Optimal Placement Research Articles

Multi-stage optimization placement of DPMUs based on node metric indices

The proliferation of distributed energy resources and the introduction of new loads in distribution networks present significant challenges for monitoring and operation. To satisfy the enhanced observability and controllability requirements of modern distribution networks, there is an increasing demand for advanced monitoring devices. Distribution Network Phasor Measurement Units (DPMUs) offer high-precision measurement data with precise timestamps, thereby improving both the accuracy and redundancy of measurements within the distribution network.This paper introduces an optimization model for the strategic placement of PMUs within distribution networks, leveraging node metric indices. The indices considered are node degree, spatiotemporal correlation, and node power ratio. The relative importance of these indices is determined using an improved entropy weight method, which quantifies the differentiation of nodes within the network. This method facilitates the prioritized placement of DPMUs at critical nodes. The proposed model also incorporates constraints such as the depth of unobservability and zero injection nodes. Utilizing a 0–1 integer programming algorithm, the model derives a multi-stage optimal placement scheme for PMU placement. This scheme evolves from incomplete observability to critical observability and ultimately to full redundancy. Importantly, this approach allows for the monitoring of key nodes within the distribution network and enhances measurement redundancy without necessitating an increase in the number of placements.

Simulation of tomato and corn growth using a modified intercropping model considering radiation interception in two-dimensional space and air temperature stress

Intercropping system has been widely applied in the agricultural production worldwide. However, the difference in agricultural productivity in tomato–corn intercropping system with different spatial arrangements has not been determined. Meanwhile, the existing intercropping models cannot precisely capture inter-species crop growth dynamics. Therefore, a 10-year (2010–2019) experimental field study was conducted in Hetao irrigation district, China, for the parameterization and evaluation of the model. The experiment data from the typical years (2018 and 2019) was adopted to analyze the crop growth dynamics in different hydrological years and different systems [two rows of tomato intercropping two rows of corn (IC2–2), four rows of tomato intercropping two rows of corn (IC4–2), sole corn (SC), and sole tomato (ST)]. Moreover, a modified intercropping model considering radiation interception in two-dimensional space and air temperature stress (MICG) was deduced. The results revealed that the MICG model can precisely simulate inter-species crop growth in tomato–corn intercropping system with different spatial arrangements. The mean relative error (MRE), normalized root mean square error (nRMSE), determination coefficient (R2), and percentage bias (PBIAS) of leaf area index (LAI) simulated by the MICG model in different spatial arrangements were 9.0 %, 7.8 %, 0.97, and –5.3 % for corn and 11.0 %, 7.8 %, 0.95, and 0.1 % for tomato, respectively. An apparent difference in the simulation accuracy was observed among different intercropping models in the rapid and senescence crop growth stages. The simulation accuracy of the MICG model was apparently higher than that of ICGw and ICG models. However, the MICG model still needs to be further improved considering its some limitations, e.g., the soil respiration and soil nitrogen transformation modules need be introduced in this model. Additionally, an apparent difference in crop growth dynamics in different spatial arrangements was observed, particularly in different hydrological years. The largest difference in crop growth dynamics in different spatial arrangements was observed in the middle crop growth stage (days after sowing [DAS] 20–100) in the wet year and in the late crop growth stage (DAS 101–140) in the dry year. In general, the highest aboveground biomass and yields of corn and tomato were observed in the IC2–2 and ST systems, respectively. However, the highest water use efficiency for corn (23.9) and tomato (268.7) was observed in the IC4–2 and ST systems, respectively. The agricultural production benefits in the IC4–2 system were better than in the IC2–2 system. Average land equivalent ratio and water equivalent ratio in both years increased by 0.8 % and 12.5 % in the IC4–2 system compared with those in the IC2–2 system, respectively. Overall, the IC4–2 system could be recommended as the most optimal spatial arrangement for the tomato–corn intercropping system.

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

Novel optimal sensor placement method towards the high-precision digital twin for complex curved structures

The complex shape of the structure and the new needs for high-precision in digital twin modeling pose challenges for sensor placement optimization. A novel optimal sensor placement towards the high-precision digital twin (OSP-HDT) method is proposed for complex curved structures. It comprises three key aspects. Firstly, leveraging the spatial dimensionality reduction method, the complex curved surface is simplified into a planar representation. Subsequently, candidate sensor placement points can be easily identified by dividing the background mesh in the plane and screening them within the curved surface. These candidate points are then binary encoded to facilitate the subsequent optimization. Secondly, the method collects result data from the finite element model, treating it as virtual sensor data. Using this data, a surrogate model is constructed and then the objective function is formulated based on both the global and local critical areas precision of the surrogate model. Thirdly, the sensor placement optimization model is constructed, followed by optimization design using the efficient multi-objective covariance matrix adaptive evolutionary strategy. Through the steps above, the optimal sensor placement can be identified. To validate the proposed OSP-HDT method, an experiment is conducted on an S-shaped variable cross-section stiffened shell, with the construction of the corresponding digital twin. Compared to the uniform placement with an equivalent number of sensors, the OSP-HDT method demonstrated a significant 9.0% improvement in global precision and a remarkable 62.1% enhancement in local precision of critical areas. Furthermore, when compared to the random sensor placement strategies, the OSP-HDT method exhibited a 20.5% increase in global precision, together with a 44.2% increase in the local precision. Notably, even when compared to the full sensor placement, the OSP-HDT method can maintain comparable local precision, while significantly reducing the number of sensors by 77.6%. The above comparison indicates that the proposed OSP-HDT method can build a digital twin model with higher global and local precision for complex structures.

SPLANDID — Optimal Sizing, PLacement, And management of centralized aNd DIstributed shareD battery energy storage systems in residential communities: Application to smart grids

This paper introduces SPLANDID, a novel techno-economic methodology for the optimal sizing, placement, and management of shared Battery Energy Storage Systems (BESSs) in residential communities that minimizes both capital and operational costs, along with energy losses within the community. To address the installation of two types of shared BESSs (i.e., single centralized BESS, multiple distributed BESSs), our methodology offers two distinct approaches: one optimizes the centralized BESS, while the other focuses on optimizing distributed BESSs. We formulate each approach as a constrained optimization problem and solve it using the particle swarm optimization (PSO) algorithm. To validate our methodology, we use real consumption and production patterns collected from households in a residential community in the United Kingdom (UK). We propose and compare three scenarios (i.e., no BESS installation, single BESS installation, and multiple BESSs installation) using various numerical metrics, such as total energy losses and total costs. Simulation results underscore the effectiveness of BESS installation, demonstrating an impressive 82.24% reduction in total costs compared to the benchmark scenario without BESS. Moreover, the installation of multiple distributed BESSs outperforms a single centralized BESS, reducing total costs and energy losses by 17.4% and 49.4%, respectively. Furthermore, the distributed installation proves efficient by decreasing the required storage capacity by 11.82% in contrast to the centralized approach. Compared to the large existing body of literature, this study contributes on several fronts. First, it explores the feasibility of installing multiple distributed shared BESSs in residential communities, departing from the dominant single centralized shared BESS installation in existing studies. The results obtained with this alternative installation strategy are highly promising from several perspectives. Secondly, our methodology is uniquely designed to base planning on actual batteries available on the market, enhancing its practical applicability in real-life scenarios. This approach contrasts with studies that often propose optimal sizes without considering the constraints imposed by the capabilities of existing batteries.

Structural damage identification based on dual sensitivity analysis from optimal sensor placement

Structural damage identification (SDI) methods using incomplete modal information can avoid the extension for unmeasured degrees of freedom, but the absence of essential damage information often leads to the failure of SDI. To address this problem, a novel SDI method based on dual sensitivity analysis and optimal sensors placement technique is proposed in this study. Firstly, in the optimal sensor placement technique, an improved eigenvector sensitivity method combined with weighted modal kinetic energy is proposed, which enables the acquisition of eigenvector information related to damage sensitivity, and incorporates it into the modal strain energy sensitivity matrix to obtain the dual sensitivity analysis matrix. Then, the sparsity of structural damage is considered, and the L1 sparse regularization is selected and introduced into the dual sensitivity analysis damage equation for better SDI results. Finally, to assess the effectiveness of the proposed method, a series of numerical simulations and experimental verifications were carried out under different structural damage scenarios. The results indicate that the proposed method can efficiently localize and quantify the structural damage with minimal modal information in one single step.

Study of vortex dynamics in a solar tower vortex generator

Vortical flows, integral to fluid mechanics, significantly influence the performance and efficiency of various engineering applications. Utilizing a validated three-dimensional computational model, this study characterizes vortex structures and examines the impact of ambient conditions and the strategic positioning of vortex generators (swirler) at critical geometric locations, such as the throat, and regions above and below it, within a converging–diverging solar tower. The research explores the influence of swirler position on recirculation zone characteristics, focusing on center-to-center (C2C) distance, penetration length, and maximum width. When the swirler is positioned above or below the throat, C2C distance shows minimal change. However, placing the swirler exit at the throat significantly reduces C2C distance at the lowest velocity (0.13 m/s), due to a stable recirculation zone. This effect diminishes at higher velocities. Swirler position greatly affects penetration length, resulting in two distinct behaviors: Behavior A, dominated by swirler position, occurs when the swirler is above or below the throat, and Behavior B, which is velocity-dependent, is observed when the swirler exit is at the throat. The study also investigates the interaction between the recirculation zone and swirler exit flow, focusing on restriction and mixing ratios. The restriction ratio is higher below the throat (31 %) compared to the throat (27 %). The mixing ratio shows minimal variation with inlet velocity but significant variation with swirler position. Among the evaluated positions, placing the swirler below the throat results in the lowest swirl strength for most axial distances, except near the exit height, rendering it less suitable for water harvesting applications. These findings provide guidance for optimal swirler placement to achieve desired recirculation zone control and system performance.

Reinforcement learning-based charging cluster determination algorithm for optimal charger placement in wireless rechargeable sensor networks

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms.

A modified Runge–Kutta optimization for optimal photovoltaic and battery storage allocation under uncertainty and load variation

The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Assessment of different optimal sensor placement methods for dynamic monitoring of civil structures and infrastructures

The optimal sensor placement is a critical aspect in the structural health monitoring of structures, as the number and layout of sensors directly impact the quality and usefulness of collected data. When developing a monitoring system, it is of paramount importance to use the minimum number of sensors to gain the maximum amount and quality of information. However, in the case of a limited number of sensors, their location on the structure becomes crucial. This study aims to assess the potential of several optimal sensor placement methods for the development of cost-efficient and well-dimensioned dynamic structural health monitoring systems to be implemented in a wide range of civil engineering structures and infrastructures. Indeed, the construction stock consists of a great variety of civil engineering structures and infrastructures that significantly differ from each other in terms of typology, historical period, construction techniques, materials, etc. Some of the most widely used optimal sensor placement methods are selected and applied in real case studies, and the obtained sensor configurations are discussed and compared. The results illustrated in this paper will contribute to defining good practice in the optimal sensor placement procedure for typical civil engineering structures and infrastructures.

Computational optimisation and modelling of sacrificial anode placement and dimension for maximising the corrosion prevention of screw piles

Computational optimisation and modelling of sacrificial anode placement and dimension for maximising the corrosion prevention of screw piles

Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

The transition to Electric Vehicles (EVs) for reducing urban greenhouse gas emissions is hindered by the lack of public charging infrastructure, particularly fast-charging stations. Given that electric vehicle fast charging stations (EVFCS) can burden the electricity grid, it is crucial for EVFCS to adopt sustainable energy supply methods while accommodating the growing demands of EVs. Despite recent research efforts to optimize the placement of renewable-powered EV charging stations, current planning methods face challenges when applied to a complex city scale and integrating with renewable energy resources. This study thus introduces a robust decision-making model for optimal EVFCS placement planning integrated with solar power supply in a large and complex urban environment (e.g., Chicago), utilizing an advantage actor-critic (A2C) deep reinforcement learning (DRL) approach. The model balances traffic demand with energy supply, strategically placing charging stations in areas with high traffic density and solar potential. As a result, the model is used to optimally place 1,000 charging stations with a random starting search approach, achieving total reward values of 74.30 %, and estimated the capacities of potential EVFCS. This study can inform the identification of suitable locations to advance the microgrid-based charging infrastructure systems in large urban environments.

Image-guided patient-specific optimization of catheter placement for convection-enhanced nanoparticle delivery in recurrent glioblastoma

BackgroundProper catheter placement for convection-enhanced delivery (CED) is required to maximize tumor coverage and minimize exposure to healthy tissue. We developed an image-based model to patient-specifically optimize the catheter placement for rhenium-186 (186Re)-nanoliposomes (RNL) delivery to treat recurrent glioblastoma (rGBM). MethodsThe model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient (CCC) was used to quantify the agreement between the predicted and measured SPECT signals. The model was then used to simulate RNL distributions from a range of catheter placements, resulting in a ratio of the cumulative RNL dose outside versus inside the tumor, the “off-target ratio” (OTR). Optimal catheter placement) was identified by minimizing OTR. ResultsFifteen patients with rGBM from a Phase I/II clinical trial (NCT01906385) were recruited to the study. Our model, with either individual-calibrated or population-assigned parameters, achieved high accuracy (CCC > 0.80) for predicting RNL distributions up to 24 h after delivery. The optimal catheter placements identified using this model achieved a median (range) of 34.56 % (14.70 %–61.12 %) reduction on OTR at the 24 h post-delivery in comparison to the original placements. ConclusionsOur image-guided model achieved high accuracy for predicting patient-specific RNL distributions and indicates value for optimizing catheter placement for CED of radiolabeled liposomes.

Optimal layout of four anchors to improve accuracy of Ultra-Wide band based indoor positioning

Despite extensive research on three-dimensional (3D) indoor positioning algorithms, there remains room for improvement in the optimal placement strategy for four ultra-wideband (UWB) anchors in line-of-sight (LOS) situations. We conducted 20 observations at five different heights within a 210 m3 experimental area, performing a total of 4845 weighted trilaterations. The experimental results demonstrated that a larger virtual polyhedron volume between the Mobile Terminal (MT) and the anchors resulted in a smaller 3D error. This 3D error-volume relationship was statistically verified. Furthermore, this study revealed that the mask angle can be disregarded in indoor positioning and that anchor placement need not be limited to the Z-axis, unlike Global Navigation Satellite System (GNSS). Thus, all axes should be considered within a cuboid in space. Consequently, the optimal anchor placement to minimize the 3D error is one that maximizes the volume between the anchors and the MT along any direction in the three-dimensional space.

Optimization of Sensor Placement for a Measurement System for the Determination of Local Magnetic Material Properties

The aim of this work is to optimize the sensor positions of a sensor–actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before solving the actual identification problem, i.e., finding the material distribution, the sensor placement of the measurement setup should be improved in order to reduce the uncertainty of the identification of the material distribution. The Fisher information matrix (FIM), which allows one to quantify the amount of information that the measurements carry about the unknown parameters, is used as the main metric for the objective function of this design optimization. The forward problem is solved by the finite element method. The results show that the proposed method is able to find optimal sensor positions as well as the minimum number of sensors to achieve a desired maximum parameter uncertainty.

Optimal Implant Positioning Following Total Knee Arthroplasty Using Predictive Dynamic Simulation.

In this paper, a novel method is proposed for the determination of the optimal subject-specific placement of knee implants based on predictive dynamic simulations of human movement following total knee arthroplasty (TKA). Two knee implant models are introduced. The first model is a comprehensive 12-degree-of-freedom (DoF) representation that incorporates volumetric contact between femoral and tibial implants, as well as patellofemoral contact. The second model employs a single-degree-of-freedom equivalent kinematic (SEK) approach for the knee joint. A cosimulation framework is proposed to leverage both knee models in our simulations. The knee model is calibrated and validated using patient-specific data, including knee kinematics and ground reaction forces. Additionally, quantitative indices are introduced to evaluate the optimality of implant positioning based on three criteria: balancing medial and lateral load distributions, ligament balancing, and varus/valgus alignment. The knee implant placement is optimized by minimizing the deviation of the indices from their user-defined desired values during predicted sit-to-stand motion. The method presented in this paper has the potential to enhance the results of knee arthroplasty and serve as a valuable instrument for surgeons when planning and performing this procedure.

How building arrangements can improve outdoor thermal comfort and indoor sunlight?

In the context of frequent extreme hot weather conditions, high-dense urban areas posed more life-threatening challenges. Such as university dormitories, as important residential spaces, have received very little attention. Moreover, how building arrangements can simultaneously impact internal and external environments remain poorly understood. To bridge this gap, student residential areas of Jiangsu University in Zhenjiang, China were considered as a case study, with the aim of optimal building arrangements that can simultaneously improve the outdoor thermal comfort (OTC) and indoor sunlight (ISL). A combination of on-site measurement and numerical models were employed to simulate various scenarios with different building orientations, building heights and layouts. The result revealed that: first, the building orientation emerged as a dominant factor influencing both OTC and ISL, with the optimal value identified as SE-30°. Second, in the context of extremely hot weather conditions, increasing building height or floor-area ratio contributed to improved OTC. Finally, the building arrangement with inconsistent heights of north-to-south descending order, exhibited a notable reduction in hot outdoor spaces by 18.6%, and attributed to enhanced airflow. The findings provide clear guidelines for improved strategies at the detailed urban design scale, and can further contribute to the development of sustainable cities.

Optimal Placement of Multiple Sources in a Mesh-Type DC Microgrid Using Dijkstra’s Algorithm

This research paper introduces an optimization methodology for the strategic electric sources’ placement at multiple positions in a DC islanded microgrid characterized by a mesh network, aiming to minimize line losses while considering minimal cable weight. The DC microgrid studied in this paper is composed of PV panels, batteries, a diesel generator, and 20 residential loads. Employing Dijkstra’s algorithm, a graph algorithm used in Google Maps, the study identifies the shortest path (resistance) between potential source nodes and various variable loads within a predefined electric distribution mesh network topology. This study focuses on active power considerations and offers valuable insights into the placement optimization of multiple sources’ positions in DC microgrid mesh networks. A key contribution of this paper lies in the ranking of source node positions based on minimal to maximal line losses, taking into consideration optimal cable weights, while using MATPOWER to validate sources’ ranking based on Dijkstra’s hypothesis. The research further includes a techno-economic study to assess the viability of sources’ placement at multiple positions within the mesh network, comparing it with the optimal placement scenario involving a single position for all sources. This methodology serves as a valuable resource for system designers and operators aiming to minimize line losses and optimize energy distribution in DC microgrids in a mesh topology.

Laparoscopic caudate lobe resections: How I do it: tips and pitfalls (with video).

The caudate lobe (S1) of the liver, due to its deep central position, presents a formidable challenge for laparoscopic resection. Historical skepticism about laparoscopic approaches has been overshadowed by advancements in technology and technique, with recent studies showing comparable outcomes to open surgery. This paper introduces the "Easy First" technique and the Sextet strategies for laparoscopic hepatic caudate lobectomy. The strategies include meticulous preoperative planning, optimal trocar placement, and team positioning, tailored to the anatomical complexities of the caudate lobe. With a 0% conversion and mortality rate, our series demonstrates the safety of the "Easy First" technique. The Sextet strategies have been instrumental in navigating the technical challenges, emphasizing the importance of patient selection and surgeon expertise. The "Easy First" technique, with its structured approach and the Sextet strategies, offers a replicable method for laparoscopic caudate lobectomy. It underscores the need for stringent patient selection, advanced technical skill, and high-volume center expertise to ensure procedural success and patient safety.