Integration of Electric Vehicles and Wind Energy in Power Systems

Abstract

Today, internal combustion engines are considered as essential equipment of transportation industry and business. Concerns about oil price fluctuation and climate changes are reasons to find an alternative for fossil fuel-based vehicles. Electric vehicle (EV) with electric drive, good efficiency and no pollution can be a suitable alternative for conventional vehicles. The presence of EVs in the distribution network, especially those with the ability to connect to the grid (V2G), allows the distribution network to meet its required reserve at a lower cost. An individual EV does not have a significant impact on the grid; however, the optimal charge/discharge aggregation of EVs may lead to the better technical and economic performance of the distribution network. In this chapter, an optimal operation model of EVs is presented in the presence of wind turbines and the effects of the coordinate and non-coordinate operation of EVs on the stochastic generation of wind turbines are assessed. The stochastic generation of wind turbine are modeled through Weibull distribution function under various scenarios.

Appendix A

Nomenclature
Indices
t	Index for time period
w	Index for wind scenario
g	Index for dispatchable units
ev	Index for Electric Vehicles in parking lot
Parameters
${P}_{t,w}^{Wind}$	Wind power generation in scenario w of period t
π t, w	Probability of scenario w in period t
v aw	Average wind speed in scenario w
P rated	Rated power of wind unit
v ci	Cut-in wind speed
v r	Rated wind speed
v co	Cut-out wind speed
λ t	Day-ahead Market price
${\lambda}_t^{ch}$	Charging price in period t
${\lambda}_t^{dch}$	Dis-charging price in period t
η ev	Efficiency of EVs
MUT g	Minimum up time of unit g
${E}_{ev}^{EV,\mathit{\operatorname{Min}}}$	Minimum allowed energy stored in EV
${P}_{ev,t}^{dch,\mathit{\operatorname{Max}}}$	Maximum allowed energy stored in EV
${E}_{ev,0}^{EV}$	Remained stored energy in EV at arrival hour
MDN g	Minimum down time of unit g
SUC g, t, w	Start-up cost of unit g in period t and scenario w
SDC g, t, w	Shut-down cost of unit g in period t and scenario w
${P}_g^{Min}$	Minimum limit of power generation of unit g
${P}_g^{Max}$	Maximum limit of power generation of unit g
${\underline{P}}_{g,t}$	Minimum time-dependent operating limit of unit g in period t
${\overline{P}}_{g,t}$	Maximum time-dependent operating limit of unit g in period t
${P}_t^{Dem}$	Demand power in period t
Variables
P g, t, w	Power produced by unit g in day-ahead market in period t and scenario w
${P}_{ev,t,w}^{ch}$	Power Charge of EV in period t and scenario w
${P}_{ev,t,w}^{dch}$	Power Dis-charge of EV in period t and scenario w
FC g, t, w	Cost of unit g in period t and scenario w
Cost(t,w)	Total cost of problem in period t and scenario w
Revenue(t,w)	Total revenue of problem in period t and scenario w
Profit	Total profit of problem
${E}_{ev, cap}^{EV}$	Stored energy in EV in period t and scenario w
${\alpha}_{t,w}^{ch}$	Binary variable for EV related to charge status in period t and scenario w
${\alpha}_{t,w}^{dch}$	Binary variable for EV related to dis-charge status in period t and scenario w
α g, t, w	Binary variable, 1 if unit g is on, 0 otherwise
β g, t, w	Binary variable, 1 if unit g starts up
γ g, t, w	Binary variable, 1 if unit g shuts down