In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost‐effective and efficient network service. Various meta‐heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta‐heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large‐scale 295‐bus system (composed …

In interconnected microgrids, facade thermal photovoltaics (TPVs) systems have to be efficiently scaled and allocated for cost-effective building energy consumption and network operation. This paper aims at defining pertinent innovative solutions for reducing the undesired severe voltage dips and minimizing the relevant total costs of the PVs allocation within interconnected microgrids. To optimally place and size the TPVs, different meta-heuristic optimization tools are considered. Dealing with several scenarios of loads and solar energy output uncertainties, the ability of the novel modified meta-heuristic optimizer based on coronavirus herd immunity optimizer (CHIO) to capture a global optimal solution is evaluated. Using Matlab T M numerical simulations, fair comparison with grey wolf optimization, particle swarm optimization, arithmetic optimization algorithm, and chimp optimization algorithm is presented. The …

Abstract

Due to the scarcity of freshwater resources in many arid regions of the world, as well as rapidly growing populations and industrialization, various desalination technologies have been developed and enhanced to improve the performance of saline water purification with high quality. Integrating solar energy technologies with desalination systems would alleviate the running out of fossil fuel sources, reduce costs, and improve energy efficiency. Solar-powered desalination systems could be a viable and efficient method for treating highly saline water for human consumption. Obtaining reliable and accurate design parameters for such hybrid systems plays a significant role in determining the system performance of solar-driven desalination systems. The present review provides a comprehensive review of various solar-driven membrane-based desalination systems to investigate the impact of design and operation parameters for solar and desalination units on the effectiveness of the hybrid solar/desalination system. Recent advancements in utilizing numerous solar energy sources for desalination are analyzed herein. The economic implications of various membrane desalination operations for different solar energy sources are also discussed. It was revealed that the solar system design parameters, desalination unit characteristics, feed water properties, and climate conditions all affect the functionality and productivity of the membrane-based solar-powered desalination system. The feed pressure, number and shape of membranes, and the integrated solar system, all have significant impacts on the performance of the hybrid system. This article provides a pathway for desalination researchers to select the optimal design and operation parameters for hybrid solar-powered membrane-based desalination systems. Notably, they are found more feasible and sustainable than traditional desalination processes. Several related conclusions and future perspectives are reported herein.