Abstract
A nano-grid is an independent hybrid sustainable framework that utilizes non-renewable and renewable power resources for supplying continuous electrical energy to the load. Considering this scenario, in this research work, photovoltaic (PV) array, wind turbine, and fuel cell are taken as the three generation resources that have been used in the nano-grid. The active and reactive power of the all three generation resources is controlled using various controllers, i.e. integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, fractional order proportional integral derivative (FOPID) and sliding mode controller (SMC). An advanced optimization technique based on a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers. The integral square error is taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes is evaluated to find the best optimal solution. The performance of SMC has surpassed the performance of all other optimized controllers for power stability. In less than 0.267 seconds, the actual power yielded by using SMC is within 1% of the desired power. PSO algorithm has performed better than GA algorithm with all controllers. The worst performance is by FOPID controller with a steady state error of 6071.3W using GA algorithm and have a high magnitude of overshoot and undershoot. Moreover, a smart switching algorithm has been introduced for switching between the generation resources in accordance with the load demand and cost of the system in order to operate the nano-grid more economically. Finally, a case study has been performed in which the smart switching algorithm is utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads.
Highlights
IntroductionOne other leading problem is that approximately 1.2 billion individuals throughout the world don’t have access to electrical energy [4]
The transients in the power that occur due to variations in the attached load or generated power have been minimized for the proposed nano-grid system by optimizing various controllers using genetic algorithm (GA), particle swarm optimization (PSO) algorithms
Smart switching algorithm is introduced that connects the source of generation with the load in accordance with the demand of the load
Summary
One other leading problem is that approximately 1.2 billion individuals throughout the world don’t have access to electrical energy [4] Among these 1.2 billion individuals, most people are living in rural or separated communities, and expanding the grid to such community is usually regarded as uneconomical in terms of societal, ecological and financial factors [5]. The remedy to these types of inadequacies urges is the distributed generation [6], [7], which is an independent power system. They can be used with other localized load and generators to develop a self-sustaining power network [8], [9]
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