Sensitivity Analysis Of Parameters Research Articles

Multi-criteria study and optimization of an innovative combined scheme utilizing compressed air energy storage for a modified solid oxide fuel cell-driven gas turbine power plant fueled by biomass feedstock

This paper explores the potential of a combined solid oxide fuel cell and gas turbine technology for medium- to large-scale power generation, emphasizing its applicability and sustainability, particularly with biomass feedstock. An innovative heat integration process is developed for a modified solid oxide fuel cell and gas turbine power plant, incorporating a steam power cycle, compressed air energy storage, a Kalina cycle, and a domestic hot water production subsystem. The system utilizes biomass through a downdraft gasifier, enabling a comprehensive evaluation of thermodynamic, economic, and environmental performance during both charging and discharging phases. A detailed parametric sensitivity analysis is performed to investigate two operational modes. Subsequently, five multi-objective optimization scenarios are formulated and optimized using the cuckoo search algorithm and two decision-making approaches. The results indicate that the optimization scenario focusing on exergetic round-trip efficiency and the unit cost of products during the discharging phase achieves superior thermodynamic and environmental performance. Specifically, the system exhibits energetic and exergetic round-trip efficiencies of 59.20 % and 51.06 %, respectively, with a levelized total emission of 0.64 kg/MWh. Furthermore, when considering the objectives of exergetic round-trip efficiency and net present value, the optimal economic performance is achieved with a payback period of 1.54 years and a net present value of $7.44 million.

A new integrated intelligent computing paradigm for predicting joints shear strength

Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures. The prevailing models mostly adopt the form of empirical functions, employing mathematical regression techniques to represent experimental data. As an alternative approach, this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength. Five metaheuristic optimization algorithms, including the chameleon swarm algorithm (CSA), slime mold algorithm, transient search optimization algorithm, equilibrium optimizer and social network search algorithm, were employed to enhance the performance of the multilayered perception (MLP) model. Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models, employing statistical indicators such as root mean square error (RMSE), correlation coefficient (R2), mean absolute error (MAE), and variance accounted for (VAF) to evaluate the performance of each model. The sensitivity analysis of parameters that impact joints shear strength was conducted. Finally, the feasibility and limitations of this study were discussed. The results revealed that, in comparison to other models, the CSA-MLP model exhibited the most appropriate performance in terms of R2 (0.88), RMSE (0.19), MAE (0.15), and VAF (90.32%) values. The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength. This paper presented an efficacious attempt toward swift prediction of joints shear strength, thus avoiding the need for costly in-site and laboratory tests.

Uncertainty estimates in the NISAR high-resolution soil moisture retrievals from multi-scale algorithm

It is important to know the amount of systematic and random uncertainties in any state variable to improve its geophysical application potential. The expected high-resolution (200 [m]) soil moisture product from the NASA-ISRO Synthetic Aperture Radar (NISAR) mission is no exception. Thus, knowing the quality of the soil moisture retrievals through the estimation of various error sources is imperative. The estimation error sources in soil moisture retrievals can be obtained by various methods. In situ measurements provide a reliable estimate of the uncertainty of soil moisture retrievals. However, in situ measurements are available only for limited locations, as they are typically very tedious and expensive to obtain. Thus, an analytical approach has been developed to obtain an estimate of the uncertainty in the soil moisture retrievals that vary in space and time across grid-cells. This uncertainty estimation is specifically developed for the multi-scale algorithm of the upcoming NISAR mission, which will provide soil moisture retrievals at 200 [m] resolution. The multi-scale algorithm for the NISAR mission disaggregates the coarser resolution soil moisture (∼9 [km]) to high-resolution (∼200 [m]) using NISAR L-band SAR measurements. However, uncertainty in high-resolution soil moisture retrievals might be introduced due to errors in input datasets (e.g., coarse resolution soil moisture, instrument error of SAR, etc.) and multi-scale algorithm parameters. Therefore, this study carried out a detailed sensitivity analysis of input datasets and algorithm parameters using the proposed approach. The sensitivity analysis shows that error in the input coarse resolution soil moisture is one of the primary drivers of uncertainty in the high-resolution soil moisture retrievals. The other portion of the uncertainty comes from errors in the algorithm parameters, and noise in SAR co-pol and cross-pol backscatter observations. Furthermore, the approach was tested on the UAVSAR L-band data time-series that had been simulated to closely match the expected characteristics of NISAR (e.g., spatial resolution and noise). The uncertainty estimates in UAVSAR-based high-resolution retrievals were compared with the SMAPVEX-12 in situ measurements. The uncertainties estimated for different crops were found to be close to the ubRMSE metric, which is also lower than the NISAR mission accuracy goal (0.06 [m3/m3]).

Multi-objective optimization of building integrated photovoltaic windows in office building

Building-Integrated Photovoltaics (BIPV) offer a promising solution to enhance building energy efficiency and reduce building energy consumption. Among the various application of BIPV, BIPV windows stand out as an intriguing and notable example. This paper investigates an office building with BIPV windows in five different climatic cities in China. The study considers four variables, including building orientation (BO), window size (WZ), window visible light transmittance (VLT), and type of PV (TOPV). The objective is to minimize both the annual net electricity cost (ANEC) and the extra investment cost of BIPV windows. To simulate the design variables and objective functions, the jEPlus software is employed. Additionally, the jEPlus + EA software uses the SRC and Morris algorithms for sensitivity analysis of design parameters. The multi-objective problem is addressed using the NSGA-II. By conducting optimization, a set of Pareto optimal solutions were obtained. The results demonstrate that the building with BIPV windows can save a minimum of 6.83 % annual electricity costs. Moreover, the static investment payback period for all cities ranges from 7 to 14 years, indicating the economic feasibility of implementing BIPV windows.

Parameters estimation and sensitivity analysis of lithium-ion battery model uncertainty based on osprey optimization algorithm

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions.

Sensitivity analysis of the process parameters of the composite process of submerged arc surfacing and laser cladding

Sensitivity analysis of the process parameters of the composite process of submerged arc surfacing and laser cladding

A Morris sensitivity analysis of an office building's thermal design parameters under climate change in sub-Saharan Africa

Sub-Saharan Africa faces severe overheating problems during hot periods due to substandardly constructed buildings and high-priced energy services. Global warming is expected to aggravate the situation. Although literature addresses the effects of climate change on buildings in different world regions, how it will impact the Sub-Saharan region is still being determined, particularly in terms of design parameters and how they will vary. This study assesses the influence of design parameters on the cooling energy demand of a small-scale office building in Lagos and Kano, Nigeria. A Morris sensitivity analysis was carried out to rank and compare the influence of different design parameters on energy consumption, determined using EnergyPlus for present-day typical weather and the SSP5-8.5 scenario. The future scenario was generated using the Future Weather Generator, a morphing tool. The results show that, in 2080, the cooling load will increase from 1551 kWh/a to 2612 kWh/a (68.4 %) in Kano and from 1931 kWh/a to 3093 kWh/a (60.1 %) in Lagos. The cooling load in Lagos will generally be higher than in Kano by 18.4 % (596 kWh/a). The results indicate that reductions in the thermal conductivity of the east wall and decreases in the solar absorptance of the east wall, roof, and west wall elements will be the most significant factors affecting cooling in Kano. In Lagos, reductions in the thermal conductivity of the east wall and decreases in solar absorptance of both the east wall and roof elements will be the most influential.

Simulation of pulse-like ground motions with directionality effect for the 2001 Mw 7.6 Bhuj, India earthquake and sensitivity analysis of uncertain model input parameters

Ground motions exhibiting pulse-like (PL) features present a distinct and significant threat to the structural integrity of constructed environments, due to intense pulse, large amplitude, and rapid energy release. The lack of recorded PL ground motion data in the Indian region presents a significant challenge for accurate seismic risk assessment of the built environment. In the absence of recorded PL ground motions, the generation of synthetic PL ground motions can provide earthquake acceleration time histories for seismic response analysis of structures and also to evaluate ground motion prediction equations (GMPEs). Within this framework, the modified stochastic finite-fault approach based on the dynamic corner frequency is used to generate the synthetic ground motions for the 2001 Gujarat earthquake. These artificially generated ground motions are validated using available peak ground acceleration data from thirteen stations. After validation, the ground motions are generated at locations equidistant from the fault in all four directions (i.e., east, west, north, south). The expected acceleration time histories at specific sites, the spatial distribution of ground motion characteristics, and the effect of site non-linearity are investigated. Based on the geographical positioning of sites for the 2001 event, it was observed that 65.15 % of ground motions on the western side, relative to the epicenter, exhibited PL characteristics, whereas 23.64 % of ground motions on the eastern side were observed to have PL properties. The effect of uncertainty in the model input parameters on the simulated ground motions is examined by performing a sensitivity analysis. The reliability and validity of the simulated ground motions are assessed by deriving an empirical equation and compared with the results obtained from available GMPEs for the region.

Vertical matrix advection dominates transient anomalous diffusion within fracture-matrix systems using a modified diffusion model

Pollutant transport in fractured media has been well analyzed using process-based diffusion models and concise, time-nonlocal models like the tempered fractional derivative (TFD) model. While traditional diffusion models are limited to specific transport processes, TFD model parameters lack hydrogeological meaning. In addressing these knowledge gaps, a modified diffusion model is proposed to augment the traditional diffusion model and offer hydrogeological interpretations for TFD parameters. This modified diffusion model incorporates transverse advection in low-permeability matrices, allowing for the quantification of transient anomalous diffusion in a single fracture-matrix system. This model offers a semi-analytical solution, systematically validated for its applicability. Memory function analysis establishes a quantitative connection between the modified diffusion model and the TFD model, uncovering a pivotal discovery in this study. Parameter sensitivity analysis further shows that the matrix characteristic rate defined as a function of (Vm)2/(4Dm), where Vm is the transverse matrix velocity and Dm is the transverse matrix diffusion coefficient, controls the temporal evolution of anomalous sub-diffusion in the fracture. Applications are performed to check the feasibility of the modified diffusion model. The developed modified diffusion model sheds light on quantifying complex transport in fracture-matrix systems and enhances the predictability of nonlocal transport models.

Quantitative analysis of ITER Poloidal Field joints through rigorous resistivity parameterization

The lap-type twin-box joints are integral components in International Thermonuclear Experimental Reactor (ITER) fusion magnets, with profound implications for magnet stability based on their electro-magnetic, thermal, and mechanical properties. Throughout the extensive R&D process, rigorous qualification tests are conducted to meet stringent standards. However, existing tests often prioritize global performance, which lack of strand-level details due to inherent limitations in test setups. Furthermore, as the referencing test facility of SULTAN falls short in replicating relevant ITER operating conditions, numerical methods that offer both accuracy and the requisite level of detail for comprehensive magnet and component analysis and development are necessary. This paper introduces the utilization of the JackPot-AC/DC code, developed at the University of Twente, as a fundamental tool for achieving strand-level precision in handling CICCs and joints, which encompasses copper and solder components. The primary focus of this study is to obtain precise input parameters, emphasizing their role in conducting a quantitative analysis using JackPot-AC/DC. The investigation centers on an ITER PF5 joint (PFJEU6), where contact resistances and AC losses were measured under parallel magnetic fields. Given the constraints in the measured results, an enhanced parameterization is performed to derive precise resistivity and solder-related parameters. Additionally, sensitivity analyses of individual parameters and cable compact configurations are thoughtfully evaluated. With the optimal input parameters acquired, systematic simulations of the joint exposed to transverse magnetic fields, mimicking SULTAN and ITER operating conditions, are processed and validated against experimental results. This research establishes a comprehensive foundation for the analysis of lap-type twin-box joints, including DC, AC, and stability properties. The outcomes will significantly contribute to advancing the understanding of the intricate behavior of these joints in the context of fusion magnet applications.

Dynamical System for Tuberculosis Outbreak with Vaccination Treatment and Different Interventions on the Burden of Drug Resistance

Tuberculosis, a highly contagious and lethal infectious disease, remains a global health concern with challenging treatment options. To combat its widespread impact, prevention strategies, such as vaccination, are imperative. This research focuses on developing a mathematical model with the addition of a vaccination compartment to understand the dynamics of tuberculosis transmission with vaccination. Subsequently, the study proceeds to identify the equilibrium points and calculate the basic reproduction number (ℜ0). Following this, a comprehensive stability analysis is conducted, and a numerical simulation is executed to observe the population dynamics. Furthermore, parameter sensitivity analysis is undertaken to assess the extent to which these parameters impact ℜ0. Preliminary analysis shows that the modified model has a solution that remains in the non-negative and bounded region. Furthermore, model analysis reveals two equilibrium points, namely the disease-free equilibrium and the endemic equilibrium. It is established that the disease-free equilibrium exhibits local asymptotic stability when ℜ0 1. Remarkably, the numerical simulation aligns with the analytical findings, reinforcing the robustness of the results. Analysis of the sensitivity of the parameter to ℜ0 shows that the parameter of the proportion of susceptible population entering the vaccination class has a significant effect on the value of ℜ0. The parameter of proportion of susceptible population entering the vaccination class has a negative effect on the number of populations with infection.

Study on bearing capacity of corroded pipes before and after spraying rehabilitation

In recent years, there has been growing interest in trenchless technology, particularly in using centrifugal spraying technology to rehabilitate drainage pipelines. In this study, we evaluated the effectiveness of fiber-reinforced cement mortar spraying materials on pipes using the three-edge bearing test (TEBT) as per ASTM C497 standards. Six conditions of corroded and rehabilitated pipelines were tested: (1) corrosion depths, (2) corrosion widths, (3) corrosion positions, (4) pipe diameters, (5) loading rates, and (6) repair thicknesses. Through the TEBT, the structural bearing capacity and failure form of the pipe before and after repair were compared, and the bearing capacity difference of the pipe under different conditions was studied. Finally, a sensitivity analysis was conducted on the six variables. Among the many factors that affect the reliability of pipe structures, the influence degree of each factor is quite different. Through the parameter sensitivity analysis, the influence degree of each factor on the reliability of the structure can be clarified. The results show that the bearing capacity of the rehabilitated pipes is sufficient to meet the normal pipe bearing capacity requirements. The mortar lining and existing pipes can bear stress jointly, significantly increasing the maximum load of the rehabilitated pipes by up to 55.6 % and correspondingly reducing the circumferential strain. This indicates that the application of the mortar spraying lining layer effectively achieves the purpose of pipeline repair. This study provides valuable guidance for structural design optimization and safe operation.

Optimization of CO2 flooding under dual goals of oil recovery and CO2 storage: Numerical case studies of the first-ever CCUS pilot in Changqing oilfield

CCUS (CO2 capture, utilization, and storage) is an important way to reduce carbon emissions and is increasingly applied in the development of hydrocarbon resources. It is crucial to develop proper engineering parameters to achieve the dual goals of enhanced oil recovery (EOR) and CO2 storage. This paper reports systematic numerical case studies of the first-ever CCUS pilot project at Changqing Oilfield to identify further improvement opportunities compared to the current development strategy. The targeted reservoir is located in the Huang 3 (H3) block and is characterized by ultra-low permeability (<1 mD), low oil production (<5 tons/day), and miscible and near-miscible flooding. A three-phase compositional reservoir model is established and a typical well group, consisting of one injector and eight producers, is selected to perform the numerical case studies. We investigate the influence and sensitivity analysis of engineering parameters on oil production and CO2 storage in ultra-low permeability sandstone reservoirs. The results indicate that the well pattern, the timing of switching to water-alternating-gas injection, the gas-water slug size ratio, and the injection rate are controlling factors to achieve favorable performance. The simulation results show that by using optimized engineering parameters, the oil recovery factor of the target reservoir is significantly increased by 25% and the CO2 storage efficiency is increased by 60%. Finally, an optimized development plan is obtained based on the actual conditions of the target reservoir. The reported case studies are supposed to provide insights into the optimal design and operation of CO2 utilization and storage in hydrocarbon reservoirs.

Exploring the Therapeutic Potential of Defective Interfering Particles in Reducing the Replication of SARS-CoV-2

SARS-CoV-2 still presents a global threat to human health due to the continued emergence of new strains and waning immunity among vaccinated populations. Therefore, it is still relevant to investigate potential therapeutics, such as therapeutic interfering particles (TIPs). Mathematical and computational modeling are valuable tools to study viral infection dynamics for predictive analysis. Here, we expand on the previous work on SARS-CoV-2 intra-cellular replication dynamics to include defective interfering particles (DIPs) as potential therapeutic agents. We formulate a deterministic model that describes the replication of wild-type (WT) SARS-CoV-2 virus in the presence of DIPs. Sensitivity analysis of parameters to several model outputs is employed to inform us on those parameters to be carefully calibrated from experimental data. We then study the effects of co-infection on WT replication and how DIP dose perturbs the release of WT viral particles. Furthermore, we provide a stochastic formulation of the model that is compared to the deterministic one. These models could be further developed into population-level models or used to guide the development and dose of TIPs.

Theoretical model and parameter sensitivity analysis of concrete slab vibration in concrete-face rockfill dams during underwater nuclear explosions

Large reservoir dams, which are carriers of energy and resources, have historically been the main targets of military conflict and terrorist attacks. A concrete-face rockfill dam (CFRD) is a competitive hydraulic structure widely used in large-scale water conservancy projects. However, the vibration response and damping paths of CFRDs under the influence of underwater explosion shock waves remains insufficiently understood. In this study, we created a theoretical model of slab vibration with four-sided free boundaries using Cole’s formula and the motion differential equation of a slab on an elastic foundation. Furthermore, a global sensitivity analysis of the vibration model parameters on the slab displacement was performed based on the Kriging surrogate model and Sobol method. The results showed that the antiseepage slabs experienced a void phenomenon, resulting in overall instability. The explosive distance and normal stiffness of the slab had the greatest influence on the void displacement of the slab, whereas the dam height and concrete density had insignificant effect. Moreover, increasing the dam height of the CFRD did not increase the risk of slab voids during underwater explosions, which was not the case under strong seismic loads. Therefore, the key factors for active and passive defense against blast resistance in CFRDs were identified, which can provide theoretical support for the blast-resistant design and research on CFRDs. In this study, the reliability of the theoretical model was verified by establishing a numerical analysis model.

Efficient and sustainable power propulsion for all-electric ships: An integrated methanol-fueled SOFC-sCO2 system

To address marine pollution caused by fuel usage and reduce carbon emissions in ships, the development of alternative fuel electric propulsion ship power systems presents a promising solution. In this study, an integrated Methanol-fueled SOFC-sCO2 combined power system is proposed for all-electric ships. The system efficiently utilizes waste heat from SOFCs by integrating it into a partial heating sCO2 power cycle to generate additional power. Through the utilization of a thermodynamic model, parametric sensitivity analysis and multi-objective optimization are conducted to maximize the system's energy efficiency while reducing investment and maintenance costs. Comprehensive energy, exergy, and economic analyses are performed on the optimized system. The achieved energy efficiency and exergy efficiency are 66.10 % and 57.74 % respectively, with an investment and maintenance cost of 16.96 $/h and a payback period of 2.2 years. When applied to container ships and compared with other ship power systems, the proposed system demonstrates the highest energy efficiency, delivering an impressive output power of 65,092 kW. The energy efficiency design index (EEDI) of the system is moderate, reaching 5.94. Despite the relatively high fuel cost, the proposed system offers a simpler design compared to existing SOFC-sCO2 systems and is only slightly more complex than integrated systems like SOFC-GT.

Numerical analysis of shored mechanically stabilized earth walls

ABSTRACT This study investigates the deformation and stability of shored mechanically stabilized earth (SMSE) walls using numerical analysis. A finite element (FE) model is developed in Plaxis, and parametric sensitivity analysis is conducted to identify the most influential input parameters on the maximum lateral facing displacements of the mechanically stabilized earth (MSE) and soil nail (SN) walls and global factor of safety (FS) of the SMSE wall. Predictive equations are developed for these responses using multiple linear regression. The suitability and adequacy of the developed predictive equations are assessed by evaluating the summary of fit statistics. It is observed that the governing failure mechanism of the SMSE wall is a bilinear failure plane. Its inclination angle is 10° less than that of the Rankine failure surface. The external stability of the SMSE wall is more critical compared to the conventional MSE wall. It is concluded that special attention should be given to the interfaces and connections due to the vulnerability to reinforcement-facing connection failure.

Multiple dynamic targets enclosing control for a class of nonlinear uncertain multiagent systems

The multiple dynamic targets enclosing control protocol is proposed in this paper for a class of nonlinear uncertain multiagent systems. To accomplish the mission of hunting multiple targets with double-integral nonlinear dynamics, a distributed estimator is established firstly for each agent by employing the signum function, leading to an estimation of the average position of multiple targets. Specifically, only partial information about the dynamic targets is utilized to construct the enclosing formation reference beacon. Furthermore, due to the existence of the system uncertainties, it is a challenging task to design the enclosing controller. To overcome this difficulty, an augmented system is constructed to compensate for the effect caused by the uncertainties, which is introduced afterwards into the control protocol design along with comprehensive theoretical analysis. Sufficient conditions are derived and proved under Lyapunov stability criterion to ensure the closed-loop stability of nonlinear multiagent systems. The effectiveness of the proposed control scheme is finally verified through the numerical simulations. More precisely, by appropriately picking control parameters α and β based on Lemma 3.1, the designed formation reference beacon estimator is validated by achieving convergence of errors between the actual state and estimated states to zero. Meanwhile, the feasibility of the proposed enclosing control protocol is demonstrated simultaneously as it enables the regulation of enclosing errors using a self-designed parameter γ0. Additionally, the sensitivity analysis of the control parameters is presented, which indicates that the proposed reference beacon estimator remains robust to the variation of its control parameters, while larger values for the control parameters in the designed controller result in faster error convergence rates.

Examining the impact of common faults on chiller performance through experimental investigation and parameter sensitivity analysis

Chillers are the important components across various applications, and understanding the impact of common faults on their performance is crucial for monitoring fault progression and predicting degradation. Consequently, an experimental investigation was conducted on a magnetic levitation-driven chiller across different severity levels. The quantitative influence of common faults on performance was then analyzed, with a specific focus on thermodynamic mechanism parameters. From a data variation perspective, a sensitivity analysis was implemented to identify the fault-sensitive parameters that exhibited noteworthy variations due to faults. From a classification performance perspective, the diagnosis-effective parameters were identified based on their significance in fault diagnosis. The results of this study led to several key conclusions: 1) Trends and magnitudes of changes in 38 parameters related to nine typical faults are identified in comparison to their normal levels; 2) Common faults have a significant influence on thermodynamic mechanism parameters, causing big changes in two to eight thermodynamic mechanism parameters for each fault; 3) Discrepancies between fault-sensitive parameters and diagnosis-effective parameters results in a difference of up to 64% in diagnostic performance; 4) Nine decoupling parameters are identified as pivotal in diagnosing concurrent faults, and an effective set of fault diagnosis rules is recommended. These findings provide valuable insights into the selection of sensitive parameters, identification of fault-decoupling parameters, and the development of fully interpretable and reliable fault diagnosis approaches.

Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management

As electric vehicles (EVs) continue to rise in popularity, there has been an intensified focus on the distribution of power within hybrid renewable energy systems. In this context, the significance of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) models for mitigating grid pressures has been increasingly recognized. These models help mitigate grid pressures by using EVs as reliable loads. This research delves into the technical and economic aspects of a hybrid microgrid integrated with various components such as photovoltaic panels (PVs), wind turbines (WTs), battery energy storage systems (BESSs), and EV grid connections, situated at a specific latitude of 40°39.2′N and longitude of 29°13.2′E. The methodology employed is grounded in advanced stochastic metaheuristic approaches. The infrastructure accommodates two distinct loads: a primary load and another dedicated to EV charging. The cornerstone of the energy framework is renewables, particularly PV panels and wind turbines. A dynamic rule-based energy management scheme is implemented to ensure consistent fulfillment of energy demands with an emphasis on cost efficiency. In defining the dimensions of the microgrid, various algorithms including the Coati Optimization Algorithm (COA), Driving Training-Based Optimization (DTBO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Turbulent Flow of Water-based Optimization (TFWO), White Shark Optimizer (WSO), Zebra Optimization Algorithm (ZOA), and Flying Foxes Optimization (FFO) were used. This configuration was evaluated in terms of the annual system cost, present-day value, loss of power supply probability (LPSP), and leveled cost of energy (LCOE). Thanks to the strategic deployment of the TFWO algorithm, optimal results were achieved for the system, including a PV capacity of 411.0560 kW, a WT capacity of 327.0229 kW, and a BESS capacity of 561.1750 kW. With this configuration, the annual cost of the system was determined to be $4.0499 × 105, the present value was set at $4.6452 × 106, the nominal LPSP was calculated at 0.0487 %, and the LCOE was established at $0.1597/kWh. Notably, a significant portion of the energy demand, approximately 69.87 %, was met through renewables, with the remaining 30.13 % covered by traditional sources. Comprehensive sensitivity analysis of key parameters enabled forecasting of future trends. All research activities underwent thorough review, confirming the TFWO algorithm's superior efficiency and faster convergence compared to other methods. The optimization algorithms were implemented in MATLAB 2022b, and statistical evaluations performed using the R programming language.