Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.

KEYWORDS:

• Battery energy storage
• electric vehicles
• multiobjective optimization
• renewable energy resources
• system risk level
• uncertainty

Nomenclature

$a_i,b_i,c_i,d_i,e_i$	=	Fuel and valve point loading (VPL) coefficients of ith thermal generators.
$T_a$	=	Ambient temperature.
$N_{OT}$	=	Nominal operating temperature of solar cell.
$P_{STC}$	=	Maximum power of solar PV module at standard test condition (STC).
$G_{STC}$	=	Solar irradiance at STC (i.e., 1000 W/m2).
k	=	Temperature coefficient.
$T_r$	=	Reference temperature.
$P_B^{ch}$	=	Charging power of battery (negative).
${\eta }_{ch}$	=	Charging efficiency of battery.
$E$	=	Total capacity of BESS during the scheduling period.
$\delta$	=	Self-discharge rate of storage.
$P_B^{disch}$	=	Discharging power of battery (positive).
$P_B^{max}$	=	Maximum charging/discharging power of battery.
${\eta }_{disch}$	=	Discharging efficiency of battery.
${\eta }_{conv}$	=	Inverter efficiency.
$\widetilde{P_{Dev,i}}$	=	Deviation power.
β	=	Confidence level.
$s$	=	Index for probability of scenarios.
$x$	=	Decision vector.
$t_{char}$	=	Charging time.
$T$	=	Time needed for EV to be fully charged.
$E_{EV}^{max}$	=	Maximum capacity of EV.
$t_{dischar}$	=	Discharging power from time (t-1) to t.

Additional information

Funding

This work was supported by the Woosong University [Woosong University’s Academic Research Funding - (20019-2020)].