System Parameters Research Articles

Enhancing the power quality of dual rotor wind turbines using improved fuzzy space vector modulation and super twisting sliding techniques

In the field of control, many approaches have been used to control generators, where indirect vector control (IVC) is considered one of the most prominent of these approaches due to its many advantages. This approach has a fast response time (RT) and is quite easy to realize. However, its reliance on traditional controllers makes this approach less efficient and less effective if the system parameters change. Consequently, this work proposes a new IVC approach for doubly-fed induction generators (DFIG) used in contra-rotating wind turbine (CRWT) systems. The designed IVC employs a super-twisting control to eliminate the instantaneous errors of the DFIG power using the direct calculation of the control voltage required by the rotor, which will lead to the improvement of the transient performance. In addition, a constant switching frequency is obtained using the multilevel fuzzy modified space vector modulation proposed for controlling the DFIG inverter, facilitating the design of harmonic AC filters. To evaluate the proposed solution, a digital simulation of the CRWT system was verified using MATLAB with the power of the used generator being 1.5 MW. For more accuracy, two tests were used to study the efficiency of the suggested control compared to the efficiency of the IVC in terms of getting better system features. The obtained results showed the efficacy of the designed control compared to the IVC and some of the different existing techniques in terms of enhancing system features. The suggested approach minimized torque fluctuations, active power, and current in the first test compared to the IVC approach by ratios estimated at 93%, 97%, and 98%, respectively. Also, the RT for reactive power, active power, and torque was reduced by 99.05%, 98.60%, and 98.60%, respectively, compared to the conventional IVC approach. In both tests, the designed approach minimized the harmonic distortion of the stream by ratios estimated at 18.02% and 16.22% compared to the conventional IVC approach. These obtained results were verified using empirical work, where hardware-in-the-loop simulation was used for this purpose. Accordingly, the empirical results demonstrated the validity, durability, and competence of the designed approach compared to the base IVC approach. Both the simulative and empirical results validate that the designed approach is of great importance in the field of control and renewable powers, as it can be relied upon to enhance the features of the systems. Therefore, the designed control has a promising future.

A Critical Analysis of The Application of Artificial Neural Network (ANN) in the Field of DC to DC Converters.

Because of the fast advancement of renewable energy technologies, the idea of microgrid (MG) is becoming more commonly adopted in power systems. DC MG is gaining popularity due to the benefits of the DC distribution system, including as easier integration of energy storage and lower system loss. The linear controller, such as PI or PID, is established and widely used in the power electronics sector, but its performance degrades as system parameters change. In this paper, an artificial neural network (ANN)-based voltage control technique for a DC- DC boost converter is developed. The model predictive control (MPC) is employed as an expert in this study, providing data to train the suggested ANN. Because ANN is highly adjusted, it is used directly to regulate the step-up DC converter. The key benefit of the ANN is that it reduces the inaccuracy of the system model even with erroneous parameters and has a lower computing overhead than MPC owing to its parallel nature. Extensive MATLAB/Simulink simulations are run to validate the performance of the proposed ANN. The simulation findings reveal that the ANN-based control method outperforms the PI controller under various loading situations. The trained ANN model has an accuracy of roughly 97%, making it acceptable for DC microgrid applications.. Index Terms—ANN, DC Microgrid, DC/DC boost converter, MPC, Primary control.

Evaluation of the effectiveness of a corrective and rehabilitation program using gravity postisometric relaxation exercises, self-massage and myofascial release on the biogeometric profile of posture and cardiovascular system parameters of women of the second period of mature age

urpose. To scientifically substantiate, develop and experimentally verify the impact of a correctional and rehabilitation program using gravitational postisometric relaxation exercises, self-massage and myofascial release on the state of the biogeometric profile of posture and parameters of the cardiovascular system of women in the second period of adulthood. Material & Methods. The ‘School of Women's Health’ was organised. The total number of 79 people was women aged 36-45 years. The study involved two groups of women with complaints of chronic pain in the cervical spine and fluctuations in the parameters of the cardiovascular system: A group of women aged 36-40 years (n=16) and a group of women aged 41-45 years (n=15). Study procedure: heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured. Posture examination was performed in the frontal and sagittal planes using the REEDCO posture scale. Measurement of the depth of physiological cervical bend (cervical point) was performed according to the method of Z.P. Kovalkova. With correct posture, the depth of the cervical spine curve is within 5-5.5 cm. Statistical data processing was performed using the statistical package STATISTICA 13.0 (StatSoft). Results. A correctional and rehabilitation programme was developed for the subjects of the ‘School of Women's Health’, which consisted of three blocks: means of physical culture and sports rehabilitation, psychocorrection, and educational part. In the first block, it was proposed to perform isometric gymnastic exercises aimed at relaxing and strengthening the neck muscles; myofascial release using a massage roll. The aim of the educational component of the programme was to teach women how to perform self-massage of the neck on their own. The research and work on the correctional and rehabilitation programme lasted 3 months. The women spent 21 days in the School of Women's Health under the supervision of a rehabilitation specialist. Subsequently, the women worked independently under the programme, keeping in touch with the rehabilitation specialist through online communication in Viber, WhatsApp and Telegram messengers. During the initial communication, all the women complained of recurrent headaches, dizziness, palpitations, and high blood pressure. The results of the formative experiment showed a significant improvement in all parameters of the cardiovascular system (p˂0.05). After the implementation of the correctional and rehabilitation programme, the scores on the REEDCO posture scale statistically significantly increased in women aged 36-40 years by 1.04 times (p<0.05), in women aged 41-45 years by 1.12 times (p<0.05). The introduction of myofascial release into the correctional and rehabilitation programme in women of the second period of mature age had a positive effect on changes in the cervical spine: the depth of spinal curvature according to the method of Z.P. Kovalkova in women aged 36-40 years statistically significantly increased by 1.12 times (p<0.05), in women aged 41-45 years by 1.29 times (p<0.05) Conclusions. Training in the ‘School of Women's Health’ according to the developed correctional and rehabilitation programme had a positive effect on the parameters of the cardiovascular system, namely, normalisation of heart rate, blood pressure, and blood pressure. The use of gravitational post-isometric relaxation, self-massage of the cervical spine, myofascial release significantly improved the biogeometric profile according to the REEDCO posture scale, positively influenced the change in the depth of spinal curvature according to the method of Z.P. Kovalkova.

COMPARATIVE EVALUATION OF DIFFERENT DESIGNS OF COMPRESSOR-EJECTOR UNIT FOR GAS PUMPING DURING REPAIR WORKS ON MAIN GAS PIPELINES

The article describes the problem of resource saving in the process of mainline transport on the example of rational use of process gas, contained in the section switched off before repairs. At present, according to normative and technical documentation, in such cases the section is discharged from process gas by blowing through gas vents to the atmosphere. In order to save resources and reduce environmental impact it is offered to pump gas from the isolated section to the neighbouring pipeline in the technological corridor. The article presents different designs of the compressor-ejector unit and calculates its operation modes for emptying a section of the main gas pipeline in the technological corridor of two parallel gas pipelines with a length of 125 km each and operating pressure of 7.5 MPa. In the presented scenarios the compressor location relative to the ejector was changed: on the active gas line, on the pumped gas line, on the ejector outlet. Parameters of system «plant – gas pipelines» operation were calculated in hydrodynamic simulator with creating a separate model for each design of the plant. The character of changes in parameters during gas transportation along the technological corridor in case of shutdown of one 25 km long section on one of the parallel gas pipelines and gas extraction by the compressor-ejector unit using technological gas from the section before the shutdown is shown. The dependence curve of pumped gas flow rates and pressures vs. time in the given designs of the compressor-ejector unit is presented. The optimal compressor location is determined in view of maximizing the evacuated gas volume and, accordingly, minimizing the evacuation time.

Economic assessment of intellectual and innovative technologies in the context of sustainable development

The object of research is the processes of economic evaluation of intellectual and innovative technologies in the context of sustainable development. The problem of economic evaluation of such technologies is caused by the insufficiency of the current methodological framework, which is substantiated with the use of the VosViewer bibliometric toolkit. The paper identifies the need to implement intellectual and innovative technologies aimed at achieving the goals of sustainable development, as well as their projects, using the example of the Golden Ducat Jewellery Company (Ukraine). To substantiate the choice of intellectual and innovative technologies, a methodological approach has been developed based on a system of parameters for expert assessment of the level of intellectualisation of technologies (autonomy, adaptability, machine learning, interactivity, integration). The author's approach is proposed to be implemented with the help of a mathematical model based on matrices, using the methods of fuzzy set theory. The practical results of applying the model have shown that the most intellectualised technology is the interactive jewellery design studio, which corresponds to the system of parameters by 73.43 %. Other technologies have lower degrees of compliance: 3D visualisation and virtual fitting – 47.16 %, integration of online platforms – 40.50 %, interactive learning services – 34.09 %, and eco-design of products – 23.35 %. The article compares the parameters of intellectualisation of technologies with the expected economic performance of a company due to the implementation of these technologies. It is found that the parameters of ‘autonomy’ and ‘machine learning’ have the greatest impact on the level of intellectualisation. Accordingly, the most effective technologies are an interactive jewellery design studio (ROI – 64.19 %, payback period – 7.3 months) and eco-design of products (ROI – 69.47 %, payback period – 7.2 months). It is found that for a reasonable choice of intellectual and innovative technologies it is important to take into account factors such as market demand, long-term trends and business specifics.

Analysis Modulation Instability and Parametric Effect on Soliton Solutions for M-Fractional Landau–Ginzburg–Higgs (LGH) Equation Through Two Analytic Methods

This manuscript studies the M-fractional Landau–Ginzburg–Higgs (M-fLGH) equation in comprehending superconductivity and drift cyclotron waves in radially inhomogeneous plasmas, especially for coherent ion cyclotron wave propagation, aiming to explore the soliton solutions, the parameter’s effect, and modulation instability. Here, we propose a novel approach, namely a newly improved Kudryashov’s method that integrates the combination of the unified method with the generalized Kudryashov’s method. By employing the modified F-expansion and the newly improved Kudryashov’s method, we investigate the soliton wave solutions for the M-fLGH model. The solutions are in trigonometric, rational, exponential, and hyperbolic forms. We present the effect of system parameters and fractional parameters. For special values of free parameters, we derive some novel phenomena such as kink wave, anti-kink wave, periodic lump wave with soliton, interaction of kink and periodic lump wave, interaction of anti-kink and periodic wave, periodic wave, solitonic wave, multi-lump wave in periodic form, and so on. The modulation instability criterion assesses the conditions that dictate the stability or instability of soliton solutions, highlighting the interplay between fractional order and system parameters. This study advances the theoretical understanding of fractional LGH models and provides valuable insights into practical applications in plasma physics, optical communication, and fluid dynamics.

A deep learning approach for predicting and optimizing the V2X network parameters for sustainable smart transportation systems

Abstract The increasing prevalence of automated vehicles necessitates robust vehicle-to-everything (V2X) communication systems. However, challenges such as latency, reliability, transmission rates, data collisions, and security vulnerabilities require thorough investigation. To address these issues, this study proposes a deep learning-based hybrid model integrating long short-term memory (LSTM) neural networks and generative adversarial networks (GANs) to predict performance metrics in V2X communication networks. The GANs augment the sample vehicular dataset and facilitate proactive vehicular network management. The proposed approach comprises three key phases: data augmentation, feature extraction, and predictive analysis. To optimize the neural parameters, the whale optimization algorithm (WOA) is utilized and benchmarked against other optimization algorithms, including beetle swarm optimization, artificial bee colony optimization, and pelican optimization. The parameters in the developed hybrid GAN-LSTM model and autoencoder were tuned using the WO algorithm to minimize key performance metrics such as the mean absolute error, root mean square error, bit error rate, and mean square error. Results indicate that the predictive model enhances the sustainability and efficiency of transportation systems by accurately forecasting critical metrics, including latency, throughput, and reliability, within V2X networks.

Numerical Simulation of the Hybrid Precoding Algorithm in Millimeter Wave Band Using the Open Source Channel Model

Relevance. The use of multi-antenna technologies in the form of signal precoding is a basic condition for increasing spectral efficiency in modern mobile communication systems in combination with the transition to the millimeter wave frequency band. Propagation conditions in the millimeter wave require the use of antenna arrays in communication systems to compensate for propagation losses and directional transmission and reception of user signals. When transmitting multiple parallel spatial user data streams, signal precoding is used to implement spatial multiplexing and improve the spectral efficiency of the system. A hybrid architecture for constructing a multi-antenna system and precoding, consisting of analog and digital parts, is considered. But reducing the number of radio frequency paths leads to a decrease in the possibility of spatial multiplexing compared to a completely digital system. In this regard, it is important to select the optimal number of radio frequency paths to obtain maximum spatial multiplexing, taking into account the current conditions of signal propagation and spatial correlation of the communication channel. The purpose of the study is to determine the effect on the spectral efficiency of the choice of the number of used radio frequency paths in a hybrid precoding system.The research methods consist of simulation modeling of a hybrid precoding algorithm. To solve the problem of numerical modeling of hybrid precoding, implementations of a MIMO channel of millimeter waves are used, obtained using the open channel model software package QuaDRiGa.The results are presented in the form of distribution functions of the spectral efficiency of the hybrid precoding system, obtained on the basis of channel implementations in a certain propagation scenario.The novelty lies in the numerical determination of the channel parameters of a multi-antenna communication system in the millimeter range and the use of the distribution of eigenvalues of the obtained channel implementations to study the influence of the number of radio frequency paths on the spectral efficiency of a communication system with hybrid precoding achievable under given conditions.

The Application of Transformers with High-Temperature Superconducting Windings Considering the Skin Effect in Mobile Power Supply Systems

The active and passive components of transformer electrical equipment have reached their limits regarding modernization and optimization, leading to the implementation of innovative approaches. This is particularly relevant for mobile and autonomous energy complexes due to the introduction of increased frequency, which can be advantageous, especially in geoengineering, where the energy efficiency of electrical equipment is crucial. The new design of transformer equipment utilizing cryogenic technologies incorporates high-temperature superconducting (HTS) windings, a dielectric filler made of liquid nitrogen, and a three-dimensional magnetic system based on amorphous alloys. The finite element method showed that the skin effect does not impact HTS windings compared to conventional designs when the frequency increases. The analysis and synthesis of the parameters of the magnetic system made from amorphous iron and HTS windings in an HTS transformer with a dielectric medium of liquid nitrogen at a temperature of 77 K were performed, significantly reducing the mass and size characteristics of the HTS transformer compared to traditional counterparts while eliminating environmental and fire hazards. Based on these studies, an experimental prototype of an industrial HTS transformer with a capacity of 25 kVA was designed and manufactured.

Robust model predictive control framework for multi-motor driving cutterhead system with model errors in hard rock TBMs

Tunnel boring machine (TBM) is a safe and effective equipment for excavating tunnels. The advance control of the driving cutterhead system plays an important role in hard rock TBM excavation. This work presents a robust model predictive control (MPC) for optimizing the torques of driving motors. First, a model of TBM cutterhead system is established with state-space representation subject to constraints and additional disturbances. Based on real operating data, the parameters of cutterhead system are identified by using the prediction error method. To address the robustness issue, an output disturbance model is constructed. The system states are estimated for the state feedback control design by using Kalman filter. Based on the estimated state, a robust MPC is designed by presenting a compensation strategy and the constraints are considered together. Extensive simulations based on the identified system are given to test the performance under tracking control and disturbance rejection of cutterhead system.

Neutral atom quantum computers for the applications in condensed matter physics

Abstract Quantum computers have the potential to solve problems that are difficult or impossible to address using classical modes of computation. Laser cooled neutral atoms at ultracold temperatures offer unique possibilities to study interacting many-body quantum systems which is at the heart of various quantum condensed matter phenomena. The first-generation neutral atom quantum computers for performing special purpose quantum computations was realized by trapping ultracold atoms in optical lattices. These tunable and scalable machines provided tremendous opportunities to study various quantum phases of Bose and Fermi Hubbard models, topological phases, and non-equilibrium dynamics, with control over key system parameters enabling insightful explorations within specific quantum models. In a more recent advent, arrays of single neutral atoms trapped in optical tweezers have emerged as dark horse candidate for universal and fault tolerant quantum computing. Here, we review recent advances and achievements obtained with this platform and discuss future perspectives.

Sufficient conditions for the asymptotic relaxation of Kuramoto ensemble in a potential field

We study the asymptotic relaxation of Kuramoto oscillators in an analytic potential field. The Kuramoto model describes the synchronization of weakly coupled limit-cycle oscillators and it casts as a gradient flow on the Euclidean space with an analytic potential. The Kuramoto model corresponds to our proposed model with a constant force field. As a nontrivial local potential field, we consider two types of local potential fields corresponding to quadratic and linear-plus-oscillatory potentials. For the asymptotic relaxation, we provide sufficient frameworks leading to the uniform boundedness of state for each local potential field. The proposed framework is given in terms of system parameters and initial data. Then, we use the gradient flow formulation and uniform boundedness of state to find the existence of equilibrium and convergence toward them, which implies the existence of asymptotic relaxation.

Harnessing quantum chaos in spin-boson models for all-purpose quantum-enhanced sensing

Many-body quantum chaos has immense potential as a tool to accelerate the preparation of entangled states and overcome challenges due to decoherence and technical noise. Here, we study how chaos in the paradigmatic Dicke model, which describes the uniform coupling of an ensemble of qubits to a common bosonic mode, can enable the rapid generation of non-Gaussian entangled spin-boson states without fine-tuning of system parameters or initial conditions. However, the complexity of these states means that unlocking their utility for quantum-enhanced sensing with standard protocols would require the measurement of complex or typically inaccessible observables. To address this challenge, we develop a sensing scheme based on interaction-based readout that enables us to implement near-optimal quantum-enhanced metrology of global spin rotations or bosonic displacements using only spin measurements. We show that our approach is robust to technical noise and imperfections and thus opens new opportunities to exploit complex entangled states generated by chaotic dynamics in current quantum science platforms such as trapped-ion and cavity-QED experiments. Published by the American Physical Society 2025

Analysis of wavelet transform application in aircraft longitudinal motion identification

This study investigates the application of wavelet transforms in identifying aircraft longitudinal motion dynamics. The Output Error Method, enhanced with wavelet transforms, specifically Haar wavelets at varying decomposition levels, was used to estimate system parameters. The impact of filtering wavelet coefficients and the use of a coherence function in determining parameter changes were analyzed to assess their effect on model identification accuracy. The identification process involved estimating the parameters of a linear model by fitting its responses to those of a nonlinear system, which were noise-perturbed and treated as measurement data. The results demonstrate the successful application of wavelet transforms in system identification for flying objects, with recommendations for further optimization.

A unified criterion for semiglobal and global finite/fixed-time stability of stochastic systems

In this paper, semiglobal and global finite/fixed-time stability of a class of semilinear stochastic systems in Hilbert spaces are studied. By employing the stopping time technique and applying the infinite-dimensional Itô formula, a unified Lyapunov criterion is established under which global finite-time, global fixed-time, semiglobal finite-time and semiglobal fixed-time stability can all be obtained. This fills the research gaps in the finite/fixed-time stability of stochastic distributed parameter systems and the semiglobal finite/fixed-time stability of stochastic systems. Moreover, compared to existing results on finite/fixed-time stability for stochastic systems, our Lyapunov criterion can still work well under more stringent conditions. This is done by adding a term which can reflect the stabilising effect of the diffusion term and replacing original fractional power functions with a class of piecewise continuous fractional power functions that are no longer required to be increasing. Finally, illustrative examples are given and the obtained theoretical results are verified by numerical simulations.

Evaluation of health monitoring parameters for automotive pneumatic elastomeric system using soft pressure sensors for enhanced vehicle performance

Abstract In the rapidly evolving automotive industry, the need for reliable and efficient pneumatic elastomeric components necessitates cutting-edge health monitoring methods, given that the pneumatic components are directly connected to dampening properties, ride comfort, vehicle safety, and stability. A novel approach for pneumatic elastomeric component health monitoring utilizing ionic liquid-based soft sensor technology has been proposed, which has the promise to enable real-time health monitoring and prognostics of vehicle systems. The proposed polymer sensor leverages the distinctive characteristics of flexibility, stretchability, and high sensitivity. These properties are critical for precisely measuring load, vertical displacement, air pressure, force locations, and load frequency of the air pressure responsive rubber part. The sensors are attached to three key contact positions: two on the metal cover of the elastomeric component and one on the piston of the component to provide critical information about the rolled and unrolled rubber. These sensors continuously measure essential parameters for health monitoring. The collected data can identify potential issues such as leaks, wear, or structural weaknesses. The finding illustrates the effectiveness of the ionic liquid-based soft sensors in providing precise and reliable data for health monitoring parameters.

Investigating Pulsating Variables and Eclipsing Binaries in NGC 2126 using Ground and Space-based Photometry, Astrometry, Spectroscopy and Modeling

Abstract Pulsating variables are prevalent in the classical δ Scuti instability strip of intermediate-age open star clusters. The cluster membership of these stars facilitates a comparative analysis of their evolution in analogous environments. In this study, we integrate ground-based observations, TESS Full Frame Images (FFIs), and Gaia DR3 data to investigate variable stars in the intermediate-age open star cluster NGC 2126. We performed ground-based time-series observations of NGC 2126 to identify variable stars within its vicinity. Next, we determined the membership of these stars using parallax and the proper motions from Gaia DR3 archive. Then, we searched the TESS Full Frame Images (FFIs) for counterparts to the variables identified above and performed their frequency analysis and classification. Finally, we modeled the light curves (LCs) of detected eclipsing binaries (EBs), including V551 Aur, which has a pulsating component. We found 25 members and 85 field variable stars. In TESS FFIs, we found LCs for 11 known variables and a new rotational variable. We determined that the pulsating EB V551 Aur is a member of the cluster. The low- and medium-resolution spectra revealed the line profile variation and the basic parameters for the star, respectively. Simultaneous modeling of the eclipses and the embedded pulsations resulted in improved orbital parameters for the binary system. We also report the determination of orbital parameters for the previously uncharacterized EB system UCAC4 700-043174.

Research on Real-Time Operation Optimization of the Urban Rail Traction Power Supply System Integrated Storage and Feedback

Urban rail transit plays an important role in the rapid economic and social development of China. The integrated storage and feedback urban rail traction power supply system is one of the strategies for the green and low-carbon development of urban rail transit. This paper establishes a real-time operation optimization model based on the Markov decision process in the context of the integrated storage and feedback urban rail traction power supply system. It uses offline training of a deep reinforcement learning agent to optimize the control parameters of the energy feedback systems and energy storage systems in real-time to reduce the traction energy consumption of the power supply system, providing a reference for energy-saving operation of subways.

A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems

This paper presents an autonomous perfectly secure low-bit-rate voice communication system (APS-VCS) based on the mixed-excitation linear prediction voice coder (MELPe), Vernam cipher, and sequential key distillation (SKD) protocol by public discussion. An authenticated public channel can be selected in a wide range, from internet connections to specially leased radio channels. We found the source of common randomness between the locally synthesized speech signal at the transmitter and the reconstructed speech signal at the receiver side. To avoid information leakage about open input speech, the SKD protocol is not executed on the actual transmitted speech signal but on artificially synthesized speech obtained by random selection of the linear spectral pairs (LSP) parameters of the speech production model. Experimental verification of the proposed system was performed on the Vlatacom Personal Crypto Platform for Voice encryption (vPCP-V). Empirical measurements show that with an adequate selection of system parameters for voice transmission of 1.2 kb/s, a secret key rate (KR) of up to 8.8 kb/s can be achieved, with a negligible leakage rate (LR) and bit error rate (BER) of order 10−3 for various communications channels, including GSM 3G and GSM VoLTE networks. At the same time, by ensuring perfect secrecy within symmetric encryption systems, it further highlights the importance of the symmetry principle in the field of information-theoretic security. To our knowledge, this is the first autonomous, perfectly secret system for low-bit-rate voice communication that does not require explicit prior generation and distribution of secret keys.

Quantifying the Impact of Electric Vehicles and Distributed Energy Resources on Load Profile Sensitivity: A Comprehensive Analysis

<div>This research investigates how distributed energy resources (DERs) and electric vehicles (EVs) affect distribution networks. With sensitivity analysis, the research focuses on how these integrations affect load profiles. The research focuses on sizing of various DERs and EV charging/discharging strategies to optimize the load profile, voltage stability, and network loss minimization. System parameters including load profile, EV charging pattern, weather conditions, DER sizes, and electricity pricing are analyzed to quantify their individual and combined impacts on load variability. However, with increased capacity of DERs, network losses increase. A mathematical model with system and operational constraints has been developed and simulated in MATLAB Simulink environment, validation of the proposed approach in improving the load profile, and reduction in network losses, with the intermittent power generation from DERs and EV integration. Simulation result shows that optimal capacity of DERs and optimized EV integration improve the load profile, improve voltage regulation at various nodes across the distribution network and reduction in network losses considerable.</div>