Reliability evaluation of power-generating systems gives a mechanism to guarantee proper system operations in the face of different uncertainties including equipment failures. It is regularly not attainable to identify all possible failure states to figure the reliability indices because of the large number of system states engaged with system operations. Therefore, a hybrid optimization technique is required to analyse the reliability of the power system. This paper proposes a hybrid optimization technique to evaluate the reliability of a power system for the generation expansion planning incorporating wind energy source. The proposed hybrid methodology is the joined execution of both ant lion optimization algorithm (ALO) and lightning search algorithm (LSA), and it is named as ANLSA. ALO searching behavior is enhanced by LSA. Through the inherent convergence mechanisms, ANLSA search the meaningful system states.The most probable failure states contribute reliability indices of power generating system including mean down time (MDT), loss of load probability (LOLP), loss of load expectation (LOLE), loss of load frequency (LOLF), and expected demand not supplied (EDNS). Furthermore, ANLSA is utilized to assess the reliability of system under normal condition, integration of wind farm with capacity of 150 MW, and considering spinning reserve requirement (SRR). By then, the proposed work is actualized in MATLAB/Simulink platform and it is demonstrated on IEEE reliability test system (IEEE RTS-79). Furthermore, the statistical analysis of proposed and existing techniques such as Monte Carlo simulation (MCS) and discrete convolution (DC) is considered. The comparison results demonstrate that proposed approach confirms its ability for evaluating the power system reliability.
KEYWORDS
ALO, LOLE, LOLF and EDNS, LOLP, LSA, MDT, reliabilityNomenclature Symbols, indices, and parameters: P OUT , output power in watts; ρ Air , density of air in Kg/m3; V Speed , speed of wind in m/sec; A S , turbine's swept area in m 2 ; P C , coefficient of power; V C − In V C − Out , cut-in speed, cut-out speed; VRated, rated speed; PRated, rated power of the wind turbine; λRatesμRates, failure rates, repair rates; f Outage, Forced Outage Rate; XOutage, outage state; P(XOutage) and P XOutage À Á , cumulative probabilities of the capacity outage state before and after the units are added; R, random variable representing the down time (outage duration); f R (r), probability density functionR; T i , number of all possible transition states i; F i , failure probability of state i; E[⋅], expectation operator; r, estimator of MDT; T i d , duration of ith interruption encountered during the sequential simulation; N c , number of cycles simulated; N F , number of failure states previously attained; q i ,exp rand, shaping parameter, exponential random number; LD max , maximum load demand; Loadloss, j, loss of load occurs at hour t for state j; EGcapi, t, total effective generating capacity of state at hour t; PFailure, j, failure probability of state...
