacity strategy in the constraints. If the constraints are met, determine the location and capacity of the charging station. If the constraints are not met, delete the optimal location and capacity from the strategy combination. Strategy, go to step (2) again.
 The planning model of the distribution network is
 minL=ηC imv (x)+C loss (x)(1)
 In formula (1), x is the decision variable, η is the annual average cost coefficient of fixed asset investment in the distribution network, C imv (x) is the investment cost of grid construction, C loss (x) is the operating loss cost of the distribution network.
 among them:
 C i m v ( x ) = X i A S k X i A S k i C i j x i j + X k A S A X k A S A k C k l x k l - - - ( 2 )
 C l o s s = β 1 X X i = 1 r τ i P i l o s s - - - ( 3 )
 In formula (2), the first group of formulas indicates whether the line needs to be replaced, S k Is a collection of replaceable lines, For S k The set of replaceable plans for the i-th line in the middle, C ij Is the cost of the j-th replacement plan for the i-th route, x ij It is a 0-1 decision variable, 0 means that the replacement plan is not selected, 1 means that the replacement plan is selected; the second group of formulas indicates whether the line is newly built, S A Is a collection of new lines, For S A Set of new plans for the kth line in the middle, C kl Is the cost of the first new scheme for the kth route, x kl It is a 0-1 decision variable, 0 means the new plan is not selected, 1 means the new plan is selected;
 In formula (3), β 1 Is the current electricity price, τ 1 Is the annual maximum load loss hours of the i-th branch, P iloss Is the power loss of the i-th branch;
 The constraints are:
 U imin ≤U i ≤U imax
 I i ≤I imax
 λP k-L -P k-max <0(4)
 X j A S k i x i j ≤ 1
 X k A S A k x k l ≤ 1
 In formula (4), U i Are the i-node voltage, I i Is the i-node current, λP k-L Represents the power of λ electric vehicles connected to the kth node of line L, P k-max Indicates the maximum load power at the kth node.
 The constraints are determined in the following way:
 Determine the required substation capacity for the horizontal plan
 S=P×r+P 1 ×r(5)
 Total capacity of new substation S n for:
 S n = S - S 0 - X j = 1 m S t - - - ( 6 )
 In formula (5), P is the normal load, P 1 Is the charging station load after an orderly charging strategy, r is the capacity ratio of the substation; in formula (6), S 0 Is the number of existing substations, m is the number of existing substations, S t It is the annual capacity increase of the t-th existing substation at the planned level, and the constraint is that the capacity of the charging station shall not be greater than S n.
 After determining the total capacity of new substations, determine the profit and loss of substation capacity in this level year, and further determine the number of new substations that need to be built in this level year. The calculation formula is as follows:
 n = [ S n S N ] S n 0 0 S n ≤ 0 - - - ( 7 )
 In formula (7), S N Is the standard capacity, [] is the rounding calculation.
 FA is mainly based on the following three ideal rules:
 (1) All fireflies are unisexual, and they attract each other regardless of gender;
 (2) The attractiveness of fireflies is proportional to the brightness of the fireflies. For any two fireflies, the low-brightness fireflies are attracted by the brighter fireflies and move in the direction of the brighter fireflies. The brightness and attractiveness of fireflies are inversely proportional to the distance between the fireflies, and decrease as the distance increases. If the brightness of the fireflies is the same, the fireflies move randomly.
 (3) The brightness of the firefly is determined by the objective function of the given problem.
 In FA, brightness and attractiveness are the two main factors, which are defined as follows:
 Definition 1: The brightness of a firefly describes the pros and cons of the objective function value. A firefly with a high brightness can attract a firefly with a weaker brightness in the line of sight. The brightness I is defined as:
 I = I 0 X e - γ r i j 2 - - - ( 5 - 8 )
 Where: I 0 Is the maximum fluorescence brightness of the firefly, that is, the fluorescence brightness of itself (r=0), which is related to the selected objective function value. The better the objective function value, the higher its own brightness; γ is the light intensity absorption coefficient, the firefly Fluorescence will gradually weaken as the distance increases and the absorption of the propagation medium, so the light intensity absorption coefficient is set to reflect this characteristic, which can be set as a constant; r ij Is the space distance between firefly i and j.
 Definition 2: The attractiveness of fireflies is related to brightness. The brighter the fireflies have the higher attractiveness, the attractiveness β is defined as:
 β = β 0 X e - γ r i j 2 - - - ( 5 - 9 )
 Where β 0 Is the maximum attraction, that is, the attraction of the firefly itself.
 Definition 3: The Cartesian distance r between fireflies i and j ij defined as:
 r i j = | | x i - x j | | = X k = 1 d ( x i , k - x j , k ) 2 - - - ( 5 - 10 )
 Where x i,k , X j,k For fireflies i and j at x i The k-th position in dimensional space coordinates.
 Definition 4: The position update formula of firefly i attracted by firefly j is:
 x i = X i +(x j -x i )+×(rand-1/2)(5-11)
 Where x i , X j Is the position of firefly i and j; and rand are both perturbed random parameters, used to enlarge the search area to avoid falling into the local optimum prematurely, which reflects that the firefly has a good global optimization ability and a certain local Optimization ability.
 The process of FA optimization is as follows: First, the fireflies are randomly scattered in the solution space, and the brightness of each firefly is different due to different positions. By comparison, the high-brightness fireflies can attract the low-brightness fireflies to move in its direction. The moving distance mainly depends on the attractiveness of the firefly; in order to increase the search area of ​​the firefly and avoid falling into the local optimum prematurely, the disturbance term α×(rand-1/2) is added in the process of updating the position of the firefly, Calculate the updated position of the fireflies according to the formula; after multiple moves, all the individual fireflies will gather at the position of the fireflies with the highest brightness, achieving the goal optimization.
 Reference figure 1 As shown, the specific steps of the firefly algorithm of the present invention are as follows:
 (1) Enter each function and related parameters; set the number of firefly individuals in FA, the light absorption coefficient of the medium, the maximum attraction, the number of iterations, and the disturbance factor to determine the objective function;
 (2) Randomly generate the initial feasible strategy combination x = {x 1 ,...,X i ,...,X n }, where the micro-source strategy is micro-source capacity, and the load-point strategy is load active power;
 (3) Maintain strategy portfolio x -i ={x 1 ,...,X i-1 ,x i+1 ,...,X n }constant;
 (4) Calculate different positions x i The maximum brightness, attractiveness, distance between the fireflies, and the moving direction of the target function of the fireflies, and update the position of the fireflies;
 (5) Judge whether the Firefly algorithm meets the convergence criterion, if it is satisfied, the new position is taken as the best strategy x i * :
 x i * = arg max x i u X ( x i , x - i ) - - - ( 8 )
 If it is not satisfied, return to step (4), continue to change the position of the firefly, and perform the next round of position update;
 (6) Follow steps (3)-(5) to optimize each position to obtain a new strategy combination:
 x * = { x 1 * , ... , x i * , ... , x n * } - - - ( 9 )
 Convergence test, judge the optimal combination Whether the original combination x satisfies the convergence criterion If it is satisfied, it indicates that the newly obtained combination is an optimal strategy, the process ends, and step (8) is entered; otherwise, it returns to step (3) and iteratively optimizes again.
 The above are only the preferred embodiments of the present invention, so the scope of implementation of the present invention cannot be limited by this. That is, the equivalent changes and modifications made according to the scope of the patent application of the present invention and the content of the specification shall still belong to the present invention. The scope of patent coverage.

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