Real Site Research Articles

Robust condition identification against label noise in industrial processes based on trusted connection dictionary learning

In the era of big data, the pervasive use of artificial intelligence (AI) technology has revolutionized various industries. AI-powered systems, particularly those utilizing data-driven interpretable and reliable methods for accurate condition identification, have emerged as a crucial factor in achieving intelligent industrial systems. However, in real industrial sites, the labels of offline data often contain incorrect labels, i.e. label noise, due to human error, sensor failure, and other sources, which reduces the accuracy of the model and affects the performance and safety of the industrial system. To tackle the challenge of precarious performance caused by label noise, this paper proposes a robust method called Trusted Connection Dictionary Learning (TCDL) which effectively mitigates the adverse effects of label noise through interpretable constraints and guarantees the trustworthiness of the model for industrial applications. Specifically, to address the issue of data structure and label inconsistency arising from label noise, this paper presents a dynamic graph model that jointly optimizes data features and label information to obtain consistent representations of them. Subsequently, to effectively fit the labeled data, TCDL leverages the label information from the nearest neighbors through the optimized graph structure and facilitates the self-correction of label noise, leading to robust representations of process features and a reliable condition classifier. Finally, a novel iterative optimization algorithm that combines the Block Coordinate Descent (BCD) method and the Alternating Direction Multiplier Method (ADMM) is also developed to ensure the convergence of the algorithm. Due to simple constraints, it can obtain accurately optimized variables with few iterations. Numerical simulations and industrial experiments demonstrate the superiority of the proposed method compared to some state-of-the-art methods.

An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites

Construction robots have been increasingly adopted in construction projects to improve productivity, reduce risk, and speed up work cycles. Visual Simultaneous Localization and Mapping (V-SLAM) technology is widely used in construction robots due to its lightweight weight, cost-effectiveness, and provision of semantic information. On real construction sites, lighting conditions are often challenging due to factors such as uneven lighting intensity, inadequate exposure, or robots in backlit positions (e.g. roughcast houses without artificial lighting, underground car parks), which cause images captured under these conditions to exhibit low signal-to-noise ratio, uneven lighting, color distortion, noise, and other problems. These challenges make the extraction and matching of feature points in V-SLAM difficult, resulting in significant positioning errors or the inability to generate positioning trajectories for construction robotics. Although existing methods for enhancing low-light images can certainly improve image brightness, they still cannot effectively address the localization challenges brought about by low-light construction scenes with uneven illumination and noise. To address the interference of uneven and changing lighting conditions on V-SLAM localization in construction sites, this paper proposes the Unsupervised Reflectance Retinex and Noise model (URRN-Net) to enhance low-light construction images. By using a CNN model based on the Retinex theory to decompose the illumination characteristics of images, we achieve effective brightness restoration of uneven low-light images by utilizing unsupervised loss functions. URRE-Net can reduce the root mean square localization error by >65% compared to low-light images in the ORB-SLAM3 method, and the maximum error is reduced by >71% in on-site experiments. The proposed URRE-Net can be integrated with existing V-SLAM algorithms to provide more robust localization services for low-light construction site applications such as building operations (e.g., interior wall spraying robotic) or construction management tasks (e.g., automatic tunnel inspection).

Self‐assembly aryl cyclopalladated imine film supported on the silicon surface—Fabrication, formation of active PdPd/PdO sites, and catalytic properties for Sonogashira cross‐coupling reaction

Tailoring the structure and arrangement of the organometallic catalyst are essential for the fundamental investigating and applications, and the impacting challenges, such as the size, active species distribution, and composition, as well as the orientation of catalyst, are critical for determining their activities and efficiencies. Herein, a series of aryl cyclopalladated catalysts containing different alkane chains were synthesized, and their self‐assembly films covalent binding to the silicon surface were fabricated and characterized with water drop contact angle, Raman, atomic force microscopy, scanning electron microscopy, and X‐ray photoelectron spectroscopy. The catalytic performances of these catalytic films were explored using Sonogashira cross‐coupling reaction as a template. One of these catalytic films exhibited a higher catalytic activity with a TON value of up to 68,333 h−1, which was 20 times higher than that of homogeneous catalyst due to the ordered arrangement of this catalyst on the silicon surface, and could be reused at least seven times. The catalytic mechanism was heterogeneous catalysis occurred at interface confirmed by hot filtration tests, poisoning tests, and ReactIR. The real active site was PdPd/PdO, and the synergistic effect between Pd and PdO in the active sites (PdPd/PdO) made the key initial oxidation step easily proceed, improving its catalytic activity. The loss of active sites and residues absorbed on the surface of catalytic film were the main reason for deactivation.

CONFUSION OF REAL SITE IMPLEMENTATION OF VSD UTILIZATION ON WATER PUMP OF A CHILLER PLANT SYSTEM IN OFFICE BUILDING JAKARTA

The utilization of VSD in centralized air conditioning systems has been applied for years for efficiency purposes. However, found in real implementation has not been really applied as its function yet. This paper will present real site implementation of applying VSD on regulating the motor speed of chilled water pump as its function [1] to control the chilled water flow rate [2] go into the evaporator to let the chiller work as correspond on cooling demand at the actual load level and at its necessary power input. By analyzing some related parameters such as temperature, water pressure and amperes measured and observed to be the raw data then they will show the working performance of chiller impact on energy consumed to be evaluated on its efficiency impact. Found the real implementation at this site the VSD does not look like varying the speed of motor pump as usually applied but more by being set at certain frequency such as 32 Hz. Then mostly the chiller cannot be run as high as cooling demand as running at only about 50-60% as lower chilled water flow rate flowing into the evaporator. Other than that it looks efficiency of refrigeration effect on handling the building load is not good enough as mentioned in catalogue/reference with difficulties on reaching the target chilled water leaving temperature.

Object-based terminal positioning solution within task-boosted global constraint for improving mobile robotic stacking accuracy

Wall building is labor-intensive and time-consuming as a typical construction task. It is expected to perform this heavy task using robots. However, laborers are still largely employed, mainly because high continuous stacking precision for mobile robots has always been a bottleneck problem. This paper examines the characteristics of continuous robotic stacking in wall-building tasks and finds the key challenge lies in precisely manipulating the robot arm terminal to the target pose and guaranteeing the wall’s verticality. We propose an object-based terminal positioning solution for this issue. The core of this solution is establishing a reference coordinate system as a measurement reference and designing a multi-sensor measurement system to measure the relative pose between the robot and the object. The solution allows the robot to reposition multiple times and iteratively correct the terminal’s pose, which greatly reduces the impact of positioning errors and loaded deflections of mobile systems on terminal positioning accuracy. Furthermore, we propose for the first time the construction of an active reference feature using a planar laser as a global constraint to provide a consistent reference for establishing reference coordinate systems. Global constraint eliminates the cumulative stacking errors due to relying only on the previously stacked object’s reference features for terminal positioning in continuous stacking tasks and guarantees the wall’s verticality. Finally, an autonomous mobile stacking robot is developed, and continuous block stacking experiments are conducted with this robot in a laboratory environment simulating real application sites. Experimental results demonstrate that the developed robotic system can achieve millimeter-level stacking accuracy, where a single vertical stack error of 3 mm meets the high requirements of the construction industry. Thus, it is reasonable to believe the proposed solution is extremely competitive compared to existing studies and provides valuable insights for improving the continuous stacking accuracy of mobile robots in large-scale unstructured environments.

New Displacement Method for Free Embedded Cantilever Walls in Sand

In the current literature, there is no practical formula to calculate the horizontal displacement of cantilever walls. To fill this gap, in the present study, eight formulae for the estimation of wall displacement were developed based on 431 FE wall model configurations. Each formula considers factors such as the wall height, embedment depth, surcharge load, unit weight, internal friction angle, elastic modulus of the surrounding soil, and flexural rigidity of the wall. The FE model, which was used in the development of the formula, was also validated against a physical laboratory study. In addition, the outputs obtained from the formulae were compared with the results of two laboratory studies and a real site study. Finally, a parametric study was performed to estimate the influence of formula input parameters on wall displacement.

In Situ Reconstruction to Surface Sulfide Adsorbed Metal Scaffold for Enhanced Electrocatalytic Hydrogen Evolution Activity

AbstractTransition‐metal‐based compounds have been intensively explored as efficient electrocatalysts for hydrogen evolution reaction (HER). Feasible reconstruction to the real active sites, which is yet to be identified, endows the promotion of HER activity. Here, it is reported that the incoming S coordinates and anion vacancies prompt the structural reconstruction of S‐doped Co3O4 on carbon cloth (S‐Co3O4/CC) during HER. A list of in situ studies reveals that the real active sites for HER are the “metallic surface‐adparticles” system embracing metallic Co scaffold and the dilute coverage of S coordinated Coδ+. Reaction mechanism exploration illustrates that interfacial perimeters between the coverage of Co3S4 moieties and metallic Co considerably facilitate the adsorption of H*, improve the kinetics of water dissociation, and consequently promote HER activity. The exemplified sulfide‐mediated topotactic transformation strategy is extended to the preparation of S, Fe codoped Ni(OH)2 (S‐NiFe/CC) as a bifunctional electrocatalyst. The assembled anion exchange membrane water electrolyzer achieves a current density of 1.0 A cm−2 at 1.72 V, showing excellent capability in catalyzing overall water splitting at ampere level. This study, showing a feasible strategy that enables the facile reconstruction to identify active sites, would inspire the development of efficient electrocatalysts for HER and other electrochemical hydrogenation reaction.

Atomically Dispersed Metal‐Nitrogen‐Carbon Electrocatalysts for the Oxygen Reduction Reaction

AbstractThe quest for alternatives to Pt as an oxygen reduction electrocatalyst, possessing high activity, stability, and abundant reserves, holds great significance for H2/O2 fuel cells. Recently, metal‐nitrogen‐carbon (M‐N‐C) electrocatalysts have garnered substantial attention as promising substitutes. These electrocatalysts not only exhibit well‐defined structures but also offer the flexibility to adjust the central metal atoms and coordination atoms. It is beneficial in elucidating the active sites during the catalytic process and in the design of highly active electrocatalysts. In this review, the real active site of M‐N‐C electrocatalyst‐driven ORR is investigated in depth by in situ characterization techniques such as X‐ray absorption spectroscopy, Raman spectroscopy, Fourier‐transform infrared spectroscopy. It′s worth noting that the catalytic activity of M‐N‐C electrocatalysts originates from the dynamic evolution of electrocatalyst structure. Subsequently, we review various synthetic strategies, including the wet chemistry method, spatial confinement, and template‐assisted method, aimed at the rational design of M‐N‐C electrocatalysts. Moreover, recent progresses of M‐N‐C electrocatalysts with varying configurations, encompassing single‐atom, and double‐atom electrocatalysts are discussed, Finally, summary and perspectives on the development of M‐N‐C are provided.

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.

Regulating the Electronic Synergy of Asymmetric Atomic Fe Sites with Adjacent Defects for Boosting Activity and Durability toward Oxygen Reduction

AbstractThe oxygen reduction reaction (ORR) plays a fundamental role in sustainable energy technologies. However, the creation of non‐precious metal electrocatalysts with high ORR activity and durability under all pH conditions is of great significance but remains challenging. Herein, the aim is to overcome this challenge by creating a Fe single atom catalyst on a 2D defect‐containing nitrogen‐doped carbon support (Fe1/DNC) via a microenvironment engineering strategy. Microkinetic modeling reveals that FeN4(OH) moieties are the real active sites under reaction conditions. Due to the synergistic promotion effect of denser accessible FeN4(OH) moieties and defect‐induced electronic properties, Fe1/DNC catalyst achieves extraordinary ORR activity under alkaline, acidic, and neutral conditions, with half‐wave potentials of 0.95, 0.82, and 0.70 V, respectively. Moreover, a negligible performance decay is observed with this Fe catalyst in stability and methanol tolerance tests. Zn‐air battery employing Fe1/DNC delivers remarkable peak power density and long‐term operational durability. Theoretical analysis provides compelling evidence that the defects adjacent to FeN4(OH) moieties can endow an inductive effect to reshape electronic properties to balance the OOH* formation and OH* reduction. This work offers insight into the regulation of asymmetric coordination structure and electronic properties of metal sites for boosting electrocatalytic activity and stability.

Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes

Mechanical, electrical, and plumbing (MEP) systems play a crucial role in providing various services and creating comfortable environments for urban residents. In order to enhance the management efficiency of these highly complex MEP systems, as-built building information models (BIMs) are being increasingly adopted worldwide. As-built BIMs accurately represent the actual conditions of facilities, making as-built BIM reconstruction significantly important for construction progress tracking, quality assurance, subsequent facility management, and renewal. To create as-built BIMs for MEP systems, laser scanners are widely utilized to capture high-resolution images and dense 3D measurements of the environment in a fast and highly accurate manner. Despite research efforts to automatically achieve “Scan-to-BIM,” there are still gaps in applying current solutions to real-world scenarios. One of the major challenges are the limited generalization of existing methods to unseen scenarios without proper training or fine-tuning on custom-designed datasets. To address this issue, this study introduces Omni-Scan2BIM, a novel approach powered by large-scale pre-trained vision foundation models. Omni-Scan2BIM enables the recognition of MEP-related components with a single shot by integrating an all-purpose feature extraction model and a class-agnostic segmentation model. Firstly, given only a single image with a reference mask, the visual features are extracted for both the target component and the collected on-site images using the vision foundation model DINOv2. Secondly, through comparing pixel features, similarity maps are generated for the on-site images. The prior points for the class-agnostic segmentation model are sampled from the local maxima of the similarity map. Thirdly, Segment Anything Model (SAM) is leveraged to sequentially segment the target component. Finally, the target component is segmented out in 3D space based on the transformation matrix describing the spatial relationship between the 2D images and the 3D point clouds. Shape analysis and label fusion are conducted for as-built BIM modeling purpose. To validate the feasibility of the proposed technique, experiments were conducted using data collected from a real construction site in Hong Kong. The results demonstrate that the proposed Omni-Scan2BIM approach can easily generalize to unseen components with significantly improved accuracy and efficiency.

In-situ active sites analysis of bifunctional metal-organic frameworks for coupled adsorption and electrochemical oxidation of PPCPs

Pharmaceuticals and personal care products (PPCPs) pose a significant wastewater pollution concern. While electrochemical oxidation is promising, it struggles with slow mass transfer. To address this, we propose a novel strategy utilizing bifunctional metal–organic frameworks (MOFs) that couple an adsorbent with an electrocatalyst. The real active sites within iron-based MOFs responsible for the adsorption of PPCPs are MIL-101(Fe) molecules, while the in-situ formed active sites, α-FeOOH, accelerate the charge transfer under bias, contributing to the electrooxidation of PPCPs. The rapid destruction of adsorbed organics on the dual-functional electrode surface enables to release adsorption sites. This, in turn, paves the way for fast and efficient re-adsorption of PPCPs molecules, consequently promoting mass transfer. Iron-based MOFs exhibit excellent adsorption capacity (∼60 mg g−1 for sulfosalicylic acid, ∼15 mg g−1 for acetaminophen) and electrooxidation ability (94 % removal in 2 h, 31.2 kWh kg−1 COD) with good stability in realistic pharmaceutical wastewater treatment. This study offers a novel strategy for removing PPCPs using molecular electrocatalysts and provides new insights for designing and constructing MOFs for refractory wastewater treatment.

Digital Twin for the SBR Cold Emulsion Copolymerization Process

AbstractDevelopment of accurate and robust dynamic models for the description of emulsion copolymerization processes is fundamental for the implementation of monitoring, advanced control, and optimization strategies. There are several studies on the dynamic modeling of styrene/1,3‐butadiene rubber (SBR) emulsion copolymerization, but most of them focus on hot conditions or only one semi‐batch reactor, as in the case of cold conditions. For this reason, the present study focuses on the dynamic modeling of SBR cold emulsion copolymerization processes considering a train of 15 continuous stirred tank reactors, as in many real industrial sites. The developed dynamic model is implemented by using the digital twin (DT) concept, which involves the online reading of process variables and an adaptive strategy for online tuning of some of the model parameters, being also sensitive to the effect of real‐time changes on the number of reactors in the train, a subject that has been overlooked previously, but which is important at the plant site. The practical application of the DT for monitoring a real industrial process illustrates the robustness and accuracy of the developed tool, making it useful for opportune detection of process anomalies and opening the way for future advanced control strategies.

Biohybrid CO2 electrolysis under external mode: using pure formic acid extracted from CO2 electroreduction for diverse microbial conversion

The demand for converting CO2 into fuels or chemicals is on the rise to achieve a carbon-efficient circular economy. Biohybrid CO2 electrolysis shows potential for increasing production rates and diversifying product spectra by combining electrocatalysts and microbial catalysts. However, it is important to note that utilizing a shared catholyte for biohybrid CO2 electrolysis has not demonstrated significant performance improvements to date. In this study, we developed a biohybrid CO2 electrolysis system utilizing a solid electrolyte operating in an external mode. The produced formic acid was extracted and used as an intermediate for microbial conversion. Impressively, the solid-electrolyte CO2 electrolysers obtained a remarkable total Faraday efficiency of 81.4% for formic acid production. In-situ mechanism studies unveiled metallic tin as the probable real active site, prompting further exploration of strategies to boost the activity and stability of electrocatalysts. In the bioconversion step, we achieved a noteworthy 8-day duration for generating bioelectricity, nearly 100% electron recovery for biomethane production, and 90.8% for acetate generation. Additionally, when ethanol was co-fed, a C6 specificity of 41.1% was observed for the generation of medium-chain fatty acids (MCFAs). This study presents groundbreaking experimental data that demonstrates the numerous advantages of utilizing hybrid systems as advanced synthesis techniques.

Activating and Identifying the Active Site of RuS2 for Alkaline Hydrogen Oxidation Electrocatalysis.

Searching for highly efficient and economical electrocatalysts for alkaline hydrogen oxidation reaction (HOR) is crucial for the development of alkaline polymer membrane fuel cells. Here, we report a valid strategy to active pyrite-type RuS2 for alkaline HOR electrocatalysis by introducing sulfur vacancies. The obtained S-vacancies modified RuS2-x exhibits outperformed HOR activity with a current density of 0.676 mA cm-2 and mass activity of 1.43 mA μg-1, which are 15-fold and 40-fold improvement than those of Ru catalyst. In situ Raman spectra demonstrate the formation of S-H bond during the HOR process, identifying the S atom of RuS2-x is the real active site for HOR catalysis. Density functional theory calculations and experimental results including in situ surface-enhanced infrared absorption spectroscopy suggest the introduction of S vacancies can rationally modify the p orbital of S atoms, leading to enhanced binding strength between the S sites and H atoms on the surface of RuS2-x, together with the promoted connectivity of hydrogen-bonding network and lowered water formation energy, contributes to the enhanced HOR performance.

Activating and Identifying the Active Site of RuS2 for Alkaline Hydrogen Oxidation Electrocatalysis

AbstractSearching for highly efficient and economical electrocatalysts for alkaline hydrogen oxidation reaction (HOR) is crucial for the development of alkaline polymer membrane fuel cells. Here, we report a valid strategy to active pyrite‐type RuS2 for alkaline HOR electrocatalysis by introducing sulfur vacancies. The obtained S‐vacancies modified RuS2−x exhibits outperformed HOR activity with a current density of 0.676 mA cm−2 and mass activity of 1.43 mA μg−1, which are 15‐fold and 40‐fold improvement than those of Ru catalyst. In situ Raman spectra demonstrate the formation of S−H bond during the HOR process, identifying the S atom of RuS2−x is the real active site for HOR catalysis. Density functional theory calculations and experimental results including in situ surface‐enhanced infrared absorption spectroscopy suggest the introduction of S vacancies can rationally modify the p orbital of S atoms, leading to enhanced binding strength between the S sites and H atoms on the surface of RuS2−x, together with the promoted connectivity of hydrogen‐bonding network and lowered water formation energy, contributes to the enhanced HOR performance.

Precast concrete project image dataset for deep learning object detection

Computer vision technology in precast concrete (PC) projects has the potential to enhance site management but faces challenges due to the lack of specialized datasets for this field. This study developed a PC components dataset for object detection (PCCODA) in four steps: component range selection, image collection, image preprocessing, and labeling. The performance of PCCODA was assessed using multiple algorithms, including You Only Look Once (YOLO), YOLO X, faster region-based convolutional neural networks (Faster R-CNN), and Double Head R-CNN. The dataset resulted in average accuracies of 0.88–0.97, average robustness of 0.84–0.96, and average detection speed of 3.64–23.3 frames per second. This performance fulfills the level required by most studies on construction automation. The applicability of the dataset was tested at a real site. This study contributes to the off-site construction project management theory and enhances productivity in PC projects by automating object detection-related tasks.

A New Approach for Durable and Sustainable Infrastructure Concrete

The durability of reinforced concrete infrastructures is a major concern related to several aspects of the sustainability. The actual concrete design is still largely based on prescriptive indications, such as the cement content and the water cement ratio, while the durability investigation started to be oriented towards a more performance-based concept. The actual laboratory durability tests depend on the environmental exposure classes. Nonetheless, the actual stringent durability limits force to produce concrete with a too rapid strength development. This fact adversely affects the rigidity of the infrastructure and reduce the durability. Furthermore, the actual concrete requirement concepts partially inhibit the use of more sustainable concrete. This work proposes a new approach and a new concrete framework to produce more eco-efficient and more durable concrete. This will be attained by introducing a better laboratory-field relationship with respect to the durability by means of combined laboratory environmental attacks, which better simulate the real infrastructure site condition.

Evaluation of a single-layer urban energy balance model using measured energy fluxes by scaled outdoor experiments in humid subtropical climate

Urban energy balance models can simulate the localized climate and fluxes in urban areas. They are often evaluated using real urban site observations. However, uncertainties in anthropogenic emission, geometry and materials can hinder interpretation. These uncertainties can be eliminated by scaled outdoor models. We assess the performance of a single-layer urban canopy model (SLUCM) offline in predicting surface energy balance (SEB) fluxes within a street canyon-like urban configuration using field measurements from a scaled outdoor setup in Guangzhou, China. We utilize observations of SEB fluxes and surface temperature from a homogenous scaled urban site characterized by street canyons. The scaled observations in June–December 2020 in a dense urban environment with a height-to-width ratio (H/W) of 2 are employed to assess seasonal variations in model performance. Additionally, the December data from two distinct urban scenarios (H/W = 2(2020) and 0.5(2019)) are utilized to compare model performance in deep and shallow street canyon settings. Results show that QS(R2 = ∼0.47, RMSE = ∼46.0) is the most challenging to predict, followed by QH (R2 = ∼0.92, RMSE = ∼65.5), Q*(R2 = ∼0.99, RMSE = ∼39.5), L↑(R2 = ∼0.95, RMSE = ∼33.7), and K↑(R2 = ∼0.99, RMSE = ∼6.2). SLUCM faces difficulties simulating L↑ particularly during nighttime and cloudy conditions. It also tends to underestimate daytime net radiation (Q*), especially during hot, humid months. Moreover, H/W ratio slightly affects SLUCM's performance in simulating SEB fluxes under dry cool conditions denser urban settings (H/W = 2) show better model performance in K↑, QH, and QS but worse performance in L↑ and Q*.

Robust generation expansion planning in power grids under renewable energy penetration via honey badger algorithm

Robust reliability Generation Expansion Planning (GEP) turns out to be a crucial step for an efficient energy management system in a modern power grid, especially under renewable energy employment. The integration of all such components in a GEP model makes it a large-scale, nonlinear, and mixed-variable mathematical modeling problem. In this paper, the presence of wind energy uncertainty is analyzed. Both long and short-term uncertainties are incorporated into the proposed GEP model. The first step concerns the impact of long-term wind uncertainties through the annual variations of the capacity credit of two real sites in Egypt at Zafaranh and Shark El-ouinate. The second step deals with the short-term uncertainties of each wind site. The wind speed uncertainty of each wind site is modeled by probability distribution function. Then, wind power is estimated from the wind power curve for each wind site and Monte-Carlo Simulation is performed. Fast Gas Turbine and/or Pump Hydro Storage are incorporated to cope with short-term uncertainties. Sensitivity analysis is implemented for 3, 6, and 12 stages as short and long planning horizons to minimize the total costs with wind energy penetration and emission reduction over planning horizons. Also, a novel Honey Badger Algorithm (HBA) with model modifications such as Virtual Mapping Procedure, Penalty Factor Approach, and the Modified of Intelligent Initial Population Generation is utilized for solving the proposed GEP problem. The obtained results are compared with other algorithms to ensure the superior performance of the proposed HBA. According to the results of the applicable test systems, the proposed HBA performs better than the others, with percentage reductions over CSA, AO, BES, and PSO ranging up to 4.2, 2.72, 2.7, and 3.4%, respectively.