In this paper, daylight harvesting is used to minimize the power consumption - required for indoor lighting - using electric roller blind. Smart controller is designed to adjust - based on the preset light intensity - the position of the roller blind’s stepper motor, and consequently the roller blind opening for better utilization of the daylight entering the room. If the desired illuminance level (IL) is not achieved for any reason, the smart controller adjusts the LED circuit current to boost the light intensity to achieve precisely the desired IL. Comprehensive tests - carried out using MATLAB-Simulink to verify the performance of the proposed smart controller – reveal that the proposed controller successfully maintains the indoor lighting intensity at the desired IL. Results of the techno-economic analysis - performed to evaluate the benefits of employing the proposed controller – show that an energy saving of about 62% is achieved, and that the lifetime of the LED circuits can extend to more than 20 years.

Energy and water crisis affects every aspect of modern human life, and thus, addressing both in one solution is a growing trend. Merging hybrid renewable energy systems (HRES) and water desalination system in one system is a promising solution. In this paper, optimization of stand-alone hybrid photovoltaiv (PV) systems for powering a house equipped with a reverse osmosis (RO) water desalination system in Sinak village, Tehran, Iran, is discussed. RO system configuration, regular house load and RO deferrable load, solar radiation capability and HRES components have been analyzed in the first part of this paper. The second part deals with optimization, cost analysis and sensitivity analysis - using HOMER Pro software - of two HRES scenarios equipped with RO: i) a PV system with battery storage and ii) a PV system with battery storage and a gas generator. Moreover, different dispatch strategies for controlling the investigated systems - namely cycle charging (CC) and load following (LF) - are described. In the last part, a comparison between the two scenarios is performed. The obtained results show that the hybrid PV-battery-RO system is more energy-effective, has less control complexity and has a capability of meeting the load demand with with zero carbon emissions. Results of the conducted economic analysis reveal that the system has a net present cost (NPC) and a cost of electricity (COE) of 10,245 US$ and 0.31 US$/kWh, respectively, while fewer sensitivity variables affect the system’s cost.

Hybrid energy systems, which are combinations of two or more renewable and non-renewable energy sources, have been identified as a viable mechanism to address the limitations of a single renewable energy source, utilized for electricity generation.In view of this, several research works have been carried out to determine the optimal mix of different renewable and non-renewable energy resources used for electricity generation.This paper presents a comprehensive review of the optimization approaches proposed and adopted by various authors in the literature for optimal sizing of hybrid energy systems.It is observed that the objective functions -considered by a large percentage of researchers to optimize the sizing of hybrid energy systems -are cost minimization of the generated electricity, system reliability enhancement and environmental pollution reduction.Other factors covered in the literature are equally discussed in this article.Similarly, simulation and optimization software used for the same purpose are covered in the paper.In essence, the main aim of this paper is to provide a scope into the works that have been carried out in the field of hybrid energy systems, used for electricity generation with the view to informing researchers and members of the public alike, on trends in methods applied in optimal sizing of hybrid energy systems.It is believed that the information provided in this paper is very crucial in advancing research in the field.

A method for modeling photovoltaic (PV) arrays - based on artificial intelligence techniques, namely genetic algorithm (GA) and cuckoo optimization algorithm (COA) - is presented. COA and GA are used to obtain the parameters of the PV array model using the PV cell’s datasheet information. The adopted models – using GA and COA - are implemented on a simulation platforms using MATLAB 2020a environment for two-diode and single-diode models. The proposed optimization method fits the mathematical current-voltage (I-V) characteristic to the three (V, I) remarkable points without the need to guess or to estimate any other parameter. The obtained models are tested and validated with experimental data taken from the Mutah university’s PV power plant. The results show that for both of the employed optimization algorithms, the two-diode model is more accurate than the single-diode model. The results also disclose that, at different values of temperature and solar irradiance, the COA – compared to the GA – better handles the optimization problem with low iterations and better fitness value.