Optimum Sizing of PVWTFCDG Hybrid Energy System using Teaching LearningBased Optimization In a hybrid energy system, fulfilling the useru0027s electricity demand costeffectively is an imperative task. In a standalone environment, diesel generator (DG) and renewable energy sources (RESs) like wind turbines (WTs), photovoltaics (PVs), and fuel cells (FCs) provide an effective and reliable solution to fulfill a household load. To achieve the economic and environmental aspects of hybrid RESs (HRESs), optimal sizing of components is indispensable. In this paper, the PVWTFCDG hybrid system is considered, optimally sized, and modeled in terms of environmental emissions and total annual cost (TAC) values for Hawksbay, Pakistan. For optimal sizing, the teaching learningbased optimization (TLBO) algorithm is proposed that does not require any algorithmspecific parameter for its execution. The results reveal that TLBO fulfills the household load at minimum TAC.

Refractionlearningbased whale optimization algorithm for highdimensional problems and parameter estimation of PV model Abstract   Whale optimization algorithm (WOA) is a relatively new metaheuristic optimization algorithm which mimics the hunting behavior of humpback whales. This paper presents a modified version of WOA, called RLWOA, for solving highdimensional optimization problems. The proposed RLWOA adopts a modified conversion parameter update rule that relies on Logistic model to balance between diversity and convergence during the search process, and a new refractionlearning strategy based on the principle of refraction of light is proposed to help the population jump out of a local optimum. The experiments on a set of benchmark test functions with various features, i.e., 12 widely used benchmark functions with 100, 1000, and 10000 dimensions, two practical engineering design problems, and parameter estimation problem of photovoltaic model. The comparisons demonstrate that the proposed RLWOA shows better or at least competitive performance against the standard WOA, WOA variants and other stateoftheart metaheuristic algorithms for solving highdimensional numerical optimization, practical engineering design optimization, and photovoltaic model parameter estimation problems.