Optimal System Parameters Research Articles

Harnessing Nth Root Gates for Energy Storage

We explore the use of fractional controlled-not gates in quantum thermodynamics. The Nth-root gate allows for a paced application of two-qubit operations. We apply it in quantum thermodynamic protocols for charging a quantum battery. Circuits for three (and two) qubits are analysed by considering the generated ergotropy and other measures of performance. We also perform an optimisation of initial system parameters, e.g.,the initial quantum coherence of one of the qubits strongly affects the efficiency of protocols and the system’s performance as a battery. Finally, we briefly discuss the feasibility for an experimental realization.

Extending battery lifetime of electric-hydraulic hybrid wheel loader through system parameter optimization

Parallel electric hydraulic hybrid powertrain (PEHH) is a promising substitute for electric direct drive to extend battery lifetime. The convex programming (CP) based simultaneous optimization method has been adopted in electric hybrid system parameter optimization, but it is difficult to extend it for PEHH due to its non-convexity. In this paper, a combined exhaustive search – convex programming (ES-CP) system parameter optimization method is proposed to address the non-convexity of PEHH system parameter optimization problem. In this method, the PEHH system parameter optimization problem is modeled and reformulated in convex form and the convex part is decoupled from the optimization problem. The CP method is used for the convex part and the ES method is used for the remained non-convex part. The ES-CP method is applied to the PEHH powertrain to extend its battery lifetime. Optimization results show that the battery lifetime of the ES-CP optimized PEHH system is 6.8% longer than that of the non-optimized system. The computing time of ES-CP is 97% lower than Latin hypercube sampling and 90% lower than genetic algorithm methods.

Increasing the efficiency of operation and management of railroad transport infrastructure based on maximum levels of fault tolerance

This paper considers the optimization of parameters for a railroad transport system. The maximum level of technological reliability and the average time spent by trains on the route are used as optimization criteria. The purpose of the study is to establish the optimal parameters for the operational process of railroad transport systems according to the criterion of the maximum level of technological reliability and the minimum time of trains on the route. Methods of technological reliability research have been proposed. Taking into account that the entire technological process is a sequential set of technological elements, a simulation model of the technological process of the transit transport-technological line along a route direction has been built. A population of agents that simulates the operation of railroad sections of the rotation of train locomotives and is a key subsystem of the simulation model has been developed and configured. The simulation model makes it possible to optimize the parameters of multi-section railroad lines. This approach is provided owing to the agent approach. As a result of the experiments, the optimal parameters of the functioning of railroad lines were established when organizing the passage of transit trains. The coefficient of utilization of the locomotive fleet fluctuates within the optimal range (0.55–0.65), which indicates the sufficiency of traction resources in the railroad system. The optimal parameters of the railroad transport system were established experimentally using the example of a train flow of 85 pairs of trains on a two-track route with five sections. The problem of "abandoned trains" has a solution but, to this end, it is necessary to increase the fleet of train locomotives by 150–200 % relative to existing standards. At the same time, even with an unlimited fleet of train locomotives, there is a fairly high probability (up to 30–50 %) of technological failures

Enhanced Broadband Tri-Stable Energy Harvesting System by Adapting Potential Energy – Experimental Study

Abstract This paper presents the process optimization of some key parameters, such as the size of the air gap and distance between fixed neodymium magnets to enhance the vibration-based energy harvesting effect in the tri-stable energy harvesting systems and the improved tri-stable energy harvesting system being the proposed solution under weak excitation. In order to do it, firstly the distributed parameters model of the magnetic coupling energy harvesting system, including macro fiber composites of the 8514 P2 with a homogenous material in the piezoelectric fiber layer and nonlinear magnetic force, was determined. The performed numerical analysis of the conventional and the improved tri-stable energy harvesting system indicated that introducing an additional magnet to the tri-stable system leads to the shallowing of the depth of a potential well by decreasing the air gap between magnets and consequently generating higher power output and improving the effectiveness of the proposed improved tri-stable energy harvesting system. Experiments carried out on the laboratory stand allowed us to verify the numerical results as well as determine the optimal parameters of the magnetic coupling system. Due to it, the effectiveness of the proposed system versus the conventional tri-stable energy harvesting system is most enhanced.

Analytical comparison of the boil-off gas recondensation process’s energy consumption and carbon emissions

With 28 operational liquefied natural gas receiving terminals in China by 2023, totaling a receiving capacity of 1.2 × 108 t/a, managing carbon emissions and boil-off gas has attracted increased attention. This study presents the optimization of 8 boil-off gas (BOG) recondensation processes, considering energy consumption and carbon emissions as assessment criteria, along with two types of vaporizers. The method involves analyzing how process parameters affect system performance using Hydraulic System Simulation Software, followed by optimizing system parameters with a genetic algorithm. It explores the impact of compressor output pressure, after-cooler inlet material ratio, and pre-cooler inlet material ratio on system energy consumption and carbon emissions. Results show that increased recondensing, compression, and cooling measures slightly elevate energy consumption but significantly reduce carbon emissions. Moreover, a higher BOG content enhances the carbon reduction effect. This study represents the first optimization of BOG recondensation processes focusing on carbon emissions, offering fresh theoretical insights and significant potential for enhancing energy efficiency and reducing carbon emissions.

An energy-environment coupled simulation framework for multi-scale and multi-facet evaluation of data center

This paper focuses on analyzing the thermal environment and optimizing energy consumption in data centers, which has largely omitted from previous studies. The thermal environmental of data centers is simulated and analyzed by utilizing the established “server-rack-room” multi-scale heat transfer numerical model. Based on this, the coupling simulation model of thermal environment and energy consumption in data centers is proposed to explore the relationship between them, and the corresponding optimization strategy is put forward. The energy consumption simulated by energy-environment coupled model and non-coupled model can reach a discrepancy of over 30 %, which indicates that the thermal environment impacts the power consumption of the data center significantly. Besides, the effect of several operational parameters of air conditioning system on the thermal environment and energy consumption of data center is analyzed. Through the particle swarm optimization algorithm, the optimal system parameters, which meet the requirements of thermal environment and lead to the lowest energy consumption, are found for typical cities in different climatic regions. The recommended settings include the supply air temperature of about 24 ∼ 26 ℃, the air supply volume of 8 m3/s, and the temperature difference of about 3.5 ℃ between the supply air temperature and the supply chilled water temperature. Under the optimized parameter setting, the highest temperature of server decreases to below 71 ℃, and the energy saving rate is more than 6 %.

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

Enhancing Energy Harvesting Efficiency from Ambient Sources through Advanced Materials and Nanotechnology

The utilization of ambient energy sources for powering small-scale electronic devices and sensors has garnered significant attention due to its potential for sustainable and autonomous operation. This research focuses on enhancing the efficiency of energy harvesting from ambient sources, such as vibrations, heat differentials, and electromagnetic fields, through the integration of advanced materials and nanotechnology into energy harvesting systems. The study begins with a comprehensive review of existing energy harvesting technologies, highlighting their limitations in terms of efficiency, scalability, and adaptability to various ambient energy sources. It identifies the key challenges faced in achieving high energy conversion rates and explores the potential of advanced materials and nanoscale structures to address these challenges. One aspect of the research involves the development and characterization of novel materials with superior energy conversion properties. This includes the synthesis of piezoelectric materials for vibration energy harvesting, thermoelectric materials for heat-to-electricity conversion, and nanomaterials for enhancing electromagnetic energy harvesting efficiency. Advanced characterization techniques, such as scanning electron microscopy (SEM) and X-ray diffraction (XRD), are employed to analyze the structural and electrical properties of these materials. Furthermore, the study investigates the design and fabrication of nanostructured energy harvesting devices optimized for specific ambient energy sources. This involves the integration of nanoscale components, such as nanostructured electrodes, into energy harvesting systems to improve energy capture and conversion rates. Finite element analysis (FEA) simulations and experimental testing are conducted to evaluate the performance and efficiency of these nano-enhanced energy harvesting devices under real-world conditions. The research also addresses the optimization of energy harvesting system parameters, including resonance tuning, impedance matching, and energy management circuits, to maximize energy extraction and utilization. Computational modeling techniques, such as multiscale modeling and finite element simulations, are utilized to optimize system configurations and improve overall energy harvesting efficiency. Overall, this research contributes to the advancement of energy harvesting technologies by leveraging advanced materials and nanotechnology, paving the way for sustainable and autonomous power generation from ambient energy sources for a wide range of applications.

Optimization of vacuum membrane distillation and advanced design of compact solar water heaters with heat recovery

With the escalating global population, freshwater scarcity has become a pressing challenge. To address this issue, this paper presents a novel compact solar water heater (CSWHT) system incorporating heat recovery and submerged-membrane filtration. The research aims to develop an efficient desalination method that reduces water production costs and maximizes output. The system comprises a feed storage tank, a vacuum membrane distillation (VMD) system positioned atop the submerged CSWHT, a liquid ring vacuum pump (LRVP) utilizing waste heat from the outlet to preheat the feed, and hollow fiber membranes.Three critical parameters, namely the mass of the feed in the storage tank, the number of hollow fiber membrane rows, and the feed volumetric flow rate, were optimized using a multi-objective non-dominated sorting genetic algorithm II (NSGA II) to minimize water production cost (WPC) while maximizing water production. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was employed to identify the optimal scenario from among the selected best solutions.For Mediterranean seawater and a 6 m2 solar collector area, the multi-objective NSGA II algorithm identified 20 membrane rows per collector, 334.4 kg feed mass in the storage tank, and 0.027 kg.s−1 feed mass flow rate as the optimal system parameters. Under these optimized conditions, the system exhibited a daily production capacity of 92.5 kg water, 4.6 kg chloride, and 0.03 g bromide, while achieving a production cost of 12.2 USD.m−3 and a gain output ratio (GOR) of 1.6. These results highlight the system's remarkable potential for desalination with high efficiency and low cost. Future research should focus on scaling up the system and exploring its applicability in various geographical locations.

Hybrid Power Generating System Sources and Load Synchronization Using Adaptive-Neural-Fuzzy-Inference-System and Pelican Optimization Algorithm Technique

The integration of renewable energy sources (RESs) into current power networks addresses rising global energy demand and mitigates greenhouse gas emissions. This transition shifts the role of frequency regulation from synchronous generators to power converters, serving as interfaces between the grid and RESs. This paper proposes a hybrid method for optimizing power flow in hybrid power-generating systems. The proposed hybrid technique is the combined execution of an Adaptive-Neural-Fuzzy-Inference-System (ANFIS) and Pelican Optimization Algorithm. Hence it is named as ANFIS-POA technique. POA optimizes current and voltage controllers, while ANFIS predicts optimal system parameters. Implemented in MATLAB, the proposed strategy achieves an impressive efficiency of 96%, outperforming existing strategies like genetic algorithm (GA) and particle swarm optimization (PSO). With a faster settling time (0.56 s), a shorter rising time (1.32 s), and a reduced peak time (5.78 s), the proposed approach performs better than existing methods. Additionally, it significantly lessens system costs (Rs. 99,450) and demonstrates superior statistical performance in terms of lower error rates and improved key metrics like accuracy, precision, and recall compared to existing methods.

Modeling the Optimal Distance Between Pipes in a Solar Water Heating Collector

This study proposes a mathematical model based on normal distribution to determine the optimal distance between pipes in a solar water heating collector. The model takes into account key system parameters such as material thermal conductivity, heat losses, and solar radiation intensity. Experimental data collected for model validation confirm its accuracy and adequacy. The main results show that the maximum efficiency (η) of the solar water heating collector is achieved at a pipe spacing of approximately 0.0469 m. The analytical method demonstrates a smooth decrease in efficiency when deviating from the optimal distance, whereas the experimental data indicate a high sensitivity of the system to changes in this parameter. Thus, the proposed model is a useful tool for designers, allowing for precise prediction of optimal system parameters and increased efficiency. Further research and experiments are planned to improve model accuracy and expand its applicability.

Automatic optimization design of laser triangulation ranging sensors using an improved genetic algorithm

Optical system parameter design is of great importance to ensure the accuracy of asymmetry systems such as laser triangulation ranging systems. However, the system parameter determination often depends on the experience and manual attempts of designers, which is not only time-consuming but also inevitable to introduce human errors. Therefore, in this paper an automatic optimization design method based on nonlinear programming genetic algorithm with elitism strategy (E-NPGA) is proposed, to accurately and fast determine the optimal system parameters of laser triangulation ranging systems assisting in improving the measurement accuracy. Firstly, an optimization model of system parameters is developed under the Scheimpflug rule establishing the constraints for various measurement resolutions and ranges. An image size constraint is constructed for the first time to improve and evaluate the parameter optimization. Secondly, the E-NPGA is proposed with nonlinear optimization and elitism strategy, which can determine the optimal system parameters in 15 iterations avoiding local extremum. In design examples, using the E-NPGA determined system parameters ZEMAX simulation and experimental results of the parameters depended image spot size show a slight relative difference below 0.6%. Moreover, the experiment results demonstrate the sensor system designed by using the E-NPGA enables a distance measurement with submicron absolute error and 10−4 relative uncertainty. The automatic optimization method proposed in this paper is compared with the conventional GA method and PSO method, and it is validated that the convergence accuracy of the proposed method is much higher than the conventional ones.

SELECTION OF A MRP SYSTEM FOR OIL AND GAS DEPOSITS

A number of serious problems arise in the development of sub-gas zones of oil and gas deposits due to the joint occurrence of oil and gas. The key problem is gas breakthrough from the gas cap to oil wells. This entails an increase in the gas factor (GOR), a rapid and intense decrease in pressure in the gas cap. If there is a reservoir pressure maintenance system or an active aquifer as a result of a decrease in reservoir pressure in the gas cap, oil may be pushed back into it, which leads to a rise in gas-oil contact, jamming of mobile oil in the «dry sandstones» of the gas cap, which in turn leads to a decrease in reserves of mobile oil and a decrease in the oil recovery factor (ORF). All this together allows us to consider oil in the sub-gas zones as hard-to-recover reserves.Preventing and combating the negative consequences of gas breakthrough from the gas cap to oil wells is a complex task and consists of several areas, such as selection of rational drilling of production and injection wells, selection of optimal operating modes for them, use of inflow control devices or performance of repair and isolation work to limit inflow from intervals with gas breakthrough, periodic shutdown of wells to disband gas cones. The ultimate goal of solving these problems is to increase the oil recovery factor and economic efficiency indicators for the development of sub-gas zones.Due to the variety of ways to optimize the development of oil and gas reservoirs, it is not possible to cover them all in one work, therefore, the tasks associated with optimizing the reservoir pressure maintenance system are considered. It is worth noting that in comparison with the development of oil reservoirs, the presence of a gas cap significantly complicates this task due to the lack of analytical solutions for three-phase filtration. For this reason, the selection of the optimal parameters of the reservoir pressure maintenance system is usually carried out either by multivariate calculations in the hydrodynamic model (HDM), or by the method of analogies or the implementation of pilot tests of various approaches.In this study, an approach based on multivariate numerical calculations using the sector model of the development element of the gas zone was used. As a result of calculations, it was shown that when choosing the optimal parameters of the reservoir pressure maintenance system, it is advisable to use a differentiated approach, which consists in the fact that these parameters must be set individually for zones with different ratios of oil-saturated and gas-saturated thicknesses.

Periodic response and stability analysis of a bistable viscoelastic von Mises truss

This paper examines the effect of viscoelasticity on the periodic response of a lumped parameter viscoelastic von Mises truss. The viscoelastic system is described by a second-order equation that governs the mechanical motion coupled to a first-order equation that governs the time evolution of the viscoelastic forces. The viscoelastic force evolves at a much slower rate than the elastic oscillations in the system. This adds additional time scales and degrees of freedom to the system compared to its viscous counterparts. The focus of this study is on the system’s behavior under harmonic loading, which is expected to show both regular and chaotic dynamics for certain combinations of forcing frequency and amplitude. While the presence of chaos in this system has already been demonstrated, we shall concentrate only on the periodic solutions. The presence of the intrawell and interwell periodic oscillations is revealed using the Harmonic Balance method. The study also looks at the influence of parameter changes on the system’s behavior through bifurcation diagrams, which enable us to identify optimal system parameters for maximum energy dissipation. Lastly, we formulate an equivalent viscous system using an energy-based approach. We observe that a naive viscous model fails to capture the behavior accurately depending on the system and excitation parameters, as well as the type of excitation. This underscores the necessity to study the full-scale viscoelastic system.

Magnetic biochar enhanced microbial electrolysis cell with anaerobic digestion for complex organic matter degradation in landfill leachate

Combining microbial electrolytic cells with anaerobic digestion (MEC-AD) was considered as an important method for enhancing complex organic matter degradation. However, the magnetic biochar (MBC) addition would be an effective approach for enhancing biodegradation in MEC-AD. By designing orthogonal experiments, the optimal parameters of MBC-enhanced MEC-AD system for landfill leachate treatment were determined. The results indicated that the optimal conditions were identified as HRT of 72 h, electrode spacing of 2.5 cm, and applied voltage of 0.8 V. Under these conditions, the COD removal efficiency reached a maximum of 54.7 %. Additionally, the UV–vis, 3D-EEM, and GC–MS indicated the macromolecules 13-Docosenamide (Z), Bis(2-ethylhexyl) benzene-1,4-dicarboxylate and bis(2-ethylhexyl) phthalate were degraded. 13-Docosenamide (Z) was almost completely removed under the conditions of 0.8 V applied voltage, 2.5 cm electrode spacing and 24 h HRT, with a removal efficiency of 99.91 %. Significant differences were observed in the microbial core genera among the MEC-AD systems. The core genera in the anodic and cathodic biofilms were primarily fermentative and electroactive bacteria, including Soehngenia (2.2 % - 32.1 %, 3.2 % - 26.4 %) and Desulfomicrobium (1.1 % - 10.2 %, 2.0 % - 29.3 %). Fermentative bacteria, norank_f__Bacteroidetes_vadinHA17, established cooperative relationships with electroactive bacteria Acinetobacter. The enrichment of electrochemically active bacteria optimized microbial interactions, thereby synergistically enhancing the biotransformation of complex organic matter in landfill leachate.

Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior

Objective: This study aimed to develop a control architecture for reactive autonomous navigation of a mobile robot by integrating Deep Learning techniques and fuzzy behaviors based on traffic signal recognition. Materials: The research utilized transfer learning with the Inception V3 network as a base for training a neural network to identify traffic signals. The experiments were conducted using a Donkey-Car, an Ackermann-steering-type open-source mobile robot, with inherent computational limitations. Results: The implementation of the transfer learning technique yielded a satisfactory result, achieving a high accuracy of 96.2% in identifying traffic signals. However, challenges were encountered due to delays in frames per second (FPS) during testing tracks, attributed to the Raspberry Pi's limited computational capacity. Conclusions: By combining Deep Learning and fuzzy behaviors, the study demonstrated the effectiveness of the control architecture in enhancing the robot's autonomous navigation capabilities. The integration of pre-trained models and fuzzy logic provided adaptability and responsiveness to dynamic traffic scenarios. Future research could focus on optimizing system parameters and exploring applications in more complex environments to further advance autonomous robotics and artificial intelligence technologies.

New X-ray based characterization technique for aluminized explosives and effect of aluminum proportion on velocity of detonation

Explosive samples of aluminized compositions based on RDX/HMX-TNT have been investigated using digital radiography comprising a flat panel detector and an ultra-fine focus X-ray tube. Melt-cast aluminized compositions have been designed and constructed into cylindrical charges with a diameter ∼50 mm and height ∼150 mm. These charges have been evaluated for velocity of detonation using ionization pin method. Projection radiography has been used to gain information about the microstructure of solidified explosive compositions using optimized system parameters. The resolution of the image has been investigated using a duplex wire type image quality indicator having thirteen wire pairs. Within the explosive samples, the patches of aluminum have been seen evenly dispersed. The size of these patches of aluminium grows with increase in aluminium proportion at the expense of HMX. The maximum VOD value has been observed for the composition with highest HMX content. Value of VOD is found to decrease by ∼4 % if HMX is replaced by RDX, keeping TNT proportion at 30 %. VOD values are found to decrease with increase in proportion of aluminium at the cost of HMX.

Optimizing energy efficiency in multi-chiller systems: A comprehensive Modelica-based approach

To achieve sustainable development of buildings, this paper investigates the energy-saving potential of multi-chiller systems by examining the interactions between sequencing optimization, load optimization, and parameter optimization in systems with varying capacities. A Modelica-based simulation platform, adaptable to various programming languages optimization engines, was developed for this purpose. The study introduces a sequencing optimization threshold function that considers both wet-bulb temperature and cooling load. Additionally, a two-stage optimization strategy that prioritizes parameter adjustments before sequencing is proposed. Case studies demonstrate that this threshold function and reversed optimization order can further enhance overall energy savings. Focusing on a system with three large chillers and one small chiller, the study reveals the energy benefits of activating the small chiller under specific conditions, improving overall system efficiency. The proposed optimization achieved a cumulative energy saving of 650,463 kWh over the summer, with sequencing contributing 5.64 %. Frequency conversion control of the water pump is identified as a significant energy-saving factor. This research provides a comprehensive approach to optimizing diverse chiller systems, highlighting substantial potential for energy conservation.

OPTIMIZATION OF CYBER-PHYSICAL SYSTEM PARAMETERS BASED ON INTELLIGENT IOT SENSORS DATA

The optimization of cyber-physical systems (CPS) parameters is researched with focusing on the integration of computation, networking, and physical processes with the Internet of Things (IoT). The use of intelligent IoT sensors is crucial for collecting real-time data, which is necessary for enhancing the efficiency, reliability, and performance of CPS. Various methods of CPS parameters optimization are analyzed and categorized into model-based approaches, data-driven approaches, and hybrid approaches. The model-based approaches rely on mathematical models to describe CPS behavior and use optimization algorithms like linear programming and evolutionary algorithms to predict system responses and optimize parameters. But, the limitations of model-based approaches are related to complex systems with uncertain or dynamic behavior. The data-driven approaches are more suitable for complex cyber-physical systems. These approaches utilize machine learning and data analytics techniques to extract patterns from sensor data, which are then used to adjust system parameters. The hybrid approaches combine elements of both model-based and data-driven methods. The method of cyber-physical system parameters optimization based on intelligent IoT sensors data processing is developed with using of distributed neural network. The optimization problem is formulated with constraints for the system parameters. The neural network mathematical model and learning algorithm are proposed. The performed research shows the importance of developing optimization methods for CPS parameters based on intelligent IoT sensor data, considering the evolving nature of IoT technology. The integrating intelligent sensors into CPS offers new opportunities for optimizing system performance but also presents challenges in data management and security that should be addressed in future.

Enhancing stability via coordinated control of generators, wind farms, and energy storage: Optimizing system parameters

This study delves into the intricacies of power system stability, specifically addressing the challenges posed by integrating renewable energy sources, primarily focusing on wind power. The intricate dynamic interactions between Synchronous Generators (SGs), Doubly Fed Induction Generators (DFIGs), and Battery Energy Storage Systems (BESS) under varying operating conditions necessitate advanced control strategies for effective stabilization. Conventional control methods, such as Power System Stabilizers (PSS) and damping controllers, are scrutinized for their inherent sensitivity to system changes and limitations in mitigating the dynamic complexities arising from integrating Wind Farms (WFs) and BESS. This research accentuates the critical need for synchronized control mechanisms to preempt dynamic instability in power systems, considering factors like wind speed fluctuations, WF integration intricacies, and responses to severe disturbances. The introduction of DFIG-based WFs is particularly examined for its impact on altering system inertia, leading to Low-Frequency Oscillations (LFOs) and diminished damping, thereby heightening stability concerns. The proposed control strategies encompass the integration of BESS, PSS, and coordinated damping controllers meticulously tailored to counteract systemic instability. Recognizing the potential inadequacy of individual control methods during severe disturbances, the study underscores the imperative of synchronized operation between PSS and BESS damping controllers. Furthermore, the investigation sheds light on the pivotal role of uncertainties in power system stability, advocating for a paradigm shift from deterministic methods to more nuanced probabilistic approaches. The proposed coordinated probabilistic control designs, leveraging participation factors for SGs, DFIGs, and BESS, offer a sophisticated solution to mitigate uncertainties and fortify system stability across diverse conditions. A rigorous evaluation employing Real-Time Digital Simulator (RTDS) simulations on the IEEE 9-bus test system substantiates the practical efficacy of the proposed coordinated control approach. The discerned outcomes underscore discernible enhancements in both small signal and transient stability, affirming the practical viability and reliability of the advanced control strategies delineated in this study.