Optimal Sizing of Multiple Renewable Energy Resources and PV Inverter Reactive Power Control Encompassing Environmental, Technical, and Economic Issues

2019 ◽  
Vol 13 (3) ◽  
pp. 3026-3037 ◽  
Author(s):  
Mikaeel Ahmadi ◽  
Mohammed E. Lotfy ◽  
Ryuto Shigenobu ◽  
Abdul Motin Howlader ◽  
Tomonobu Senjyu
Keyword(s):  
Renewable Energy ◽  
Power Control ◽  
Reactive Power ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Reactive Power Control ◽  
Optimal Sizing ◽  
Economic Issues ◽  
Pv Inverter

scholarly journals Adaptive Controllers for Enhancement of Stand-Alone Hybrid System Performance

2018 ◽  
Vol 9 (3) ◽  
pp. 979
Author(s):  
Yew Weng Kean ◽  
Agileswari Ramasamy ◽  
Shivashankar Sukumar ◽  
Marayati Marsadek
Keyword(s):  
Renewable Energy ◽  
Power Control ◽  
Reactive Power ◽  
Adaptive Controller ◽  
Pi Controller ◽  
Active Power ◽  
Reactive Power Control ◽  
Adaptive Controllers ◽  
Pv Inverter ◽  
Hybrid Renewable Energy System

<span lang="EN-US">This paper presents a stand-alone hybrid renewable energy system (SHRES) consisting of solar photovoltaic (PV), wind turbine (WT) and battery energy storage (BES) in an effort reduce the dependence on fossil fuels. The renewable energy sources have individual inverters and the PV inverter of the SHRES is operated using active and reactive power control. The PV inverter have two main control structures which are active power control and reactive power control and each contain a proportional integral (PI) controller. Accurate control of the PV inverter’s active power is essential for PV curtailment applications. Thus, this paper aims to enhance the performance of the SHRES in this work by optimizing the performance of the PV inverter’s active power PI controller parameters through the design of adaptive controllers. Therefore, an adaptive controller and an optimized adaptive controller are proposed in this paper. The performances of the proposed controllers are evaluated by minimizing the objective function which is the integral of the time weighted absolute error (ITAE) criterion and this performance is then compared with a controller that is tuned by the traditional trial and error method. Simulation results showed that the optimized adaptive controller is better as it recorded an error improvement of 42.59%. The dynamic optimized adaptive controller is more adept at handling the fast changes of the SHRES operation.</span>

Optimum sizing and simulation of hybrid renewable energy system for remote area

2021 ◽  
pp. 0958305X2110301
Author(s):  
Animesh Masih ◽  
HK Verma
Keyword(s):  
Renewable Energy ◽  
Reliability Index ◽  
Energy System ◽  
Energy Resources ◽  
Total System ◽  
Renewable Energy Resources ◽  
Optimal Sizing ◽  
Remote Areas ◽  
Grasshopper Optimization Algorithm ◽  
Hybrid Renewable Energy

In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are[Formula: see text] with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

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