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.

Abstract

This research presents an exergy analysis of a gas turbine power plant situated in Assiut, Egypt, operating under high-temperature conditions. The aim of the study is to assess the performance of the simple gas turbine cycle and identify the sources of thermodynamic inefficiencies using the second law of thermodynamics as a basis for analysis. To accomplish this, a model was developed in EES software utilizing real operational data obtained from the plant's control system. The investigation focused on the impact of varying ambient temperature on the exergy efficiency, exergy destruction, and net power output of the cycle. The results revealed that the combustion chamber accounted for the highest exergy destruction, amounting to 85.22%. This was followed by the compressor at 8.42% and the turbine at 6.36%. The overall energy and exergy efficiencies of the system were determined to be 28.8% and 27.17%, respectively. Furthermore, the study examined the effects of increasing ambient temperature from 0 to 45°C on the system's performance. It was observed that as the temperature rose, the overall exergy efficiency decreased from 27.91 to 26.63%. Simultaneously, the total exergy destruction increased from 126,407 to 138,135 kW. Additionally, the net power output exhibited a decline from 88,084 to 84,051 kW across the same ambient temperature range. These findings highlight the significant influence of ambient temperature on the thermodynamic performance of gas turbine power plants. As temperature rises, a greater amount of exergy is lost, resulting in reduced efficiency and diminished net power output. Therefore, optimizing the design of the combustion chamber is crucial for mitigating the adverse effects of hot weather conditions. The insights obtained from this study can be utilized to enhance the design and operation of gas turbine plants operating in hot climates.