Impact of electric vehicles on optimal power dispatch of a micro-grid in competitive electric market

ABSTRACT

In this study, for optimal power dispatch of a grid-connected micro-grid, a new stochastic model has been built up to minimize the operating cost of micro-grid that equipped with plug-in hybrid electric vehicles, renewable energy sources, and storage devices. Impact of electric vehicles on power dispatch is studied by considering its uncertainty charging characteristics. Monte–Carlo simulation is employed for uncertainty modeling. For this work, three different charging strategies are followed up, namely, uncontrolled, controlled, and smart charging strategy to observe the impact of electric vehicles on micro-grid. So, here an endeavor has been made using a potent and robust technique i.e. improved whale optimization algorithm for obtaining optimal power dispatch. The suggested method’s appropriateness and effectiveness are evaluated by modeling a grid-connected micro-grid. The outcomes of this technique improve the MG’s performances in terms of best solution and economic operation. On different case studies, the outcomes are compared with other methods without and with charging strategy of electric vehicles. It is seen that operating cost obtained without electric vehicles is nearly 300$ and with charging strategy, i.e. uncontrolled, controlled, and smart of electric vehicles are 664$, 390$, and 327$, respectively. So, inclusion of vehicles on MG increases operating cost but its impact on micro-grid significantly reduces the operating cost for smart charging strategy in comparison with other charging strategy.

KEYWORDS:

• Optimal power dispatch
• electric vehicles
• charging strategy
• micro-grid
• renewable energy sources
• uncertainty modeling
• improved whale optimization algorithm

Nomenclature

$F_{MG}\left(P\right)$	=	MG operating cost function
$OC\left(t\right)$	=	Operating cost of MG at time “t” hour
$Cos{t}_{GS,t}$	=	Operating cost of generating units at “t” hour
$Cos{t}_{SS,t}$	=	Operating cost of storage system at “t” hour
$Cos{t}_{GRID,t}$	=	Operating cost of grid at “t” hour
$P_{Gn},X_{Gn}\text{amp}{S}_{Gn}$	=	Power generation, price of bid quantity and start-up/shut-down cost of $n^{th}$ generating unit respectively.
$P_{Gm},X_{Gm}\text{amp}{S}_{Gm}$	=	Power generation, price of bid quantity and start-up/shut-down cost of $m^{th}$ storage system respectively.
$U_n\text{amp}{U}_m$	=	Unit commitment status of $n^{th}$ generating unit and $m^{th}$ storage system respectively. Generally chosen as 1/0 for ON/OFF status.
$P_{GRID}\text{amp}{B}_{GRID}$	=	Import/export energy from/to main utility grid and energy market price respectively.
$N_G\text{amp}{M}_{SS}$	=	Total quantity of generating units and storage system.
$D\text{amp}{D}_{PHEV,k}$	=	The demand of MG and $k^{th}$ PHEVs charging demand.
$F\left(W_V\right)$	=	Weibull PDF
$v$	=	wind speed
$p\text{amp}d$	=	Shape and scale factor
$P_W$	=	wind power output
$v_{in},v_o\text{amp}{v}_r$	=	cut-in wind-speed, cut-out wind-speed and rated wind-speed, respectively
$G_{si}\left(S_i\right)$	=	Beta PDF
$a_1\text{amp}{a}_2$	=	Beta PDF’s shape parameters
$P_{SI}$	=	Solar output power
${\eta }_{PV}\text{amp}{A}_{PV}$	=	Solar photovoltaic unit’s efficiency and total area, respectively

Abbreviation

MG	=	Micro-grid
DER	=	Distributed energy resource
EV	=	Electric-Vehicles
IWOA	=	Improved Whale Optimization Algorithm
PDF	=	Probability Distribution Function
PHEV	=	Plug-In Hybrid-Vehicles
RES	=	Renewable energy sources
WOA	=	Whale Optimization Algorithm
MCS	=	Monte-Carlo Simulation
MT	=	Micro-turbine
PV	=	Photovoltaic cell
WT	=	Wind turbine
SoC	=	State of Charge
ESS	=	Energy Storage System

Disclosure statement

No potential conflict of interest was reported by the author(s).