A Comparative Analysis of Various Controller Techniques for Optimal Control of Smart Nano-Grid Using GA and PSO Algorithms

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.

A Survey on FOPID Controllers for LFO Damping in Power Systems Using Synchronous Generators, FACTS Devices and Inverter-Based Power Plants

“…According to [20,55], design methods can be classified into three various categories, as follows: Two techniques have been applied for FOPID controller design in various studies for power system applications. The rule-based technique is based on approximation [42,56,57], and the numerical technique is based on optimization [58,59]. The first technique is based on the approximation of the fractional-order function to an integer-order function [43,57].…”

“…Energies 2021, 14, 5983 7 of 27 nique is based on the approximation of the fractional-order function to an integer-order function [43,57]. Additionally, the numerical technique is to use optimization algorithms to determine the optimal values of the parameters [39][40][41][42][43][44][45]58,59]. This technique has been used in the literature for the application of the FOPID controller as a PODC, so this will be stated in detail in the next section.…”

“…In this technique, the parameters of the FOPID controller are determined by solving an optimization problem subjected to technical constraints, [20] and [59][60][61]. As indicated in Figure 3 and based on Equation ( 14), the closed-loop transfer function is as follows [19]:…”

Cascading Failures Assessment in Renewable Integrated Power Grids Under Multiple Faults Contingencies

“…To provide cheaper electricity and in order to meet unexpected load requirements, SGs relies on clusters of renewable energy resources (RERs), which are interconnected with one another in the form of multiple renewable integrated power grids (RIPGs) [1] [2] [3]. However, despite cost-effectiveness using RERs, reliability is still a growing challenge in these RERs [4]. Due to this reason, structure vulnerabilities and uncertainties occurred in these multiple interconnected (MIRIPGs) infrastructures [5].…”

Annual Cost and Loss Minimization in a Radial Distribution Network by Capacitor Allocation Using PSO

“…The optimal dispatch problem, i.e., optimal power generation sizing, is done by PSO [27]. Meanwhile, PSO is used for optimal control of the smart nano grid [28]. Researchers have also utilized PSO for the optimal design of the grid-connected solar system in [29].…”