Real Test Research Articles

MnCo2O4 Spinel Nanorods for Highly Sensitive Electrochemical Detection of Nitrite.

The rational design of nitrite sensors has attracted significant research interest due to their widespread use and the associated risks of methemoglobinemia and carcinogenicity. The undisclosed nitrite-sensing performance of the spinel cobaltite MnCo2O4 (MCO) prepared by an oxalate-assisted coprecipitation method is reported in this study. Spectroscopy and microscopy investigations revealed the formation of uniform MCO nanorods with a high aspect ratio. The electrocatalytic nitrite oxidation at the MCO-coated glassy carbon electrode (MCO/GCE) indicated the promising performance of the synthesized material for nitrite sensing. MCO/GCE detects nitrite in a concentration range of 5 μM to 3 mM and has a limit of detection of 0.95 μM with a higher sensitivity of 857 μA mM-1 cm-2 in a response time of 4 s. In MCO, the mixed-valence states of Co2+/Co3+ confer a high electrical conductivity, and higher valent redox couples of Mn and Co impart remarkable electrocatalytic activity toward nitrite oxidation. MCO spinel undergoes facile and ultrafast faradaic reactions to mediate nitrite oxidation. Additionally, the mesopores of MCO nanorods facilitate the rapid diffusion of electrolyte and nitrite ions. Employing the electrode in sensing nitrite in milk, lake, and tap water samples further validates its potential application in real-life testing. MCO spinel nanorods showcase promising scope for utilization in the electrochemical sensing of nitrite and inspire further exploration of transition-metal oxide-based mixed-spinel materials.

Analyzing the role of emissivity in stranded conductors for overhead power lines

Emissivity and absorptivity are important parameters in overhead power line conductors that depend on the conductor surface condition, so their values change as the conductors age. They have an important effect on their thermal behavior, so it is important to know their value when designing dynamic line rating (DLR) strategies. DLR methods are becoming increasingly important, especially in areas with congested power lines, as they allow the full capacity of the lines to be utilized by dynamically determining their maximum allowable currents without exceeding the safe temperature limits of the conductors, thus ensuring that they operate within safe limits. However, measuring emissivity and absorptivity is non-trivial and requires complex tests that cannot be performed in the field. Using simulations based on a realistic thermoelectric model and experimental tests performed on three types of transmission line conductors in a high-current laboratory, this paper shows the importance and effect of emissivity on conductor temperature. It also presents a method for measuring the emissivity value based on adjusting the emissivity and absorptivity values in the thermoelectric model to match the experimental values. The results presented show that aged conductors allow an increase in the maximum current carrying capacity compared to new conductors in the order of 5–14 %, depending on the conductor type considered and the operating conditions.

Experimental Validation of Electrothermal and Aging Parameter Identification for Lithium-Ion Batteries

Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the evolution of cell voltage, capacity, and internal resistance, in relation to the cell actual operating conditions, and estimates the ongoing degradation in capacity and internal resistance due to the battery use. Thus, the model articulates into two sub-models, an electrothermal one, describing the battery voltage, and an aging one, computing the ongoing degradation. We first propose an approach to identify the parameters of both sub-models. Then, we validate the identification procedure and the accuracy of the electrothermal and aging models through an experimental campaign, also comprising two real cycle load tests at different temperatures, in which real measurements collected from real Li-ion cells are used. The overall model demonstrates good performances in simulating battery characteristics and forecasting degradation. The results show a Mean Absolute Percentage Error (MAPE) lower than 1% for battery voltage and capacity, and a maximum absolute error on internal resistance that is on par with the most up-to-date empirical models. The proposed approach is therefore well-suited for implementation in system modeling, and can be employed as an informative tool for enhancing battery design and operational strategies.

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

Hazard analysis is a vital step in developing intelligent connected vehicles, aiming to eliminate or control hazards in the initial stages of system development and to provide theoretical support for the system’s safety design. However, conventional hazard analysis methods, such as Fault Tree Analysis and Failure Mode and Effects Analysis, suffer from two shortcomings: they do not account for the impact of cybersecurity factors on system safety and do not provide sufficient quantification of hazard scenarios. To this end, we propose a quantifiable hazard analysis method with security consideration, which integrates System Theoretic Process Analysis for Security (STPA-Sec) and Generalized Stochastic Petri Net (GSPN), supporting the extraction, modeling, and quantification of hazards. Specifically, we employ STPA-Sec for qualitative analysis to identify causal scenarios, safety requirements, security requirements, and the corresponding mitigations. Then, based on the identified causal scenarios, a GSPN model is established to quantify system-level hazards. A case study on a real open-source test vehicle demonstrates that the proposed method not only offers a comprehensive analysis of hazards but also provides a quantitative assessment. Comparative assessments suggest that the proposed method exhibits an advantage in terms of analysis processes (integrating security) and results (quantification).

Effect of pooled tracheal sample testing on the probability of Mycoplasma hyopneumoniae detection

Tracheal pooling for Mycoplasma hyopneumoniae (M. hyopneumoniae) DNA detection allows for decreased diagnostic cost, one of the main constraints in surveillance programs. The objectives of this study were to estimate the sensitivity of pooled-sample testing for the detection of M. hyopneumoniae in tracheal samples and to develop probability of M. hyopneumoniae detection estimates for tracheal samples pooled by 3, 5, and 10. A total of 48 M. hyopneumoniae PCR-positive field samples were pooled 3-, 5-, and 10-times using field M. hyopneumoniae DNA-negative samples and tested in triplicate. The sensitivity was estimated at 0.96 (95% credible interval [Cred. Int.]: 0.93, 0.98) for pools of 3, 0.95 (95% Cred. Int: 0.92, 0.98) for pools of 5, and 0.93 (95% Cred. Int.: 0.89, 0.96) for pools of 10. All pool sizes resulted in PCR-positive if the individual tracheal sample Ct value was < 33. Additionally, there was no significant decrease in the probability of detecting at least one M. hyopneumoniae-infected pig given any pool size (3, 5, or 10) of tracheal swabs. Furthermore, this manuscript applies the probability of detection estimates to various real-life diagnostic testing scenarios. Combining increased total animals sampled with pooling can be a cost-effective tool to maximize the performance of M. hyopneumoniae surveillance programs.

3D magnetotelluric inversion with arbitrary data orientation angles

The survey setup of 3D magnetotelluric inversion is typically chosen to best reflect the underlying geological structure. Typically, the individual observation azimuths are distinct from this preferred survey layout orientation; in fact, measured electric and magnetic fields are aligned with local geomagnetic North. When that happens, either data preprocessing (rotation) is required before the inversion may be performed, or the orientation of the modeling grid is aligned with the data orientations. None of these approaches is optimal, possibly resulting in a reduction of data quality, the loss of statistical accuracy of the error estimates, or in unnecessary grid cells, resulting in increased computation times. In situations when the data quality may be improved by allowing distinct individual site orientations, such as when cultural noise or faulty instrumentation cause a degradation of one of the modes, data loss due to rotation is especially detrimental. In this paper, we present a modified 3D magnetotelluric inversion algorithm, based on the ModEM software, that allows for direct interpretation of observations with arbitrary data orientation angles (ModEM ADORA). In ModEM ADORA, the modeling responses are rotated to the observation orientation angles after each forward calculation and before the data misfit is evaluated. This allows for direct interpretation of observation data with distinct orientations at each site and (as needed for some historical data) at each frequency. The derivative of the penalty functional is computed by pre-multiplying the derivatives of the data functionals (or the complete Jacobian matrix) by the relevant sparse block-diagonal rotation operators. We describe the principle and implementation of ModEM ADORA, and provide a collection of synthetic and real data tests for validation and an analysis of effectiveness of the ADORA option of ModEM. We show that by decoupling modeling grid and sensor orientations, ModEM ADORA supports both computational efficiency and ease of use, as well as allows for the maximum quality and quantity of inverted data.

Analysis and Measurement of Supraharmonics in Real Textile Industries Using Hybrid Technique

In this paper proposes a hybrid ADKF-FHO method for analysis and study of supraharmonics in textile industry. The proposed hybrid approach is the combined performance of anisotropic diffusion Kuwahara filtering (ADKF)and Fire Hawk Optimizer (FHO). Commonly it is named as ADKF-FHO technique. The major objective of the proposed approach is the main aim is to analysis of Supraharmonicsin real textile industries. The Variable frequency drive is predominantly used in Textile industry for varying the speed to control the roller speed for obtaining quality of yarn from the machine with more productivity and high energy saving. The ADLF is utilized to ensure the supraharmonics in textile industry and the FHO is used to optimizing the switching states of the embedded power converter. In this paper, standards and its limits are discussed; simulation model of the drive is done to ensure the Supraharmonics. Real time testing of the motor with drive for ensuring the Supraharmonics at testing laboratory and real time Textile industry in different geographical location is discussed in detail. By then, the proposed model is implemented in MATLAB/Simulink working stage and the execution is calculated with the existing methods. The proposed method shows better results in all approaches like RFA, CSA and PSO.

A Deep Recurrent Reinforcement Learning Approach for Enhanced MPPT in PV Systems

Under partial shading condition (PSC), for efficient usage, solar photovoltaic (PV) systems are required to operate at global maximum power point (GMPP). A maximum power point tracking (MPPT) controller is usually employed for this task. In this work, for MPPT under PSC, a recurrent deep reinforcement learning (DRL) approach has been developed and investigated. The optimized neural network, augmented by long short-term memory (LSTM), is trained using the proximal policy optimization (PPO) DRL algorithm. The MPPT performance of the proposed scheme was assessed using the GMPP attainment accuracy (%), on several realistic static and dynamic test cases. The presented investigations revealed the proposed controller's average MPPT accuracy of 97.79% for the considered random static PSCs. Similarly, average MPPT accuracies of 95.69% under changing irradiance, 95.70% under change in temperature and 95.77% under changes in both irradiance and temperature were recorded. These accuracies are found to be significantly superior to recent reported works. The resulting performance enhancement is attributed to the inclusion of LSTM, which allowed the agent to retain information about past states and actions, enabling it to make informed decisions. Therefore, based on the presented investigations, it is concluded that the proposed PPO-LSTM approach is an excellent MPPT alternative for PV systems.

Next Generation Seismic Source Detection by Computer Vision: Untangling the Complexity of the 2016 Kaikōura Earthquake Sequence

AbstractSeismic source locations are fundamental to many fields of Earth and planetary sciences, such as seismology, volcanology and tectonics. However, seismic source detection and location are challenging when events cluster closely in space and time with signals tangling together at observing stations, such as they often do in major aftershock sequences. Though emerging algorithms and artificial intelligence (AI) models have made processing high volumes of seismic data easier, their performance is still limited, especially for complex aftershock sequences. In this study, we propose a novel approach that utilizes three‐dimensional image segmentation—a computer vision technique—to detect and locate seismic sources, and develop this into a complete workflow, Source Untangler Guided by Artificial intelligence image Recognition (SUGAR). In our synthetic and real data tests, SUGAR can handle complex, energetic earthquake sequences in near real time better than skillful analysts and other AI and non‐AI based algorithms. We apply SUGAR to the 2016 Kaikōura, New Zealand sequence and obtain five times more events than the analyst‐based GeoNet catalog. The improved aftershock distribution illuminates a continuous fault system with extensive fracture zones beneath the segmented, discontinuous surface ruptures. Our method has broader applicability to non‐earthquake sources and other time series image data sets.

A novel ratiometric electrochemical immunosensor for the detection of cancer antigen 125 based on three-dimensional carbon nanomaterial and MOFs

Accurate and sensitive detection of cancer antigen 125 (CA125) is important in early ovarian cancer diagnosis and monitoring. In the present work, a novel electrochemical ratiometric immunosensor for the determination of CA125 based on three-dimensional reduced graphene oxide/multi-walled carbon nanotube carboxylic acid- thionine (3DrGO/MWCNTs-Thi) and UIO-66-NH2/ferrocenecarboxylic acid (UiO-66-Fc) was constructed. 3DrGO/MWCNTs-Thi of immobilized primary antibody (Ab1) and UiO-66-Fc of labeled second antibody (Ab2) were used as capture probes and signaling probes for CA125, respectively. As the concentration of CA125 increases, the oxidation peak currents (IThi and IFc) generated by the two materials were changed in opposite directions, and therefore the ratio of the currents (IThi/IFc) was negatively correlated with the concentration of CA125. Thanks to the amplification of high loading of signaling molecules by the framework nanocomposites and the ratiometric strategy, the constructed electrochemical immunosensor exhibits outstanding accuracy, stability, and sensitivity. Under optimal conditions, the detection range spanned from 0.01 U mL−1 to 80 U mL−1, with a calculated limit of detection (LOD) of 0.0089 U mL−1. The immunosensor was successfully applied to real serum sample tests, indicating its potential for clinical detection applications.

Learning-Based Vehicle State Estimation Using Gaussian Process Regression Combined with Extended Kalman Filter

In order to address the challenge of accurate vehicle state estimation, especially in highly nonlinear and complex operating conditions, this paper proposes a learning-based method for vehicle state estimation. To achieve this, a novel theoretical framework is constructed that combines the model-based Extended Kalman Filter (EKF) with Gaussian process regression (GPR). By establishing a dynamic model of the vehicle and describing the errors using machine learning techniques, the sources of state estimation errors are analyzed. Furthermore, by considering the model uncertainty, an error prediction model is developed through GPR learning from real-world vehicle data. Combined with EKF, the proposed method enables high-precision online estimation of vehicle state. To validate the proposed method, tests are performed on a real vehicle test platform equipped with high-precision sensors and data acquisition equipment, under two different operating conditions: emergency and normal. The results are compared with those obtained using EKF and State Observer, which demonstrates a more accurate and adaptable vehicle state estimation, and provides a method for quantitatively describing the confidence interval of vehicle state estimation result.

Concept and investigation of Selective Forward Osmosis (SFO) for Salt-Salt Separation as a Pretreatment of Seawater for Resource Utilization

Seawater utilization is crucial for the sustainable human development. Despite growing interest in forward osmosis (FO) due to its unique properties, conventional FO membranes with salt-water selectivity have limitations in applying to specific salt-salt separation processes, which hinders their application in resource utilization. In this work, a new concept, “selective forward osmosis (SFO)”, was proposed, which ingeniously employed an SFO membrane consisting of an ion-selective layer on a denser substrate. The denser substrate is designed to control water flux so as to alleviate the solution dilution and improve the salt-salt separation. Moreover, the sucrose and pure water were used separately as feed solution to provide different water flux to influence the various salt fluxes, showing that pure water feed could enhance the salt-salt separation efficiency, although it could dilute the draw solution to some extent. Therefore, pure water was selected as feed in the subsequent experiments. The optimized SFO membrane achieved high Na2SO4/NaCl selectivity (∼54.8) and MgCl2/NaCl selectivity (∼9.2) in single-salt draw solutions. With mixed-salt and heavy-metal-mixed-salt draw solutions, the Mg2+/Na+ selectivity was enhanced to ∼14.5, and further to 29.3. In real seawater tests, the SFO system effectively permeated monovalent elements (such as Na flux of ∼68.6 g m−2 h−1) while maintaining a higher rejection for bivalent elements (such as Mg flux of ∼0.08 g m−2 h−1), showing high selectivities for Mg/Na, U/Na, Sr/Na, Ni/Na, and Ca/Na. These results demonstrate the potential of SFO for resource utilization, especially in complex saline environments. This work contributes a new route for salt-salt separation in the pretreatment of seawater resources.

INNOVATION IN RECRUITMENT IN TALENT ACQUISITION: A STUDY ON TECHNOLOGIES AND STRATEGIES ADOPTED FOR TALENT MANAGEMENT IN IT SECTOR

Talent acquisition and recruitment are the undergoing processes that also essential for the organization but it also created challenges for the company to leverage the social network aggressively market their employee brand and it could employment every day. Sourcing people in a new place with the help of social media tool creating and opportunity for the internal candidates and leveraging the use network of referral by making relationship within the company the study mainly focus on the innovation in recruitment internal acquisition by making a study on technologies and strategies which are adopted by the talent management in the IT sector, Apart from this the study also provided the complete background of these study by discussing the different and new methods of the equipment process that highly focused to use in technologies and other studies adopted acquired in talent in the IT sector. Overall, innovation in recruitment aims to make the process more efficient, effective, and engaging for both employers and candidates. By embracing new technologies and creative approaches, companies can stay ahead in the talent acquisition game and attract the best candidates for their organizations. Inovation in recruitment and talent acquisition refers to the use of new and creative methods to attract and hire the best candidates for a job. It involves thinking outside the box and leveraging technology and data to streamline the hiring process. With the rapid advancements in technology, there are various innovative approaches being used in recruitment. Another innovative method is the use of gamified assessments. These are interactive and engaging assessments that simulate real-life scenarios and test candidates' skills, knowledge, and problem-solving abilities. By incorporating elements of gamification, recruiters can make the assessment process more enjoyable and immersive for candidates.

Bimetallic Fe/Co nanoalloy embedded in porous carbon as electrochemical sensor for acetaminophen detection

Ferro-alloys have been extensively studied in the field of electrochemical sensing due to their abundant earth reserves and excellent performances. In this work, serials of bimetallic Fe/Co nanoalloy@carbon composites (Fe/Co@C) were successfully prepared using Co-doped MIL-101(Fe) as templates and developed as excellent electrochemical sensors for acetaminophen (AP) detection. It was found that the atomic ratio of Fe/Co in the templates can significantly affect the electrochemical activity of the product. Among the prepared composites, Fe/Co@C-G2 (Fe/Co = 2:1) with high conductivity and large specific surface area presents the best electrochemical sensing performance. Under the optimal conditions, Fe/Co@C-G2 has a high sensitivity of 55.2 [Formula: see text]A [Formula: see text]M[Formula: see text] cm[Formula: see text], a wide linear range of 0.29-407.15 [Formula: see text]M, a low LOD of 0.09 [Formula: see text]M, good selectivity, excellent stability, and good repeatability. In addition, Fe/Co@C-G2 can also be used for real sample testing. This work expands the application of Fe/Co@C-based composites in electrochemical sensing.

The potential of coupled water electrolysis with electrochemical wastewater treatments

The coupled water electrolysis is a promising strategy for green hydrogen production. Pollutants are attractive feedstocks for the fungible anode reactions of the sluggish oxygen evolution reaction, with the advantages of trash-to-treasure and cost reduction. There is an urgent need for real waste sample tests from the view of both contaminant abatement and electrochemical hydrogen production. This work offers a systematic evaluation of the coupled water electrolysis with real wastewater, where four real wastewater with sulfur element compounds in different states are taken as examples. The technical and economic potentials are evaluated particularly based on the coupled water electrolysis performances, including cell voltage, current density, hydrogen production rate, hydrogen electricity consumption, and pollution abatement. The coupled water electrolysis with the refractory wastewater, e.g. the desulfurization wastewater from coal-fired power plants and the H acid wastewater, does not show advantages from the view of hydrogen production, due to the high overpotential and limited current density. Nevertheless, hydrogen is a high-value-added byproduct for operation cost reduction from the view of wastewater treatment. The readily oxidizable pollutants, e.g. sulfite and sulfide, exhibit notable potentials for anodic depolarization and electricity consumption reduction of hydrogen production. However, the coupled water electrolysis adopting the sulfide spent caustic stream may suffer a low hydrogen production rate for sulfide to sulfur. The coupled water electrolysis adopting sulfite wastewater indicates excellent potential for both space-time yield and electricity consumption for hydrogen production. This work may offer a reference for the systematic evaluation and selection of coupled water electrolysis with real wastewater.

Seismic Response of RC Beam-Column Joints Strengthened with FRP ROPES, Using 3D Finite Element: Verification with Real Scale Tests

A 3D-finite element analysis within the numerical program ABAQUS is adopted in order to simulate the seismic behavior of reinforced concrete beam-column joints and beam-column joints strengthened with CFRP ropes. The suitability of the adopted approach is investigated herein. For this purpose, experimental and numerical cyclic tests were performed. The experiments include four reinforced concrete (RC) joints with the same ratio of shear closed-stirrup reinforcement and two different volumetric ratios of longitudinal steel reinforcing bars. Two joints were tested as-built, and the other two were strengthened with CFRP ropes. The ropes were applied as Near Surface Mounted (NSM) reinforcement, forming an X-shape around the joint body and further as flexural reinforcement at the top and bottom of the beam. The purpose of the externally mounted CFRP ropes is to allow the development of higher values of concrete principal stresses inside the joint core, compared with the specimens without ropes, and also to reduce the developing shear deformation in the joint. From the results, it is concluded that X-shaped ropes reduced the shear deformation in the joint body remarkably, especially in high drifts. Further, as a result of the comparisons between the yielded outcome from the attempted nonlinear analysis and the observed response from the tests, it is deduced that the adopted method sufficiently describes the whole behavior of the RC beam-column connections. In particular, comparisons between experimental and numerical results of principal stresses developing in the joint body of all examined specimens, along with similar comparisons of force displacement envelopes and shear deformations of the joint body, confirmed the adequacy of the applied finite element approach for the investigation of the use of CFRP-ropes as an efficient and easy-to-apply strengthening technique. The findings also reveal that the connections that have been strengthened with the FRP ropes demonstrated improved performance, and the crack system preserved its load capacity during the reversal loading tests.

Sim-to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting

Enhancing the safety of passengers by venturing beyond the limits of a human driver is one of the main ideas behind autonomous vehicles. While drifting is mostly witnessed in motorsports as an advanced driving technique, it could provide many possibilities for improving traffic safety by avoiding accidents in extreme traffic situations. The purpose of the research presented in this article is to provide a machine learning-based solution to autonomous drifting as a proof of concept for vehicle control at the limits of handling. To achieve this, reinforcement learning (RL) agents were trained for the task in a MATLAB/Simulink-based simulation environment, using the state-of-the-art Soft Actor–Critic (SAC) algorithm. The trained agents were tested in reality at the ZalaZONE proving ground on a series production sports car with zero-shot transfer. Based on the test results, the simulation environment was improved through domain randomization, until the agent could perform the task both in simulation and in reality on a real test car.

On the mitigation of the overheating caused by floor heating systems exposed to long direct solar radiation and high occupation rates

This study investigates the effects of solar radiation and human metabolic heat due to occupation on the dynamic thermal response of glazed building's floor heating system (FHS), which may entail an indoor thermal discomfort. A full experiment was conducted on a real test cell to measure various parameters such as surface and floor-depth temperature, ambient and operative temperature, and relative humidity. The sun patch was experimentally simulated with a moving heating film placed on the surface of the radiant floor. The performance of the radiant floor was simulated using a TRNSYS® model in conjunction with SketchUp®. Once validated, the model was used to predict the test cell surface, ambient, and operative temperatures under four different scenarios related to the presence or absence of shading and/or occupant gains. Under the realistic climatic data of the city of Troyes (France), the results show that when the solar and occupants gains are considered on the heating slab, the indoor temperature reach the high value of 33.9 °C. A statistical approach based on Design of Experiments (DoE) method was used to perform sensitivity analysis and evaluate the effects of factors/design parameters such as heating pipe thickness, heating slab conductivity, specific heat capacity and density on surface and indoor air temperatures. Findings indicates that heating slab with 6 cm thickness, and with low conductivity and diffusivity leads to better FHS performances with no overheating/discomfort periods. The developed model could serve as a basis for any predictive adaptive control to mitigate the overheating problem.

Mechanical shock test simulation analysis of butterfly valves developed for the naval defense industry and evaluation of real test and production data

The main purpose of this study is to investigate the mechanical shock behavior and develop the shock resistance of widely preferred butterfly valves for navy defense industries by handling the real test results with computer aided design and simulation programs. The 2D and 3D drawings were realized by using solid modeling and design programs. Mechanical analyzes to determine the mechanical strength of the specimens were carried out with the finite element analysis method by using structural simulation program. Mechanical shock test simulations were carried out by with shock response spectrum analysis. Solidification, filling-time–temperature analyzes, and simulation studies of inner stresses caused by micro and macro shrinkages were performed by using the casting simulation program. Comparisons of virtual tests simulated in computer environment with real tests were done in shock test setup. Products made of bronze were preferred due to the high corrosion resistance and the desire to be a useful research article that can respond to common applications in the defense industry. Virtual shock test simulation and real shock tests were performed according to the MIL STD 810 standard. The shock test results observations showed that by revising the design with a safety factor of 18% on the specimen, it was ensured that the product could pass the mechanical shock test even at an acceleration of 4000 m/s2. Then material become safe to use. With the use of a three-way feeder in the production of the reinforced design the difference in net weight from 19% has been reduced to 12%, while the production time has been improved by 22%.

Power generation–network–load–energy storage co-planning under uncertainty

With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning.