Standard Test System Research Articles

Digital twin-based probabilistic prediction of microcrack initiation and propagation in the weld zone of orthotropic steel deck

Orthotropic steel deck (OSD) has become the primary bridge deck structure for steel bridges, but it is vulnerable to fatigue cracks in the weld zone of OSD under traffic loading. This study proposes a digital twin (DT)-based framework to give the probabilistic prediction of microcrack initiation and propagation in the weld zone of OSD. The DT-based framework couples crystal plasticity finite element model (CPFEM) simulations with fatigue tests through Dynamic Bayesian Network (DBN). The fatigue tests of the specimen (physical entity) cut from the weld zone of OSD and sliced to the centimeter scale are carried out using the standard dynamic test system together with optical microscope. The CPFEM simulations are used to develop a multiscale virtual entity to map the physical entity. The role of the DBN is to integrate the uncertainties into a hierarchical network. After completing the updating of uncertainties in the virtual entity via DBN, the microcrack propagation is predicted probabilistically by the DBN inference, and the microcrack initiation is predicted probabilistically by the inverse DBN inference. The results demonstrate the feasibility and accuracy of the DT-based framework for a full prediction of microcrack initiation and propagation in the weld zone of OSD.

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

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

Security constrained economic dispatch with VSC-HVDC connected wind farms

The rise in wind power in electrical networks creates complex challenges for transmission system operators (TSOs), particularly regarding real-time operation. TSOs rely on grid codes to enforce wind farm (WF) participation in primary frequency control (PFC), like conventional plants. This paper addresses this need by proposing a security constrained economic dispatch (SCED) model with VSC-HVDC connected WFs based on variable speed wind generators. A key differentiator from existing models is the explicit calculation of the spinning reserve and the droop parameter of the WFs. This method uses the deloading schemes related to blade pitch angle and mechanical rotor speed to ensure compliance with grid codes related to PFC. We show that the wind speed and these deloading schemes significantly impact the WF droop parameter. For efficient studies, linear shift factors are used to model the transmission grid and the VSC-HVDC connected WFs. The performance of the SCED model was assessed using two standard test systems that incorporate WFs connected by VSC-HVDC lines. Its effectiveness was confirmed by comparing it with the security constrained optimal power flow (SCOPF). The results showed agreement between both models, with differences less than 3 % and with the SCED saving about 70 % of the computing time.

Robust optimization of transactive flexibility oriented scheduling of joint electricity and gas supplies

The power system with flexibility provision will lead to the expansion of the utilization of renewable energy resources. Providing flexibility is one of the basic challenges for power system operators and developers, which is addressed in this study. Moreover, the security of the gas supply system is important for power system operators, because the operation of gas-fired units depends significantly on the gas supply. In this study, the availability of pipelines has been considered as a gas supply uncertainty. Then, an optimal uncertainty set is proposed for security constraint unit commitment (SCUC) which is calculated for managing the uncertainty of wind and gas supply in different scenarios of risk. Robust-based optimization has been implemented to obtain the optimal uncertainty set in which both feasibility and economic optimal robustness have been guaranteed. A new robust flexibility-based SCUC model has been applied using a demand response program (RFSCUCDRP). The transactive flexibility between flexible providers, such as thermal units and DR, and uncertainty producers, such as wind resources, has been determined in the proposed model. The problem has been implemented on the 6-bus and IEEE-118-bus standard test systems with different risk percentages. The operation costs of the 6-bus system with and without robust optimization are 89,094 $ and 119,475 $, respectively. It is concluded that robust optimization is ultimately economically better.

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart Grid involves application of Information and Communication Technology (ICT) for monitoring, protection, operation, and control of interconnected power systems under various scenarios. Smart Grid Control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats, and attacks under central and distributed control architectures. Cyber Security of smart grid control, especially, Load Frequency Control (LFC) is an important issue that is addressed in this article. The state-of-the-art in cyber security and attacks on smart grid control and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic Vulnerability Assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for Vulnerability Assessment of the standard 39-bus New England test system and the 96 bus reliability test system to illustrate the concept of cyber security and Vulnerability Assessment of smart grid LFC.

Mixed Integer Nonlinear Programming-Based Unit Commitment of Conventional Thermal Generators Using Hybrid Evolutionary Algorithms

Unit Commitment (UC) discusses the optimized generation resources (to turn on economical generators and turn off expensive generators),which are subjected to satisfy all the operational constraints. The operational constraints such as load balancing, security maximization, minimum up and down time, spinning reserve, and ramp up and down constraints are difficult to satisfy. Although, UC is a cost minimization problem that is realized by committing less expensive units while satisfying the corresponding constraints, and dispatching the committed units economically. The UC problem is an np-hard Mixed Integer Nonlinear Problem (MINLP). Therefore, in this paper, hybrid EA based on a Genetic Algorithm (GA) has been applied to find the optimal solution to the UC problem. Moreover, during the search process, it is very difficult to discard infeasible solutions in EAs. Hence, the Genetic Algorithm (GA) is integrated with the feasibility rule constraint handling technique to emphasize feasible solutions. IEEE RTS Eleven Thermal Generator Standard Test system is used to validate the performance of proposed methods. For the validation and the superiority of the proposed algorithm, simulation results are compared with the classical Lagrangian Relaxation (LR) methods. Results show that the proposed method can find the global optimal solution to the UC problem which is subjected to satisfy all the operational constraints.

Techno‐economic assessment of distribution system considering different types of electric vehicles and distributed generators

AbstractThe global warming crisis, together with environmental concerns, has already led governments to replace traditional fossil‐fuel cars with low‐emission Electric Vehicles (EVs). This replacement has resulted in the addition of a large number of EVs capable of connecting to the grid. A coordinated charging method is needed to promote EVs on a large scale while preventing grid overloading. In this work, EVs are categorized based on their battery capacity, as cars, trucks, and buses. A charging/discharging mechanism based on Real Time Pricing for each hour is developed for a 20‐min block that has been formed. The locations of EV charging stations have been identified using Voltage Stability Index.In the present work, the power loss when Distributed Generator (DG) units are deployed after the EVs are located in the system for charging and discharging is analyzed. The DGs are considered as solar‐based or wind‐based generators for studying the economic benefit of such an investment under two different weather scenarios and topologies on standard test systems. The results thus obtained give an insight into the various technical and economic benefits.

Accurate computing paradigm for decoupled harmonic load flow analysis with CONOPT solvers for non-sinusoidal radial distribution systems

In this article, a novel method for performing Decoupled Harmonic Load Flow (DHLF) analysis in radial distribution systems is presented. The proposed method involves solving load flow equations for both fundamental and higher frequencies as constrained minimization problems utilizing a nonlinear programming (NLP) CONOPT solver integrated into the General Algebraic Modeling Systems (GAMS) environment. The effectiveness of the proposed method is assessed across three IEEE test systems: 18-bus, 33-bus, and 69-bus. Five cases of DHLF analysis are taken into consideration. DHLF was performed on standard test systems with incorporated non-linear loads in the first case. Cases two through five examine the DHLF in the presence of distributed generators (DGs), shunt capacitors (SCs), simultaneous DGs and SCs, and electric vehicles (EVs). Comparing the obtained results to those of DHLF analysis tools ETAP and DigSilent, as well as to those found in the literature, proved that the proposed CONOPT solver provides precise solutions.

Associations of cartilage intermediate layer protein 1 and hypoxia-inducible factor-1-alpha with transthoracic echocardiography results in patients with heart failure with preserved ejection fraction

Aim. To evaluate the relationship of serum concentrations of myocardial remodeling and cellular hypoxia biomarkers cartilage intermediate layer protein 1 (CILP-1) and hypoxia-inducible factor-1-alpha (HIF-1α) with paraclinical parameters in patients with heart failure with preserved ejection fraction (HFpEF) and in the control group.Material and methods. The study included 47 patients diagnosed with HFpEF, aged from 47 to 79 years, who were treated from May 2018 to December 2019 in the hospital of the National Medical Research Center for Therapy and Preventive Medicine. The control group consisted of 32 people without a diagnosis of HFpEF, matched by sex and age. All participants underwent transthoracic echocardiography with assessment of diastolic function. Serum concentrations of CILP-1 and HIF-1α were determined by enzyme immunoassay using standardized test systems (RayBio and Clone-Cloud, USA).Results. In patients with HFpEF, the median serum concentrations of CILP-1 (3,24 ng/ml) and HIF-1α (14,3 pg/ml) were not significantly different from the values obtained in the control group (3,6 ng/ml and 7,5 pg/ml, respectively). Significant correlations of CILP-1 with echocardiographic indicators of the left ventricular interstitial fibrosis severity were revealed, while echocardiographic markers of HFpEF positively correlated with the HIF-1α level.Conclusion. Although the serum concentrations of CILP-1 and HIF-1α do not differ depending on HFpEF presence, it demonstrates an association with a number of echocardiographic parameters both in subgroups of patients with HFpEF and in subgroups of controls with different body mass index.

FEATURES OF THE METABOLISM OF SIALOGLYCOPROTEINS IN THE TISSUES OF THE LIVER, STOMACH AND SMALL INTESTINE OF RATS UNDER IMMOBILIZATION STRESS

Introduction. Immobilization stress leads to the intensification of free-radical reactions leading to the destruction of sialoglycoproteins of the gastroduodenal zone. The aim of the study. To evaluate the features of the metabolism of sialoglycoproteins in the tissues of the liver, stomach and small intestine of rats under immobilization stress. Methods. Total sialic acids (TSA) and levels of sialidase activity (SA) were determined in the serum, liver, stomach, and small intestine tissues of rats by standard testing systems in dynamics on the 21st, 35th, 60th and 90th days of observation. Results. The blood serum of laboratory rats showed a maximum increase in total sialic acids and sialidases by 35 days of stress exposure, while in the liver, on the contrary, a decrease in the above-mentioned indicators. In the mucous overlays of the stomach and small intestine, the number of TSA increased throughout the dynamics of observation, and the activity of sialidase decreased by day 21 and increased in the following days of the experiment. In the wall of the stomach and small intestine of experimental rodents, the level of TSA increased by the 35th day of observation. Conclusion. During immobilization stress in the exchange of sialoglycoproteins, a compensatory predominance of anabolic processes in liver tissues was noted against the background of the prevalence of catabolism in the mucosal layer of the stomach and small intestine on days 21, 35 of observation, changes in the walls of the above organs are less pronounced and manifest themselves at a later date.

A novel solution to optimal power flow problems using composite differential evolution integrating effective constrained handling techniques

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm.

Reconfiguration of low-voltage distributed power sources within electric power's distribution network based on improved particle swarm-fish swarm fusibility algorithm

With the development of distributed power sources in the distribution network, the algorithm of distribution network reconfiguration is gaining attention from experts and scholars. Its goal is to reduce the power loss during power transmission, so as to reduce the power grid loss during power transmission. And weaken the electric heating effect in the process of electric energy transmission, thus maintaining the safety of the surrounding residents. Due to the wire impedance effect, a lot of electric energy of the circuit is lost to electric heating, which is easy to cause local overheating and lead to fire. This will not only cause power loss, but also endanger the safety of surrounding residents. To address the issue, experiments on distribution grid reconstruction are performed using the enhanced particle swarm-fish swarm algorithm with the Elecgrid self-constructed dataset. Initially, low-voltage distributed power sources in parallel are connected to the circuit, thereby decreasing internal resistance and electrical heat. Then, by controlling the circuit in the system, the double separation relay adjusts the inductance and capacitance of the conductor, thus reducing the reactance length. Additionally, particle swarm particles are mutated to enable them to jump out of the local optimum, and elite fish approach is used to expand the search area. Finally, the proposed fusion algorithm is applied to the self-built data set of Elecgrid and compared with the other three algorithms. The fusion algorithm serves as the standard test system for this comparison. The active power loss of the hybrid algorithm is 63 kW at an operating voltage of 0.74 V. The loss work of the other three algorithms is 74 kW, 97 kW and 109 kW respectively. The mixed algorithm has the lowest loss among the four algorithms. The experiments are repeated for six times, and the linear fitting degrees of the four algorithms are 0.9804, 0.9527, 0.9612 and 0.9503, respectively. The experimental results show that the application of this algorithm can effectively reduce the active loss in the process of distribution network reconfiguration, thus reducing energy consumption; At the same time, it can reduce the electric heating in the process of electric energy transmission, and then prevent the occurrence of fire. There are three main contributions of this study. Firstly, the resistance in the transmission path is reduced by using this algorithm, so that the power transmission efficiency can be analyzed more accurately. Secondly, the new algorithm enriches the power safety maintenance method; Finally, the fire caused by local overheating of the line is reduced by fusion algorithm.

Optimal stochastic power flow using enhanced multi-objective mayfly algorithm

For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2–4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results.

Evaluation of FACTS Contributions Using Branch Flow Model and Newton–Raphson Algorithm

Flexible alternating current transmission systems (FACTSs) have been widely incorporated in electric power systems in order to control system parameters. This paper proposes the modeling of four FACTS devices, using the Branch Flow Model (BF) as an optimization problem to reduce the complexity of the Newton–Raphson (NR) load flow code with FACTS devices. The devices are represented as variable impedances, as a function of a firing angle, and as voltage source converters (VSCs) located on the buses and transmission lines. This proposed model solves the problem associated with the selection of appropriate initial conditions of the parameters of each device that guarantee convergence. The model is validated by evaluating its percentage deviation with respect to the NR method, using the standard test systems, IEEE 5-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems.

An interferometric-based tensile tester to resolve damage events within reconstituted multi-filaments collagen bundles

Understanding how mechanical damage propagates in load-bearing tissues such as skin, tendons and ligaments, is key to developing regenerative medicine solutions for when these tissues fail. For collagenous tissues in particular, damage is typically assessed after mechanical testing using a broad range of microscopy techniques because standard tensile testing systems do not have the time and force sensitivity to resolve mechanical damage events. Here we introduce an interferometric detection scheme to measure the displacement of a cantilever with a resolution of 0.03% of full scale at a sampling rate of 5000 samples/s. The system is validated using collagen fibers engineered to mimic mammalian tendons. The system can detect sudden decrease in force due to slippage between collagen filaments, one to five microns in diameter, within a fiber in air. It can also detect yield events associated with local collagen unfolding or sliding within collagen fibrils within a fiber in liquid. This is opening the road to the sub-failure study of damage propagation within a broad range of hierarchical biomaterials.

Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.

Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management

This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pumped-storage hydroelectric units. Acknowledging the variability of solar and wind energy sources and the existence of a pumped-storage hydroelectric system, this study integrates a solar-wind-thermal energy system. The DSM program not only enhances power grid security but also lowers operational costs. The research addresses the DED problem with and without DSM implementation to analyze its impact. Demonstrating effectiveness on two test systems, the suggested method's efficacy is showcased. The recommended method's simulation results have been compared to those obtained using Cheetah Optimizer Algorithm (COA) and Grey Wolf Optimizer. The optimization results indicate that, for both the 10-unit and 20-unit systems, the proposed ECOA algorithm achieves savings of 0.24% and 0.43%, respectively, in operation costs when Dynamic Economic Dispatch is conducted with Demand-Side Management (DSM). This underscores the advantageous capability of DSM in minimizing costs and enhancing the economic efficiency of the power systems. Our ECOA has greater adaptability and reliability, making it a promising solution for addressing multi-objective energy management difficulties within microgrids, particularly when demand response mechanisms are incorporated. Furthermore, the suggested ECOA has the ability to elucidate the multi-objective dynamic optimal power flow problem in IEEE standard test systems, particularly when electric vehicles and renewable energy sources are integrated.

Hybrid Siting and Sizing of Distributed Generators and Shunt Capacitors with System Reconfiguration using Wild Horse Optimizer

Injection of Distributed generators (DGS) and Shunt capacitors (SCS) simultaneously with system reconfiguration significantly promotes smart grid performance. In addition, system reconfiguration increases the injected distributed generation capacity in the system. This work proposes a wild horse optimizer (WHO) for the optimal siting and sizing of DGS and SCS in parallel with network reconfiguration. The proposed method aims to attain single and multi-objectives: minimizing active power loss, maximizing voltage stability index (VSI), and minimizing voltage deviation index (VDI). Five operational cases are introduced to elucidate the superior performance of the proposed method. The five cases are executed on IEEE 33-bus standard radial distribution test system. The single-objective function results are compared with other optimization algorithms. The simulation results belay that the proposed WHO optimizer has the best results for unfixed DGS and SCS locations.

Evaluation of human mononuclear umbilical cord blood cells systemic administration efficiency in the acute period of experimental severe spinal cord injury

Aim. To evaluate the efficiency of systemic (intravenous) application of cryopreserved human umbilical cord blood mononuclear cells (HUCBCs) in animal models of acute contusion spinal cord injury for the restoration of hind limb motor function and formation of posttraumatic cysts using clinically significant examination methods.Materials and methods. Adult female Sprague–Dowley rats were used for the study. Severe acute contusion spinal cord injury model was performed using standard “weight‑drop” method. All samples of cryopreserved HUCBCs concentrate were prestored prior to infusion for 3 to 4 years at –196 °C. Hind limbs motor function was evaluated using open‑field technique and standard BBB testing system. Magnetic resonance scanning was performed using high‑field magnetic resonance CleanScan 7.0 T tomography (Bruker BioSpin, Germany).Results. Intravenous infusions of HUCBCs were performed on Day 1 following acute severe spinal cord injury. Motor function assessment demonstrated significant (p <0.05) improvement of hind limbs motor function (up to 40–50 %) comparing to self‑healing outcomes. Moreover, by the Days 4 and 5 after severe spinal cord injury, the volume of posttraumatic cystic cavity decreases significantly (up to 40 %) (p <0.05).Conclusion. The obtained results demonstrated that cryopreserved HUCBCs can be used as an effective source for cell therapy of acute contusion spinal cord injury.

A novel optimal allocation of STATCOM to enhance voltage stability in power networks

Utilizing a static synchronous compensator (STATCOM) in the electrical power grid greatly improves the grid's voltage profile by enhancing voltage stability. This article proposes a novel approach based on Mixed Integer Distributed Ant Colony Optimization (MIDACO) to determine the optimal STATCOM installation in the electrical power grid. This approach has two control variables to optimize: the STATCOM size and location. This optimization aims to enhance voltage stability with minimum cost by minimizing two objectives: the voltage deviation index and the STATCOM cost. Also, this article presents a sensitivity analysis to show the stochastic nature of MIDACO and to explain the effect of MIDACO parameters on the optimization approach and the process of reaching the optimal solution. The proposed method has been evaluated on three standard test systems: IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus. In addition, the MIDACO results are compared to those of the artificial bee colony algorithm, the genetic algorithm, and particle swarm optimization.