Multi-objective search group algorithm for thermo-economic optimization of flat-plate solar collector

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Techno-economic and environmental analysis of a hybrid PV/T solar system based on vegetable and synthetic oils coupled with TiO2 in Cameroon

To assess the production potential, economic profitability and ecobalance of the photovoltaic/thermal (PV/T) system in Cameroon, different configurations of HTF based on water, vegetable and synthetic oils, coupled with different forms of titanium dioxide (TiO 2 ) are used. A numerical code is written in Matlab. The PV/T model connected in direct contact PV-absorber is validated and a multi-objective optimization of the system is performed. The hourly evolution of PV cell temperature for the six HTF configurations revealed a value below 36 °C with Coton/TiO2. The platelets-and spherical-shaped nanoparticles increase the convection transfer coefficient between the fluids and the tubes. TiO 2 showed a higher thermal influence in vegetable and synthetic oils than in water at a volume concentration of 4 %. The cotton/TiO2 configuration showed a 12.08 % improvement in electrical efficiency over conventional PV systems with low exergy efficiency compared to water. Configurations with therminolVP-1/TiO2 are better, with the proposed energy cost reduced to 33 % of the price of electricity in Cameroon. The PV/T-Palm/TiO2 system showed an energy cost of $0.03 with a net present value of $568.45, an emission rate of 7.78 kg, a reversibility index of 1.95, an annual cost of $7.07 and a payback time of 5.97yr. This shows that PV/T systems based on vegetable oils are economical.

Performance investigation of state-of-the-art metaheuristic techniques for parameter extraction of solar cells/module

One of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell's precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E-03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E-03 and 4.7571E-05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.