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In order to maximize the electricity supply from clean energy sources, the goal of the smart power system is to unite all renewable energy sources. The goal of the present study is to use three optimization techniques, artificial rabbits optimization algorithm (ARO), grey wolf optimizer (GWO), and whale optimization algorithm (WOA), to reduce the cost of electricity (COE) while improving the reliability of the power supply for rural areas. While using the same control variables for the optimization methods and load profile, various hybrid system configurations are explored. Photovoltaic, wind turbine, fuel cell, and electrolyser systems are all involved in the proposed hybrid renewable system. The ARO methodology is more effective than the GWO, WOA, and PSO procedures in terms of net present cost (NPC) and cost of energy (COE) generation, according to data comparing the three optimization techniques with the traditional Particle Swarm Optimization (PSO) method. The proposed ARO reached a value of COE of 0.4412$/kWh compared to 0.4438$/kWh for GWO, 0.4443$/kWh for WOA, and 0.44378$/kWh for PSO. [ABSTRACT FROM AUTHOR]

In the quest for sustainable and efficient energy solutions, hydrogen fuel cells emerge as a beacon of hope, offering a promising pathway towards a greener future. Accurate Identification of the ungiven parameters of proton exchange membrane fuel cell (PEMFC) mathematical models is indispensable for designing, managing, and simulating the practical PEMFC. In order to identify the parameters of PEMFC punctually, this paper presents a modified version of the slime mould algorithm (MSMA). In order to increase capability of the MSMA in the exploitation phase, both locally and globally, the sine-cosine technique has been utilized to boost the search capabilities. To assess the performance of MSMA, MSMA is first utilized to address ten well-known benchmark functions. The obtained results confirm that MSMA outperforms SMA on all benchmark functions. Then, MSMA is employed to solve the optimization problem of different mechanical design problems and also the MSMA provides superior performance over the standard SMA. Finally, the MSMA is used to identify the unknown parameters of four typical PEMFCs: 250W PEMFC, BCS 500W PEMFC, AVISTA SR-12 model, and the Temasek 1 kW PEMFC model. Experimental results boost the supremacy of MSMA in the PEMFC parameters extraction by comparing it with the original SMA and well-known potent optimization techniques. Furthermore, MATLAB/Simulink is employed for advanced dynamic PEMFC modeling, facilitating a comprehensive assessment of fuel cell parameters. The validation of this dynamic PEMFC model, using MSMA-optimized parameters, establishes its practical utility in system analysis and real-world fuel cell operation, marking a significant advancement in PEMFC technology management and simulation. [ABSTRACT FROM AUTHOR]

Tianjin, as one of the pioneering and most prominent cities in China, has a long history of harnessing geothermal energy. The geothermal resource available in Tianjin is primarily characterized as a low- to medium-temperature hydrothermal geothermal resource. This manuscript introduces the ongoing status and potential of geothermal utilization in China, with a particular focus on the characteristics and utilization status of geothermal resources in Tianjin, China. Moreover, the relevant strategies and challenges for cost-efficient sustainable utilization of Tianjin geothermal resources are identified. The formation parameters of heat storage characteristics of Tianjin geothermal resources are also discussed. In addition, the key paths, guidelines and challenges on how to solve the obstacles related to the geothermal resources development in Tianjin are also suggested. The summarized results indicate that the geothermal reservoirs exploited in Tianjin vary greatly, which include sandstone of Neogene Minghuazhen formation, Guantao formation, Ordovician and Cambrian and carbonate of Proterozoic Wumishan formation. Most of the exploitative geothermal resources (146 geothermal wells) in Tianjin have mainly been produced from the Wumishan formation of the Jixian system and the Guantao formation of the Neogene system. The current production capacity has been doubled, and a two-stage cascade utilization system has been established, incorporating geothermal power generation and geothermal heating. The geothermal utilization share in Tianjin is estimated to be 81.66% for heating, 16.6% for domestic hot water and 1.35% for bathing. In conclusion, notwithstanding the diversity of geothermal resources in Tianjin, it is difficult to guarantee the sustainable development and utilization of geothermal resources in Tianjin due to the unreasonable layout of geothermal wells, imbalance of production and reinjection. Hence, the integration of distributed temperature sensing and distributed strain sensing monitoring demonstrates significant promise and effectiveness in tracking water circulation and detecting flow localization problems as dynamic monitoring processes and smart thermal response tests should be recommended and established as a substantial feature required in the future utilization and development of geothermal resources in Tianjin. [ABSTRACT FROM AUTHOR]

In this article, the improved chaotic electromagnetic field optimization (ICEFO) algorithm is adopted to generate the optimal values of unknown parameters of fuel cells (FCs). The mathematical model of polymer electrolyte membrane FC (PEMFC) considers nonlinear and complex optimization problems with different control variables. The sum of squared error between the measured and computed stack voltages is considered as the main objective function. The performance of the ICEFO algorithm is tested on three PEMFC stacks. Moreover, sensitivity and statistical measures are presented to confirm the reliability and accuracy of ICEFO. In addition, the effect of changing the cell temperature and reactants pressures is studied for more validation of ICEFO. Furthermore, the results obtained by ICEFO are competitive compared with other optimization methods. These results confirm the effectiveness of ICEFO in solving the optimization problem of PEMFC parameter estimation. Finally, based on the optimization results a Simulink model for PEMFC is developed to examine the dynamic performance of the PEMFCs. [ABSTRACT FROM AUTHOR]

Demonstrating an accurate mathematical model is a mandatory issue for realistic simulation, optimization and performance evaluation of proton exchange membrane fuel cells (PEMFCs). The main goal of this study is to demonstrate a precise mathematical model of PEMFCs through estimating the optimal values of the unknown parameters of these cells. In this paper, an efficient optimization technique, namely, Tree Growth Algorithm (TGA) is applied for extracting the optimal parameters of different PEMFC stacks. The total of the squared deviations (TSD) between the experimentally measured data and the estimated ones is adopted as the objective function. The effectiveness of the developed parameter identification algorithm is validated through four case studies of commercial PEMFC stacks under various operating conditions. Moreover, comprehensive comparisons with other optimization algorithms under the same study cases are demonstrated. Statistical analysis is presented to evaluate the accuracy and reliability of the developed algorithm in solving the studied optimization problem. [ABSTRACT FROM AUTHOR]