Academic Journal of Science and Technology 
ISSN: 2771-3032 | Vol. 1, No. 3, 2022 

 

43 

Research	on	Optimal	Design	of	Microgrid	Including	
Electric	Vehicles 

Zhongwei Qi1, 2, Hongyu Li1, Limei Yan1, Haishuang Yin1 

1College of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163000, Heilongjiang Province, China 
2The Second Oil Production Plant of Daqing Oilfield, Daqing 163000, Heilongjiang Province, China 

 

Abstract: In view of the lack of consideration of electric vehicles in the design stage of microgrid, the use of traditional optimization 
algorithm in optimization design method, and only considering economic factors in optimization design index, this paper focuses on the 
mathematical model of electric vehicles, considering pollutants emission and other factors, and establishes a mathematical model of distributed 
energy. At the same time, the optimization design model of the microgrid and the constraints of the optimization problem were established, 
and the sparrow search algorithm with better comprehensive performance was used for reference. Finally, the optimization design and operation 
results of the microgrid were obtained. The simulation results proved the scientific and feasibility of the microgrid with electric vehicles under 
the new algorithm. 

Keywords: Power system, Microgrid, New energy, Sparrow optimization algorithm, Optimization design. 
 

1. Introduction 

1.1. Mathematical Models of Climatic Factors 
Both wind power generation and photovoltaic power 

generation are affected by weather factors. It is mentioned in 
literature [7] that wind speed changes constantly in a 
particular location, which depends on the local land terrain, 
weather system and ground height. Therefore, it is necessary 
to capture the change of wind speed in the model to predict 
power generation [8]. The Weibull probability density 
function can describe the change of wind speed. The Weibull 
probability function of wind speed (V) can be expressed as: 

 
1

( , , ) exp
k k

k V V
f V k c

c c c



 
     
     

     
     (1) 

 
As for the probability of temperature distribution data, the 

probability function described in literature [9] allows 
modeling of temperature behavior in system design. 
Distribution parameters are calculated with temperature data, 
and the formula is as follows: 

 

  ,

1

1
, ,

n
a a i

a
i

T T
f T h n K

nh h

     
   

        (2) 

 
Compare and select the calculation formula of light 

radiation conforming to probability density function: 
 

1 1

max max

( )
( ) 1

( ) ( )

r r
f r

r r

 
 
 

 
 

 
 

   
   
   

     (3) 

1.2. Distributed Energy Mathematical Model 
According to the current research on photovoltaic power 

generation, the output power of photovoltaic power 
generation can be expressed as: 

 

 0

0

( )
( ) ( )MT

pv pv pv Pm M M

G t
P t f P T t T

G
          (4) 

 
Many wind power models such as WPMS, WPPT, 

Prediktor, ARMINES, Previento and WPFS Ver1.0 have been 
developed internationally. Among them, the commonly used 
mathematical model of wind power generation is: 

 
2 2

2 2

 or 

;

( ) ;

0;

M C
wt C R

R C

wt R R F

C F

v v
P v v v
v v

P t P v v v

v v v v


 



  

 









     (5) 

 
The output power of the diesel generator (PG) is: 
 

( ) ( )dg DG dgiP t P t             (6) 

 
The energy storage formula established in this study for 

pumped storage system is as follows: 
 

    ( 1) ( ) 1 3600 ( ) ( )ba ba ba ba tE t E t q t q t       (7) 

 
The energy in the battery described in reference [10] 

accords with the expression in Formula (8), and takes into 
account that the charging and discharging powers are 
expressed by different variables Pbc and Pbd, both of which 
are positive. 

 

  1
( 1) ( ) 1 ( ) ( )b b sd bc bc bd

bd

E t E t P t P t 


    
 
 
 

 (8) 

 



 

44 

1.3. Mathematical Model of Electric Vehicle 
System 

The types of electric vehicles are mainly divided into two 
types: the first type of electric vehicles, can only be used as 
charging load, can not provide energy output system. The 
second type of electric vehicle can be used as charging load 
or energy source to provide energy to the system. The second 
type of electric vehicle obeys the probability density function 
shown in Formula (9). 

 
2

2

1 (ln )
( ) exp

22

d
f d

d


 


 

 
 
 

      (9) 

 
Therefore, through statistical random sampling of the 

cumulative probability of the above normal distribution and 
lognormal distribution, PEV's departure time, arrival time and 
daily driving distance can be obtained. 

2. Optimization Design Model of 
Microgrid Including Electric 
Vehicles 

2.1. Modeling of Microgrid Optimization 
Design Model Including Electric Vehicles 

In order to clarify the optimization problem, the battery needs to 
consider the different charge and discharge power, the power and 
pumping power generated by the turbine in the pumped storage 
system, the different types of charge and discharge power of electric 
vehicles, etc. Therefore, the degree of freedom of the design of the 
optimization variable can be obtained as follows: Number of 
photovoltaic panels (Npv), number of wind turbines (Nwt), 
Number of batteries (Nb), volume of water tank of pumped storage 
system (V), power transmitted by diesel generator set per hour 
(PGi(T)), battery charging power per hour (Pbc(T)), battery 
discharge power per hour (Pbd(T)), The power of water pumped per 
hour (Pba(t)), the power of water wheel delivered per hour (Pt(t)), 
the charging power of class I electric vehicles per hour (Pv1c(t)), the 
charging power of class II electric vehicles per hour (Pv2c(t)), 
Second class ev discharge power per hour (Pv2d(t)) and unsupplied 
power demand per hour (Pns(t)). 

The optimization variable can be expressed as a one-dimensional 
matrix: 

 

1 2 2

( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) (

=

)

T

pv wt b

Gi bc bd ba

t v c v c v d ns

x

N N N V

P t P t P t P t

P t P t P t P t P t

 
 
 
  

    (10) 

 
The objective function of this study is a multi-objective 

optimization function, which is essentially a multi-objective 
optimization problem. The objective function mainly includes the 
annual total cost CT of the system and the total carbon dioxide 
emission ET of the system. 

Combined with the research focus of this paper, the total annual 
cost of the system can be described as follows: 

 

T cp mt GC C C C                 (11) 

 
Total system carbon dioxide emissions, total system emissions 

generated during component construction and diesel generator 
operation, ET is calculated by: 

          
1

max ( ) max ( ) ( )
2

c
T

M pv M wt M b Gba

T pv pv c wt wt c b b c ba t op cd

t c

E T
E N P E N P E N P E P t P t E G t

T

     
 
  
 (12) 

 
To solve this multi-objective optimization problem, a weighted 

summation method will be used, in which a weight factor is assigned 
to each objective, as shown in Formula (20) (a weight factor is 
assigned to CT and a weight factor wEET to total emissions). 

 

 min x T E Tf C w E              (13) 

2.2. Optimize Design Model Constraints 
Battery pack, type 2 EV battery and pumped storage system must 

be kept within the permitted operating range. The specific 
requirement constraint formula is as follows: 

 

( )m M
b b b b bN E E t N E            (14) 

 

0 ( )baE t V              (15) 

 

  3 ( ) 3m M
v v vE E t E           (16) 

 
The daily discharge of type 2 electric vehicle must be greater than 

or equal to 80% of the energy used in daily travel during the running 
time. The specific demand constraint formula is as follows: 

 

2
2

2

0.8 3 v
b v d

v

G
M P

h

 
  

 
         (17) 

2.3. Research on Sparrow Optimization 
Algorithm 

The economic dispatching of microgrid is a nonlinear, multi-
model, multi-objective complex system optimization problem. At 
the same time, based on the advanced in the field of artificial 
intelligence all sorts of modern intelligent algorithm is also gradually 
be applied to the actual power system engineering design, the early 
main using functional analysis, fuzzy system, genetic algorithm (ga) 
optimization method, but with the development of computer 
technology, the traditional intelligent optimization algorithm in 
convergence speed, precision, etc have been unable to meet the 
increasingly complex problems. In recent years, new intelligent 
algorithms such as new swarm intelligence optimization algorithm 
and new intelligent optimization algorithm have been proposed 
gradually. Through detailed comparative analysis, it is found that the 
performance of the latest sparrow search algorithm is outstanding, 
and it has good results in solving accuracy, convergence speed and 
execution time. Sparrow Search Algorithm (SSA) is an effective 
optimization technology, which simulates the group intelligence 
foraging and anti-predation behavior of sparrows. Compared with 
many other small birds, sparrows have strong intelligence and strong 
memory, so SSA can be rapidly promoted and applied. The 
techniques involved are used to solve global optimization problems 
because of their simplicity, flexibility, and efficiency. 

3. An Example of Optimal Design of 
Microgrid with Electric Vehicle 

3.1. Optimization Design Results of Microgrid 
Including Electric Vehicles 

Containing micro grid optimization design of electric cars through 
the sparrow search algorithm to calculate the optimal design results 
are shown in table 1, as can be seen from the table, the amount of 
electric cars in 50 vehicles, including type 1 as charging electric cars 
only 20, type 2 can be two-way transmission of the electric car 30 
energy, in order to avoid the randomness of wind power generation, 
There are only 6 wind turbines and 823 photovoltaic panels. The 
total annual cost of the optimized system includes the emission cost 



 

45 

of 5.8654*106 RMB, the diesel saving cost is 1.3582*106 RMB, the 
total carbon dioxide of the system is 3731 tons, and the carbon 

dioxide emission reduction of the system is 1578 tons. 

 
Table 1. Micro-grid design optimization design results 

Optimization parameters optimization design results Optimization parameters optimization design results 

The number of iterations is 460 The number of iterations is 460 

The best score is 10-300 The best score is 10-300 

Number of wind turbines: 6 Number of wind turbines: 6 

The number of photovoltaic panels is 823 The number of photovoltaic panels is 823 

The battery quantity is 17 The battery quantity is 17 

The number of type 1 electric bullet train is 20 The number of type 1 electric bullet train is 20 

The number of type 2 electric vehicles is 30 The number of type 2 electric vehicles is 30 

Pumped storage system water tank volume 2030m3 Pumped storage system water tank volume 2030m3 

The rated power of the turbine is 23.896 kW The rated power of the turbine is 23.896 kW 

Pump rated power 334.83kW Pump rated power 334.83kW 

Total cost (including emission cost) 5.8654*106¥ Total cost (including emission cost) 5.8654*106¥ 

Diesel saves 1.3582*106¥ Diesel saves 1.3582*106¥ 

The system emits 3731 tons of carbon dioxide The system emits 3731 tons of carbon dioxide 

3.2. Optimization Design Results of Microgrid 
Including Electric Vehicles 

In the day of wind power output is affected by wind speed is larger, 
with strong randomness and uncertainty, so in the actual power grid 
in order to obtain steady power output, should choose wind 
generating set less, and the tendency of photovoltaic power 
generation is affected by radiation and temperature, Power 
generation in the one day there is a clear and predictable trend, 
observation of photovoltaic power from the figure, you can see the 
photovoltaic and above for radiation and the trend of climate factors 
of temperature, beginning early in the morning the sun rises with 
optical radiation, photovoltaic power begins to increase gradually, 
until noon ray radiation and temperature height, photovoltaic power 
maximum. After noon, the sun gradually falls, the temperature 
gradually decreases, and the power of photovoltaic power generation 
gradually decreases. Therefore, when the output of photovoltaic 
power generation is large, energy storage can be used to store the 
energy beyond the power demand, and energy will be released by the 
way of energy storage battery discharge at the peak of electricity 
consumption in the evening. 

The power demand, diesel power generation and the operating 
status of new energy, where the power of new energy is the sum of 
the combined output of photovoltaic power generation and wind 
power generation. Can be seen in the figure, the photovoltaic (pv) 
power optimization results than the results of the wind power when 
the output of the new energy is mainly composed of photovoltaic 
power, thus has the high regularity and predictability, can see from. 
at the same time, in the case of lack of new energy output, system 
mainly with the method of diesel generator, and when the new 
energy power generation output is large, the power demand of 
the system mainly adopts the output mode of new energy. 

It can be seen that at the initial output stage of new energy, the 
output of diesel generator gradually decreases, and the power 
demand of micro-grid is mainly new energy. When the output of new 
energy increases to a certain power value, the output of diesel power 
generation does not. At this point, the charging power of the energy 
storage battery begins to increase, the electric quantity stored by the 
energy storage battery gradually rises, and the power generation 
output of new energy begins to decline. In order to meet the power 
demand of the system, the charging power of the energy storage 
battery begins to decline, and the energy storage of the energy 
storage battery reaches its peak. When a new output dropped to a 
lower energy level, the system first by the energy storage battery 
discharge meet the demand of electric power system, energy storage 

battery discharge power gradually rise, when the energy storage 
battery power down to a certain level, just as the evening peak, began 
to increase the output power of diesel generators, storage battery 
output power decreases, energy storage batteries store energy also 
gradually reduced, At this time, the power demand of the system is 
mainly diesel power generation. 

The electric power demand, pumped storage charge and discharge 
and water can run energy state, can be seen from the diagram, and 
the energy storage battery energy storage and put to work idea is 
consistent, in new energy output is large, by a gradual increase in 
pump pumping pumped storage power, at the peak, can begin to put 
through the turbine, So that the whole system can effectively use new 
energy to generate electricity, and at the same time meet the work of 
the system peak regulation operation. 

4. Conclusion 
In this study, an optimization design method of microgrid 

containing electric vehicles based on sparrow search intelligent 
algorithm is proposed, and the mathematical model of distributed 
energy of microgrid and the mathematical model of two kinds of 
electric vehicles are established, and the optimization model is 
further established and the constraint conditions are clarified. At the 
same time, the algorithm optimization of the microgrid and 
programming with MATLAB software verify the scientific and 
feasibility of the proposed method. This study initially solves the 
problems of electric vehicle integration and climate data considered 
in the design stage, and uses the new intelligent optimization 
algorithm to avoid lighter and wind losses, reflecting the due 
economic value. At the same time, the emission of CO2, SO2, NO 
and NO2 of the unit is the lowest, so as to achieve the effect of energy 
saving and economic improvement. 

Acknowledgment 
This paper is supported by the higher education teaching 

reform project of Heilongjiang Province (No. 
SJGY20210111). 

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