Energy Transport Research Articles

Risk-aware electricity dispatch with large-scale distributed renewable integration under climate extremes

Distribution networks, with large-scale integration of distributed renewable resources, particularly rooftop solar photovoltaic systems, represent the most extensive yet vulnerable components of modern electric power systems during climate extremes such as hurricanes. However, existing day-ahead electricity dispatch approaches primarily focus on the transmission network and lack the capability to manage the spatiotemporal risks associated with the vast distribution networks, which can potentially lead to significant power imbalances due to the mismatches between scheduled generation and actual demand. To address this increasingly critical gap under intensifying climate extremes and growing distributed renewable integration, we introduce Risk-aware Electricity Dispatch under Climate Extremes with Renewable integration (REDUCER), a risk-aware day-ahead electricity dispatch model that incorporates high-resolution spatiotemporal risk analysis for distribution networks with large-scale distributed renewable integration into an Entropic Value-at-Risk-constrained mixed-integer convex optimization framework. Applied to the 2022 Puerto Rico power grid under Hurricane Fiona, the proposed REDUCER model is seen to effectively manage these risks with substantially less reliance on additional flexibility resources to cope with power imbalances, reducing overall operational costs by about 30% under extreme cases compared to standard unit commitment strategies already informed by average demand loss. Also, the proposed REDUCER model consistently demonstrates its effectiveness in managing the increasing temporal net demand variability introduced by growing large-scale distributed solar integration while maintaining minimal operational costs. This model offers a practical solution for cost-effective and resilient electricity dispatch of modern power systems with large-scale renewable integration facing intensifying climate risks.

A Study on the Device Topology and Control Strategy of a Hybrid Three-Port Photovoltaic Energy Storage Grid-Connected Converter

A grid-connected converter is the interface between renewable energy power generation systems, such as solar power generation, wind power, hydropower, etc., and the power grid, responsible for the stable and efficient transmission of electric energy generated by renewable energy power generation systems to the grid. In order to realize local access for distributed photovoltaic power generation devices and energy storage devices, a composite three-port converter has the advantages of small size, low cost and high power density compared with a combined three-port converter. In view of the current problems of the existing compound three-port (AC/DC/DC) converters, such as DC and AC circulating current in current composite three-port converters and the harmonic control problem, the proposed compound three-port topology consists of a full-bridge inverter with six switching tubes, a zigzag transformer, two sets of filter inductors and two filter capacitors. Among them, the power frequency transformer adopts the zigzag connection method, which can effectively restrain the AC circulation and eliminate the DC magnetic flux of the iron core while introducing the third port. Firstly, the principle of AC/DC and DC/DC power conversion in the composite three-port topology is analyzed, which has higher efficiency than other topologies. Secondly, the topology control strategy is analyzed, and a two-loop hybrid current control method with improved current loop is proposed. When the DC-side voltage fluctuates, the DC offset of the battery can effectively improve the stability of the network side. Through the MATLAB/Simulink simulation experiment platform, the high efficiency of energy conversion and stable grid-connected operation characteristics are verified. Finally, the experiment of integrating into the power grid was carried out. Experiments were used to verify the effectiveness and feasibility of the proposed topology and strategy. The experimental results show that Total Harmonic Distortion (THD) can be controlled below 3%.

Advances in Operation, Optimization, and Control of Smart Grids

Power systems are equipped with a set of equipment for the generation, transmission, and distribution of electrical energy to consumption centers in a continuous manner and with quality indices [...]

Analysis of the Carbon Footprint of a Textile Company for the Automotive Industry

This study aims to develop a process to calculate the carbon footprint of a company in the textile sector for the automotive industry, thus addressing a research gap identified in this sector. Based on a structured calculation model, the project aspires to innovate by quantifying not only the greenhouse gas emissions at different stages of the company’s operations, including those generated by the consumed electricity and gas, but also the emissions related to external and in-house transportation and solid waste management. The approach includes the design of a specific calculator, capable of integrating variables such as energy consumption, transport and types of waste, analysing them in the light of recognised conversion factors. This tool not only allows for a detailed assessment of emissions but also supports strategic decision-making, guiding the implementation of more sustainable business practices. The results indicate that, considering the use of renewable energy sources, the company’s total emissions amount to approximately 18 thousand tonnes of carbon dioxide equivalent. On the other hand, considering non-renewable energy, purchased electricity accounts for 31 thousand megawatt-hours per year, corresponding to 5 thousand tonnes of carbon dioxide equivalent, with the twisting area being the largest consumer at 89% of total usage, followed by the dipping area. In terms of mobile combustion, raw materials contribute 1373 million tonnes of carbon dioxide equivalent, while finished products generate 1869 million tonnes of carbon dioxide equivalent. Among the most impactful variables, solid waste, and stationary combustion stand out as the main contributors. These findings highlight the need for concrete measures to mitigate climate change, such as transitioning from stationary natural gas combustion to green electric power; identifying companies with more suitable waste treatment solutions, process changes that reduce disposable, and easily substitutable materials; making use of green electricity; exploring alternative transport methods or combining different modes, such as using electric vehicles for short distances; and optimizing transport routes. These initiatives reinforce the company’s commitment to sustainable development goals and the promotion of responsible environmental practices.

Design and Optimization of 30/40MVA, 132/33kV Power Transformer Using Responses Surface Methodology

Transformer is the main apparatus of the power system for both transmission and distribution of electrical energy. It is the important component of electrical engineering because of its high efficiency and helps in step up and step down the voltage, impedance matching and circuit isolation. Team of designers, engineers and building owners struggled for high performance in order to maximize the transformer efficiencies and minimized losses, weight, volume and costs. Design and selection of material to construct a transformer core is a significant process. When designing transformers, especially power transformers, incorrect sizing of active components such as core geometry, low voltage winding and high voltage winding dimensions and tank dimensions can cause additional losses in the transformer. Determining these parameters at the design stage using optimization techniques has a very significant impact on the efficiency and cost effectiveness of the transformer. The purpose of this work is to design and optimized a practical 30/40MVA, 132/33kV, three phase power transformer using Octave and Responses Surface Methodology (RSM). From the work, it was concluded that the optimization of power transformer gives more accurate results as compared to the assume values. The percentage variations of core loss and copper loss in the optimized power transformer with respect to classical value were 4.35% and 13.48%, while that of efficiency was 0.14%.The percentage variations of reactance as well as the core area with classical values were 29.22% and 14.73%. Thus, for the accurate analysis of the result, it is important to optimize the power transformer.

Overview of the Research on Improved YOLO Model in Insulator Defect Detection

People’s everyday lives depend on electricity, and insulators are essential to the reliable and safe functioning of high-voltage transmission lines. Therefore, defect detection of insulators has always been a concern for many research teams and researchers. The You Only Look Once(YOLO) series algorithm has always been the most widely used algorithm in the research of insulator defect detection. This paper seeks to examine the future directions for enhancing the YOLO model, evaluate the research problems in the field of insulator defect detection, and methodically study the pertinent theories for doing so. First, the paper outlines the main research progress in the field of insulator defect detection. Then focus on discussing the basic structure, dataset, improvement research, and experimental results of the three generations of YOLOv3, YOLOv5, and YOLOv8 models. Based on the above review, the paper proposes directions for enhancing the dataset of insulator defects and provides insights on improving the model for insulator defect detection. In addition, this article also points out the difficulties in collecting datasets in current research, providing research suggestions for future researchers.

Automatic construction of risk transmission network about subway construction based on deep learning models

Safety risks management is a critical part during the subway construction. However, conventional methods for risk identification heavily rely on experience from experts and fail to effectively identify the relationship between risk factors and events embedded in accident texts, which fail to provide substantial guidance for subway safety risks management. With a dataset comprising 562 occurrences of subway construction accidents, this study devised a domain-specific entity recognition model for identifying safety hazards during the subway construction. The model was constructed by a Bidirectional Long Short-Term Memory Network with Conditional Random Fields (BiLSTM-CRF). Additionally, a domain-specific entity causal relation extraction model employing Convolutional Neural Networks (CNN) was also developed in thsi model. The constructed models automatically extract safety risk factors, safety events, and their causal relationships from the texts about subway accidents. The precision, recall, and F1 scores of Metro Construction Safety Risk Named Entity Recognition Model (MCSR-NER-Model) all exceeded 77%. Its performance in the specialized domain named entity recognition (NER) with a limited volume of textual data is satisfactory. The Metro Construction Safety Risk Domain Entity Causal Relationship Extraction Model (MCSR-CE-Model) achieved an impressive accuracy, recall, and F1 score of 98.96%, exhibiting excellent performance. Moreover, the extracted entities were normalized and domain dictionary was developed. Based on the processed entities and relationships processed by the domain dictionary, 533 domain entity causal relation triplets were obtained, facilitating the establishment of the directed and unweighted complex network and case database about the risks of subway construction. This research successfully converted accident texts into a causal chain structure of “safety risk factors to risk events,” providing detailed categorization of safety risks and events. Concurrently, it revealed the interrelationships and historical statistical patterns among various safety risk factors and categories of risk events through the complex safety risks network. The construction of the database facilitated project managers in conducting management decisions about safety risks.

Determining Equations for a Wave Equation Arising Due to Collapse of Shafts in Power Transmission System

The study of wave propagation in mechanical systems plays a crucial role in understanding the dynamic behavior of components under stress or failure. One such scenario is the collapse of shafts in power transmission systems, where the sudden failure of structural components can trigger wave-like disturbances that propagate through the system, potentially causing further damage and system-wide disruptions. The ability to predict and analyze these wave phenomena is essential for ensuring the stability, safety, and efficiency of power transmission networks. In this paper, we study a fourth-order nonlinear wave equation which arises due to collapse of shafts in power transmission systems. Lie symmetry analysis is used to derive the corresponding determining equations that describe the symmetries of the system, which can then be used to reduce the problem to lower-order equations, find exact solutions, or gain insights into the underlying dynamics. By applying Lie group analysis, we systematically prolonged the corresponding infinitesimal generator and applied the symmetry condition to the given wave equation to yield the required determining equations. These equations enable a pathway to exact solutions and system simplification. The work reinforces the value of symmetry-based methods in understanding the dynamic behavior of mechanical systems under collapse-induced wave propagation.

Accelerating two-dimensional electronic spectroscopy simulations with a probe qubit protocol

Two-dimensional electronic spectroscopy (2DES) is a powerful tool for exploring quantum effects in energy transport within photosynthetic systems and investigating novel material properties. However, simulating the dynamics of these experiments poses significant challenges for classical computers due to the large system sizes, long timescales, and numerous experiment repetitions involved. This paper introduces the probe qubit protocol (PQP)-for quantum simulation of 2DES on quantum devices-addressing these challenges. The PQP offers several enhancements over standard methods, notably reducing computational resources, by requiring only a single-qubit measurement per circuit run and achieving Heisenberg scaling in detection frequency resolution, without the need to apply expensive controlled evolution operators in the quantum circuit. The implementation of the PQP protocol requires only one additional ancilla qubit, the probe qubit, with one-to-all connectivity and two-qubit interactions between each system and probe qubits. We evaluate the computational resources necessary for this protocol in detail, demonstrating its function as a dynamic frequency-filtering method through numerical simulations. We find that simulations of the PQP on classical and quantum computers enable a reduction on the number of measurements, i.e., simulation runtime, and memory savings of several orders of magnitude relative to standard quantum simulation protocols of 2DES. The paper discusses the applicability of the PQP on near-term quantum devices and highlights potential applications where this spectroscopy simulation protocol could provide significant speedups over standard approaches such as the quantum simulation of 2DES applied to the Fenna-Matthews-Olson (FMO) complex in green sulfur bacteria. Published by the American Physical Society 2025

Improved subseasonal prediction of Atmospheric Energy Transport over the Northwest Pacific using an atmosphere-ocean coupled model

Abstract Poleward transport of energy is essential for reducing the Earth’s energy imbalance. During boreal summer, equatorward dry static energy transport (DSET) and poleward latent heat transport (LHT) are located in the upper and lower tropical atmosphere, respectively. This study investigates changes in meridional atmospheric energy transport and their underlying mechanisms using a global coupled atmosphere-ocean model. In the July 2017 simulations, the coupled model better captures the phase variation of DSET and exhibits a small LHT at 20°N (approximately 1 PW) compared with the standalone atmospheric model. Ocean coupling decreases meridional gradient of ocean skin temperature, which mitigates the poleward propagation of convection. Consequently, the well-established meridional circulation not only increases low-level divergence but is also linked to changes in the spatial distribution of moisture. Our results show that ignoring the seasonal variability in ocean temperature can accelerate the hemispheric energy imbalance through the northwest Pacific region.

Investigating the Influence of Renewable Energy Use and Innovative Investments in the Transportation Sector on Environmental Sustainability—A Nonlinear Assessment

Ecologically sustainable economic development is increasingly recognized as essential to global efforts to improve and protect environmental and socio-economic conditions. The transportation sector is also important regarding the movement of human beings and goods. Fossil fuels are primarily used in transport vehicles and emit carbon dioxide into the atmosphere. Hence, innovative investments in the transportation system and the use of renewable energy play a key role in overcoming this lingering problem. This study utilizes nonlinear autoregressive distributed lag (NARDL) methods to uncover key drivers influencing innovative investments in the transportation sector and the impact of renewable energy use on environmental sustainability in France between 1995 and 2020. The results indicate that renewable energy use and transport infrastructure innovations positively and negatively impact environmental sustainability. Both variables have different influences on the dependent variable depending on the economic shock period. Based on the outcomes, this study offers the following significant policy insights: (i) France could invest in innovations in renewable energy sourcing and incentivize switching from combustion engine-based transport systems. (ii) France should commit to the Europe 2020 strategy for green growth to ensure resource efficiency and promote environmental sustainability, which requires a coordinated effort to invest in smart transport systems that leverage technologies like the Internet of Things, artificial intelligence, and big data analytics. (iii) Given that two-thirds of France’s electricity is produced from nuclear sources, the government needs to implement policies in the renewable energy sector to reduce over-reliance on nuclear energy sourcing.

Exposure-based smart ventilation and occupancy control for optimizing ventilation energy consumption and long-range airborne transmission of COVID-19 in school environments

Abstract Mechanical ventilation is an effective measure to control indoor long-range airborne transmission of COVID-19, but it often leads to substantial energy expenditure. This study introduces a novel exposure-based smart ventilation and occupancy control strategy to reduce infection risk and save energy in school environments that are typically characterized by fixed occupants and long exposure time. This exposure-based approach allows the quanta concentration to vary over time rather than keeping it constantly below certain thresholds. This enables us to: (1) adjust ventilation and occupant schedule to facilitate passive cooling/heating potential in response to outdoor weather conditions; (2) consider the interaction between ventilation and occupant schedule to maximize their benefits in reducing infection risk and energy consumption. Taking a typical classroom as a base case, ventilation and occupant schedule are optimized individually and jointly through Genetic Algorithm, to control infection risk, minimize energy consumption, maintain thermal comfort, and promise sufficient schooling time. Our results show that the most energy-efficient strategy is the concurrent optimization of both occupant schedule and ventilation, achieving an energy reduction of up to ∼60% compared to traditional constant ventilation methods. Solely optimizing occupant schedule is the least energy-efficient strategy, yielding an energy reduction ratio (over base case) of only half of the most efficient strategy. Our study reveals the possibility of optimizing occupant schedule and ventilation to balance building energy consumption and transmission control. The viability of these control strategies has been proven across various climate zones and seasons in China, highlighting their broad applicability.

Selection of Criteria for Load Analysis on Electrical Grid in the Context of Transport Electrification in Latvia

The following research paper addresses the pressing issue of the projected load on the electricity grid in the context of transportation electrification in Latvia. This is examined in relation to potential Electric Vehicle (EV) growth models, the current status, and implementation plans aimed at achieving the goals set by the Latvian Updated National Energy and Climate Plan 2021–2030, the Alternative Fuels Infrastructure Regulation (AFIR), and the Renewable Energy Directive (RED). The analysis considers local legislation, electricity and Renewable Energy Sources (RES) production and consumption profiles, as well as the charging habits of EV owners. The goal of this study is to create a comprehensive model for electricity demand prediction with Distributed Energy Solutions (DES) and Smart Charging capabilities for grid infrastructure development. According to research conducted by Danish scientists, “in a favorable scenario where 25 % of vehicles are electric by 2030, they are expected to contribute approximately 4 % to the peak load on a winter workday between 6 and 8 PM. In the worst-case scenario, they contribute 15 % to the peak load. In 2040, 80 % of the vehicles are expected to be electric and contribute between 15 % and 55 % to the annual peak consumption in Denmark.” The tendency behind shift in peak loads stresses the need for proactive grid planning highly relevant to Distribution System Operators (DSOs). The current grid development related to public ‘Destination’ EV charging primarily responds based on unsystematic requests for electricity connections by Charging Point Operators (CPOs) and an internally developed general simultaneity coefficient methodology. Furthermore, the ‘Highway’ EV charging infrastructure within Trans-European Transport Network (TEN-T) must be built from scratch in accordance to AFIR regulation, with specific attention given to the core sections as well as the transport flow context to the comprehend sections of it. The author also examines the shift in the EV charging profile, from predominantly “Home” overnight Alternating Current (AC) slow charging to Direct Current (DC) fast charging on-the-go. The modeling of network system scenarios for case of Latvia considers changes in charging patterns relatively to Electric Vehicle Charging Infrastructure (EVCI) availability by System Thinking approach. The causal loop diagram incorporates not only social (users’ habits), economic (growing portfolio of new affordable and used EVs, and future price parity with Internal Combustion Engine Vehicles (ICEV)), and governmental (incentives, tax tariffs, and fines for missed CO2 targets) part, but also the technical – flexible transportation electrification options such as Battery Energy Storage Systems (BESS) and Vehicle-to-Grid (V2G). As a result, the paper states that the change of charging habits and, thus, a raising public EV Charging Infrastructure growth and its utilization rate could severely impact the peak load and must be addressed.

Analytical and numerical solutions of equilibrium models for solvent-temperature gradient elution chromatography

This study presents analytical and numerical investigations of gradient elution chromatography using equilibrium-based models. In the first phase, a single-solute system under a linear solvent gradient is analyzed. An exact solution is derived using Laplace transforms under Dirichlet boundary conditions and rectangular pulse injection, and the results are validated through a high-resolution finite volume scheme (HR-FVS), showing excellent agreement across a broad range of parameters. In the second phase, a more complex two-component, nonlinear, and non-isothermal equilibrium dispersive model (EDM) is formulated to account for simultaneous solvent and temperature gradients. The model incorporates coupled mass and energy transport equations and is numerically solved using the HR-FVS approach. Numerical case studies reveal that both solvent and thermal gradients significantly affect retention behavior, peak shape, and temperature profiles, ultimately enhancing separation efficiency. These findings provide valuable insights for optimizing chromatographic performance under dual-gradient conditions and contribute to the theoretical foundation for advanced gradient elution strategies.

Dependence of the Electromagnetic Radiation Transmission Coefficient of Liquid Crystal π – Cells in the Terahertz Range

In this article, a modified Abeles technique is proposed for calculating the energy transmission coefficient of electromagnetic radiation of a liquid crystal modulator operating on the basis of a π-cell. The proposed technique has an accuracy of modeling the characteristics of the liquid crystal modulator comparable to known analogs. The results of computer modeling allow us to draw the following practical conclusions. The effect of changing the design parameters on the transmission coefficient of the liquid crystal modulator increases with a decrease in the wavelength of the electromagnetic range within the range from 16 μm to 1 μm. The maximum effect on the transmission coefficient of the liquid crystal modulator in the short-wave part of electromagnetic radiation is exerted by the thickness of the electrode layer, while there is no such dependence in the long-wave part of this radiation. The thickness of the orienting layer of the liquid crystal modulator has the same effect on its transmission coefficient as the thickness of the electrode layer, but with a smaller amplitude. The control voltage of the liquid crystal modulator has a significant effect on its transmission coefficient of electromagnetic radiation for short wavelengths and almost does not affect it in the long-wave region of electromagnetic radiation. Received: 14 January 2025 | Revised: 13 March 2025 | Accepted: 18 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Anfal Fadhil Ahmed Mulamahawsh: Validation, Formal analysis, Investigation, Writing – original draft, Visualization. G. V. Simonenko: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing – review & editing, Visualization, Supervision, Project administration.

POTENTIAL HUMAN HEALTH RISK ASSESSMENT OF HEAVY METALS VIA CASSAVA TUBERS FROM OGWASHI-UKU, NIGERIA

Heavy metals such as Cu, Pb, Zn and Cd are ubiquitous in our environment and this study seeks to investigate the concentration of heavy metals in cassava tubers cultivated under high voltage electric power lines and thus assess the associated human health risk arising from its consumption in three communities in Aniocha South LGA, Delta State. Cassava tuber samples were collected from farm under the power lines using a spade. Standard laboratory procedures were adopted for determination of the concentration of heavy metals in the samples using Atomic Absorption Spectrophotometer (AAS). Results revealed that the concentration of heavy metals in the samples range from 7.350.05, 9.573.24, 0.840.43 and 0.220.08 mg/kg for Cu, Zn, Pb and Cd respectively. Bioaccumulation of heavy metals from soil to root revealed Zn 1 in samples across all sampled locations. The life carcinogenic risk value showed that consumption of cassava tubers cultivated under high voltage power lines in the sampled communities by the local populace are not under any immediate carcinogenic threat. However, THI revealed that continuous consumption of these cassava tubers may pose the emergence of carcinogenic threat.

Design of Digital Simulation Monitoring System for High-Voltage Power Transmission Lines with Ad-hoc MAC Access

To realize the digital simulation monitoring for high-voltage transmission lines, a transmission line monitoring system based on the Media Access Control (MAC) mode of a wireless Ad-hoc network is studied. We analyze the transmission services of transmission lines comprehensively and adopt the Linear Hybrid Time Division Multiple Access (LHTDMA) hybrid access mode with logic integration and services to improve the resource utilization efficiency of the algorithm. In the optimization process of the routing algorithm, the optimization scheme based on deep Q-learning is selected to enhance the system life cycle and reduce the system delay. The algorithm of this study is verified by experiments. The experimental results confirm that the LHTDMA hybrid access method has higher data throughput and lower delay effect. Compared with other routing algorithms, under the same conditions, the time cost of this algorithm is lower than other comparison algorithms. As the size of the transfer file gradually increases, the advantages of this algorithm are more obvious for its more accurate iterative output results, which proves its superiority.

Fifteen millijoule, few-cycle pulse compression using a large-bore hollow fiber for relativistic laser–matter interactions

We demonstrate the scaling of few-cycle post-compression to the multi-TW regime by producing 15 mJ, 6.9 fs pulses through spectral broadening of 800 nm light in a large-bore (⊘1 mm) hollow-capillary fiber (HCF) of 5 m in length. The good beam quality allows for focusing of the beam to near diffraction limit to achieve a peak intensity of 6.5 · 1018 W/cm2 for relativistic laser–matter interaction studies. The performance of the helium-filled HCF is characterized under various operating conditions. We find that a reverse pressure gradient allows for operation above the critical power for self-focusing with >88% transmission for input energies of 23 mJ.

Advances in Digital Twin Deployment for Real-Time Asset Monitoring and Optimization in Power Transmission Systems

The integration of digital twin technology into power transmission systems marks a significant evolution in the way grid assets are monitored and optimized. This paper explores recent advances in the deployment of digital twins, emphasizing their role in enabling real-time, data-driven decision-making and enhancing operational resilience. Beginning with a discussion on the structural architecture of digital twins, the study outlines their core components—virtual models, sensor networks, and cyber-physical synchronization—and how they interoperate with existing infrastructure through standardized communication protocols. The paper then delves into the real-time monitoring capabilities of digital twins, highlighting their ability to support condition-based maintenance, detect anomalies through embedded AI/ML models, and provide actionable insights via immersive visualization tools. Further, the study examines optimization functionalities such as dynamic line rating, energy flow simulations, and renewable energy integration, demonstrating how digital twins improve both short-term efficiency and long-term grid planning. Despite their transformative potential, challenges remain, including data quality concerns, cybersecurity risks, cost constraints, and interoperability issues. The paper concludes by recommending future research priorities and policy interventions, including standardization efforts, investment incentives, and workforce development, to promote widespread and sustainable adoption. Overall, the deployment of digital twins represents a pivotal step toward intelligent, adaptive, and future-ready power transmission networks.

Host specificity of gastrointestinal parasites in free-ranging sloths from Costa Rica.

The diversity and host specificity of gastrointestinal parasites infecting free-ranging sloths is poorly known. We compared gastrointestinal parasites of two sloth species from Costa Rica-three-fingered sloths (Bradypus variegatus) and two-fingered sloths (Choloepus hoffmanni)-for the first time in both a primary forest and an urban habitat. We asked whether host-parasite interactions were predominantly structured by host identity, the habitats in which hosts occurred, or both. Coproparasitology revealed protozoa and nematode eggs from both host species, but cestode eggs were recorded only in C. hoffmanni. We found eight parasitic morphotypes in 38 samples, which matches the total number of these parasites described in sloths over the past 100 years. We found no significant difference in overall parasite richness between sloth species or habitats, but the parasite richness of C. hoffmanni was 2-fold greater in the primary forest vs. urban habitat. As no parasite sharing was observed between sloth species, we found strong and significant differences in parasite composition between host species regardless of habitat. In B. variegatus, we observed eggs of four nematode taxa (Spirocercidae, Subuluroidea, Spirurida, Ascaridida) and cysts of Eimeriidae (Apicomplexa). By contrast, in C. hoffmanni, we observed cestodes (Anoplocephalidae), a different nematode from the family Spirocercidae, and also different cysts of Eimeriidae (Apicomplexa). Many rare taxa were recorded only in samples from the primary forest, and these did not match any sloth parasites that had been previously described in the literature, suggesting that at least some could be undescribed species. Together, these results highlight the paucity of comparative parasitology involving tropical wildlife, the importance of characterizing host-parasite transmission networks, and the potential relevance of intermediate hosts that may be relevant to sloth health.