Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method

Abstract

Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow (SCOPF) for obtaining the best scheduled power from wind farms while lowering total operational costs. A novel metaheuristics method called Aquila Optimizer (AO) is used to address the SCOPF problem due to its highly nonconvex and nonlinear nature. Wind speed is represented by the Weibull probability distribution function (PDF), which is used to anticipate the cost of wind-generated power from a wind farm based on scheduled power. Weibull parameters that provide the best match to wind data are estimated using the AO approach. The suggested wind generation cost model includes the opportunity costs of wind power underestimation and overestimation. Three IEEE systems (30, 57, and 118) are utilized to solve optimal power flow (OPF) using the AO method to prove the accuracy of this method, and results are compared with other metaheuristic methods. With six scenarios for the penalty and reverse cost coefficients, SCOPF is applied to a modified IEEE-30 bus system with two wind farms to obtain the optimal scheduled power from the two wind farms which reduces total operation cost.

A Comprehensive Survey on Aquila Optimizer

“…In order to maximizing scheduled electricity from wind farms while minimizing total operational expenses, Khamees et al [ 58 ] introduced a stochastic optimum power flow (SCOPF) technique. Since the SCOPF problem is significantly nonconvex and nonlinear, the AO algorithm was employed to solve it.…”

An Enhanced Turbulent Flow of Water-based Optimization for Optimal Power Flow of Power System Integrated Wind Turbine and Solar Photovoltaic Generators

“…Then, the variation of the position of the 𝑗th whirlpool to decrease in its objective function is stated by Eqs. ( 39) and (40), where 𝛿 𝑗 is the 𝑗th whirlpool's angle.…”

“…The fuel cost using ETFWO is 800.4792 ($/h), according to simulation results shown in Table 1. In comparison to solutions from cutting-edge existing optimization approaches in Table 2, such as MSA 34 , MICA-TLA 35 , MHBMO, HFAJAYA 36 , IEP 37 , EP 38 , JAYA, GWO, DE 39 , AO 40 , MGBICA 41 , ARCBBO 42 , PSOGSA 43 , MRFO 44 , SKH 45 , TS 46 , ABC 47 , PPSOGSA 48 , SFLA-SA 49 , FA 50 , FPA 51 , MFO 52 , MPSO-SFLA 53 , and TFWO, the proposed ETFWO has meaningfully reduced the total cost. The convergence rate for this case is shown in Fig.…”

A linearized AC optimal power flow model based on a piecewise linear approximation along with a Taylor series

“…An interesting study about the Newton-based solution of optimality conditions, the linear programing, the hybrid linear and quadratic programming, and the interior point method have been reviewed in [11]. A stochastic OPF problem considering nondeterministic constraints of wind and solar units is presented in [12][13][14][15][16]. Trodden et al [17], present a relaxed optimization-based method for intentional islanding, where it uses a Piecewise Linear (PWL) model of the AC power flow, considering voltage and reactive power constraints.…”