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Frontiers in Energy

Front. Energy    2016, Vol. 10 Issue (3) : 355-362
Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm
S. SURENDER REDDY1,Jae Young PARK1,*(),Chan Mook JUNG2
1. Department of Railroad and Electrical Engineering, Woosong University, Republic of Korea
2. Department of Railroad and Civil Engineering, Woosong University, Republic of Korea
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This paper proposes the generation scheduling approach for a microgrid comprised of conventional generators, wind energy generators, solar photovoltaic (PV) systems, battery storage, and electric vehicles. The electrical vehicles (EVs) play two different roles: as load demands during charging, and as storage units to supply energy to remaining load demands in the MG when they are plugged into the microgrid (MG). Wind and solar PV powers are intermittent in nature; hence by including the battery storage and EVs, the MG becomes more stable. Here, the total cost objective is minimized considering the cost of conventional generators, wind generators, solar PV systems and EVs. The proposed optimal scheduling problem is solved using the hybrid differential evolution and harmony search (hybrid DE-HS) algorithm including the wind energy generators and solar PV system along with the battery storage and EVs. Moreover, it requires the least investment.

Keywords battery storage      electric vehicles (EVs)      microgrid (MG)      optimal scheduling      solar photovoltaic (PV) system      wind energy conversion system     
Corresponding Authors: Jae Young PARK   
Just Accepted Date: 12 May 2016   Online First Date: 15 June 2016    Issue Date: 07 September 2016
 Cite this article:   
S. SURENDER REDDY,Jae Young PARK,Chan Mook JUNG. Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm[J]. Front. Energy, 2016, 10(3): 355-362.
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Jae Young PARK
Chan Mook JUNG
Fig.1  Solar PV energy system with battery storage
Fig.2  Flowchart of hybrid differential evolution and harmony search (Hybrid DE-HS) algorithm
Objective function values Case 1 Case 2
Cost of conventional generation/$ 170976.45 148209.8
Cost of wind generation/$ 32683.02 28954.17
Cost of solar generation/$ 28750.71 24682.56
Cost of battery storage and EVs/$ 12369.2
Total cost/$ 232410.18 214215.73
Cost reduction/% 7.83
Tab.1  Optimum objective function values for Cases 1 and 2
Fig.3  Scheduled wind and solar PV power outputs for Cases 1 and 2
<?PubTbl tgroup dispwid="1778.00px"?>
ai,bi,ciCost coefficients of ith thermal generator
C,kScale factor and shape factor of the Weibull distribution at a given location
ηbBattery efficiency during the charging period
ηcon?vEfficiency of the inverter
πChtCharging price of EVs in the period t
πDchtDischarging price of EVs in the period t
πOMtOpen market electricity price in the period t
ωPre-determined weight
diCost coefficient of ith wind energy generator
EEVtEnergy of electric vehicle (EV) at interval t
EEVt1Energy of EV at interval (t- 1)
EEVmax?Maximum capacity of EV
GForecast solar irradiation/(W•m–2)
GstdStandard solar irradiation (i.e., 1000 W/m2)
NSize of the population
PWind energy generator power output/MW
PbPower output from the storage battery
PdisCharDischarging power
PCh,EVk,tCharge power of kth EV in period t
PDch,EVk,tDischarge power of kth EV in period t
PrjRated wind power from the wind energy generator ‘j
PWiPower output from ith wind generator
PSjPower output from jth solar energy system
PSrRated solar PV output
RcCertain solar irradiation point, and it is set as 150 W/m2
SOChighEVUpper limit of charge at every interval
SOClowEVLower limit of discharge at every interval
Si(k)ith chromosome in kth iteration
Si'(k)Trial chromosome
Si1(k)Target chromosome
TTime needed for the EV to charge fully
tcharCharging time
tdisCharDischarging time from the interval (t- 1) to the interval t
tjCost coefficient of jth solar PV system
vWind speed/(m•s–2)
vj,vi,v0Rated, cut-in and cut-out wind speeds/(m•s–2)
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